@article{zahan_shohan_harris_williams_2023, title={Do Software Security Practices Yield Fewer Vulnerabilities?}, ISSN={["2832-7640"]}, DOI={10.1109/ICSE-SEIP58684.2023.00032}, abstractNote={Due to the ever-increasing number of security breaches, practitioners are motivated to produce more secure software. In the United States, the White House Office released a memorandum on Executive Order (EO) 14028 that mandates organizations provide self-attestation of the use of secure software development practices. The OpenSSF Scorecard project allows practitioners to measure the use of software security practices automatically. However, little research has been done to determine whether the use of security practices improves package security, particularly which security practices have the biggest impact on security outcomes. The goal of this study is to assist practitioners and researchers in making informed decisions on which security practices to adopt through the development of models between software security practice scores and security vulnerability counts.To that end, we developed five supervised machine learning models for npm and PyPI packages using the OpenSSF Scorecard security practices scores and aggregate security scores as predictors and the number of externally-reported vulnerabilities as a target variable. Our models found that four security practices (Maintained, Code Review, Branch Protection, and Security Policy) were the most important practices influencing vulnerability count. However, we had low R2 (ranging from 9% to 12%) when we tested the models to predict vulnerability counts. Additionally, we observed that the number of reported vulnerabilities increased rather than reduced as the aggregate security score of the packages increased. Both findings indicate that additional factors may influence the package vulnerability count. Other factors, such as the scarcity of vulnerability data, time to implicate security practices vs. time to detect vulnerabilities, and the need for more adequate scripts to detect security practices, may impede the data-driven studies to indicate that a practice can aid in the reduction of externally-reported vulnerabilities. We suggest that vulnerability count and security score data be refined such that these measures may be used to provide actionable guidance on security practices.}, journal={2023 IEEE/ACM 45TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING: SOFTWARE ENGINEERING IN PRACTICE, ICSE-SEIP}, author={Zahan, Nusrat and Shohan, Shohanuzzaman and Harris, Dan and Williams, Laurie}, year={2023}, pages={292–303} }
 @article{shohan_hasan_starly_shirwaiker_2022, title={Investigating Autoregressive and Machine Learning-based Time Series Modeling with Dielectric Spectroscopy for Predicting Quality of Biofabricated Constructs}, volume={33}, url={http://dx.doi.org/10.1016/j.mfglet.2022.07.110}, DOI={10.1016/j.mfglet.2022.07.110}, abstractNote={Advances in biofabrication processes need to be complemented with appropriate nondestructive quality engineering techniques that can be integrated into scalable engineered tissue manufacturing systems. Previous studies have demonstrated the feasibility of dielectric spectroscopy (DS) as a inline, real time biological quality monitoring alternative. Time series modeling can help improve the efficiency and accuracy of quality prediction by analyzing trends in DS data as the biofabricated constructs mature over time. These models can help forecast potential future deviations in quality attributes and provide opportunities to take preemptive, corrective actions, leading to better yields and higher quality of final products. In this study, we investigated time series modeling of DS data to characterize the effects of two critical biofabrication parameters on constructs of gelatin methacryloyl (GelMA) hydrogel containing human adipose-derived stem cells (hASC) over 11 days of in vitro culture. The performance of standard autoregressive time series models (Exponential Smoothing, ARMA, ARIMA, SARIMA) and conventional sequence-based machine learning (ML) models (SVM, ANN, CNN and LSTM) were analyzed to forecast trends in Δɛ, a key DS metric that directly correlates to the volume of viable cells in constructs. The ML-based time series models, in general, showed superior performance in predicting future trends in Δɛ, with LSTM providing the lowest least mean square errors (MSE) in Δɛ forecasts. The outcomes of this study highlight the benefits of using DS and time series modeling synergistically for efficient quality monitoring in biofabrication.}, journal={Manufacturing Letters}, publisher={Elsevier BV}, author={Shohan, Shohanuzzaman and Hasan, Mahmud and Starly, Binil and Shirwaiker, Rohan}, year={2022}, month={Sep}, pages={902–908} }
 @article{shohan_zeng_chen_jin_shirwaiker_2022, title={Investigating dielectric spectroscopy and soft sensing for nondestructive quality assessment of engineered tissues}, volume={216}, ISSN={["1873-4235"]}, url={https://publons.com/wos-op/publon/52527065/}, DOI={10.1016/J.BIOS.2022.114286}, abstractNote={Non-destructive, inline quality monitoring techniques that can overcome the limitations of traditional, offline assays are essential to support the scale-up production of tissue engineered medical products (TEMP). In this work, we investigate a new soft-sensing approach with non-destructive dielectric spectroscopy (DS) that synergistically utilizes inline sensor data and predictive analytics to estimate unmeasured TEMP quality profiles. First, the performance of DS during the assessment of gelatin methacrylate (GelMA) constructs containing human adipose-derived stem cells was investigated in comparison to a traditional biochemical assay. The effects of two critical biofabrication parameters (photocrosslinking duration and volume of growth media) on a key scalar metric (Δϵ) were determined over 11 days of in vitro culture, where the metric was associated with the permittivity response of cells to alternating electric fields during DS and corresponding cellular metabolic activity. To enable accurate quality prediction while minimizing direct data collection to reduce the risk of cytotoxicity from prolonged exposure to the DS sensor electrodes and electric fields, we then developed a bilinear basis mixed model (BBMM) as a soft sensor. With comprehensive consideration of different variation sources, this model was designed to estimate missing permittivity profiles of constructs based on the measured DS dataset and biofabrication parameters. Results of benchmarking showed that BBMM outperformed state-of-the-art vector-prediction methods from literature in two different missing data estimation mechanisms. The high-accuracy BBMM provides a novel DS-driven soft sensing system as an inline monitoring tool suitable for scaled-up or scaled-out TEMP production systems.