TY - JOUR TI - Performance tradeoffs for spare parts supply chains with additive manufacturing capability servicing intermittent demand AU - McDermott, Kyle C AU - Winz, Ryan D AU - Hodgson, Thom J AU - Kay, Michael G AU - King, Russell E AU - McConnell, Brandon M AB - Purpose - Investigate the impact of additive manufacturing (AM) on the performance of a spare parts supply chain with a particular focus on underlying spare part demand patterns. Design/Methodology/Approach - This work evaluates various AM-enabled supply chain configurations through Monte Carlo simulation. Historical demand simulation and intermittent demand forecasting are used in conjunction with a mixed integer linear program to determine optimal network nodal inventory policies. By varying demand characteristics and AM capacity this work assesses how to best employ AM capability within the network. Findings - This research assesses the preferred AM-enabled supply chain configuration for varying levels of intermittent demand patterns and AM production capacity. The research shows that variation in demand patterns alone directly affects the preferred network configuration. The relationship between the demand volume and relative AM production capacity affects the regions of superior network configuration performance. Research limitations/implications - This research makes several simplifying assumptions regarding AM technical capabilities. AM production time is assumed to be deterministic and does not consider build failure probability, build chamber capacity, part size, part complexity, and post-processing requirements. Originality/value - This research is the first study to link realistic spare part demand characterization to AM supply chain design using quantitative modeling. DA - 2021/11/11/ PY - 2021/11/11/ DO - 10.31224/osf.io/bdq23 UR - https://doi.org/10.31224/osf.io/bdq23 ER - TY - JOUR TI - Modeling and transportation planning for US noncombatant evacuation operations in South Korea AU - Kearby, John A AU - Winz, Ryan D AU - McConnell, Brandon M AU - Hodgson, Thom J AU - Kay, Michael G AU - King, Russell E AB - Purpose: The purpose of this paper is to investigate US noncombatant evacuation operations (NEO) in South Korea and devise planning and management procedures that improve the efficiency of those missions. Design/methodology/approach: It formulates a time-staged network model of the South Korean noncombatant evacuation system as a mixed integer linear program to determine an optimal flow configuration that minimizes the time required to complete an evacuation. This solution considers the capacity and resource constraints of multiple transportation modes and effectively allocates the limited assets across a time-staged network to create a feasible evacuation plan. That solution is post-processed and a vehicle routing procedure then produces a high resolution schedule for each individual asset throughout the entire duration of the NEO. Findings: This work makes a clear improvement in the decision-making and resource allocation methodology currently used in a NEO on the Korea peninsula. It immediately provides previously unidentifiable information regarding the scope and requirements of a particular evacuation scenario and then produces an executable schedule for assets to facilitate mission accomplishment. Originality/value: The significance of this work is not relegated only to evacuation operations on the Korean peninsula; there are numerous other NEO and natural disaster related scenarios that can benefit from this approach. DA - 2021/11/17/ PY - 2021/11/17/ DO - 10.31224/osf.io/fxvqg UR - https://doi.org/10.31224/osf.io/fxvqg ER - TY - JOUR TI - Improving Chemotherapy Infusion Operations through the Simulation of Scheduling Heuristics: a case study AU - Slocum, Ryan F AU - Jones, Herbert Lee AU - Fletcher, Matthew T AU - Hodgson, Thom J AU - Taheri, Javad AU - Wilson, James R AU - McConnell, Brandon M AB - Over the last decade, chemotherapy treatments have dramatically shifted to outpatient services such that nearly 90% of all infusions are now administered outpatient. This shift has challenged oncology clinics to make chemotherapy treatment as widely available as possible while attempting to treat all patients within a fixed period of time. Historical data from a Veterans Affairs chemotherapy clinic in the United States and staff input informed a discrete event simulation model of the clinic. The case study examines the impact of altering the current schedule, where all patients arrive at 8:00 AM, to a schedule that assigns patients to two or three different appointment times based on the expected length of their chemotherapy infusion. The results identify multiple scheduling policies that could be easily implemented with the best solutions reducing both average patient waiting time and average nurse overtime requirements. DA - 2021/11/11/ PY - 2021/11/11/ DO - 10.31224/osf.io/sg6qp UR - https://doi.org/10.31224/osf.io/sg6qp ER - TY - JOUR TI - Assessing uncertainty and risk in an expeditionary military logistics network AU - McConnell, Brandon M AU - Hodgson, Thom J AU - Kay, Michael G AU - King, Russell E AU - Liu, Yunan AU - Parlier, Greg AU - Thoney-Barletta, Kristin AU - Wilson, James R AB - Uncertainty is rampant in military expeditionary operations spanning high-intensity combat to humanitarian operations. These missions require rapid planning and decision-support tools to address the logistical challenges involved in providing support in often austere environments. The US Army’s adoption of an enterprise resource planning system provides an opportunity to develop automated decision-support tools and other analytical models designed to take advantage of newly available logistical data. This research presents a tool that runs in near-real time to assess risk while conducting capacity planning and performance analysis designed for inclusion in a suite of applications dubbed the Military Logistics Network Planning System, which previously only evaluated the mean sample path. Logistical data from combat operations during Operation Iraqi Freedom drive supply requisition forecasts for a contingency scenario in a similar geographic environment. A nonstationary queueing network model is linked with a heuristic logistics scheduling methodology to provide a stochastic framework to account for uncertainty and assess risk. DA - 2021/11/15/ PY - 2021/11/15/ DO - 10.31224/osf.io/ynx5k UR - https://doi.org/10.31224/osf.io/ynx5k ER - TY - JOUR TI - A Military Logistics Network Planning System AU - Rogers, Matthew B AU - McConnell, Brandon M AU - Hodgson, Thom J AU - Kay, Michael G AU - King, Russell E AU - Parlier, Greg AU - Thoney-Barletta, Kristin AB - This paper presents a proof of concept for a Military Logistics Network Planning System (MLNPS) to be used during mission planning to quickly identify a robust logistical footprint that can adequately sustain units deployed in an expeditionary environment. The logistical network is modeled using an efficient form of goal-seeking deterministic discrete event simulation to process supply requisitions through the logistical network. The queuing information obtained from the simulation informs capacity adjustments to the network to maximize efficiency. This process of simulation and network tuning continues interactively until an adequate and robust logistical footprint is found. During the planning stages, the MLNPS can be used to identify and mitigate logistical problems instead of waiting to react to backlogs when the military's operations would have already been affected. Designed to run as an app on the Army's enterprise resource planning (ERP) system (Global Combat Support System-Army), the MLNPS can also be used during operations to inform commanders of expected operational impacts on logistics. Contingency operation scenarios are used to demonstrate the MLNPS' capabilities. DA - 2021/11/12/ PY - 2021/11/12/ DO - 10.31224/osf.io/wnvpf UR - https://doi.org/10.31224/osf.io/wnvpf ER - TY - CONF TI - On-Demand Logistics Service for Packages: Package Bidding Mechanism vs. Platform Pricing AU - Tripathy, Manish AU - Ahmed, Ramin AU - Kay, Michael AB - This paper is an exploratory analysis of an on-demand service platform for packages, where the packages bid for transportation service through various auction mechanisms, trucks offer transportation services, and distribution centers match demand and supply. All agents are independent and individually incentivized to participate. Using a utility-based model, we characterize the participation incentives for all the agents, implement the state-of-the-art pricing mechanisms from industry and academia, and design and implement a first-price auction-based mechanism. Using simulation and through performance indicators like throughput, profit of the distribution center, consumer surplus, among others, we find that the package bidding mechanism significantly outperforms the status quo. Furthermore, we extend our analysis to include uniform price and Vickrey-Clarke-Groves auctions. We find that the packages prefer the Vickrey-Clarke-Groves auction, whereas the trucks and distribution centers prefer the first-price auction; although all of them prefer the bidding mechanism to the status-quo pricing mechanism. C2 - 2021/12/12/ C3 - 2021 Winter Simulation Conference (WSC) DA - 2021/12/12/ DO - 10.1109/wsc52266.2021.9715312 PB - IEEE UR - http://dx.doi.org/10.1109/wsc52266.2021.9715312 ER - TY - BOOK TI - An Introduction to Partial Differential Equations (with Maple) AU - Li, Zhilin AU - Norris, Larry DA - 2021/9/9/ PY - 2021/9/9/ DO - 10.1142/12052 OP - PB - WORLD SCIENTIFIC SN - 9789811228629 9789811228636 UR - http://dx.doi.org/10.1142/12052 DB - Crossref ER - TY - JOUR TI - Explaining Drug-Discovery Hypotheses Using Knowledge-Graph Patterns AU - Schatz, Kara AU - Melo-Filho, Cleber AU - Tropsha, Alexander AU - Chirkova, Rada T2 - 2021 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA) AB - Drug discovery is an important process used by biomedical experts to identify potential treatments for diseases. In its traditional form, the process requires significant expert time and manual effort. By encoding a wealth of information about relationships between drugs and diseases, modern large-scale biomedical knowledge graphs provide excellent opportunities to accelerate drug discovery, by automating aspects of the process. One opportunity is to use explainable fact-checking tools to generate explanations for hypothesized drug-disease treatment relationships in a given knowledge graph, with a reliability score assigned to each explanation. The explanations and their scores can then be used by experts to determine which drug-disease pairs to consider for clinical trials.In our collaboration with a biomedical team, we have found that existing explainable fact-checking tools are not necessarily helpful in drug discovery, as their explanation formats and evaluation metrics do not match well the requirements of scientific discovery in the biomedical domain. To address these challenges in using fact-checking tools in drug discovery, we introduce a scalable automated approach for generating explanations that are modeled after existing biomedical concepts and supplemented with data-supported evaluation metrics. Our explanations are based on knowledge-graph patterns, which are readily understood by biomedical experts. Our experimental results suggest that our proposed metrics are accurate and useful on largescale biomedical knowledge graphs, and our explanations are understandable and reasonable to experts doing drug discovery. DA - 2021/// PY - 2021/// DO - 10.1109/BigData52589.2021.9672006 SP - 3709-3716 SN - 2639-1589 KW - Drug discovery KW - knowledge discovery KW - explainable fact checking KW - link prediction KW - knowledge graph mining ER - TY - JOUR TI - Trustworthy Knowledge Graph Population From Texts for Domain Query Answering AU - Ao, Jing AU - Dinakaran, Swathi AU - Yang, Hungjian AU - Wright, David AU - Chirkova, Rada T2 - 2021 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA) AB - Obtaining answers to domain-specific questions over large-scale unstructured (text) data is an important component of data analytics in many application domains. As manual question answering does not scale to large text corpora, it is common to use information extraction (IE) to preprocess the texts of interest prior to posing the questions. This is often done by transforming text corpora into the knowledge-graph (KG) triple format that is suitable for efficient processing of the user questions in graph-oriented data-intensive systems.In a number of real-life scenarios, trustworthiness of the answers obtained from domain-specific texts is vital for downstream decision making. In this paper we focus on one critical aspect of trustworthiness, which concerns aligning with the given domain vocabularies (ontologies) those KG triples that are obtained from the source texts via IE solutions. To address this problem, we introduce a scalable domain-independent text-to-KG approach that adapts to specific domains by using domain ontologies, without having to consult external triple repositories. Our IE solution builds on the power of neural-based learning models and leverages feature engineering to distinguish ontology-aligned data from generic data in the source texts. Our experimental results indicate that the proposed approach could be more dependable than a state-of-the-art IE baseline in constructing KGs that are suitable for trustworthy domain question answering on text data. DA - 2021/// PY - 2021/// DO - 10.1109/BigData52589.2021.9671514 SP - 4590-4599 SN - 2639-1589 KW - Text data KW - populating knowledge graphs KW - ontology-based information extraction KW - feature engineering ER - TY - JOUR TI - Parameterized Exhaustive Routing with First Fit for RSA Problem Variants AU - Rouskas, George N. AU - Bandikatla, Chaitanya T2 - 2021 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM) AB - We present a new single-step solution approach for the routing and spectrum allocation (RSA) problem that integrates the first-fit (FF) heuristic with a new routing strategy that we refer to as “parameterized exhaustive routing.” Our approach is to explore the whole routing space for a subset of the traffic requests, e.g., those with the largest demands or those of higher priority or importance. For each of the remaining requests we employ a greedy heuristic to select one of the candidate paths jointly with spectrum allocation. Our solution represents a two-parameter family of algorithms that bridges the gap between an exhaustive search of the routing space and current two-step methodologies for the RSA problem that select paths for each traffic request in isolation. The parameter values may be used to trade off the quality of the final solution and the computational requirements. Our results indicate that exploring the joint routing space of even a few large requests leads to better solutions than purely greedy approaches. DA - 2021/// PY - 2021/// DO - 10.1109/GLOBECOM46510.2021.9685126 SP - SN - 2576-6813 ER - TY - JOUR TI - Temporal and Spectral Analysis of Spectrum Hole Distributions in an LTE Cell AU - Zou, Rui AU - Wang, Wenye AU - Dai, Huaiyu T2 - 2021 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM) AB - Dynamic Spectrum Access (DSA) is proposed to improve spectrum efficiency by enabling opportunistic access of underutilized spectrum resources. The key to successful DSA operations is the correct understanding of spectrum hole distributions. Though huge amounts of studies have been conducted on spectrum tenancy due to the significance of spectrum hole distributions, there are still two overlooked aspects. One is the measurement resolution, and the other is the spectrum distribution in the spectral perspective. Since the spectrum hole analysis relies on the measurement data, we decode the LTE downlink control information to obtain the spectrum tenancy at the same time-frequency granularity with LTE scheduling. We analyze the spectrum hole distributions in fine resolutions along both the temporal and the spectral dimensions, and investigate the performance of two widely used spectrum tenancy models, the Markov and the on/off models, in terms of their capabilities on capturing the distributions of spectrum holes. Our observations include but are not limited to the following. The spectrum holes follow the power law distributions when examined in the LTE scheduling unit from both the time and the frequency perspectives. Both Markov and on/off models should be fitted to the spectrum tenancy along the frequency perspective to achieve their best performance. DA - 2021/// PY - 2021/// DO - 10.1109/GLOBECOM46510.2021.9685339 SP - SN - 2576-6813 KW - dynamic spectrum access KW - spectrum hole distribution KW - measurement granularity KW - power law ER - TY - JOUR TI - Maintenance of Social Commitments in Multiagent Systems AU - Telang, Pankaj R. AU - Singh, Munindar P. AU - Yorke-Smith, Neil T2 - Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI) DA - 2021/2// PY - 2021/2// VL - 35 IS - 13 SP - 11369-11377 UR - https://ojs.aaai.org/index.php/AAAI/article/view/17355 ER - TY - CONF TI - Tango: Declarative Semantics for Multiagent Communication Protocols AU - Singh, Munindar P. AU - V., Samuel H. Christie AB - A flexible communication protocol is necessary to build a decentralized multiagent system whose member agents are not coupled to each other's decision making. Information-based protocol languages capture a protocol in terms of causality and integrity constraints based on the information exchanged by the agents. Thus, they enable highly flexible enactments in which the agents proceed asynchronously and messages may be arbitrarily reordered. However, the existing semantics for such languages can produce a large number of protocol enactments, which makes verification of a protocol property intractable. This paper formulates a protocol semantics declaratively via inference rules that determine when a message emission or reception becomes enabled during an enactment, and its effect on the local state of an agent. The semantics enables heuristics for determining when alternative extensions of a current enactment would be equivalent, thereby helping produce parsimonious models and yielding improved protocol verification methods. C2 - 2021/8// C3 - Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence DA - 2021/8// DO - 10.24963/ijcai.2021/55 PB - International Joint Conferences on Artificial Intelligence Organization UR - http://dx.doi.org/10.24963/ijcai.2021/55 ER - TY - JOUR TI - COVID-19 Knowledge Extractor (COKE): A Curated Repository of Drug-Target Associations Extracted from the CORD-19 Corpus of Scientific Publications on COVID-19 AU - Korn, Daniel AU - Pervitsky, Vera AU - Bobrowski, Tesia AU - Alves, Vinicius M. AU - Schmitt, Charles AU - Bizon, Chris AU - Baker, Nancy AU - Chirkova, Rada AU - Cherkasov, Artem AU - Muratov, Eugene AU - Tropsha, Alexander T2 - JOURNAL OF CHEMICAL INFORMATION AND MODELING AB - The COVID-19 pandemic has catalyzed a widespread effort to identify drug candidates and biological targets of relevance to SARS-COV-2 infection, which resulted in large numbers of publications on this subject. We have built the COVID-19 Knowledge Extractor (COKE), a web application to extract, curate, and annotate essential drug–target relationships from the research literature on COVID-19. SciBiteAI ontological tagging of the COVID Open Research Data set (CORD-19), a repository of COVID-19 scientific publications, was employed to identify drug–target relationships. Entity identifiers were resolved through lookup routines using UniProt and DrugBank. A custom algorithm was used to identify co-occurrences of the target protein and drug terms, and confidence scores were calculated for each entity pair. COKE processing of the current CORD-19 database identified about 3000 drug–protein pairs, including 29 unique proteins and 500 investigational, experimental, and approved drugs. Some of these drugs are presently undergoing clinical trials for COVID-19. The COKE repository and web application can serve as a useful resource for drug repurposing against SARS-CoV-2. COKE is freely available at https://coke.mml.unc.edu/, and the code is available at https://github.com/DnlRKorn/CoKE. DA - 2021/12/27/ PY - 2021/12/27/ DO - 10.1021/acs.jcim.1c01285 VL - 61 IS - 12 SP - 5734-5741 SN - 1549-960X ER - TY - SOUND TI - Fast Approximate Solutions for Capacitated Production Planning Under Uncertain Demand AU - Uzsoy, R. DA - 2021/1// PY - 2021/1// ER - TY - CONF TI - A Resource Coordination Model for Managing Product Transitions AU - Leca, C.A. AU - Kempf, K.G. AU - Uzsoy, R. T2 - IEEE Conference on Automation Science and Engineering (CASE) C2 - 2021/8// CY - Lyons, France DA - 2021/8// PY - 2021/8/23/ ER - TY - ER - TY - JOUR TI - Ensuring Data Readiness for Quality Requirements with Help from Procedure Reuse AU - Chirkova, Rada AU - Doyle, Jon AU - Reutter, Juan T2 - ACM JOURNAL OF DATA AND INFORMATION QUALITY AB - Assessing and improving the quality of data are fundamental challenges in Big-Data applications. These challenges have given rise to numerous solutions targeting transformation, integration, and cleaning of data. However, while schema design, data cleaning, and data migration are nowadays reasonably well understood in isolation, not much attention has been given to the interplay between standalone tools in these areas. In this article, we focus on the problem of determining whether the available data-transforming procedures can be used together to bring about the desired quality characteristics of the data in business or analytics processes. For example, to help an organization avoid building a data-quality solution from scratch when facing a new analytics task, we ask whether the data quality can be improved by reusing the tools that are already available, and if so, which tools to apply, and in which order, all without presuming knowledge of the internals of the tools, which may be external or proprietary. Toward addressing this problem, we conduct a formal study in which individual data cleaning, data migration, or other data-transforming tools are abstracted as black-box procedures with only some of the properties exposed, such as their applicability requirements, the parts of the data that