@article{west_grigolini_kerick_franaszczuk_mahmoodi_2023, title={Complexity Synchronization of Organ Networks}, volume={25}, ISSN={["1099-4300"]}, DOI={10.3390/e25101393}, abstractNote={The transdisciplinary nature of science as a whole became evident as the necessity for the complex nature of phenomena to explain social and life science, along with the physical sciences, blossomed into complexity theory and most recently into complexitysynchronization. This science motif is based on the scaling arising from the 1/f-variability in complex dynamic networks and the need for a network of networks to exchange information internally during intra-network dynamics and externally during inter-network dynamics. The measure of complexity adopted herein is the multifractal dimension of the crucial event time series generated by an organ network, and the difference in the multifractal dimensions of two organ networks quantifies the relative complexity between interacting complex networks. Information flows from dynamic networks at a higher level of complexity to those at lower levels of complexity, as summarized in the ‘complexity matching effect’, and the flow is maximally efficient when the complexities are equal. Herein, we use the scaling of empirical datasets from the brain, cardiovascular and respiratory networks to support the hypothesis that complexity synchronization occurs between scaling indices or equivalently with the matching of the time dependencies of the networks’ multifractal dimensions.}, number={10}, journal={ENTROPY}, author={West, Bruce J. and Grigolini, Paolo and Kerick, Scott E. and Franaszczuk, Piotr J. and Mahmoodi, Korosh}, year={2023}, month={Oct} }
 @article{mahmoodi_kerick_grigolini_franaszczuk_west_2023, title={Complexity synchronization: a measure of interaction between the brain, heart and lungs}, volume={13}, ISSN={["2045-2322"]}, DOI={10.1038/s41598-023-38622-8}, abstractNote={Abstract}, number={1}, journal={SCIENTIFIC REPORTS}, author={Mahmoodi, Korosh and Kerick, Scott E. E. and Grigolini, Paolo and Franaszczuk, Piotr J. J. and West, Bruce J. J.}, year={2023}, month={Jul} }
 @article{mahmoodi_kerick_grigolini_franaszczuk_west_2023, title={Temporal complexity measure of reaction time series: Operational versus event time}, ISSN={["2162-3279"]}, DOI={10.1002/brb3.3069}, abstractNote={Abstract}, journal={BRAIN AND BEHAVIOR}, author={Mahmoodi, Korosh and Kerick, Scott E. and Grigolini, Paolo and Franaszczuk, Piotr J. and West, Bruce J.}, year={2023}, month={May} }
 @article{west_2022, title={The Fractal Tapestry of Life: III Multifractals Entail the Fractional Calculus}, volume={6}, ISSN={["2504-3110"]}, DOI={10.3390/fractalfract6040225}, abstractNote={This is the third essay advocating the use the (non-integer) fractional calculus (FC) to capture the dynamics of complex networks in the twilight of the Newtonian era. Herein, the focus is on drawing a distinction between networks described by monfractal time series extensively discussed in the prequels and how they differ in function from multifractal time series, using physiological phenomena as exemplars. In prequel II, the network effect was introduced to explain how the collective dynamics of a complex network can transform a many-body non-linear dynamical system modeled using the integer calculus (IC) into a single-body fractional stochastic rate equation. Note that these essays are about biomedical phenomena that have historically been improperly modeled using the IC and how fractional calculus (FC) models better explain experimental results. This essay presents the biomedical entailment of the FC, but it is not a mathematical discussion in the sense that we are not concerned with the formal infrastucture, which is cited, but we are concerned with what that infrastructure entails. For example, the health of a physiologic network is characterized by the width of the multifractal spectrum associated with its time series, and which becomes narrower with the onset of certain pathologies. Physiologic time series that have explicitly related pathology to a narrowing of multifractal time series include but are not limited to heart rate variability (HRV), stride rate variability (SRV) and breath rate variability (BRV). The efficiency of the transfer of information due to the interaction between two such complex networks is determined by their relative spectral width, with information being transferred from the network with the broader to that with the narrower width. A fractional-order differential equation, whose order is random, is shown to generate a multifractal time series, thereby providing a FC model of the information exchange between complex networks. This equivalence between random fractional derivatives and multifractality has not received the recognition in the bioapplications literature we believe it warrants.}, number={4}, journal={FRACTAL AND FRACTIONAL}, author={West, Bruce J.}, year={2022}, month={Apr} }