}, journal={BIOSENSORS & BIOELECTRONICS}, author={Shohan, Shohanuzzaman and Zeng, Yingyan and Chen, Xiaoyu and Jin, Ran and Shirwaiker, Rohan}, year={2022}, month={Nov} }
 @article{shohan_harm_hasan_starly_shirwaiker_2021, title={Non-destructive quality monitoring of 3D printed tissue scaffolds via dielectric impedance spectroscopy and supervised machine learning}, volume={53}, ISSN={["2351-9789"]}, url={http://dx.doi.org/10.1016/j.promfg.2021.06.063}, DOI={10.1016/j.promfg.2021.06.063}, abstractNote={Majority of methods currently used for quality assessment of tissue engineered medical products (TEMPs) are offline and destructive in nature, which is one of the factors impeding the scale up and translation of these technologies. In this study, we investigate quality assessment of TEMP via dielectric impedance spectroscopy (DIS) and supervised machine learning (ML) as a non-destructive alternative that requires minimal human intervention. 3D printed, NaOH-treated polycaprolactone (PCL) scaffolds seeded with human adipose-derived stem cells (hASC), NIH 3T3, MG63, and human chondrocyte cells were assessed via DIS over 4 days of in vitro culture. The results showed that the cell type and duration in culture had a significant effect on the delta permittivity (Δε, an important DIS metric. Five supervised ML algorithms – K Nearest Neighbors (KNN), Logistic Regression, Random Forest Classifiers, Support Vector Machines, and artificial neural network – were then used to analyze the comprehensive structured permittivity datasets to determine their ability to discern between different cell types and culture durations. The KNN algorithm demonstrated the best accuracy (99%). The outcomes of this study demonstrate the approach of using DIS and supervised ML in conjunction for assessment of TEMPs in an automated manufacturing system.}, journal={49TH SME NORTH AMERICAN MANUFACTURING RESEARCH CONFERENCE (NAMRC 49, 2021)}, publisher={Elsevier BV}, author={Shohan, Shohanuzzaman and Harm, Jordan and Hasan, Mahmud and Starly, Binil and Shirwaiker, Rohan}, year={2021}, pages={636–643} }
 @article{shohan_ali_kabir_ahmed_haque_suhi_2020, title={Building theory of green supply chain management for the chemical industry An emerging economy context}, volume={31}, ISSN={["1758-6119"]}, url={https://publons.com/publon/43226636/}, DOI={10.1108/MEQ-11-2019-0239}, abstractNote={PurposeIn Bangladesh, the chemical industry is one of the expanding industries based on current statistical data analysis. Green supply chain management (GSCM) is pivotal in order to compete with the global competition. This paper main aim is to discuss a systematic approach to build a structural outline. The purpose of the proposed structural outline is to predict the constructive implementation of GSCM especially on chemical industry in Bangladesh.}, number={5}, journal={MANAGEMENT OF ENVIRONMENTAL QUALITY}, author={Shohan, Shohanuzzaman and Ali, Syed Mithun and Kabir, Golam and Ahmed, S. K. Kafi and Haque, Tasmiah and Suhi, Saima Ahmed}, year={2020}, month={Aug}, pages={1285–1308} }
 @article{shohan_ali_kabir_ahmed_suhi_haque_2019, title={Green supply chain management in the chemical industry: structural framework of drivers}, volume={26}, ISSN={["1745-2627"]}, DOI={10.1080/13504509.2019.1674406}, abstractNote={ABSTRACT The main purpose of Green Supply Chain Management (GSCM) is to improve the quality of supply chain management strategies and environmental performance. As per current statistics, the chemical industry is growing fast in Bangladesh. In order to compete for global competition, GSCM is essential in this sector. This paper proposes a systematic approach of structural framework whose aim is to enhance the probability of constructive implementation of GSCM in the field chemical industry in Bangladesh. Therefore, this framework evaluates the appropriate interrelationship along with the drivers of GSCM in the chemical industry. In total, eight drivers were finalized from an associated literature review with the help of survey and by taking expert opinions via the Delphi methodology. In addition to MICMAC analysis, the driving and the dependence powers for all the drivers were determined. Moreover, the structural frameworks for the drivers were developed by means of total interpretive structural modeling (TISM) technique. As a result, the findings indicate that the most significant driver was supplier pressure and willingness and the most important barrier was high cost. Finally, the main objective of this research is expected to help industrial managers to evaluate and understand the critical areas where they should emphasize to implement GSCM in the chemical industry.}, number={8}, journal={INTERNATIONAL JOURNAL OF SUSTAINABLE DEVELOPMENT AND WORLD ECOLOGY}, author={Shohan, S. and Ali, S. M. and Kabir, G. and Ahmed, S. K. K. and Suhi, S. A. and Haque, T.}, year={2019}, month={Nov}, pages={752–768} }
 @article{aggregate planning using transportation method: a case study in cable industry_2014, url={https://airccse.org/journal/mvsc/vol5.html}, journal={International Journal of Managing Value and Supply Chains}, year={2014}, month={Sep} }