the procedure modifies, and the conditions that the data satisfy once the procedure has been applied. As a proof of concept, we provide foundational results on sequential applications of procedures abstracted in this way, to achieve prespecified data-quality objectives, for the use case of relational data and for procedures described by standard relational constraints. We show that, while reasoning in this framework may be computationally infeasible in general, there exist well-behaved cases in which these foundational results can be applied in practice for achieving desired data-quality results on Big Data. DA - 2021/9// PY - 2021/9// DO - 10.1145/3428154 VL - 13 IS - 3 SP - SN - 1936-1955 KW - Data and information quality KW - data integration in Big Data KW - data cleaning in Big Data KW - Big Data quality and analytics KW - Big Data quality in business process KW - Big Data quality management processes, frameworks and models ER - TY - JOUR TI - From Euclid to Corner Sums - a Trail of Telescoping Tricks AU - Patricio, Pedro AU - Hartwig, Robert E. T2 - FILOMAT AB - Euclid?s algorithm is extended to binomials, geometric sums and corner sums. Two-sided non-commuting, non-constant linear difference equations will be solved, and the solution is applied to corner sums, thereby presenting an explicit formula for the generator of the bi-module spanned by the two starting corner sums. DA - 2021/// PY - 2021/// DO - 10.2298/FIL2114613P VL - 35 IS - 14 SP - 4613-4636 SN - 0354-5180 KW - Telescoping Sum KW - Corner Sum KW - Polynomials KW - Recurrence Relation ER - TY - JOUR TI - Interaction-Oriented Programming: An Application Semantics Approach for Engineering Decentralized Applications AU - Chopra, Amit K. AU - Christie, Samuel H. AU - Singh, Munindar P. T2 - PROCEEDINGS OF THE 2021 ACM SYMPOSIUM ON PRINCIPLES OF DISTRIBUTED COMPUTING (PODC '21) AB - Interaction-Oriented Programming (IOP) refers to multiagent concepts, languages, and programming models for engineering applications that are characterized by interactions between autonomous parties. Such applications arise in domains such as e-commerce, health care, and finance. Owing to the autonomy of the principals involved, such applications are conceptually decentralized. We demonstrate how to specify a decentralized application flexibly and how to engineer correct, fault-tolerant endpoints (agents) for the principals in a straightforward manner. Notably, the entire application is realized as agents communicating over an unordered, unreliable messaging infrastructure (our implementations in fact use UDP). IOP departs from traditional distributed systems approaches that rely on guarantees in the application's communication infrastructure, e.g., for ordering and fault tolerance. Notably, IOP shows how to address application semantics, the holy grail of distributed systems. DA - 2021/// PY - 2021/// DO - 10.1145/3465084.3467486 SP - 575-576 KW - Commitments KW - information protocol KW - programming model ER - TY - BOOK TI - Purchasing & supply chain management AU - Monczka, Robert M. AU - Handfield, Robert B. AU - Giunipero, Larry C. AU - Patterson, James L. DA - 2021/// PY - 2021/// PB - Cengage ER - TY - BOOK TI - An Introduction to IoT Analytics AU - Perros, Harry G. AB - This book covers techniques that can be used to analyze data from IoT sensors and addresses questions regarding the performance of an IoT system. It strikes a balance between practice and theory so one can learn how to apply these tools in practice with a good understanding of their inner workings. This is an introductory book for readers who have no familiarity with these techniques. The techniques presented in An Introduction to IoT Analytics come from the areas of machine learning, statistics, and operations research. Machine learning techniques are described that can be used to analyze IoT data generated from sensors for clustering, classification, and regression. The statistical techniques described can be used to carry out regression and forecasting of IoT sensor data and dimensionality reduction of data sets. Operations research is concerned with the performance of an IoT system by constructing a model of the system under study and then carrying out a what-if analysis. The book also describes simulation techniques. Key Features IoT analytics is not just machine learning but also involves other tools, such as forecasting and simulation techniques. Many diagrams and examples are given throughout the book to fully explain the material presented. Each chapter concludes with a project designed to help readers better understand the techniques described. The material in this book has been class tested over several semesters. Practice exercises are included with solutions provided online at www.routledge.com/9780367686314 Harry G. Perros is a Professor of Computer Science at North Carolina State University, an Alumni Distinguished Graduate Professor, and an IEEE Fellow. He has published extensively in the area of performance modeling of computer and communication systems. DA - 2021/3/31/ PY - 2021/3/31/ DO - 10.1201/9781003139041 OP - PB - Chapman and Hall/CRC SN - 9781003139041 UR - http://dx.doi.org/10.1201/9781003139041 DB - Crossref ER - TY - JOUR TI - Quantitative Trait Locus Mapping for Common Scab Resistance in a Tetraploid Potato Full-Sib Population AU - Pereira, Guilherme da Silva AU - Mollinari, Marcelo AU - Qu, Xinshun AU - Thill, Christian AU - Zeng, Zhao-Bang AU - Haynes, Kathleen AU - Yencho, G. Craig T2 - PLANT DISEASE AB - Despite the negative impact of common scab (Streptomyces spp.) on the potato industry, little is known about the genetic architecture of resistance to this bacterial disease in the crop. We evaluated a mapping population (∼150 full sibs) derived from a cross between two tetraploid potatoes ('Atlantic' × B1829-5) in three environments (MN11, PA11, ME12) under natural common scab pressure. Three measures to common scab reaction, namely percentage of scabby tubers and disease area and lesion indices, were found to be highly correlated (>0.76). Because of the large environmental effect, heritability values were zero for all three traits in MN11, but moderate to high in PA11 and ME12 (∼0.44 to 0.79). We identified a single quantitative trait locus (QTL) for lesion index in PA11, ME12, and joint analyses on linkage group 3, explaining ∼22 to 30% of the total variation. The identification of QTL haplotypes and candidate genes contributing to disease resistance can support genomics-assisted breeding approaches in the crop.[Formula: see text] Copyright © 2021 The Author(s). This is an open access article distributed under the CC BY 4.0 International license. DA - 2021/10// PY - 2021/10// DO - 10.1094/PDIS-10-20-2270-RE VL - 105 IS - 10 SP - 3048-3054 SN - 1943-7692 KW - disease resistance KW - polyploid QTL model KW - single-nucleotide polymorphism KW - Solanum tuberosum KW - Streptomyces spp ER - TY - JOUR TI - A DUAL-CHANNEL SUPPLY CHAIN PROBLEM WITH RESOURCE-UTILIZATION PENALTY: WHO CAN BENEFIT FROM SALES EFFORT? AU - Zhao, Lianxia AU - You, Jianxin AU - Fang, Shu-Cherng T2 - JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION AB -

As manufacturers may engage in both direct sale and wholesale, the channel conflict between manufacturer and retailer becomes inevitable. This paper considers a dual-channel supply chain in which a retailer sells the product through store channel with sales effort while the manufacturer holds a direct channel and may provide an incentive measure to share the cost of sales effort. To meet social responsibility, a penalty on the total resource consumed is imposed on the manufacturer. We present a manufacturer-led decentralized model in which both members maximize individual profit, and then derive the corresponding optimal direct/store price and wholesale price. The dual-channel supply chain model without sales effort policy is also considered so as to explain the effects of sales effort policy and sharing cost measure on both parties. Special properties are presented to show (ⅰ) the influence of retailer's sales effort and manufacturer's sharing cost on the optimal strategies; (ⅱ) the resource-utilized penalty on the optimal decisions. Finally, numerical experiments are conducted to highlight the influence of various parameters on optimal solutions. We find that if the market response to retailer's sales effort is strong or the manufacturer's sharing portion of sales effort cost is increased, the retailer's profit and store selling price increase while the manufacturer's profit decreases and the direct sale and wholesale prices do not change. We also show that if the consumer's value on direct channel exceeds a threshold, the manufacturer's profit will be greater than that of the retailer. Moreover, if the market response to retailer's sales effort is strong, manufacturer's profit will be lesser than retailer's profit. DA - 2021/9// PY - 2021/9// DO - 10.3934/jimo.2020097 VL - 17 IS - 5 SP - 2837-2853 SN - 1553-166X KW - dual-channel KW - pricing strategy KW - sales effort KW - resource-utilization penalty KW - Supply chain management ER - TY - JOUR TI - A NEW HYBRID l(p)-l(2) MODEL FOR SPARSE SOLUTIONS WITH APPLICATIONS TO IMAGE PROCESSING AU - Gao, Xuerui AU - Bai, Yanqin AU - Fang, Shu-cherng AU - Luo, Jian AU - LI, Qian T2 - JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION AB - <p style='text-indent:20px;'>Finding sparse solutions to a linear system has many real-world applications. In this paper, we study a new hybrid of the <inline-formula><tex-math id="M3">\begin{document}$ l_p $\end{document}</tex-math></inline-formula> quasi-norm (<inline-formula><tex-math id="M4">\begin{document}$ 0 &lt;p&lt; 1 $\end{document}</tex-math></inline-formula>) and <inline-formula><tex-math id="M5">\begin{document}$ l_2 $\end{document}</tex-math></inline-formula> norm to approximate the <inline-formula><tex-math id="M6">\begin{document}$ l_0 $\end{document}</tex-math></inline-formula> norm and propose a new model for sparse optimization. The optimality conditions of the proposed model are carefully analyzed for constructing a partial linear approximation fixed-point algorithm. A convergence proof of the algorithm is provided. Computational experiments on image recovery and deblurring problems clearly confirm the superiority of the proposed model over several state-of-the-art models in terms of the signal-to-noise ratio and computational time.</p> DA - 2021/12// PY - 2021/12// DO - 10.3934/jimo.2021211 VL - 12 SP - SN - 1553-166X KW - Sparse optimization KW - hybrid of the l(P) quasi-norm and l(2) norm KW - optimality conditions KW - image processing ER - TY - JOUR TI - Sociotechnical Perspectives on AI Ethics and Accountability AU - Kokciyan, Nadin AU - Srivastava, Biplav AU - Huhns, Michael AU - Singh, Munindar T2 - IEEE INTERNET COMPUTING AB - The articles in this special section focus on sociotechnical perspectives on artificial intelligence (AI) ethics and accountability. Suppose we were to develop a loan-processing tool based on artificial intelligence (AI) to process applications by people for financial loan products. The tool would consider application data and recommend whether to give a loan and for how much. It would even seek out prospective borrowers online for new business and offer loans. Or, suppose we were to develop a career coach that recommends career tracks and training based on a user’s career goal, biosketch, and time and money available to invest in training. Applications of AI in decision support are not hypothetical, and applications such as loan processing and career coaching are becoming mainstream. However, although like other algorithms, their inputs and outputs are data; these AI applications are embedded in society, their decisions and recommendations have direct effects on people’s lives. Denial of a loan reduces financial options and may harm a borrower’s wellbeing, while giving a loan but at usurious interest rates might expose a borrower to financial ruin. Likewise, whereas career advice can be valuable to someone who does not have strong mentors, narrow or biased career advice can impede their future and, through them, their family’s prospects. DA - 2021/11// PY - 2021/11// DO - 10.1109/MIC.2021.3117611 VL - 25 IS - 6 SP - 5-6 SN - 1941-0131 UR - https://doi.org/10.1109/MIC.2021.3117611 ER - TY - JOUR TI - Accountability as a Foundation for Requirements in Sociotechnical Systems AU - Chopra, Amit K. AU - Singh, Munindar P. T2 - IEEE INTERNET COMPUTING AB - We understand sociotechnical systems (STSs) as uniting social and technical tiers to provide abstractions for capturing how autonomous principals interact with each other. Accountability is a foundational concept in STSs and an essential component of achieving ethical outcomes. In simple terms, accountability involves identifying who can call whom to account and who must provide an accounting of what and when. Although accountability is essential in any application involving autonomous parties, established methods do not support it. We formulate an accountability requirement as one where one principal is accountable to another regarding some conditional expectation. Our metamodel for STSs captures accountability requirements as relational constructs inspired from legal concepts, such as commitments, authorization, and prohibition. We apply our metamodel to a healthcare process and show how it helps address the problems of ineffective interaction identified in the original case study. DA - 2021/11// PY - 2021/11// DO - 10.1109/MIC.2021.3106835 VL - 25 IS - 6 SP - 33-41 SN - 1941-0131 UR - https://doi.org/10.1109/MIC.2021.3106835 KW - Authorization KW - Hospitals KW - Contracts KW - Sociotechnical systems KW - Law KW - Internet KW - Delays ER - TY - JOUR TI - SEAGLE: A Scalable Exact Algorithm for Large-Scale Set-Based Gene-Environment Interaction Tests in Biobank Data AU - Chi, Jocelyn T. AU - Ipsen, Ilse C. F. AU - Hsiao, Tzu-Hung AU - Lin, Ching-Heng AU - Wang, Li-San AU - Lee, Wan-Ping AU - Lu, Tzu-Pin AU - Tzeng, Jung-Ying T2 - FRONTIERS IN GENETICS AB - The explosion of biobank data offers unprecedented opportunities for gene-environment interaction (GxE) studies of complex diseases because of the large sample sizes and the rich collection in genetic and non-genetic information. However, the extremely large sample size also introduces new computational challenges in G×E assessment, especially for set-based G×E variance component (VC) tests, which are a widely used strategy to boost overall G×E signals and to evaluate the joint G×E effect of multiple variants from a biologically meaningful unit (e.g., gene). In this work, we focus on continuous traits and present SEAGLE, a Scalable Exact AlGorithm for Large-scale set-based G×E tests, to permit G×E VC tests for biobank-scale data. SEAGLE employs modern matrix computations to calculate the test statistic and p-value of the GxE VC test in a computationally efficient fashion, without imposing additional assumptions or relying on approximations. SEAGLE can easily accommodate sample sizes in the order of 105, is implementable on standard laptops, and does not require specialized computing equipment. We demonstrate the performance of SEAGLE using extensive simulations. We illustrate its utility by conducting genome-wide gene-based G×E analysis on the Taiwan Biobank data to explore the interaction of gene and physical activity status on body mass index. DA - 2021/11/2/ PY - 2021/11/2/ DO - 10.3389/fgene.2021.710055 VL - 12 SP - SN - 1664-8021 KW - gene-based GxE test for biobank data KW - GxE collapsing test for biobank data KW - GxE test for large-scale sequencing data KW - scalable GEI test KW - gene-environment variance component test KW - gene-environment kernel test KW - regional-based gene-environment test ER - TY - JOUR TI - Optimal convergence of three iterative methods based on nonconforming finite element discretization for 2D/3D MHD equations AU - Xu, Jiali AU - Su, Haiyan AU - Li, Zhilin T2 - NUMERICAL ALGORITHMS DA - 2021/11/13/ PY - 2021/11/13/ DO - 10.1007/s11075-021-01224-4 VL - 11 SP - SN - 1572-9265 KW - Incompressible magneto-hydrodynamics equations KW - Nonconforming finite element KW - Iterative method KW - Stability KW - Optimal error estimate ER - TY - JOUR TI - Remaining useful life prediction of PEMFC based on cycle reservoir with jump model AU - Jin, Jiashu AU - Chen, Yuepeng AU - Xie, Changjun AU - Zhu, Wenchao AU - Wu, Fen T2 - INTERNATIONAL JOURNAL OF HYDROGEN ENERGY AB - Accurate prognosis of limited durability is one of the key factors for the commercialization of proton exchange membrane fuel cell (PEMFC) on a large scale. Thanks to ignoring the structure of the PEMFC and simplifying the prognostic process, the data-driven prognostic approaches was the commonly used for predicting remaining useful life (RUL) at present. In this paper, the proposed cycle reservoir with jump (CRJ) model improves the ESN model, changes the connection mode of neurons in the reservoir and speeds up the linear fitting process. The experiment will verify the performance of CRJ model to predict stacks voltage under static current and quasi-dynamic current conditions. In addition, the reliability of the CRJ model is verified with different amount of data as the training and test sets. The experimental results demonstrate that the CRJ model can achieve better effect in the remaining useful life prognosis of fuel cells. DA - 2021/11/18/ PY - 2021/11/18/ DO - 10.1016/j.ijhydene.2021.09.233 VL - 46 IS - 80 SP - 40001-40013 SN - 1879-3487 KW - Proton exchange membrane fuel cell KW - Remaining useful life KW - Reservoir computing KW - Data-driven KW - Cycle reservoir with jump ER - TY - JOUR TI - A Survey of Defensive Deception: Approaches Using Game Theory and Machine Learning AU - Zhu, Mu AU - Anwar, Ahmed H. AU - Wan, Zelin AU - Cho, Jin-Hee AU - Kamhoua, Charles A. AU - Singh, Munindar P. T2 - IEEE COMMUNICATIONS SURVEYS AND TUTORIALS AB - Defensive deception is a promising approach for cyber defense. Via defensive deception, a defender can anticipate and prevent attacks by misleading or luring an attacker, or hiding some of its resources. Although defensive deception is garnering increasing research attention, there has not been a systematic investigation of its key components, the underlying principles, and its tradeoffs in various problem settings. This survey focuses on defensive deception research centered on game theory and machine learning, since these are prominent families of artificial intelligence approaches that are widely employed in defensive deception. This paper brings forth insights, lessons, and limitations from prior work. It closes with an outline of some research directions to tackle major gaps in current defensive deception research. DA - 2021/// PY - 2021/// DO - 10.1109/COMST.2021.3102874 VL - 23 IS - 4 SP - 2460-2493 SN - 1553-877X UR - https://doi.org/10.1109/COMST.2021.3102874 KW - Games KW - Tutorials KW - Taxonomy KW - Computer security KW - Planning KW - Monitoring KW - Measurement KW - Defensive deception KW - cybersecurity KW - game theory KW - machine learning ER - TY - JOUR TI - Probabilistic Iterative Methods for Linear Systems T2 - Journal of Machine Learning Research DA - 2021/// PY - 2021/// UR - http://jmlr.org/papers/v22/21-0031.html ER - TY - JOUR TI - A projector-based approach to quantifying total and excess uncertainties for sketched linear regression AU - Chi, Jocelyn T. AU - Ipsen, Ilse C. F. T2 - INFORMATION AND INFERENCE-A JOURNAL OF THE IMA AB - Abstract Linear regression is a classic method of data analysis. In recent years, sketching—a method of dimension reduction using random sampling, random projections or both—has gained popularity as an effective computational approximation when the number of observations greatly exceeds the number of variables. In this paper, we address the following question: how does sketching affect the statistical properties of the solution and key quantities derived from it? To answer this question, we present a projector-based approach to sketched linear regression that is exact and that requires minimal assumptions on the sketching matrix. Therefore, downstream analyses hold exactly and generally for all sketching schemes. Additionally, a projector-based approach enables derivation of key quantities from classic linear regression that account for the combined model- and algorithm-induced uncertainties. We demonstrate the usefulness of a projector-based approach in quantifying and enabling insight on excess uncertainties and bias-variance decompositions for sketched linear regression. Finally, we demonstrate how the insights from our projector-based analyses can be used to produce practical sketching diagnostics to aid the design of judicious sketching schemes. DA - 2021/8/11/ PY - 2021/8/11/ DO - 10.1093/imaiai/iaab016 VL - 8 SP - SN - 2049-8772 UR - http://dx.doi.org/10.1093/imaiai/iaab016 KW - expectation KW - variance KW - bias KW - mean squared error KW - predictive risk ER - TY - JOUR TI - Order release in production planning and control systems: challenges and opportunities AU - Missbauer, Hubert AU - Uzsoy, Reha T2 - INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH AB - Production planning and control (PPC) systems are important to the competitiveness of manufacturing firms and their ability to benefit from recent technological advances. We give an overview of research and unsolved research questions in PPC systems related to the order release function that sets output targets for autonomous production units that manage production at the shop-floor level. We describe the hierarchical Manufacturing Planning and Control (MPC) and Advanced Planning and Scheduling (APS) architectures for PPC systems prevalent in practice and the positioning of the order release task within these frameworks. We then describe the research streams relevant to order release, optimisation models for order release planning, and the relation of these models to the overall PPC system. Modelling the dynamic response of production units to time-varying work input, which represents an extension of classical production theory by incorporating the time dimension, is identified as the major modelling challenge. We conclude by suggesting several unsolved research questions that should encourage researchers to work on this topic which is far from mature. DA - 2021/11/3/ PY - 2021/11/3/ DO - 10.1080/00207543.2021.1994165 VL - 11 SP - SN - 1366-588X KW - Production planning KW - order release KW - cycle times KW - hierarchical production planning KW - optimization ER - TY - JOUR TI - Editorial AU - Uzsoy, Reha T2 - IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING AB - As another year draws to a close, TSM can look back on a busy but successful year. In February 2021 we underwent our five-yearly review by IEEE’s Publications Review and Advisory Committee (PRAC), and I am happy to say that we passed with flying colors. The reviewers made several excellent suggestions for further improvement, including conducting a survey of our readers and authors to assess their satisfaction with the current state of TSM and receive their input for the future. We have already begun acting on these suggestions, and I will keep the community informed as they develop. DA - 2021/11// PY - 2021/11// DO - 10.1109/TSM.2021.3119086 VL - 34 IS - 4 SP - 443-443 SN - 1558-2345 UR - https://doi.org/10.1109/TSM.2021.3119086 ER - TY - JOUR TI - Deserv: Decentralized Serverless Computing AU - Christie, Samuel H. AU - Chopra, Amit K. AU - Singh, Munindar P. T2 - 2021 IEEE INTERNATIONAL CONFERENCE ON WEB SERVICES, ICWS 2021 AB - A decentralized application involves multiple autonomous principals, e.g., humans and organizations. Autonomy motivates (i) specifying a decentralized application via a protocol that captures the interactions between the principals, and (ii) a programming model that enables each principal to independently (from other principals) construct its own protocol-compliant agent. An agent encodes its principal's decision making and represents it in the application. We contribute Deserv, the first protocol-based programming model for decentralized applications that is suited to the cloud. Specifically, Deserv demonstrates how to leverage function-as-a-service (FaaS), a popular serverless programming model, to implement agents. A notable feature of Deserv is the use declarative protocols to specify interactions. Declarative protocols support implementing stateful agents in a manner that naturally exploits the concurrency and autoscaling benefits offered by serverless computing. DA - 2021/// PY - 2021/// DO - 10.1109/ICWS53863.2021.00020 SP - 51-60 UR - https://doi.org/10.1109/ICWS53863.2021.00020 KW - multiagent systems KW - protocols KW - programming model ER - TY - JOUR TI - A simple and robust approach for expected shortfall estimation AU - Pan, Zhibin AU - Pang, Tao AU - Zhao, Yang T2 - JOURNAL OF COMPUTATIONAL FINANCE AB - In risk management, estimating expected shortfall, though important and indispensable, is difficult when the sample size is small. This paper suggests a recipe for meeting such a challenge. A tail-based normal approximation with explicit formulas is derived by matching a specific quantile and the mean excess square of the sample observations. To enhance the estimation accuracy, we propose an adjusted tail-based normal approximation based on the sample's tail weight. The adjusted expected shortfall estimator is robust and efficient in the sense that it can be applied to various heavy-tailed distributions, such as Student t, lognormal, Gamma and Weibull, and the errors are all small. Moreover, compared with two common expected shortfall estimators -- the arithmetic average of excessive losses and extreme value theory estimator -- the proposed estimator achieves smaller mean squared errors for small samples, especially at high confidence levels. The properties of linear transformations on the expected shortfall estimator are also investigated to ensure its practicality. DA - 2021/6// PY - 2021/6// DO - 10.21314/JCF.2021.003 VL - 25 IS - 1 SP - 77-107 SN - 1755-2850 KW - expected shortfall KW - tail-based normal approximation KW - conditional skewness KW - tail-weight adjustment KW - heavy-tailed distribution KW - small sample ER - TY - JOUR TI - A genetic algorithm for order acceptance and scheduling in additive manufacturing AU - Kapadia, Maaz Saleem AU - Uzsoy, Reha AU - Starly, Binil AU - Warsing, Donald P., Jr. T2 - INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH AB - We consider the problem of order acceptance and scheduling faced by an additive manufacturing facility consisting of multiple build chambers and postprocessing operations for support removal and surface finishing. We model each build chamber as a batch processing machine with processing times determined by the nesting and orientation of parts within the chamber. Due to the difficulty of developing an explicit functional relation between part batching, batch processing time, and postprocessing requirements we develop random-keys based genetic algorithms to select orders for complete or partial acceptance and produce a high-quality schedule satisfying all technological constraints, including part orientation and rotation within the build chamber. Extensive computational experiments show that the proposed approaches yield significant improvements in profit over the situation where all orders must be accepted, and produce solutions that compare favourably to statistically estimated bounds. DA - 2021/10/23/ PY - 2021/10/23/ DO - 10.1080/00207543.2021.1991023 VL - 10 SP - SN - 1366-588X KW - Order acceptance scheduling KW - genetic algorithms KW - additive manufacturing KW - statistical optimum estimation KW - batch machine scheduling ER - TY - JOUR TI - The maximum entropy method for data fusion and uncertainty quantification in multifunctional materials and structures AU - Gao, Wei AU - Miles, Paul R. AU - Smith, Ralph C. AU - Oates, William S. T2 - JOURNAL OF INTELLIGENT MATERIAL SYSTEMS AND STRUCTURES AB - The quantification of uncertainty in intelligent material systems and structures requires methods to objectively compare complex models to measurements, where the majority of cases include multiple model outputs and quantities of interests given multiphysics coupling. This creates questions about constructing appropriate measures of uncertainty during fusion of data and comparisons between data and models. Novel materials with complex or poorly understood coupling can benefit from advanced statistical analysis to judge models in light of multiphysics data. Here, we apply the Maximum Entropy (ME) method to more complicated ferroelectric single crystals containing domain structures and soft electrostrictive membranes under both mechanical and electrical loading. Multiple quantities of interest are considered, which requires fusing heterogeneous information together when quantifying the uncertainty of lower fidelity models. We find that parameters, which were initially unidentifiable using a single quantity of interest, become identifiable using multiple quantities of interest. We also show that posterior densities may broaden or narrow when multiple data sets are fused together. This is likely due to conflict or agreement, respectively, between the different quantities of interest and the multiple model outputs. Such information is important to advance our predictions of intelligent materials and structures from multi-model inputs and heterogeneous data. DA - 2021/10/8/ PY - 2021/10/8/ DO - 10.1177/1045389X211048220 VL - 10 SP - SN - 1530-8138 KW - Maximum entropy KW - data fusion KW - uncertainty quantification KW - dielectric elastomer membrane KW - ferroelectric domain wall ER - TY - JOUR TI - Interpolation Methods for Molecular Potential Energy Surface Construction AU - Kwon, Hyuk-Yong AU - Morrow, Zachary AU - Kelley, C. T. AU - Jakubikova, Elena T2 - JOURNAL OF PHYSICAL CHEMISTRY A AB - The concept of a potential energy surface (PES) is one of the most important concepts in modern chemistry. A PES represents the relationship between the chemical system's energy and its geometry (i.e., atom positions) and can provide useful information about the system's chemical properties and reactivity. Construction of accurate PESs with high-level theoretical methodologies, such as density functional theory, is still challenging due to a steep increase in the computational cost with the increase of the system size. Thus, over the past few decades, many different mathematical approaches have been applied to the problem of the cost-efficient PES construction. This article serves as a short overview of interpolative methods for the PES construction, including global polynomial interpolation, trigonometric interpolation, modified Shepard interpolation, interpolative moving least-squares, and the automated PES construction derived from these. DA - 2021/11/18/ PY - 2021/11/18/ DO - 10.1021/acs.jpca.1c06812 VL - 125 IS - 45 SP - 9725-9735 SN - 1520-5215 UR - https://doi.org/10.1021/acs.jpca.1c06812 ER - TY - JOUR TI - Establishing Operational Norms for Labor Rights Standards Implementation in Low-Cost Apparel Production AU - Hasan, Rejaul AU - Moore, Marguerite AU - Handfield, Robert T2 - SUSTAINABILITY AB - Low-cost production has driven many global apparel brands and retailers to source apparel from less developed countries. However, low-cost apparel production is often accompanied by labor rights violations. A persistent pattern of labor rights violations exists in the global apparel supply chains, including minimum wage violations, unpaid overtime, forced overtime, worker abuse, restricting workers’ unions, and many other violations. Research suggests that low-cost pressures restrict factory level resources, which often leads to labor rights violations in global apparel supply chains. To date, academics and practitioners remain unaware of the actual cost of implementing labor rights standards in factories. We sought to establish a baseline taxonomy of the fundamental cost-bearing activities required to provide a safe and ethical factory workplace. A Delphi survey was adopted to capture data from an expert group of experienced factory compliance auditors in Asian apparel production. The research provides practical insights for factory adoption of actions that can improve enforcement of multiple labor standards, as well as specific actions required to enforce unique requirements that arose in our analysis. DA - 2021/11// PY - 2021/11// DO - 10.3390/su132112120 VL - 13 IS - 21 SP - SN - 2071-1050 UR - https://doi.org/10.3390/su132112120 KW - apparel KW - production KW - labor rights standards KW - Delphi KW - minimum cost KW - safe factory ER - TY - JOUR TI - Scheduling to Differentiate Service in a Multiclass Service System AU - Liu, Yunan AU - Sun, Xu AU - Hovey, Kyle T2 - OPERATIONS RESEARCH AB - Dynamic Scheduling to Differentiate Delay-Based Service Levels in Multiclass Service Systems DA - 2021/3/8/ PY - 2021/3/8/ DO - 10.1287/opre.2020.2075 SP - SN - 0030-364X KW - dynamic scheduling KW - dynamic prioritization KW - time-varying staffing KW - efficiency-driven KW - heavy-traffic approximations KW - service differentiation KW - tail probability of delay ER - TY - JOUR TI - Visual Fatigue Alleviating in Stereo Imaging of Anaglyphs by Reducing Retinal Rivalry and Color Distortion Based on Mobile Virtual Reality Technology AU - Qi, Min AU - Cui, Shanshan AU - Du, Qianmin AU - Xu, Yuelei AU - McAllister, David F. T2 - WIRELESS COMMUNICATIONS & MOBILE COMPUTING AB - Stereoscopic display is the means of showing scenes in Virtual Reality (VR). As a type of stereo images, anaglyphs can be displayed not only on the screen, but are currently the only solution of stereo images that can be displayed on paper. However, its deficiencies, like retinal rivalry and color distortion, could cause visual fatigue. To address this issue, an algorithm is proposed for anaglyph generation. Unlike previous studies only considering one aspect, it considers both retinal rivalry and color distortion at the same time. The algorithm works in the CIE L a b color space and focuses on matching the perceptual color attributes especially the hue, rather than directly minimizes the sum of the distances between the perceived anaglyph color and the stereo image pair. In addition, the paper builds a relatively complete framework to generate anaglyphs so that it is more controllable to adjust the parameters and choose the appropriate process. The subjective tests are conducted to compare the results with several techniques which generate anaglyphs including empirical methods and computing methods. Results show that the proposed algorithm has a good performance. DA - 2021/9/16/ PY - 2021/9/16/ DO - 10.1155/2021/1285712 VL - 2021 SP - SN - 1530-8677 ER - TY - JOUR TI - Minimizing Total Logistics Cost for Long-Haul Multi-Stop Truck Transportation AU - Kay, Michael G. AU - Karagul, Kenan AU - Sahin, Yusuf AU - Gunduz, Gurhan T2 - TRANSPORTATION RESEARCH RECORD AB - Whenever there is sufficient demand, companies generally prefer the full truckload (TL) option for long-distance transport, resulting in large and less frequent shipment operations that can be costly if inventory carrying costs are high. Less than truckload (LTL) is another option for transport when carrying costs are high and/or there is insufficient demand. Shipment consolidation provides another option that combines many of the benefits of both TL and LTL. Shipment consolidation is a cost-effective transport solution that combines different size shipments into a single truckload. Combining many loads as a single load brings together economies of scale and potential savings. Traditional routing techniques that minimize distance are not suitable for shipments that have different origins and destinations because it can be beneficial to travel further to minimize overall transport and inventory cost, or what is termed total logistics cost (TLC). Effective consolidation of multi-stop routes to minimize TLC requires routing procedures that are more computationally intensive to find beneficial combinations of loads into consolidated shipments. In this study, we have developed a saving-based procedure to determine consolidated route sequences that minimize the TLC of shipments. Twenty-one data sets were produced using real city coordinates and population densities in North Carolina to demonstrate the effectiveness of the procedure. The solutions of the proposed method are compared with the solutions of the traditional Clarke and Wright (C-W) algorithm. Although the traditional C-W algorithm provides very fast solution times, the proposed method has produced much better solution values. DA - 2021/9/23/ PY - 2021/9/23/ DO - 10.1177/03611981211041596 VL - 9 SP - SN - 2169-4052 ER - TY - JOUR TI - An Analytical Tool for Constructing and Evaluating Testing Strategic for COVID-19 AU - Kouri, Richard AU - Warsing, Donald AU - Singh, Nikhil AU - Thomas, Beena AU - Handfield, Robert B AB - Abstract Background This paper describes the utilization of a mathematical modeling tool for evaluating alternative testing cadences for the SARS-CoV-2 virus that are applicable to any well-contained congregate setting. These settings include long-term care facilities, and public-school systems. Results Variables analyzed include population sizes, contagion factor, and unique testing objectives that congregate settings might have (e.g., differing susceptibilities, or varying underlying health conditions). The tool helps evaluate cost vs benefit for a range of testing cadences (e.g., daily, every 2 days, every 3 days, every week, every 2 weeks every 3 weeks and every 4 weeks) based on use of a commercially available antigen testing kit that costs $5 per test. Conclusions Critical parameters derived as output of the model include total persons tested, average number in quarantine, average percent positives in quarantine, total testing cost, total infections allowed, cases averted, and cost per case averted. These parameters allow public health officials, site managers and/or on-site healthcare workers to optimize testing plans to align with available resources and support fact-based decision making. We also discuss how this tool can work with vaccine roll-out both in the United States and elsewhere. DA - 2021/10/5/ PY - 2021/10/5/ DO - 10.21203/rs.3.rs-812275/v1 VL - 10 UR - https://doi.org/10.21203/rs.3.rs-812275/v1 ER - TY - JOUR TI - The role of psychological distance in organizational responses to modern slavery risk in supply chains AU - Simpson, Dayna AU - Segrave, Marie AU - Quarshie, Anne AU - Kach, Andrew AU - Handfield, Robert AU - Panas, George AU - Moore, Heather T2 - JOURNAL OF OPERATIONS MANAGEMENT AB - Abstract Modern slavery is used to describe forms of coercive labor exploitation that affect more than 40 million persons globally. Such practices are difficult to identify given they exist in the informal economy, and involve vulnerable individuals. Addressing modern slavery by organizations requires awareness of its context and complexities. While corporations have increasingly sought to manage modern slavery risk in their supply chains, their understanding of what modern slavery is and what should be managed remains limited. We argue a key problem with firms’ efforts to manage modern slavery risk is that it is a psychologically distant concept for them. We apply construal level theory to explore how organizations’ psychological distance from modern slavery risk affects their management of risk. We interviewed purchasing executives at 41 global organizations in Australia, Finland, and the U.S and identified four approaches to managing modern slavery risk at different levels of psychological distance. We also identified that conflicts between organizations' approaches to risk and what they identify in their operating environment, precedes important construal shifts that help to improve organizational understanding of labor‐related risk. We highlight ways that organizations' understanding of modern slavery risk plays a role in their governance of such risk in supply chains. DA - 2021/9/24/ PY - 2021/9/24/ DO - 10.1002/joom.1157 VL - 9 SP - SN - 1873-1317 KW - Modern Slavery KW - Social Responsibility KW - Sustainable Supply Chain KW - Labor Governance KW - Social Impacts ER - TY - JOUR TI - Mind your own customers and ignore the others: Asymptotic optimality of a local policy in multi-class queueing systems with customer feedback AU - Yang, Jiankui AU - Huang, Junfei AU - Liu, Yunan T2 - IISE TRANSACTIONS DA - 2021/7/25/ PY - 2021/7/25/ DO - 10.1080/24725854.2021.1952358 SP - SN - 2472-5862 KW - Multi-class queueing systems KW - asymptotic optimality KW - customer feedback KW - local information KW - convex delay cost KW - cl-rule ER - TY - JOUR TI - An interpretable framework for investigating the neighborhood effect in POI recommendation AU - Yuan, Guangchao AU - Singh, Munindar P. AU - Murukannaiah, Pradeep K. T2 - PLOS ONE AB - Geographical characteristics have been proven to be effective in improving the quality of point-of-interest (POI) recommendation. However, existing works on POI recommendation focus on cost (time or money) of travel for a user. An important geographical aspect that has not been studied adequately is the neighborhood effect , which captures a user’s POI visiting behavior based on the user’s preference not only to a POI, but also to the POI’s neighborhood. To provide an interpretable framework to fully study the neighborhood effect, first, we develop different sets of insightful features, representing different aspects of neighborhood effect. We employ a Yelp data set to evaluate how different aspects of the neighborhood effect affect a user’s POI visiting behavior. Second, we propose a deep learning–based recommendation framework that exploits the neighborhood effect. Experimental results show that our approach is more effective than two state-of-the-art matrix factorization–based POI recommendation techniques. DA - 2021/8/5/ PY - 2021/8/5/ DO - 10.1371/journal.pone.0255685 VL - 16 IS - 8 SP - SN - 1932-6203 ER - TY - JOUR TI - Performance tradeoffs for spare parts supply chains with additive manufacturing capability servicing intermittent demand AU - McDermott, Kyle C. AU - Winz, Ryan D. AU - Hodgson, Thom J. AU - Kay, Michael G. AU - King, Russell E. AU - McConnell, Brandon M. T2 - Journal of Defense Analytics and Logistics AB - Purpose The study aims to investigate the impact of additive manufacturing (AM) on the performance of a spare parts supply chain with a particular focus on underlying spare part demand patterns. Design/methodology/approach This work evaluates various AM-enabled supply chain configurations through Monte Carlo simulation. Historical demand simulation and intermittent demand forecasting are used in conjunction with a mixed integer linear program to determine optimal network nodal inventory policies. By varying demand characteristics and AM capacity this work assesses how to best employ AM capability within the network. Findings This research assesses the preferred AM-enabled supply chain configuration for varying levels of intermittent demand patterns and AM production capacity. The research shows that variation in demand patterns alone directly affects the preferred network configuration. The relationship between the demand volume and relative AM production capacity affects the regions of superior network configuration performance. Research limitations/implications This research makes several simplifying assumptions regarding AM technical capabilities. AM production time is assumed to be deterministic and does not consider build failure probability, build chamber capacity, part size, part complexity and post-processing requirements. Originality/value This research is the first study to link realistic spare part demand characterization to AM supply chain design using quantitative modeling. DA - 2021/12/14/ PY - 2021/12/14/ DO - 10.1108/JDAL-08-2020-0016 VL - ahead-of-print IS - ahead-of-print SP - 179-213 UR - https://doi.org/10.1108/JDAL-08-2020-0016 ER - TY - JOUR TI - Optimal strategies of contract-farming supply chain under the cooperative mode of bank-insurance: loan guarantee insurance versus yield insurance AU - Shi, Ligang AU - Pang, Tao AU - Peng, Hongjun T2 - INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH AB - Abstract We investigate the impact of two insurance mechanisms: loan guarantee insurance and yield insurance, on a capital‐constrained contract‐farming supply chain where the farmer is risk‐averse and faces yield uncertainty. Using the sequential model, we derive the farmer's optimal farm size, the agrodealer's optimal wholesale price and the insurance company's optimal premium rate. The result shows that under both insurance mechanisms, premium subsidies can promote the farmer to increase the farm size. However, we find that under loan guarantee insurance, when the premium subsidy is relatively high, the farmer's farm size increases with the poor harvest risk. Under yield insurance, the farmer's conditional value‐at‐risk (CVaR) value increases with respect to the premium subsidy rate. Under loan guarantee insurance, the premium rate increases with respect to the premium subsidy. As a result, the farmer's CVaR value decreases with respect to the premium subsidy rate when it is relatively high. Further analysis indicates that with the same government subsidy expenditure, the efficiency of premium subsidies to increase the farmer's production quantity is better under loan guarantee insurance mode, but the efficiency of premium subsidies to increase the farmer's CVaR value is better under yield insurance. DA - 2021/8/30/ PY - 2021/8/30/ DO - 10.1111/itor.13051 SP - SN - 1475-3995 KW - contract farming KW - loan guarantee insurance KW - yield insurance KW - capital constraint KW - premium subsidy ER - TY - JOUR TI - Toward a rational and ethical sociotechnical system of autonomous vehicles: A novel application of multi-criteria decision analysis AU - Dubljevic, Veljko AU - List, George AU - Milojevich, Jovan AU - Ajmeri, Nirav AU - Bauer, William A. AU - Singh, Munindar P. AU - Bardaka, Eleni AU - Birkland, Thomas A. AU - Edwards, Charles H. W. AU - Mayer, Roger C. AU - Muntean, Ioan AU - Powers, Thomas M. AU - Rakha, Hesham A. AU - Ricks, Vance A. AU - Samandar, M. Shoaib T2 - PLOS ONE AB - The impacts of autonomous vehicles (AV) are widely anticipated to be socially, economically, and ethically significant. A reliable assessment of the harms and benefits of their large-scale deployment requires a multi-disciplinary approach. To that end, we employed Multi-Criteria Decision Analysis to make such an assessment. We obtained opinions from 19 disciplinary experts to assess the significance of 13 potential harms and eight potential benefits that might arise under four deployments schemes. Specifically, we considered: (1) the status quo, i.e., no AVs are deployed; (2) unfettered assimilation, i.e., no regulatory control would be exercised and commercial entities would "push" the development and deployment; (3) regulated introduction, i.e., regulatory control would be applied and either private individuals or commercial fleet operators could own the AVs; and (4) fleets only, i.e., regulatory control would be applied and only commercial fleet operators could own the AVs. Our results suggest that two of these scenarios, (3) and (4), namely regulated privately-owned introduction or fleet ownership or autonomous vehicles would be less likely to cause harm than either the status quo or the unfettered options. DA - 2021/// PY - 2021/// DO - 10.1371/journal.pone.0256224 VL - 16 IS - 8 SP - SN - 1932-6203 ER - TY - JOUR TI - Optimal asset allocation with restrictions on liquidity AU - Medhin, Negash AU - Xu, Chuan T2 - STOCHASTIC ANALYSIS AND APPLICATIONS AB - An optimal asset allocation problem involving restrictions on liquidity is studied in this article. The portfolio consists of liquid and illiquid asset. The portfolio is only allowed to rebalance at particular times. An investor tries to maximize the total utility of a hyperbolic absolute risk aversion function depending on the consumption, which is sourced only from the liquid asset. The optimal policies of the consumption, investment, and allocation are derived. A numerical approximation scheme is developed to show the optimal allocation policy in our model is path-dependent. Paths of the value function and other optimal controls are illustrated to validate our results. DA - 2021/7/24/ PY - 2021/7/24/ DO - 10.1080/07362994.2021.1959349 SP - SN - 1532-9356 KW - Optimal asset allocation KW - liquidity KW - hyperbolic absolute risk aversion utility function KW - Hamilton-Jacobi-Bellman equation ER - TY - JOUR TI - Deep learning and regression approaches to forecasting blood glucose levels for type 1 diabetes AU - Zhang, Meng AU - Flores, Kevin B. AU - Tran, Hien T. T2 - BIOMEDICAL SIGNAL PROCESSING AND CONTROL AB - Objective: Controlling blood glucose in the euglycemic range is the main goal of developing the closed-loop insulin delivery system for type 1 diabetes patients. The closed-loop system delivers the amount of insulin dose determined by glucose predictions through the use of computational algorithms. A computationally efficient and accurate model that can capture the physiological nonlinear dynamics is critical for developing an efficient closed-loop system. Methods: Four data-driven models are compared, including different neural network architectures, a reservoir computing model, and a novel linear regression approach. Model predictions are evaluated over continuous 30 and 60 min time horizons using real-world data from wearable sensor measurements, a continuous glucose monitor, and self-reported events through mobile applications. The four data-driven models are trained on 12 data contributors for around 32 days, 8 days of data are used for validation, with an additional 10 days of data for out-of-sample testing. Model performance was evaluated by the root mean squared error and the mean absolute error. Results: A neural network model using an encoder-decoder architecture has the most stable performance and is able to recover missing dynamics in short time intervals. Regression models performed better at long-time prediction horizons (i.e., 60 min) and with lower computational costs. Significance: The performance of several distinct models was tested for individual-level data from a type 1 diabetes data set. These results may enable a feasible solution with low computational cost for the time-dependent adjustment of artificial pancreas for diabetes patients. DA - 2021/8// PY - 2021/8// DO - 10.1016/j.bspc.2021.102923 VL - 69 SP - SN - 1746-8108 KW - Neural networks KW - Regression KW - Encoder decoder KW - Time series forecasting KW - Diabetes ER - TY - JOUR TI - Significance of multi-hazard risk in design of buildings under earthquake and wind loads AU - Kwag, Shinyoung AU - Gupta, Abhinav AU - Baugh, John AU - Kim, Hyun-Su T2 - ENGINEERING STRUCTURES AB - • Development of a performance-based framework to consider multiple hazards. • Significance of multi-hazard design is shown through retrofit solutions in buildings. • Cost-effective damper design is explored under two different hazards. Traditionally, external hazards are considered in the design of a building through the various combinations of loads prescribed in relevant design codes and standards. It is often the case that the design is governed by a single dominant hazard at a given geographic location. This is particularly true for earthquake and wind hazards, both of which impart time-dependent dynamic loads on the structure. Engineers may nevertheless wonder if a building designed for one of the two dominant hazards will satisfactorily withstand the other. Prior studies have indicated that in some cases, when a building is designed for a single dominant hazard, it does not necessarily provide satisfactory performance against the other hazard. In this paper, we propose a novel framework that builds upon performance-based design requirements and determines whether the design of a building is governed primarily by a single hazard or multiple hazards. It integrates site-dependent hazard characteristics with the performance criteria for a given building type and building geometry. The framework is consistent with the burgeoning area of probabilistic risk assessment, and yet can easily be extended to traditional, deterministically characterized design requirements as illustrated herein. DA - 2021/9/15/ PY - 2021/9/15/ DO - 10.1016/j.engstruct.2021.112623 VL - 243 SP - SN - 1873-7323 KW - Earthquake and wind hazards KW - Performance-based design KW - Risk-based multi-hazard approach KW - Multi-hazard risk map KW - Multi-hazard scenario KW - Magneto-rheological damper KW - Adjacent buildings ER - TY - JOUR TI - Hercule: Representing and Reasoning About Norms as a Foundation for Declarative Contracts Over Blockchain AU - Christie, Samuel H. AU - Singh, Munindar P. AU - Chopra, Amit K. T2 - IEEE INTERNET COMPUTING AB - Current blockchain approaches for business contracts are based on smart contracts, namely, software programs placed on a blockchain that are automatically executed to realize a contract. However, smart contracts lack flexibility and interfere with the autonomy of the parties concerned. We propose Hercule, an approach for declaratively specifying blockchain applications in a manner that reflects business contracts. Hercule represents a contract via regulatory norms that capture the involved parties’ expectations of one another. It computes the states of norms (hence, of contracts) from events in the blockchain. Hercule’s novelty and significance lie in that it operationalizes declarative contracts over semistructured databases, the underlying representation for practical blockchain such as Hyperledger Fabric and Ethereum. Specifically, it exploits the map–reduce capabilities of such stores to compute norm states. We demonstrate that our implementation over Hyperledger Fabric can process thousands of events per second, sufficient for many applications. DA - 2021/// PY - 2021/// DO - 10.1109/MIC.2021.3080982 VL - 25 IS - 4 SP - 67-75 SN - 1941-0131 UR - https://doi.org/10.1109/MIC.2021.3080982 KW - Blockchain KW - History KW - Smart contracts KW - Distributed ledger KW - Law KW - Authorization KW - Blockchain KW - Contract KW - Regulatory norm KW - Document store ER - TY - JOUR TI - A Gray Box Conceptual Model for Accountability and Ethics in Business Contracts AU - Singh, Munindar P. AU - Gao, Xibin T2 - IEEE INTERNET COMPUTING AB - Current computational models are inadequate for the purposes of modeling interactions between autonomous parties in a way that highlights and supports their accountability. We propose a new conceptual model for business contracts based on norms motivated by a review of real-life business contracts. Our conception is of a gray box, reflecting the idea that a contract makes the participants accountable to one another and to outside entities, and therefore calls for the exposure of sufficient implementation details. The model consists of a recursively applicable taxonomy of clause types. In a preliminary study, we found that computer scientists are able to effectively identify the concepts introduced in this model, thereby indicating its potential for building Internet applications that support accountability. DA - 2021/// PY - 2021/// DO - 10.1109/MIC.2021.3083295 VL - 25 IS - 4 SP - 13-19 SN - 1941-0131 UR - https://doi.org/10.1109/MIC.2021.3083295 KW - Ethics KW - Computational modeling KW - Taxonomy KW - Business KW - Internet KW - Contracts ER - TY - JOUR TI - Reduced-dimensional surface hopping with offline-online computations AU - Morrow, Zachary AU - Kwon, Hyuk-Yong AU - Kelley, C. T. AU - Jakubikova, Elena T2 - PHYSICAL CHEMISTRY CHEMICAL PHYSICS AB - We simulate the photodissociation of azomethane with a fewest-switches surface hopping method on reduced-dimensional potential energy surfaces constructed with sparse grid interpolation. DA - 2021/8/19/ PY - 2021/8/19/ DO - 10.1039/D1CP03446D VL - 8 SP - SN - 1463-9084 UR - https://doi.org/10.1039/D1CP03446D ER - TY - JOUR TI - Efficient Approximation of Potential Energy Surfaces with Mixed-Basis Interpolation AU - Morrow, Zachary AU - Kwon, Hyuk-Yong AU - Kelley, C. T. AU - Jakubikova, Elena T2 - JOURNAL OF CHEMICAL THEORY AND COMPUTATION AB - The potential energy surface (PES) describes the energy of a chemical system as a function of its geometry and is a fundamental concept in modern chemistry. A PES provides much useful information about the system, including the structures and energies of various stationary points, such as stable conformers (local minima) and transition states (first-order saddle points) connected by a minimum-energy path. Our group has previously produced surrogate reduced-dimensional PESs using sparse interpolation along chemically significant reaction coordinates, such as bond lengths, bond angles, and torsion angles. These surrogates used a single interpolation basis, either polynomials or trigonometric functions, in every dimension. However, relevant molecular dynamics (MD) simulations often involve some combination of both periodic and nonperiodic coordinates. Using a trigonometric basis on nonperiodic coordinates, such as bond lengths, leads to inaccuracies near the domain boundary. Conversely, polynomial interpolation on the periodic coordinates does not enforce the periodicity of the surrogate PES gradient, leading to nonconservation of total energy even in a microcanonical ensemble. In this work, we present an interpolation method that uses trigonometric interpolation on the periodic reaction coordinates and polynomial interpolation on the nonperiodic coordinates. We apply this method to MD simulations of possible isomerization pathways of azomethane between cis and trans conformers. This method is the only known interpolative method that appropriately conserves total energy in systems with both periodic and nonperiodic reaction coordinates. In addition, compared to all-polynomial interpolation, the mixed basis requires fewer electronic structure calculations to obtain a given level of accuracy, is an order of magnitude faster, and is freely available on GitHub. DA - 2021/9/14/ PY - 2021/9/14/ DO - 10.1021/acs.jctc.1c00569 VL - 17 IS - 9 SP - 5673-5683 SN - 1549-9626 UR - https://doi.org/10.1021/acs.jctc.1c00569 ER - TY - JOUR TI - Original Learning Drug-Disease-Target Embedding (DDTE) from knowledge graphs to inform drug repurposing hypotheses AU - Moon, Changsung AU - Jin, Chunming AU - Dong, Xialan AU - Abrar, Saad AU - Zheng, Weifan AU - Chirkova, Rada Y. AU - Tropsha, Alexander T2 - JOURNAL OF BIOMEDICAL INFORMATICS AB - We aimed to develop and validate a new graph embedding algorithm for embedding drug-disease-target networks to generate novel drug repurposing hypotheses. Our model denotes drugs, diseases and targets as subjects, predicates and objects, respectively. Each entity is represented by a multidimensional vector and the predicate is regarded as a translation vector from a subject to an object vectors. These vectors are optimized so that when a subject-predicate-object triple represents a known drug-disease-target relationship, the summed vector between the subject and the predicate is to be close to that of the object; otherwise, the summed vector is distant from the object. The DTINet dataset was utilized to test this algorithm and discover unknown links between drugs and diseases. In cross-validation experiments, this new algorithm outperformed the original DTINet model. The MRR (Mean Reciprocal Rank) values of our models were around 0.80 while those of the original model were about 0.70. In addition, we have identified and verified several pairs of new therapeutic relations as well as adverse effect relations that were not recorded in the original DTINet dataset. This approach showed excellent performance, and the predicted drug-disease and drug-side-effect relationships were found to be consistent with literature reports. This novel method can be used to analyze diverse types of emerging biomedical and healthcare-related knowledge graphs (KG). DA - 2021/7// PY - 2021/7// DO - 10.1016/j.jbi.2021.103838 VL - 119 SP - SN - 1532-0480 KW - Data mining KW - Graph embedding KW - Knowledge graph KW - Drug repurposing ER - TY - JOUR TI - Covid-19 response of an additive manufacturing cluster in Australia AU - Boehme, Tillmann AU - Aitken, James AU - Turner, Neil AU - Handfield, Robert T2 - SUPPLY CHAIN MANAGEMENT-AN INTERNATIONAL JOURNAL AB - Purpose The sudden arrival of Covid-19 severely disrupted the supply chain of personal protective equipment (PPE) in Australia. This paper aims to examine the development of a geographical cluster, which, through the application of additive manufacturing (AM), responded to the PPE supply crisis. Design/methodology/approach This longitudinal case study focuses on an AM cluster, which was developed to supply PPE in a responsive and flexible manner from 2019/2020. The study gathered data over three stages of cluster evolution: pre, during and post-peak Covid-19. Findings The type and nature of exchanges between organizations involved in the cluster established important insights into success factors for cluster creation and development. Using an established complexity framework, this study identifies the characteristics of establishing a cluster. The importance of cluster alignment created initially by a common PPE supply goal led to an emerging commercial and relational imperative to address the longer-term configuration after the disruption. Practical implications Clusters can be a viable option for a technology-driven sector when there is a “buzz” that drives and rapidly diffuses knowledge to support cluster formation. This research identifies the structural, socio-political and emergent dimensions, which need to be considered by stakeholders when aiming at improving competitiveness using clusters. Originality/value Covid-19 has rapidly and unexpectedly disrupted the supply chain for many industries. Responding to challenges, businesses will investigate different pathways to improve the overall resilience including on-/near-shoring. The results provide insights into how clusters are formed, grow and develop and the differentiating factors that result in successful impacts of clusters on local economies. DA - 2021/7/22/ PY - 2021/7/22/ DO - 10.1108/SCM-07-2020-0350 VL - 7 SP - SN - 1758-6852 KW - Complexity KW - Cluster analysis KW - Disaster relief KW - Supply chain vulnerability KW - Supply chain disruptions KW - Agile systems ER - TY - JOUR TI - Nova: Value-based Negotiation of Norms AU - Aydogan, Reyhan AU - Kafali, Ozgur AU - Arslan, Furkan AU - Jonker, Catholijn M. AU - Singh, Munindar P. T2 - ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY AB - Specifying a normative multiagent system (nMAS) is challenging, because different agents often have conflicting requirements. Whereas existing approaches can resolve clear-cut conflicts, tradeoffs might occur in practice among alternative nMAS specifications with no apparent resolution. To produce an nMAS specification that is acceptable to each agent, we model the specification process as a negotiation over a set of norms. We propose an agent-based negotiation framework, where agents’ requirements are represented as values (e.g., patient safety, privacy, and national security), and an agent revises the nMAS specification to promote its values by executing a set of norm revision rules that incorporate ontology-based reasoning. To demonstrate that our framework supports creating a transparent and accountable nMAS specification, we conduct an experiment with human participants who negotiate against our agent. Our findings show that our negotiation agent reaches better agreements (with small p -value and large effect size) faster than a baseline strategy. Moreover, participants perceive that our agent enables more collaborative and transparent negotiations than the baseline (with small p -value and large effect size in particular settings) toward reaching an agreement. DA - 2021/8// PY - 2021/8// DO - 10.1145/3465054 VL - 12 IS - 4 SP - SN - 2157-6912 UR - https://doi.org/10.1145/3465054 KW - Sociotechnical systems KW - conflicting requirements KW - human-agent negotiation ER - TY - JOUR TI - A High Order Compact FD Framework for Elliptic BVPs Involving Singular Sources, Interfaces, and Irregular Domains AU - Pan, Kejia AU - He, Dongdong AU - Li, Zhilin T2 - JOURNAL OF SCIENTIFIC COMPUTING DA - 2021/9// PY - 2021/9// DO - 10.1007/s10915-021-01570-4 VL - 88 IS - 3 SP - SN - 1573-7691 UR - https://doi.org/10.1007/s10915-021-01570-4 KW - Elliptic BVP KW - Interface problem KW - Irregular domain KW - High order compact Cartesian mesh method KW - IIM & augmented IIM KW - Discontinuous coefficients KW - Gradient KW - Helmholtz equation ER - TY - JOUR TI - Impacts of Private Autonomous and Connected Vehicles on Transportation Network Demand in the Triangle Region, North Carolina AU - Hasnat, Md. Mehedi AU - Bardaka, Eleni AU - Samandar, M. Shoaib AU - Rouphail, Nagui AU - List, George AU - Williams, Billy T2 - JOURNAL OF URBAN PLANNING AND DEVELOPMENT AB - Autonomous and connected vehicle technologies have the potential to bring profound changes in travel behavior and transportation network performance with moderate to significant market penetration rates (MPRs) within the next few decades. To better understand the long-term impacts of these technologies, this study predicts the network-level effects of privately owned autonomous vehicles (AVs) and connected and autonomous vehicles (CAVs) for the Triangle Region, North Carolina, in the year 2045. Market penetration scenarios of personal AVs and CAVs along with results from microscopic mixed-traffic simulations and travel behavior assumptions are incorporated into a regional travel demand model. Results indicate that a 75% MPR of personal AVs deteriorates the performance of the network, leading to a 5.4% increase in vehicle-hours traveled, and a 17.2% increase in hours of delay. The opposite holds for private CAV adoption, which is found to result in higher peak-period link speed and less congestion. The results of this research help planners and engineers to make informed transportation planning decisions and work toward harnessing the benefits of these technologies while minimizing any negative impacts. DA - 2021/3// PY - 2021/3// DO - 10.1061/(ASCE)UP.1943-5444.0000649 VL - 147 IS - 1 SP - SN - 1943-5444 ER - TY - JOUR TI - A discrete-event simulation model for the Bitcoin blockchain network with strategic miners and mining pool managers AU - Li, Kejun AU - Liu, Yunan AU - Wan, Hong AU - Huang, Yining T2 - COMPUTERS & OPERATIONS RESEARCH AB - As the first and most famous cryptocurrency-based blockchain technology, Bitcoin has attracted tremendous attention from both academic and industrial communities in the past decade. A Bitcoin network is comprised of two interactive parties: individual miners and mining pool managers, each of which strives to maximize its own utility. In particular, individual miners choose which mining pool to join and decide on how much mining power to commit under limited constraints on the mining budget and mining power capacity; managers of mining pools determine how to allocate the mining reward and how to adjust the membership fee. In this work we investigate the miners’ and mining pool managers’ decisions in repeated Bitcoin mining competitions by building a Monte-Carlo discrete-event simulation model. Our simulation model (i) captures the behavior of these two parties and how their decisions affect each other, and (ii) characterizes the system-level dynamics of the blockchain in terms of the mining difficulty level and total mining power. In addition, we study the sensitivity of system performance metrics with respect to various control parameters. Our analysis may provide useful guidelines to mining activity participants in the Bitcoin network. DA - 2021/10// PY - 2021/10// DO - 10.1016/j.cor.2021.105365 VL - 134 SP - SN - 1873-765X KW - Blockchain KW - Discrete-event simulation KW - Bitcoin mining policy KW - Mining competition ER - TY - JOUR TI - Managing product transitions with learning and congestion effects AU - Manda, A. B. AU - Uzsoy, Reha T2 - INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS AB - The introduction of a new product into an operating factory can have significant adverse impacts on the throughput and cycle time of all products produced in the factory, and thus needs to be managed carefully. In previous work we proposed a production planning model for new product introductions that captures the impact of additional variability caused by the new product and of learning as experience producing the new product is gained. This paper extends the earlier work by incorporating learning through deliberate experimentation using engineering lots and the impact of cycle time on line yield due to delays in detecting adverse events. We formulate a non-convex nonlinear optimization model to determine the mix of production and engineering lots to be processed, and obtain approximate solutions using a genetic algorithm. Numerical experiments with different scenarios show the importance of carefully managing the releases of production and engineering lots and of accelerating learning early in the time horizon through judicious use of engineering lots. DA - 2021/9// PY - 2021/9// DO - 10.1016/j.ijpe.2021.108190 VL - 239 SP - SN - 1873-7579 KW - New product introduction KW - Learning effects KW - Non-linear programming KW - Genetic algorithms KW - Semiconductor manufacturing ER - TY - JOUR TI - Double Blind Peer-Review in Logistics AU - Handfield, Robert T2 - LOGISTICS-BASEL AB - Pre-publication peer-review forms the basis for how scholarly journals assess whether an article is suitable for publication [...] DA - 2021/6// PY - 2021/6// DO - 10.3390/logistics5020040 VL - 5 IS - 2 SP - SN - 2305-6290 ER - TY - JOUR TI - Resource planning for direct fabrication of customized orthopedic implants using EBM technology AU - Hauser, Margaret AU - King, Russell AU - Wysk, Richard AU - Harrysson, Ola T2 - JOURNAL OF MANUFACTURING SYSTEMS AB - Medical innovations and patient expectations are pushing healthcare toward personalized medicine. In orthopedics, the concept of patient-specific implants could be economically realized with the use of additive manufacturing. Knee and hip replacements are some of the most common musculoskeletal procedures performed in the United States. Joint replacement implants are typically offered in standard sizes and geometries. The mass customization of theses prostheses, however, can improve patient outcomes and reduce medical costs. Mass customization is not economically feasible with traditional manufacturing methods because of the high fixed tooling costs for each geometry. The freedom of design offered by additive manufacturing presents a viable production alternative for unique personal geometry. The objective of this paper is to develop two new analytic models that can be used to investigate a complex additive manufacturing supply chain. The focus of the model is to provide planning tools and a methodology for the direct production of customized orthopedic implants using electron beam melting, an additive manufacturing technology. First, a production model for an additive manufacturing-based system is created. Next, resource planning for a single customized implant system is performed using a simulation model. A queuing model is developed for rapid systems analysis. The staffing requirement predictions of the two models align closely for production of a singular, customized implant. A detailed systems analysis of an additive manufacturing supply chain is conducted to illustrate the use of these models. The queueing model is analytically tractable, so it is extended to describe the production of standard and customized versions of multiple implant families. DA - 2021/7// PY - 2021/7// DO - 10.1016/j.jmsy.2021.07.003 VL - 60 SP - 500-511 SN - 1878-6642 UR - https://doi.org/10.1016/j.jmsy.2021.07.003 KW - Production model KW - Resource planning KW - Electron beam melting KW - Customized implants ER - TY - JOUR TI - An analysis of optimal ordering policies for a two-supplier system with disruption risk AU - Luo, Sha AU - Ahiska, S. Sebnem AU - Fang, Shu-Cherng AU - King, Russell E. AU - Warsing, Donald P., Jr. AU - Wu, Shuohao T2 - Omega - The International Journal of Management Science AB - • Optimal policy can be proved for a two-supplier system with an unreliable supplier. • Stable (s,S) policy is robust outside of parameter conditions for optimality. • There is an easily computed condition for exclusive unreliable supplier ordering. • Optimal policies move from exclusive ordering to splitting as key parameters vary. We study a single-product, periodic-review inventory system with the presence of fixed ordering cost. There are two suppliers: One is perfectly reliable while the other offers a cost advantage but is subject to possible supply interruptions. We present a theoretical framework with mathematical proofs for the optimal ordering policy in the finite-horizon setting, which exhibits an ( s , S ) structure, but with multiple, sometimes overlapping, reorder points and order-up-to levels. Then, we analyze the limiting behavior of our ( s , S ) policy and show that both the optimal cost and ordering policy parameters converge over time. This steady-state ( s , S ) policy characterizes the optimal sourcing strategy for the infinite-horizon setting. Through computational studies, we investigate the effects of parameter changes on the optimal policy and demonstrate that our two-supplier ( s , S ) ordering policy is optimal under a wide range of system parameters beyond the conditions required in the optimality proof. DA - 2021/12// PY - 2021/12// DO - 10.1016/j.omega.2021.102517 VL - 105 SP - 102517 UR - https://doi.org/10.1016/j.omega.2021.102517 KW - Dual sourcing KW - Unreliable supply KW - (s, S) policy ER - TY - JOUR TI - Acceleration Technique for the Augmented IIM for 3D Elliptic Interface Problems AU - Zhang, Changjuan AU - Li, Zhilin AU - Yue, Xingye T2 - NUMERICAL MATHEMATICS-THEORY METHODS AND APPLICATIONS DA - 2021/8// PY - 2021/8// DO - 10.4208/nmtma.OA-2020-0112 VL - 14 IS - 3 SP - 773-796 SN - 2079-7338 KW - 3D elliptic interface problem KW - augmented IIM KW - fast Poisson solver KW - directional least squares interpolation ER - TY - JOUR TI - A novel kernel-free least squares twin support vector machine for fast and accurate multi-class classification AU - Gao, Z. AU - Fang, S-C AU - Gao, X. AU - Luo, J. AU - Medhin, N. T2 - Knowledge-Based Systems AB - Multi-class classification is an important and challenging research topic with many real-life applications. The problem is much harder than the classical binary classification, especially when the given data set is imbalanced. Hidden nonlinear patterns in the data set can further complicate the task of multi-class classification. In this paper, we propose a kernel-free least squares twin support vector machine for multi-class classification. The proposed model employs a special fourth order polynomial surface, namely the double well potential surface, and adopts the ”one-verses-all” classification strategy. An ℓ2 regularization term is added to accommodate data sets with different levels of nonlinearity. We provide some theoretical analysis of the proposed model. Computational results using artificial data sets and public benchmarks clearly show the superior performance of the proposed model over other well-known multi-class classification methods, in particular for imbalanced data sets. DA - 2021/8/17/ PY - 2021/8/17/ DO - 10.1016/j.knosys.2021.107123 VL - 226 SP - 107123 SN - 1872-7409 KW - Multi-class classification KW - Least squares twin support vector machine KW - Double well potential KW - Kernel-free SVM KW - Imbalanced data ER - TY - JOUR TI - Efficient and sustainable closed-loop supply chain network design: A two-stage stochastic formulation with a hybrid solution methodology AU - Moheb-Alizadeh, Hadi AU - Handfield, Robert AU - Warsing, Donald T2 - JOURNAL OF CLEANER PRODUCTION AB - In recent years, consumers and legislators have pushed companies to design their supply chain networks to consider environmental and social impacts as an important performance outcome. Due to the role of resource utilization as a key component of logistics network design, another primary goal of design is ensuring available scarce resources are used as efficiently as possible across all facilities. To address efficiency issues in a sustainable closed-loop supply chain network, a stochastic integrated multi-objective mixed integer nonlinear programming model is developed in this paper, in which sustainability outcomes as well as efficiency of facility resource utilization are considered in the design of a sustainable supply chain network. In doing so, efficiency is assessed using a bi-objective output-oriented data envelopment analysis model. A hybrid three-step solution methodology is presented that creates a linear form of the original mixed integer nonlinear programming problem using piecewise McCormick envelopes approach. In the second step, an aggregated single objective programming model is derived by exploiting the multi-choice goal programming. Finally, a Lagrangian relaxation algorithm is developed to effectively solve the latter stochastic single objective mixed integer linear programming problem. The application of the proposed approach is investigated with data drawn from a case study in the electronics industry. This case study illustrates how firms may balance sustainability and efficiency in the supply chain network design problem. Further, it demonstrates the integration of efficiency results in improving economic aspects of sustainability as well as social responsibility outcomes, but also highlights the trade-offs that exist between efficiency and environmental impacts. DA - 2021/7/25/ PY - 2021/7/25/ DO - 10.1016/j.jclepro.2021.127323 VL - 308 SP - SN - 1879-1786 KW - Closed-loop supply chain network KW - Sustainability KW - Data envelopment analysis KW - Stochastic programming KW - Multi-choice goal programming KW - Lagrangian relaxation ER - TY - JOUR TI - The Deming management method and digital partnering in a construction procurement contract AU - Ganguly, Joydeep AU - Handfield, Robert AU - Harvey, Delvin AU - Rasovsky, Lily T2 - Journal of Strategic Contracting and Negotiation AB - In this case study of a research and development facility construction project at a large biopharmaceutical organization, we explored how digital investments must be accompanied by a partnership approach and a transformation of the cultural values of an organization tied to operational principles. The project faced considerable challenges, including a highly constrained market environment, time and cost constraints, and a multiyear organization transformation with a diverse mix of stakeholder objectives. Despite these challenges, the project was brought in under budget and on schedule, achieving other objectives that often seem at odds with each other (best-in-class sustainability ratings, quality scores from customers, and with a remarkably low number of change requests). We found that significant stakeholder engagement early in the architect and contractor selection process leads to the right contract management process and ultimately successful outcomes. A key insight from this case involves the need for differentiated supplier relationship management for procurement-project team integration. DA - 2021/9// PY - 2021/9// DO - 10.1177/20555636211022432 VL - 5 IS - 3 SP - 170-195 UR - https://doi.org/10.1177/20555636211022432 ER - TY - JOUR TI - Sparse Solutions by a Quadratically Constrained l(q) (0 < q < 1) Minimization Model AU - Jiang, Shan AU - Fang, Shu-Cherng AU - Jin, Qingwei T2 - INFORMS JOURNAL ON COMPUTING AB - Finding sparse solutions to a system of equations and/or inequalities is an important topic in many application areas such as signal processing, statistical regression and nonparametric modeling. Various continuous relaxation models have been proposed and widely studied to deal with the discrete nature of the underlying problem. In this paper, we propose a quadratically constrained [Formula: see text] (0 < q < 1) minimization model for finding sparse solutions to a quadratic system. We prove that solving the proposed model is strongly NP-hard. To tackle the computation difficulty, a first order necessary condition for local minimizers is derived. Various properties of the proposed model are studied for designing an active-set-based descent algorithm to find candidate solutions satisfying the proposed condition. In addition to providing a theoretical convergence proof, we conduct extensive computational experiments using synthetic and real-life data to validate the effectiveness of the proposed algorithm and to show the superior capability in finding sparse solutions of the proposed model compared with other known models in the literature. We also extend our results to a quadratically constrained [Formula: see text] (0 < q < 1) minimization model with multiple convex quadratic constraints for further potential applications. Summary of Contribution: In this paper, we propose and study a quadratically constrained [Formula: see text] minimization (0 < q < 1) model for finding sparse solutions to a quadratic system which has wide applications in sparse signal recovery, image processing and machine learning. The proposed quadratically constrained [Formula: see text] minimization model extends the linearly constrained [Formula: see text] and unconstrained [Formula: see text]-[Formula: see text] models. We study various properties of the proposed model in aim of designing an efficient algorithm. Especially, we propose an unrelaxed KKT condition for local/global minimizers. Followed by the properties studied, an active-set based descent algorithm is then proposed with its convergence proof being given. Extensive numerical experiments with synthetic and real-life Sparco datasets are conducted to show that the proposed algorithm works very effectively and efficiently. Its sparse recovery capability is superior to that of other known models in the literature. DA - 2021/// PY - 2021/// DO - 10.1287/ijoc.2020.1004 VL - 33 IS - 2 SP - 511-530 SN - 1526-5528 KW - nonsmooth optimization KW - nonconvex optimization KW - optimality condition KW - sparse solution KW - sparse signal recovery KW - image processing ER - TY - JOUR TI - Semi-decoupling hybrid asymptotic and augmented finite volume method for nonlinear singular interface problems AU - Zhao, Tengjin AU - Ito, Kazufumi AU - Zhang, Zhiyue T2 - JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS AB - An accurate semi-decoupling numerical method has been proposed for nonlinear singular differential equation with interface, which combines Puiseux series asymptotic technique with augmented compact finite volume method. The main motivation is to decouple the singular interface problem and get high order accurate numerical solution. Key to our proposed new method is introducing three augmented variables involving the interface and the singularity, and reconstructing the representative of the solution as the Puiseux series expansions on the interface to decouple original problem as two standard nonlinear singular problems with the second jump condition. In this way, the augmented variables related with semi-analytic solutions near the interface and numerical solutions can be simultaneously solved in the remaining interval on both sides of the interface. It demonstrates that our method does not take more heavier works for handling jump conditions like other methods, and is independent of the interface and jump ratio. A rigorous error estimate for the solution of nonlinear singular differential equation with interface and augmented variables is obtained. Numerical experiments for those singular differential equations with interface confirm the theoretical analysis and accuracy of the new approach. In particular, an interesting example with blow-up coefficient at singular point shows that our approach can be extended to numerically solve strongly singular interface problem. DA - 2021/11// PY - 2021/11// DO - 10.1016/j.cam.2021.113606 VL - 396 SP - SN - 1879-1778 KW - Semi-decoupling KW - Nonlinear singular problem with interface KW - Puiseux series KW - Compact finite volume method KW - Augmented variable KW - High order convergence ER - TY - JOUR TI - SURROGATE BASED MUTUAL INFORMATION APPROXIMATION AND OPTIMIZATION FOR URBAN SOURCE LOCALIZATION AU - Hollis, Andrew N. AU - Smith, Ralph C. AU - Wilson, Alyson G. T2 - INTERNATIONAL JOURNAL FOR UNCERTAINTY QUANTIFICATION AB - The ability to efficiently and accurately localize potentially threatening nuclear radiation sources in urban environments is of critical importance to national security. Techniques to infer the location and intensity of a source using data from a configuration of radiation detectors, and the effectiveness of the source localization depends critically on how the detectors are configured. In this paper, we introduce a framework that uses surrogate models to efficiently compare and optimize different detector configurations. We compare our technique to others and demonstrate its effectiveness for selecting optimal detector configurations in the context of urban source localization. DA - 2021/// PY - 2021/// DO - 10.1615/Int.J.UncertaintyQuantification.2021034400 VL - 11 IS - 5 SP - 39-55 SN - 2152-5099 KW - computational statistics KW - information theory KW - bayesian inference KW - spatial statistics ER - TY - JOUR TI - Tuning value chains for better signals in the post-COVID era: vaccine supply chain concerns AU - Finkenstadt, Daniel J. AU - Handfield, Robert B. T2 - INTERNATIONAL JOURNAL OF OPERATIONS & PRODUCTION MANAGEMENT AB - Purpose The authors identify the critical bottlenecks that exist in the vaccine supply chain that are preventing a robust coronavirus disease (COVID) response. The authors posit that improved supply chain signals can result in improved handling and distribution of vaccines in a post-COVID world and identify recommendations for redesign of the vaccine supply chain as well as future research questions for scholars. Design/methodology/approach The supply chain operating reference (SCOR) model is used as a framework to identify each of the major gaps that exist in the supply chain for the COVID vaccine. The critical bottlenecks and delays that exist within this supply chain are identified through this framework and validated through the ongoing research and interviews in the field. Findings Whilst the vaccine supply chain for influenza is perfectly sized for development and distribution of this cyclical virus, the emergence of a new virus created a pandemic, which has exposed a number of critical shortages. The authors find that the design of the COVID vaccine supply chain suffers from a flawed structure. To date, less than 3% of the United States and global population has been fully vaccinated. The authors advocate a “back to front design”, beginning with demand planning for actual vaccinations and working backwards toward supply planning and distribution planning. These lessons may be helpful for capacity planning and supply chain strategy for future vaccinations as variants of the COVID vaccine emerge. Originality/value The authors provide a unique approach for viewing the current shortages that exist in the vaccine supply chain and offer suggestions for new variants of this supply chain for the future. DA - 2021/6/18/ PY - 2021/6/18/ DO - 10.1108/IJOPM-01-2021-0039 VL - 41 IS - 8 SP - SN - 1758-6593 UR - https://doi.org/10.1108/IJOPM-01-2021-0039 KW - Humanitarian logistics KW - Supply chain integration KW - Healthcare sector KW - Vaccines ER - TY - JOUR TI - COVID-KOP: integrating emerging COVID-19 data with the ROBOKOP database AU - Korn, Daniel AU - Bobrowski, Tesia AU - Li, Michael AU - Kebede, Yaphet AU - Wang, Patrick AU - Owen, Phillips AU - Vaidya, Gaurav AU - Muratov, Eugene AU - Chirkova, Rada AU - Bizon, Chris AU - Tropsha, Alexander T2 - BIOINFORMATICS AB - Abstract Summary In response to the COVID-19 pandemic, we established COVID-KOP, a new knowledgebase integrating the existing Reasoning Over Biomedical Objects linked in Knowledge Oriented Pathways (ROBOKOP) biomedical knowledge graph with information from recent biomedical literature on COVID-19 annotated in the CORD-19 collection. COVID-KOP can be used effectively to generate new hypotheses concerning repurposing of known drugs and clinical drug candidates against COVID-19 by establishing respective confirmatory pathways of drug action. Availability and implementation COVID-KOP is freely accessible at https://covidkop.renci.org/. For code and instructions for the original ROBOKOP, see: https://github.com/NCATS-Gamma/robokop. DA - 2021/2/15/ PY - 2021/2/15/ DO - 10.1093/bioinformatics/btaa718 VL - 37 IS - 4 SP - 586-587 SN - 1460-2059 ER - TY - JOUR TI - A hierarchical Bayesian model for background variation in radiation source localization AU - Michaud, Isaac J. AU - Schmidt, Kathleen AU - Smith, Ralph C. AU - Mattingly, John T2 - NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT AB - In this paper, we apply a new model to account for varying background radiation in radiological source localization. We present a hierarchical Bayesian model that simultaneously infers background and source location parameters without requiring separate estimation of the background radiation at each detector location. We employ a simplified photon transport model to reduce the computational expense of Bayesian model calibration. We demonstrate the model accuracy by localizing a cesium-137 source in a simulated city block, and we analyze experimental field measurements with varying background. In both cases, the model provides sufficient fidelity that we can locate the source while simultaneously estimating background radiation. DA - 2021/6/21/ PY - 2021/6/21/ DO - 10.1016/j.nima.2021.165288 VL - 1002 SP - SN - 1872-9576 KW - Mixed-effects statistical model KW - Radiation detection KW - Inverse problem KW - Bayesian inference KW - Wide-area search ER - TY - JOUR TI - Crosstalk-Aware Shared Backup Path Protection in Multi-Core Fiber Elastic Optical Networks AU - Tang, Fengxian AU - Shen, Gangxiang AU - Rouskas, George N. T2 - JOURNAL OF LIGHTWAVE TECHNOLOGY AB - Elastic optical networks employing multi-core fibers (MCF-EON) have the potential to expand significantly the transmission capacity of optical transport. However, wide deployment of such networks depends on addressing effectively two critical challenges: inter-core crosstalk, which may cause serious signal performance degradation in an MCF link, and survivability against network failures that may cause enormous data loss. In this article, we consider the design of MCF-EONs with shared-backup path protection (SBPP), one of the most efficient techniques for protecting network traffic. Specifically, we tackle the crosstalk-aware routing, core, and spectrum assignment (CA-RCSA) problem with the objective of jointly minimizing the network spectrum resources used and the total inter-core crosstalk. We formulate the problem as an integer linear programming (ILP) model subject to strict inter-core crosstalk limits for each provisioned lightpath, and we also propose an auxiliary graph (AG) based heuristic algorithm for lightpath provisioning. Simulation studies show that our algorithm is effective in terms of the objectives, and it is efficient to perform close to the ILP model in small networks, for which solving the ILP is feasible. DA - 2021/5/15/ PY - 2021/5/15/ DO - 10.1109/JLT.2021.3064935 VL - 39 IS - 10 SP - 3025-3036 SN - 1558-2213 UR - https://doi.org/10.1109/JLT.2021.3064935 KW - Crosstalk KW - Optical fiber networks KW - Optical crosstalk KW - Resource management KW - Routing KW - Optical fibers KW - Heuristic algorithms KW - Inter-core crosstalk KW - MCF-EON KW - RCSA KW - SBPP KW - survivability ER - TY - JOUR TI - Editorial AU - Uzsoy, Reha T2 - IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING AB - One of the most pleasant aspects of the Editor in Chief’s job is to recognize outstanding contributions from the TSM community. The TSM Best Paper Award is presented annually to a paper published in the previous calendar year that is recognized by the Editorial Board to have made an outstanding contribution to the field. Papers are nominated at the time of acceptance by the handling Associate Editor; nominated papers are then reviewed and ranked by a subcommittee of the Editorial Board. DA - 2021/5// PY - 2021/5// DO - 10.1109/TSM.2021.3073760 VL - 34 IS - 2 SP - 139-139 SN - 1558-2345 UR - https://doi.org/10.1109/TSM.2021.3073760 ER - TY - JOUR TI - A generalized modulus-based Newton method for solving a class of non-linear complementarity problems with P-matrices AU - Li, Rui AU - Li, Zhi-Lin AU - Yin, Jun-Feng T2 - NUMERICAL ALGORITHMS DA - 2021/6/3/ PY - 2021/6/3/ DO - 10.1007/s11075-021-01136-3 VL - 6 SP - SN - 1572-9265 KW - Non-linear complementarity problems KW - Modulus-based Newton methods KW - P-matrix ER - TY - JOUR TI - Optimal planar facility location with dense demands along a curve AU - Zhou, Jianqin AU - Fang, Shu-Cherng AU - Jiang, Shan AU - Ju, Songdong T2 - JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY AB - This paper studies a multi-facility location problem with demand distributed continuously along a curve (MFLCD) in a planar space. We first model a single facility location problem in this setting. When the curve is approximated by a collection of line segments, an exact algorithm is proposed to solve the problem. Then we extend the model to the multi-facility case and propose an “alternate location and allocation” (ALA) based algorithm. Extensive computational experiments are conducted to justify the proposed models and algorithms. We show that using the proposed model to deal with continuous demand directly is much more effective than using other models with discrete demand generated by demand aggregation. As a by-product, some sufficient conditions that ensure an error-free demand aggregation from continuous demand to discrete demand are provided. Computational results are in support of the solution quality and computational efficiency of the proposed ALA-based algorithm for solving MFLCD problems. Our sensitivity analysis also offers some insights and suggestions. DA - 2021/// PY - 2021/// DO - 10.1080/01605682.2021.1907237 KW - Facility location KW - dense demand KW - alternative location and allocation approach KW - p-median problem ER - TY - JOUR TI - Uncertainty Quantification and Sensitivity Analysis of Partial Charges on Macroscopic Solvent Properties in Molecular Dynamics Simulations with a Machine Learning Model AU - Peerless, James S. AU - Kwansa, Albert L. AU - Hawkins, Branden S. AU - Smith, Ralph C. AU - Yingling, Yaroslava G. T2 - JOURNAL OF CHEMICAL INFORMATION AND MODELING AB - The molecular dynamics (MD) simulation technique is among the most broadly used computational methods to investigate atomistic phenomena in a variety of chemical and biological systems. One of the most common (and most uncertain) parametrization steps in MD simulations of soft materials is the assignment of partial charges to atoms. Here, we apply uncertainty quantification and sensitivity analysis calculations to assess the uncertainty associated with partial charge assignment in the context of MD simulations of an organic solvent. Our results indicate that the effect of partial charge variance on bulk properties, such as solubility parameters, diffusivity, dipole moment, and density, measured from MD simulations is significant; however, measured properties are observed to be less sensitive to partial charges of less accessible (or buried) atoms. Diffusivity, for example, exhibits a global sensitivity of up to 22 × 10–5 cm2/s per electron charge on some acetonitrile atoms. We then demonstrate that machine learning techniques, such as Gaussian process regression (GPR), can be effective and rapid tools for uncertainty quantification of MD simulations. We show that the formulation and application of an efficient GPR surrogate model for the prediction of responses effectively reduces the computational time of additional sample points from hours to milliseconds. This study provides a much-needed context for the effect that partial charge uncertainty has on MD-derived material properties to illustrate the benefit of considering partial charges as distributions rather than point-values. To aid in this treatment, this work then demonstrates methods for rapid characterization of resulting sensitivity in MD simulations. DA - 2021/4/26/ PY - 2021/4/26/ DO - 10.1021/acs.jcim.0c01204 VL - 61 IS - 4 SP - 1745-1761 SN - 1549-960X ER - TY - JOUR TI - Fourth order compact FD methods for convection diffusion equations with variable coefficients AU - Tong, Fenghua AU - Feng, Xinlong AU - Li, Zhilin T2 - APPLIED MATHEMATICS LETTERS AB - Fourth order finite difference methods combined with an integrating factor strategy for steady convection and diffusion partial differential equations with variable coefficients in both 2D and 3D are proposed using uniform Cartesian grids. An integrating factor strategy is applied to transform the convection and diffusion PDE to a self-adjoint form. Then, a fourth order finite difference method is obtained through a second order scheme followed by the Richardson extrapolation. Another approach is a direct fourth order compact finite difference scheme. The developed integrating factor strategy provides an efficient way for dealing with large convection coefficients. Several numerical examples are presented to demonstrate the convergence order and compare the two fourth order methods. DA - 2021/11// PY - 2021/11// DO - 10.1016/j.aml.2021.107413 VL - 121 SP - SN - 1873-5452 UR - https://doi.org/10.1016/j.aml.2021.107413 KW - Convection and diffusion PDEs KW - Integrating factor KW - 4th order compact scheme ER - TY - JOUR TI - SEAGLE: A Scalable Exact Algorithm for Large-Scale Set-based GxE Tests in Biobank Data AB - The explosion of biobank data offers unprecedented opportunities for gene-environment interaction (GxE) studies of complex diseases because of the large sample sizes and the rich collection in genetic and non-genetic information. However, the extremely large sample size also introduces new computational challenges in G×E assessment, especially for set-based G×E variance component (VC) tests, which are a widely used strategy to boost overall G×E signals and to evaluate the joint G×E effect of multiple variants from a biologically meaningful unit (e.g., gene). In this work, we focus on continuous traits and present SEAGLE, a Scalable Exact AlGorithm for Large-scale set-based G×E tests, to permit G×E VC tests for biobank-scale data. SEAGLE employs modern matrix computations to calculate the test statistic and p-value of the GxE VC test in a computationally efficient fashion, without imposing additional assumptions or relying on approximations. SEAGLE can easily accommodate sample sizes in the order of 105, is implementable on standard laptops, and does not require specialized computing equipment. We demonstrate the performance of SEAGLE using extensive simulations. We illustrate its utility by conducting genome-wide gene-based G×E analysis on the Taiwan Biobank data to explore the interaction of gene and physical activity status on body mass index. DA - 2021/5/7/ PY - 2021/5/7/ DO - https://doi.org/10.3389/fgene.2021.710055 ER - TY - JOUR TI - Bungie: Improving Fault Tolerance via Extensible Application-Level Protocols AU - Christie, Samuel H. AU - Chopra, Amit Khushwant AU - Singh, Munindar P. T2 - COMPUTER AB - We present Bungie, an approach based on applicationlevel protocols that precisely capture the causality inherent to the interactions among agents. We show through patterns and examples how Bungie provides abstractions for achieving fault tolerance. DA - 2021/5// PY - 2021/5// DO - 10.1109/MC.2021.3052147 VL - 54 IS - 5 SP - 44-53 SN - 1558-0814 UR - https://doi.org/10.1109/MC.2021.3052147 KW - Fault tolerance KW - Protocols KW - Fault tolerant systems ER - TY - JOUR TI - Multiplicative perturbation bounds for multivariate multiple linear regression in Schatten p-norms AU - Chi, Jocelyn T. AU - Ipsen, Ilse C. F. T2 - LINEAR ALGEBRA AND ITS APPLICATIONS AB - Multivariate multiple linear regression (MMLR), which occurs in a number of practical applications, generalizes traditional least squares (multivariate linear regression) to multiple right-hand sides. We extend recent MLR analyses to sketched MMLR in general Schatten p-norms by interpreting the sketched problem as a multiplicative perturbation. Our work represents an extension of Maher's results on Schatten p-norms. We derive expressions for the exact and perturbed solutions in terms of projectors for easy geometric interpretation. We also present a geometric interpretation of the action of the sketching matrix in terms of relevant subspaces. We show that a key term in assessing the accuracy of the sketched MMLR solution can be viewed as a tangent of a largest principal angle between subspaces under some assumptions. Our results enable additional interpretation of the difference between an orthogonal and oblique projector with the same range. DA - 2021/9/1/ PY - 2021/9/1/ DO - 10.1016/j.laa.2021.03.039 VL - 624 SP - 87-102 SN - 1873-1856 UR - https://doi.org/10.1016/j.laa.2021.03.039 KW - Projector Multiplicative perturbations KW - Moore Penrose inverse KW - Schatten p-norms KW - Multivariate multiple linear KW - regression ER - TY - JOUR TI - Discovery of a major QTL for root-knot nematode (Meloidogyne incognita) resistance in cultivated sweetpotato (Ipomoea batatas) AU - Oloka, Bonny Michael AU - Pereira, Guilherme da Silva AU - Amankwaah, Victor A. AU - Mollinari, Marcelo AU - Pecota, Kenneth V AU - Yada, Benard AU - Olukolu, Bode A. AU - Zeng, Zhao-Bang AU - Yencho, G. Craig T2 - THEORETICAL AND APPLIED GENETICS AB - Utilizing a high-density integrated genetic linkage map of hexaploid sweetpotato, we discovered a major dominant QTL for root-knot nematode (RKN) resistance and modeled its effects. This discovery is useful for development of a modern sweetpotato breeding program that utilizes marker-assisted selection and genomic selection approaches for faster genetic gain of RKN resistance. The root-knot nematode [Meloidogyne incognita (Kofoid & White) Chitwood] (RKN) causes significant storage root quality reduction and yields losses in cultivated sweetpotato [Ipomoea batatas (L.) Lam.]. In this study, resistance to RKN was examined in a mapping population consisting of 244 progenies derived from a cross (TB) between 'Tanzania,' a predominant African landrace cultivar with resistance to RKN, and 'Beauregard,' an RKN susceptible major cultivar in the USA. We performed quantitative trait loci (QTL) analysis using a random-effect QTL mapping model on the TB genetic map. An RKN bioassay incorporating potted cuttings of each genotype was conducted in the greenhouse and replicated five times over a period of 10 weeks. For each replication, each genotype was inoculated with ca. 20,000 RKN eggs, and root-knot galls were counted ~62 days after inoculation. Resistance to RKN in the progeny was highly skewed toward the resistant parent, exhibiting medium to high levels of resistance. We identified one major QTL on linkage group 7, dominant in nature, which explained 58.3% of the phenotypic variation in RKN counts. This work represents a significant step forward in our understanding of the genetic architecture of RKN resistance and sets the stage for future utilization of genomics-assisted breeding in sweetpotato breeding programs. DA - 2021/7// PY - 2021/7// DO - 10.1007/s00122-021-03797-z VL - 134 IS - 7 SP - 1945-1955 SN - 1432-2242 ER - TY - JOUR TI - Optimal strategies for a capital constrained contract-farming supply chain with yield insurance AU - Shi, Ligang AU - Pang, Tao AU - Peng, Hongjun T2 - RAIRO-OPERATIONS RESEARCH AB - We consider a capital-constrained contract-farming supply chain with a risk-averse farmer and a risk-neutral agro-dealer, where the farmer faces some yield uncertainty that can be covered by insurance. Using the Stackelberg model, we derive the optimal strategies on the insured level, production and wholesale price. The result shows that farmers with low risk aversion tend not to be insured, while those with high risk aversion tend to insure. Further analysis indicates that, as the degree of the farmer’s risk aversion increases, the farm size decreases, but the yield per unit area and the wholesale price of the agricultural product increases. In addition, yield insurance and premium subsidies can lead to a decrease of the yield per unit area. However, the expansion of the farm size can compensate for the inhibitory effect of the decrease of yield per unit area on the total yield, and thus the total yield increases. We also find that when the premium subsidy rate is low, the yield insurance’s value to farmers is negative. Moreover, the yield insurance’s value to farmers increases with respect to the bank’s interest rate. DA - 2021/3/31/ PY - 2021/3/31/ DO - 10.1051/ro/2021006 VL - 55 IS - 2 SP - 521-544 SN - 2804-7303 KW - Contract-farming KW - yield uncertainty KW - yield insurance KW - capital constraint KW - risk-averse ER - TY - JOUR TI - An ADI-Yee's scheme for Maxwell's equations with discontinuous coefficients AU - Deng, Shaozhong AU - Li, Zhilin AU - Pan, Kejia T2 - JOURNAL OF COMPUTATIONAL PHYSICS AB - An alternating directional implicit (ADI)-Yee's scheme is developed for Maxwell's equations with discontinuous material coefficients along one or several interfaces. In order to use Yee's scheme with the presence of discontinuities, some intermediate quantities along the interface are introduced. The intermediate quantities are from the solutions and their derivatives on the interface and should satisfy some interface conditions. In discretization, those quantities are actually determined implicitly. For a fixed interface and a fixed time step size, the linear system of equations for the intermediate quantities can be pre-determined, so is the PLU or SVD decomposition of the coefficient matrix of the linear system. The ADI-Yee's scheme maintains the structure (the finite difference scheme with modified right-hand sides) as well as the accuracy and stability of Yee's scheme even with the presence of discontinuities. Theoretical analysis and numerical examples are also provided. DA - 2021/8/1/ PY - 2021/8/1/ DO - 10.1016/j.jcp.2021.110356 VL - 438 SP - SN - 1090-2716 UR - https://doi.org/10.1016/j.jcp.2021.110356 KW - Maxwell's equations KW - Yee's scheme KW - Interface problem KW - ADI method KW - Augmented immersed interface method KW - Scattering ER - TY - JOUR TI - Conic programming models for production planning with clearing functions: Formulations and duality AU - Gopalswamy, Karthick AU - Uzsoy, Reha T2 - European Journal of Operational Research AB - Concave clearing functions that model the expected throughput of a production resource as a function of its planned workload have yielded promising results when used in production planning models. The most common of these models take the form of linear programs (LPs) obtained by piecewise linearization of the clearing function constraints, leading to large formulations and inaccurate estimates of dual prices for resources. We show that several clearing function forms considered in the literature to date can be reformulated as conic programs (CPs), for which efficient solution methods and an elegant duality theory analogous to that for linear programming exist. We derive expressions for the optimal values of the dual variables and present computational experiments showing that the dual prices obtained from the CP formulation are more accurate than those obtained from the piecewise linearized LP models. In addition, the CP solution outperforms the LP solutions with respect to nervousness. DA - 2021/8// PY - 2021/8// DO - 10.1016/j.ejor.2020.11.039 VL - 292 IS - 3 SP - 953-966 J2 - European Journal of Operational Research LA - en OP - SN - 0377-2217 UR - http://dx.doi.org/10.1016/j.ejor.2020.11.039 DB - Crossref KW - Capacity pricing KW - Congestion KW - Conic programming KW - Production planning ER - TY - JOUR TI - Quadratic hyper-surface kernel-free least squares support vector regression AU - Ye, Junyou AU - Yang, Zhixia AU - Li, Zhilin T2 - INTELLIGENT DATA ANALYSIS AB - We present a novel kernel-free regressor, called quadratic hyper-surface kernel-free least squares support vector regression (QLSSVR), for some regression problems. The task of this approach is to find a quadratic function as the regression function, which is obtained by solving a quadratic programming problem with the equality constraints. Basically, the new model just needs to solve a system of linear equations to achieve the optimal solution instead of solving a quadratic programming problem. Therefore, compared with the standard support vector regression, our approach is much efficient due to kernel-free and solving a set of linear equations. Numerical results illustrate that our approach has better performance than other existing regression approaches in terms of regression criterion and CPU time. DA - 2021/// PY - 2021/// DO - 10.3233/IDA-205094 VL - 25 IS - 2 SP - 265-281 SN - 1571-4128 KW - Regression problem KW - support vector regression KW - quadratic kernel-free least squares support vector regression ER - TY - JOUR TI - The recombination landscape and multiple QTL mapping in a Solanum tuberosum cv. 'Atlantic'-derived F-1 population AU - Pereira, Guilherme da Silva AU - Mollinari, Marcelo AU - Schumann, Mitchell J. AU - Clough, Mark E. AU - Zeng, Zhao-Bang AU - Yencho, G. Craig T2 - HEREDITY AB - There are many challenges involved with the genetic analyses of autopolyploid species, such as the tetraploid potato, Solanum tuberosum (2n = 4x = 48). The development of new analytical methods has made it valuable to re-analyze an F1 population (n = 156) derived from a cross involving 'Atlantic', a widely grown chipping variety in the USA. A fully integrated genetic map with 4285 single nucleotide polymorphisms, spanning 1630 cM, was constructed with MAPpoly software. We observed that bivalent configurations were the most abundant ones (51.0~72.4% depending on parent and linkage group), though multivalent configurations were also observed (2.2~39.2%). Seven traits were evaluated over four years (2006-8 and 2014) and quantitative trait loci (QTL) mapping was carried out using QTLpoly software. Based on a multiple-QTL model approach, we detected 21 QTL for 15 out of 27 trait-year combination phenotypes. A hotspot on linkage group 5 was identified with co-located QTL for maturity, plant yield, specific gravity, and internal heat necrosis resistance evaluated over different years. Additional QTL for specific gravity and dry matter were detected with maturity-corrected phenotypes. Among the genes around QTL peaks, we found those on chromosome 5 that have been previously implicated in maturity (StCDF1) and tuber formation (POTH1). These analyses have the potential to provide insights into the biology and breeding of tetraploid potato and other autopolyploid species. DA - 2021/5// PY - 2021/5// DO - 10.1038/s41437-021-00416-x VL - 126 IS - 5 SP - 817-830 SN - 1365-2540 ER - TY - JOUR TI - Modeling and Parameter Subset Selection for Fibrin Polymerization Kinetics with Applications to Wound Healing AU - Pearce, Katherine J. AU - Nellenbach, Kimberly AU - Smith, Ralph C. AU - Brown, Ashley C. AU - Haider, Mansoor A. T2 - BULLETIN OF MATHEMATICAL BIOLOGY AB - During the hemostatic phase of wound healing, vascular injury leads to endothelial cell damage, initiation of a coagulation cascade involving platelets, and formation of a fibrin-rich clot. As this cascade culminates, activation of the protease thrombin occurs and soluble fibrinogen is converted into an insoluble polymerized fibrin network. Fibrin polymerization is critical for bleeding cessation and subsequent stages of wound healing. We develop a cooperative enzyme kinetics model for in vitro fibrin matrix polymerization capturing dynamic interactions among fibrinogen, thrombin, fibrin, and intermediate complexes. A tailored parameter subset selection technique is also developed to evaluate parameter identifiability for a representative data curve for fibrin accumulation in a short-duration in vitro polymerization experiment. Our approach is based on systematic analysis of eigenvalues and eigenvectors of the classical information matrix for simulations of accumulating fibrin matrix via optimization based on a least squares objective function. Results demonstrate robustness of our approach in that a significant reduction in objective function cost is achieved relative to a more ad hoc curve-fitting procedure. Capabilities of this approach to integrate non-overlapping subsets of the data to enhance the evaluation of parameter identifiability are also demonstrated. Unidentifiable reaction rate parameters are screened to determine whether individual reactions can be eliminated from the overall system while preserving the low objective cost. These findings demonstrate the high degree of information within a single fibrin accumulation curve, and a tailored model and parameter subset selection approach for improving optimization and reducing model complexity in the context of polymerization experiments. DA - 2021/5// PY - 2021/5// DO - 10.1007/s11538-021-00876-6 VL - 83 IS - 5 SP - SN - 1522-9602 KW - Wound healing KW - Fibrin polymerization KW - Kinetics model KW - Parameter identifiability KW - Subset selection ER - TY - JOUR TI - EXAMINATION OF SOLVING OPTIMAL CONTROL PROBLEMS WITH DELAYS USING GPOPS-II AU - Betts, John T. AU - Campbell, Stephen AU - Digirolamo, Claire T2 - NUMERICAL ALGEBRA CONTROL AND OPTIMIZATION AB -

There are a limited number of user-friendly, publicly available optimal control software packages that are designed to accommodate problems with delays. GPOPS-Ⅱ is a well developed MATLAB based optimal control code that was not originally designed to accommodate problems with delays. The use of GPOPS-Ⅱ on optimal control problems with delays is examined for the first time. The use of various formulations of delayed optimal control problems is also discussed. It is seen that GPOPS-Ⅱ finds a suboptimal solution when used as a direct transcription delayed optimal control problem solver but that it is often able to produce a good solution of the optimal control problem when used as a delayed boundary value solver of the necessary conditions. DA - 2021/6// PY - 2021/6// DO - 10.3934/naco.2020026 VL - 11 IS - 2 SP - 283-305 SN - 2155-3297 KW - Optimal control KW - delayed dynamics KW - waveform relaxation KW - direct transcription KW - control parameterization KW - SOS KW - GPOPS-II ER - TY - JOUR TI - Blurry vision: Supply chain visibility for personal protective equipment during COVID-19* AU - Finkenstadt, Daniel Joseph AU - Handfield, Robert T2 - JOURNAL OF PURCHASING AND SUPPLY MANAGEMENT AB - We explore supply chain visibility challenges in the context of our contemporary COVID pandemic, and offer insights, models and potential solutions to remove barriers to clear supply chain visibility. In this paper, we describe how visibility and velocity are the two key attributes that are required to enabling critical decision-making accuracy which will in turn increase the ability of local, state and federal healthcare and public health decision-makers to response to shifts in the U.S. system. We describe the problems in current systems due to the lack of visibility of material in global supply chains, which in turn leads to problems such as the lack of PPE that occurred during the COVID pandemic. We conclude with recommendations on how to render inventory more visible for the future. DA - 2021/6// PY - 2021/6// DO - 10.1016/j.pursup.2021.100689 VL - 27 IS - 3 SP - SN - 1873-6505 UR - https://doi.org/10.1016/j.pursup.2021.100689 KW - COVID KW - Public purchasing KW - Inventory KW - Visibility KW - Global sourcing ER - TY - JOUR TI - Afforestation, reforestation and new challenges from COVID-19: Thirty-three recommendations to support civil society organizations (CSOs) AU - Mohan, Midhun AU - Rue, Hayden A. AU - Bajaj, Shaurya AU - Galgamuwa, G.A. Pabodha AU - Adrah, Esmaeel AU - Aghai, Matthew Mehdi AU - Broadbent, Eben North AU - Khadamkar, Omkar AU - Sasmito, Sigit D. AU - Roise, Joseph AU - Doaemo, Willie AU - Cardil, Adrian T2 - Journal of Environmental Management AB - Afforestation/reforestation (A/R) programs spearheaded by Civil Society Organizations (CSOs) play a significant role in reaching global climate policy targets and helping low-income nations meet the United Nations (UN) Sustainable Development Goals (SDGs). However, these organizations face unprecedented challenges due to the COVID-19 pandemic. Consequently, these challenges affect their ability to address issues associated with deforestation and forest degradation in a timely manner. We discuss the influence COVID-19 can have on previous, present and future A/R initiatives, in particular, the ones led by International Non-governmental Organizations (INGOs). We provide thirty-three recommendations for exploring underlying deforestation patterns and optimizing forest policy reforms to support forest cover expansion during the pandemic. The recommendations are classified into four groups - i) curbing deforestation and improving A/R, ii) protecting the environment and mitigating climate change, iii) enhancing socio-economic conditions, and iv) amending policy and law enforcement practices. DA - 2021/6// PY - 2021/6// DO - 10.1016/j.jenvman.2021.112277 VL - 287 SP - 112277 J2 - Journal of Environmental Management LA - en OP - SN - 0301-4797 UR - http://dx.doi.org/10.1016/j.jenvman.2021.112277 DB - Crossref KW - Deforestation and forest degradation KW - Real-time forest monitoring and management KW - Sustainable development goals (SDGs) KW - Planting trees with drones KW - International non-governmental organizations KW - (INGOs) KW - Impacts of COVID-19 on forests ER - TY - JOUR TI - Selecting green third party logistics providers for a loss-averse fourth party logistics provider in a multiattribute reverse auction AU - Qian, Xiaohu AU - Fang, Shu-Cherng AU - Yin, Mingqiang AU - Huang, Min AU - Li, Xin T2 - Information Sciences AB - Existing winner determination models tend to overlook the sustainable attributes of third party logistics (3PL) providers. This paper investigates a novel green winner determination problem that has several features, including (i) the sustainable attributes with conflicting and interactive properties of potential 3PLs, and (ii) the loss-averse behavior with an internal reference point of a fourth party logistics (4PL) provider. For the attributes with a combination of crisp data, interval numbers and intuitionistic 2-tuple linguistic terms, we integrate the prospect theory (PT) and Choquet integral with the “benefits, opportunities, costs and risks (BOCR)” framework to propose a novel PTC-BOCR solution method. Numerical experiments are conducted to illustrate the effectiveness and applicability of PTC-BOCR by comparing it with some known methods. Comparison analysis indicates that PTC-BOCR is robust with respect to the variance of 3PLs’ attribute values, while behavioral parameter analysis reveals that the loss-averse behavior of the 4PL is intensified as the difference of 3PLs varies. Managerial insights are also drawn for green 3PLs to win the auction. This study is a significant extension of traditional decision-making methods, which could benefit the realization of a sustainable logistics system in a cost-effective way for 4PLs. DA - 2021/2// PY - 2021/2// DO - 10.1016/j.ins.2020.09.011 VL - 548 SP - 357-377 J2 - Information Sciences LA - en OP - SN - 0020-0255 UR - http://dx.doi.org/10.1016/j.ins.2020.09.011 DB - Crossref KW - Green winner determination KW - Multiattribute decision making KW - Prospect theory KW - Choquet integral KW - Intuitionistic 2-tuple linguistic terms ER - TY - JOUR TI - A kernel-free double well potential support vector machine with applications AU - Gao, Zheming AU - Fang, Shu-Cherng AU - Luo, Jian AU - Medhin, Negash T2 - European Journal of Operational Research AB - As a well-known machine learning technique, support vector machine (SVM) with a kernel function achieves much success in nonlinear binary classification tasks. Recently, some quadratic surface SVM models are proposed and studied by utilizing quadratic surfaces for nonlinear binary separations. In this paper, a kernel-free soft quartic surface SVM model is proposed by utilizing the double well potential function for highly nonlinear binary classification. Mathematical analysis on the theoretical properties of the proposed model, including the existence, uniqueness and support vector representation of optimal solutions, is shown. The sequential minimal optimization algorithm is adopted to implement the proposed model for computational efficiency. Numerical results on some artificial and public benchmark data sets demonstrate its effectiveness over well-known SVM models with or without kernel functions. The proposed model is extended to successfully handle some real-life corporate and personal credit data sets for applications. DA - 2021/4// PY - 2021/4// DO - 10.1016/j.ejor.2020.10.040 VL - 290 IS - 1 SP - 248-262 J2 - European Journal of Operational Research LA - en OP - SN - 0377-2217 UR - http://dx.doi.org/10.1016/j.ejor.2020.10.040 DB - Crossref KW - Data science KW - Support vector machine KW - Double well potential function KW - Kernel-free SVM KW - Binary classification ER - TY - JOUR TI - A Prime-Logarithmic Method for Optimal Reliability Design AU - Li, Han-Lin AU - Huang, Yao-Huei AU - Fang, Shu-Cherng AU - Kuo, Way T2 - IEEE Transactions on Reliability AB - Optimal reliability design (ORD) problem is challenging and fundamental to the study of system reliability. For a system with u components/stages where each of them can be set in m possible reliability levels, state-of-the-art linear reformulation models of ORD problem require O(um) binary variables, O(mn) continuous variables together with either O(mn) inequality constraints or O(um) equality constraints. Using the special property of prime factorization and adopting the logarithmic expression technique, in this article, we propose a novel linear reformulation model of the ORD problem requiring O(um) binary variables, O(mn n! ) continuous variables, and very few linear constraints. This theoretic reduction in variables and constraints can lead to significant savings in computational efforts. Our numerical experiments further confirm the drastic reduction in computational time for solving ORD problems in large size. DA - 2021/3// PY - 2021/3// DO - 10.1109/TR.2020.3020597 VL - 70 IS - 1 SP - 146-162 J2 - IEEE Trans. Rel. OP - SN - 0018-9529 1558-1721 UR - http://dx.doi.org/10.1109/TR.2020.3020597 DB - Crossref KW - Linear reformulation KW - optimal reliability design (ORD) problem KW - prime numbers KW - prime-logarithmic linearization technique ER - TY - JOUR TI - Detecting Framing Changes in Topical News AU - Sheshadri, Karthik AU - Shivade, Chaitanya AU - Singh, Munindar P. T2 - IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS AB - Changes in the framing of topical news are known to foreshadow significant public, legislative, and commercial events. Automated detection of framing changes is, therefore, an important problem, which existing research has not considered. Previous approaches are manual surveys that rely on human effort and are consequently limited in scope. This article systematizes the discovery of framing changes through a fully unsupervised computational method that seeks to isolate framing change trends over several years. We demonstrate our approach by isolating framing change periods that correlate with previously known framing changes. We have prepared a new data set, consisting of over 12 000 articles from seven news topics or domains, in which earlier surveys have found framing changes. Finally, our work highlights the predictive utility of framing change detection, by identifying two domains in which framing changes foreshadowed substantial legislative activity, or preceded judicial interest. DA - 2021/6// PY - 2021/6// DO - 10.1109/TCSS.2021.3063108 VL - 8 IS - 3 SP - 780-791 SN - 2329-924X UR - https://doi.org/10.1109/TCSS.2021.3063108 KW - Obesity KW - Market research KW - Benchmark testing KW - Media KW - Current measurement KW - Standards KW - Sociology KW - Framing KW - news media ER - TY - JOUR TI - Fault-Tolerant Attitude Control for Rigid Spacecraft Without Angular Velocity Measurements AU - Wang, Xianghua AU - Tan, Chee Pin AU - Wu, Fen AU - Wang, Jiandong T2 - IEEE TRANSACTIONS ON CYBERNETICS AB - In this paper, a fault-tolerant control scheme is proposed for the rigid spacecraft attitude control system subject to external disturbances, multiple system uncertainties, and actuator faults. The angular velocity measurement is unavailable, which increases the complexity of the problem. An observer is first designed based on the super-twisting sliding mode method, which can provide accurate estimates of the angular velocity in finite time. Then, an adaptive fault-tolerant controller is proposed based on neural networks using the information from the observer. It is shown that the attitude orientations converge to the desired values exponentially. Finally, a simulation example is utilized to verify the effectiveness of the proposed scheme. DA - 2021/3// PY - 2021/3// DO - 10.1109/TCYB.2019.2905427 VL - 51 IS - 3 SP - 1216-1229 SN - 2168-2275 KW - Space vehicles KW - Actuators KW - Angular velocity KW - Observers KW - Attitude control KW - Velocity measurement KW - Fault tolerance KW - Fault-tolerant control (FTC) KW - finite-time observer KW - neural networks (NNs) KW - rigid spacecraft attitude control ER - TY - JOUR TI - Modeling and Control of Drill-String System With Stick-Slip Vibrations Using LPV Technique AU - Cheng, Jun AU - Wu, Min AU - Wu, Fen AU - Lu, Chengda AU - Chen, Xin AU - Cao, Weihua T2 - IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY AB - In this article, a systematic linear parameter-varying (LPV) model and a gain-scheduled control methodology for drill-string systems are proposed to analyze drill-string dynamics and suppress stick-slip vibrations, finally achieving efficient drilling. First, the changing length of drill string over the entire drilling process is emphasized and the corresponding LPV model is presented by combining the existing multi-degree-of-freedom (DOF) drill-string model, so as to capture length-varying effect. Then, we construct a generalized gain-scheduled control structure based on the H framework and gain-scheduling technique. Procedures for obtaining original and reduced gain-scheduled controllers are designed. Using the measured top drive speed and drill-string length in real time, the gains of the proposed controllers are automatically scheduled, enforcing satisfactory tracking and disturbance rejection performances. Finally, simulations and comparisons between our model and finite-element method-based model and our control method and existing active damping controller are carried out. The simulation results illustrate the effectiveness of the proposal. DA - 2021/3// PY - 2021/3// DO - 10.1109/TCST.2020.2978892 VL - 29 IS - 2 SP - 718-730 SN - 1558-0865 KW - Drill-string system KW - gain-scheduled control KW - linear parameter-varying (LPV) system KW - multi-degree-of-freedom (DOF) model KW - stick-slip vibration ER - TY - JOUR TI - A little bit flexibility on headway distribution is enough: Data-driven optimization of subway regenerative energy AU - Li, Xiang AU - Zhang, Bowen AU - Liu, Yunan T2 - INFORMATION SCIENCES AB - As an emerging energy-efficient management approach, the essential idea of subway regenerative energy optimization is to maximize the absorption of energy generated from train deceleration by adjusting train schedules. In the extant literature, performance evaluation and optimization rely on synthetic data (e.g., computer simulations) and train energy-efficient operation (EEO) strategy. However, when compared to real automatic train operation (ATO) data, the above-mentioned method exhibits significant errors. In this work, we develop a new optimization method driven by real ATO data for maximizing subway regenerative energy based on the following three steps: first, we provide a high-frequency ATO data-driven method for simulating the amount of regenerative energy absorption; second, we propose a concept of uniformity to measure the homogeneous degree of headway distribution; third, we formulate a headway optimization model to maximize the regenerative energy absorption under uniformity constraint. To handle the high complexity of the ATO data-driven objective function (e.g., non-monotonicity, non-convexity, multi-modality), we propose an improved genetic algorithm with multiple crossover and mutation operators to search for near-optimal solutions, in which an adaptive operator selection mechanism with reduction process on ATO data is considered for speeding up the regenerative energy simulation and optimization. The effectiveness of the proposed method is confirmed by using the real ATO data of Beijing Subway Changping line. Our numerical study reveals that, benchmarked with the uniform headway distribution (the policy that is presently in use), our proposed approach achieves a relative improvement of 7.75% at off-peak hours and 42.44% at peak hours for the regenerative energy absorption; and we show that such a significant performance improvement is obtained by allowing a small level of scheduling flexibility (less than 6% relaxation on uniformity level). DA - 2021/4// PY - 2021/4// DO - 10.1016/j.ins.2020.12.030 VL - 554 SP - 276-296 SN - 1872-6291 KW - Subway KW - Regenerative energy KW - ATO data KW - Headway distribution KW - Improved genetic algorithm ER - TY - JOUR TI - Partially penalized IFE methods and convergence analysis for elasticity interface problems T2 - Journal of Computational and Applied Mathematics DA - 2021/1// PY - 2021/1// ER - TY - JOUR TI - Forecasting US Yield Curve Using the Dynamic Nelson-Siegel Model with Random Level Shift Parameters AU - Luo, Deqing AU - Pang, Tao AU - Xu, Jiawen T2 - ECONOMIC MODELLING AB - In this paper, we develop a new model based on the classical dynamic Nelson-Siegel model by introducing random level shift (RLS) parameters. The built-in RLS can capture cyclical fluctuations in interest rates and structural breaks induced by technological progress, financial crisis, major monetary policy interventions, etc. In addition, the model can be used to forecast future structural breaks. We apply the model to fit and forecast daily U.S. Treasury yield curves and the model outperforms other widely used models. The empirical results show that the model not only has a better in-sample fit with residuals exhibiting less persistence but also has superior out-of-sample performance. Moreover, the model performs very well especially for short-term and long-term bonds, and the performance improves as the forecasting horizon increases. DA - 2021/1// PY - 2021/1// DO - 10.1016/j.econmod.2020.10.015 VL - 94 SP - 340-350 SN - 1873-6122 KW - US treasury yield curves KW - Dynamic Nelson-Siegel model KW - Random level shift (RLS) KW - Forecasting ER - TY - JOUR TI - ANDERSON ACCELERATION FOR A CLASS OF NONSMOOTH FIXED-POINT PROBLEMS AU - Bian, Wei AU - Chen, Xiaojun AU - Kelley, C. T. T2 - SIAM JOURNAL ON SCIENTIFIC COMPUTING AB - Related DatabasesWeb of Science You must be logged in with an active subscription to view this.Article DataHistorySubmitted: 6 April 2020Accepted: 23 November 2020Published online: 20 January 2021Keywordsnonsmooth equatioins, Anderson acceleration, integral equations, nonlinear equations, fixed-point problemsAMS Subject Headings65H10, 45G10Publication DataISSN (print): 1064-8275ISSN (online): 1095-7197Publisher: Society for Industrial and Applied MathematicsCODEN: sjoce3 DA - 2021/// PY - 2021/// DO - 10.1137/20M132938X VL - 43 IS - 5 SP - S1-S20 SN - 1095-7197 UR - https://doi.org/10.1137/20M132938X KW - Key words KW - nonsmooth equatioins KW - Anderson acceleration KW - integral equations KW - nonlinear equations KW - fixed-point problems ER - TY - JOUR TI - The local tangential lifting method for moving interface problems on surfaces with applications AU - Xiao, Xufeng AU - Feng, Xinlong AU - Li, Zhilin T2 - JOURNAL OF COMPUTATIONAL PHYSICS AB - In this paper, a new numerical computational frame is presented for solving moving interface problems modeled by parabolic PDEs on static and evolving surfaces. The surface PDEs can have Dirac delta source distributions and discontinuous coefficients. One application is for thermally driven moving interfaces on surfaces such as Stefan problems and dendritic solidification phenomena on solid surfaces. One novelty of the new method is the local tangential lifting method to construct discrete delta functions on surfaces. The idea of the local tangential lifting method is to transform a local surface problem to a local two dimensional one on the tangent planes of surfaces at some selected surface nodes. Moreover, a surface version of the front tracking method is developed to track moving interfaces on surfaces. Strategies have been developed for computing geodesic curvatures of interfaces on surfaces. Various numerical examples are presented to demonstrate the accuracy of the new methods. It is also interesting to see the comparison of the dendritic solidification processes in two dimensional spaces and on surfaces. DA - 2021/4/15/ PY - 2021/4/15/ DO - 10.1016/j.jcp.2021.110146 VL - 431 SP - SN - 1090-2716 UR - https://doi.org/10.1016/j.jcp.2021.110146 KW - Surface and evolving surface PDEs KW - Local tangential lifting method KW - Moving interface on surface KW - Dendritic solidification KW - Front tracking method KW - Discrete delta function on surface ER - TY - JOUR TI - Loss rate forecasting framework based on macroeconomic changes: Application to US credit card industry AU - Taghiyeh, Sajjad AU - Lengacher, David C. AU - Handfield, Robert B. T2 - EXPERT SYSTEMS WITH APPLICATIONS AB - A major part of the balance sheets of the largest U.S. banks consists of credit card portfolios. Hence, managing the charge-off rates is a vital task for the profitability of the credit card industry. Different macroeconomic conditions affect individuals’ behavior in paying down their debts. In this paper, we propose an expert system for loss forecasting in the credit card industry using macroeconomic indicators. We select the indicators based on a thorough review of the literature and experts’ opinions covering all aspects of the economy, consumer, business, and government sectors. The state of the art machine learning models are used to develop the proposed expert system framework. We develop two versions of the forecasting expert system, which utilize different approaches to select between the lags added to each indicator. Among 19 macroeconomic indicators that were used as the input, six were used in the model with optimal lags, and seven indicators were selected by the model using all lags. The features that were selected by each of these models covered all three sectors of the economy. Using the charge-off data for the top 100 US banks ranked by assets from the first quarter of 1985 to the second quarter of 2019, we achieve mean squared error values of 1.15E−03 and 1.04E−03 using the model with optimal lags and the model with all lags, respectively. The proposed expert system gives a holistic view of the economy to the practitioners in the credit card industry and helps them to see the impact of different macroeconomic conditions on their future loss. DA - 2021/3/1/ PY - 2021/3/1/ DO - 10.1016/j.eswa.2020.113954 VL - 165 SP - SN - 1873-6793 UR - http://www.scopus.com/inward/record.url?eid=2-s2.0-85090568347&partnerID=MN8TOARS KW - Expert system KW - Time series forecasting KW - Loss forecasting KW - Macroeconomic indicators KW - Financial industry ER - TY - JOUR TI - A framework for designing and evaluating realistic blockchain-based local energy markets AU - Christidis, Konstantinos AU - Sikeridis, Dimitrios AU - Wang, Yun AU - Devetsikiotis, Michael T2 - APPLIED ENERGY AB - A growing customer base for solar-plus-storage at the grid edge has resulted in stronger interest at the regulatory level towards energy markets at the distribution level. Local energy markets (LEMs) running on blockchains are being studied as a possible direction, but the relevant literature treats the blockchain component as a black box. We make the case that this approach is flawed because the choices in this layer affect the market’s performance significantly. We explicitly identify the design space that the blockchain layer introduces, and analyze how the design choices made therein affect the performance, governance, and degree of decentralization of these markets. As an exercise, we consider three distinct configurations for a next-generation LEM, and compare their performance on both the blockchain and the market layer via a case study. We demonstrate that simple changes in the data model can decrease the market efficiency by up to 90%. We also show that changes in the way bids get encrypted may result in economic improvements, but they do so at the risk of subverting the proper operation and resilience of the market. The simulations for our case study are conducted via a framework that we developed and open-sourced as part of this work. DA - 2021/1/1/ PY - 2021/1/1/ DO - 10.1016/j.apenergy.2020.115963 VL - 281 SP - SN - 1872-9118 KW - Transactive energy KW - Local energy market KW - Blockchain KW - Decentralized market KW - Peer-to-peer trading KW - Renewable energy ER - TY - JOUR TI - Staffing many-server queues with autoregressive inputs AU - Sun, Xu AU - Liu, Yunan T2 - NAVAL RESEARCH LOGISTICS AB - Abstract Recent studies reveal significant overdispersion and autocorrelation in arrival data at service systems such as call centers and hospital emergency departments. These findings stimulate the needs for more practical non‐Poisson customer arrival models, and more importantly, new staffing formulas to account for the autocorrelative features in the arrival model. For this purpose, we study a multiserver queueing system where customer arrivals follow a doubly stochastic Poisson point process whose intensities are driven by a Cox–Ingersoll–Ross (CIR) process. The nonnegativity and autoregressive feature of the CIR process makes it a good candidate for modeling temporary dips and surges in arrivals. First, we devise an effective statistical procedure to calibrate our new arrival model to data which can be seen as a specification of the celebrated expectation–maximization algorithm. Second, we establish functional limit theorems for the CIR process, which in turn facilitate the derivation of functional limit theorems for our queueing model under suitable heavy‐traffic regimes. Third, using the corresponding heavy traffic limits, we asymptotically solve an optimal staffing problem subject to delay‐based constraints on the service levels. We find that, in order to achieve the designated service level, such an autoregressive feature in the arrival model translates into notable adjustment in the staffing formula, and such an adjustment can be fully characterized by the parameters of our new arrival model. In this respect, the staffing formulas acknowledge the presence of autoregressive structure in arrivals. Finally, we extend our analysis to queues having customer abandonment and conduct simulation experiments to provide engineering confirmations of our new staffing rules. DA - 2021/4// PY - 2021/4// DO - 10.1002/nav.21960 VL - 68 IS - 3 SP - 312-326 SN - 1520-6750 KW - autocorrelation KW - heavy‐ KW - traffic approximations KW - many‐ KW - server queues KW - mean‐ KW - reverting process KW - non‐ KW - Poisson arrivals KW - optimal staffing KW - parameter uncertainty KW - queues with customer abandonment ER - TY - JOUR TI - Bayesian inference and uncertainty propagation using efficient fractional-order viscoelastic models for dielectric elastomers AU - Miles, Paul R. AU - Pash, Graham T. AU - Smith, Ralph C. AU - Oates, William S. T2 - JOURNAL OF INTELLIGENT MATERIAL SYSTEMS AND STRUCTURES AB - Dielectric elastomers are employed for a wide variety of adaptive structures. Many of these soft elastomers exhibit significant rate-dependencies in their response. Accurately quantifying this viscoelastic behavior is non-trivial and in many cases a nonlinear modeling framework is required. Fractional-order operators have been applied to modeling viscoelastic behavior for many years, and recent research has shown fractional-order methods to be effective for nonlinear frameworks. This implementation can become computationally expensive to achieve an accurate approximation of the fractional-order derivative. Accurate estimation of the elastomer’s viscoelastic behavior to quantify parameter uncertainty motivates the use of Markov Chain Monte Carlo (MCMC) methods. Since MCMC is a sampling based method, requiring many model evaluations, efficient estimation of the fractional derivative operator is crucial. In this paper, we demonstrate the effectiveness of using quadrature techniques to approximate the Riemann–Liouville definition for fractional derivatives in the context of estimating the uncertainty of a nonlinear viscoelastic model. We also demonstrate the use of parameter subset selection techniques to isolate parameters that are identifiable in the sense that they are uniquely determined by measured data. For those identifiable parameters, we employ Bayesian inference to compute posterior distributions for parameters. Finally, we propagate parameter uncertainties through the models to compute prediction intervals for quantities of interest. DA - 2021/3// PY - 2021/3// DO - 10.1177/1045389X20969847 VL - 32 IS - 4 SP - 486-496 SN - 1530-8138 UR - https://doi.org/10.1177/1045389X20969847 KW - Fractional derivative KW - Riemann– KW - Liouville KW - dielectric elastomers KW - viscoelasticity KW - Bayesian inference KW - uncertainty propagation ER - TY - JOUR TI - Non-parallel hyperplanes ordinal regression machine AU - Jiang, Haitao AU - Yang, Zhixia AU - Li, Zhilin T2 - KNOWLEDGE-BASED SYSTEMS AB - This paper proposes a method to solve ordinal regression problems, namely the non-parallel hyperplanes ordinal regression machine (NPHORM). The goal of this approach is to find K different hyperplanes for the K classes with ordinal information, so that each class is as close as possible to the corresponding hyperplane while as far as possible from the adjacent to the left and right classes. The more flexible separate hyperplanes are preferred using the order information of the data. As a result, this approach only needs to solve K quadratic programming problems independently. Our approach NPHORM is validated on 2 artificial datasets, 16 discretized regression datasets and 17 real ordinal regression datasets and compared with 8 outstanding SVM-based ordinal regression approaches. The results show that our approach NPHORM is comparable with the other SVM-based approaches, especially in real ordinal regression datasets. In addition, our NPHORM is also carried out on the historical color image dataset to compare the performance of deep learning method. Experimental results demonstrate that the performance of our NPHORM outperforms the deep learning methods on MAE. DA - 2021/3/15/ PY - 2021/3/15/ DO - 10.1016/j.knosys.2020.106593 VL - 216 SP - SN - 1872-7409 UR - https://doi.org/10.1016/j.knosys.2020.106593 KW - Ordinal regression KW - Support vector machine KW - Twin support vector machine KW - Non-parallel hyperplanes ordinal regression machine ER - TY - JOUR TI - Efficient algorithms for finding2-mediansof a tree AU - Oudjit, Aissa AU - Stallmann, Matthias F. T2 - NETWORKS AB - Abstract The p ‐median problem for networks is NP‐hard, but polynomial time algorithms exist for trees ( n is the number of nodes): O( pn 2 ) by Tamir, and O( n lg p + 2 n ) by Benkoczi and Bhattacharya. Goldman gave an O( n ) algorithm for the 1‐median problem on trees. Mirchandani and Oudjit proved localization properties for 2‐medians on trees; these were later used to obtain an O( n lg n ) bound, and, in special cases, O( n ) . We present a framework that unifies all efficient algorithms for the 2‐median problem on trees. Our framework isolates the nonlinear part of the computation so that future time‐bound improvements are easily incorporated. We also introduce a method for reducing the search space, improving all known runtimes in many instances. Finally, we give a new algorithm for the case where edge lengths are positive integers. The associated time bound is O( n + D ) , where D is the sum of the logarithms of edge lengths. This is O( n ) if edge lengths are bounded by a constant and O( n lglg n ) if they are O(lg n ) . The algorithm is flexible enough to extend to noninteger edge lengths, preserving the time bound in some circumstances. DA - 2021/4// PY - 2021/4// DO - 10.1002/net.21978 VL - 77 IS - 3 SP - 383-402 SN - 1097-0037 UR - https://doi.org/10.1002/net.21978 KW - 2-median KW - binary search KW - linear time KW - priority queue KW - sorting KW - trees ER - TY - JOUR TI - Partially penalized IFE methods and convergence analysis for elasticity interface problems AU - Huang, Peiqi AU - Li, Zhilin T2 - JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS AB - In this paper, some partial penalized immersed finite element methods (PPIFEMs) are proposed and analyzed for solving elasticity interface problems. Through verifying the inverse trace inequality on the interface edges, the optimal convergence in the energy norm is derived. A new test function is constructed to obtain the discrete inf–sup condition of the penalty-free nonsymmetric PPIFEM and is utilized in the proof of the optimal convergence. Furthermore, the effect of the Lamé parameters on convergence is also studied. Various numerical examples and comparisons are provided to confirm the theoretical results. DA - 2021/1/15/ PY - 2021/1/15/ DO - 10.1016/j.cam.2020.113059 VL - 382 SP - SN - 1879-1778 KW - Elasticity interface problems KW - Immersed finite element KW - Discontinuous coefficients KW - Coercivity KW - Inf-sup condition KW - Error estimates ER - TY - JOUR TI - Radiation Source Localization Using Surrogate Models Constructed from 3-D Monte Carlo Transport Physics Simulations AU - Miles, Paul R. AU - Cook, Jared A. AU - Angers, Zoey V. AU - Swenson, Christopher J. AU - Kiedrowski, Brian C. AU - Mattingly, John AU - Smith, Ralph C. T2 - NUCLEAR TECHNOLOGY AB - Recent research has focused on the development of surrogate models for radiation source localization in a simulated urban domain. We employ the Monte Carlo N-Particle (MCNP) code to provide high-fidelity simulations of radiation transport within an urban domain. The model is constructed to employ a source location (x,y,z) as input and return the estimated count rate for a set of specified detector locations. Because MCNP simulations are computationally expensive, we develop efficient and accurate surrogate models of the detector responses. We construct surrogate models using Gaussian processes and neural networks that we train and verify using the MCNP simulations. The trained surrogate models provide an efficient framework for Bayesian inference and experimental design. We employ Delayed Rejection Adaptive Metropolis (DRAM), a Markov Chain Monte Carlo algorithm, to infer the location and intensity of an unknown source. The DRAM results yield a posterior probability distribution for the source’s location conditioned on the observed detector count rates. The posterior distribution exhibits regions of high and low probability within the simulated environment identifying potential source locations. In this manner, we can quantify the source location to within at least one of these regions of high probability in the considered cases. Employing these methods, we are able to reduce the space of potential source locations by at least 60%. DA - 2021/1/2/ PY - 2021/1/2/ DO - 10.1080/00295450.2020.1738796 VL - 207 IS - 1 SP - 37-53 SN - 1943-7471 KW - Radiation detection KW - inverse problem KW - Bayesian inference KW - MCNP KW - surrogate modeling ER - TY - JOUR TI - A Neural Network-Based Policy Iteration Algorithm with Global H-2-Superlinear Convergence for Stochastic Games on Domains AU - Ito, Kazufumi AU - Reisinger, Christoph AU - Zhang, Yufei T2 - FOUNDATIONS OF COMPUTATIONAL MATHEMATICS AB - Abstract In this work, we propose a class of numerical schemes for solving semilinear Hamilton–Jacobi–Bellman–Isaacs (HJBI) boundary value problems which arise naturally from exit time problems of diffusion processes with controlled drift. We exploit policy iteration to reduce the semilinear problem into a sequence of linear Dirichlet problems, which are subsequently approximated by a multilayer feedforward neural network ansatz. We establish that the numerical solutions converge globally in the $$H^2$$ H 2 -norm and further demonstrate that this convergence is superlinear, by interpreting the algorithm as an inexact Newton iteration for the HJBI equation. Moreover, we construct the optimal feedback controls from the numerical value functions and deduce convergence. The numerical schemes and convergence results are then extended to oblique derivative boundary conditions. Numerical experiments on the stochastic Zermelo navigation problem are presented to illustrate the theoretical results and to demonstrate the effectiveness of the method. DA - 2021/4// PY - 2021/4// DO - 10.1007/s10208-020-09460-1 VL - 21 IS - 2 SP - 331-374 SN - 1615-3383 KW - Hamilton-Jacobi-Bellman-Isaacs equations KW - Neural networks KW - Policy iteration KW - Inexact semismooth Newton method KW - Global convergence KW - q-superlinear convergence ER - TY - JOUR TI - Constraint violation reduction search for 0-1 mixed integer linear programming problems AU - Bansal, Ankit AU - Uzsoy, Reha T2 - ENGINEERING OPTIMIZATION AB - This article presents Constraint Violation Reduction Search (CVRS), a primal heuristic for 0–1 Mixed Integer Linear Programming (MILP) problems. CVRS constructs a series of MILP subproblems by adding artificial variables representing the amount by which each constraint is violated and minimizing their sum in the objective function. A cutoff constraint added to each subproblem ensures that the objective function value of the original MILP problem improves at each iteration. If no integer feasible solution to the MILP subproblem can be found, a neighbourhood search is used to repair the infeasibility. CVRS is tested on 99 hard instances of resource constrained project scheduling, Mixed Integer Programming Library (MIPLIB) and capacitated warehouse location, and its performance is compared to a recent neighbourhood search based primal heuristic for MILP and the CPLEX® MILP solver with promising results. DA - 2021/4/3/ PY - 2021/4/3/ DO - 10.1080/0305215X.2020.1742710 VL - 53 IS - 4 SP - 609-626 SN - 1029-0273 KW - Primal heuristic KW - MILP KW - resource constrained project scheduling KW - MIPLIB KW - capacitated warehouse location ER - TY - JOUR TI - Improving chemotherapy infusion operations through the simulation of scheduling heuristics: a case study AU - Slocum, Ryan F. AU - Jones, Herbert L. AU - Fletcher, Matthew T. AU - McConnell, Brandon M. AU - Hodgson, Thom J. AU - Taheri, Javad AU - Wilson, James R. T2 - Health Systems AB - Over the last decade, chemotherapy treatments have dramatically shifted to outpatient services such that nearly 90% of all infusions are now administered outpatient. This shift has challenged oncology clinics to make chemotherapy treatment as widely available as possible while attempting to treat all patients within a fixed period of time. Historical data from a Veterans Affairs chemotherapy clinic in the United States and staff input informed a discrete event simulation model of the clinic. The case study examines the impact of altering the current schedule, where all patients arrive at 8:00 AM, to a schedule that assigns patients to two or three different appointment times based on the expected length of their chemotherapy infusion. The results identify multiple scheduling policies that could be easily implemented with the best solutions reducing both average patient waiting time and average nurse overtime requirements. DA - 2021/7/3/ PY - 2021/7/3/ DO - 10.1080/20476965.2019.1709908 VL - 2 SP - 1-16 UR - https://doi.org/10.1080/20476965.2019.1709908 KW - Discrete event simulation (DES) KW - scheduling KW - healthcare KW - chemotherapy ER - TY - JOUR TI - Assessing uncertainty and risk in an expeditionary military logistics network AU - McConnell, Brandon M AU - Hodgson, Thom J AU - Kay, Michael G AU - King, Russell E AU - Liu, Yunan AU - Parlier, Greg H AU - Thoney-Barletta, Kristin AU - Wilson, James R T2 - The Journal of Defense Modeling and Simulation: Applications, Methodology, Technology AB - Uncertainty is rampant in military expeditionary operations spanning high-intensity combat to humanitarian operations. These missions require rapid planning and decision-support tools to address the logistical challenges involved in providing support in often austere environments. The US Army’s adoption of an enterprise resource planning system provides an opportunity to develop automated decision-support tools and other analytical models designed to take advantage of newly available logistical data. This research presents a tool that runs in near-real time to assess risk while conducting capacity planning and performance analysis designed for inclusion in a suite of applications dubbed the Military Logistics Network Planning System, which previously only evaluated the mean sample path. Logistical data from combat operations during Operation Iraqi Freedom drive supply requisition forecasts for a contingency scenario in a similar geographic environment. A nonstationary queueing network model is linked with a heuristic logistics scheduling methodology to provide a stochastic framework to account for uncertainty and assess risk. DA - 2021/4/10/ PY - 2021/4/10/ DO - 10.1177/1548512919860595 VL - 7 SP - 154851291986059 UR - https://doi.org/10.1177/1548512919860595 KW - Logistics KW - capacity planning KW - queueing KW - networks KW - forecasting KW - risk analysis KW - nonstationary arrival process KW - time-dependent arrival rate KW - dispersion ratio KW - index of dispersion for counts ER - TY - JOUR TI - Robust consensus for linear multi-agent systems with structured uncertainties AU - Xue, Xiangming AU - Wu, Fen AU - Yuan, Chengzhi T2 - INTERNATIONAL JOURNAL OF CONTROL AB - This paper addresses a robust H∞ consensus problem for linear multi-agent systems subject to structured uncertainty and external disturbances under the leaderless framework. A distributed dynamic output-feedback protocol is proposed, which utilises not only relative output information of neighbouring agents but also relative state information of neighbouring controllers. Through model transformations, the robust H∞ consensus control problem of multi-agents network is reduced to a set of independent H∞ stabilisation problems for single linear subsystems. For robust H∞ consensus, it is shown that the analysis and full state-feedback synthesis conditions for such subsystems can be formulated as linear matrix inequality (LMI) optimisation problems. On the other hand, the synthesis condition for dynamic output-feedback protocol is formulated as non-convex bilinear matrix inequality (BMI) optimisation problem. An iterative LMI algorithm is then presented to solve the resulting BMI optimisation problem. An example of multiple mass-spring-damper systems has been used to demonstrate theoretical results. DA - 2021/3/4/ PY - 2021/3/4/ DO - 10.1080/00207179.2019.1612096 VL - 94 IS - 3 SP - 675-686 SN - 1366-5820 KW - Uncertain linear multi-agent systems KW - structured uncertainty KW - distributed control KW - output-feedback protocol KW - bilinear matrix inequality ER -