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Because of the manufacturing variations of circuit elements and the effects of parasitic parameters, the switching elements of parallel-connected modules cannot be synchronously switched. Consequently, the characteristics of these converters, which are multi-mode, high-order systems, cannot be comprehensively described using reduced-order models based on the symmetry of circuit topology and parameters. It is also difficult to fully describe the characteristics of these systems using discrete mapping models and averaged models. Therefore, we developed a smooth, sigmoid function-based continuous-time model for systems comprising parallel-connected buck converters with average current-sharing control. The proposed model provides a unified description of the continuous and discontinuous conduction modes of the system. The criteria for ensuring the stability of the system were derived based on the Floquet theory. The nonlinear dynamic behavior of the system and the effects of the key parameters on the stability of the system were investigated. The results showed that the system may undergo period doubling bifurcation, border collision bifurcation, and Neimark-Sacker bifurcation as the system parameters varied. The theoretical analysis and simulation results were verified experimentally. Our results provide a basis for the configuration of controller parameters.
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http://dx.doi.org/10.1063/5.0201373 | DOI Listing |
Lipids Health Dis
September 2025
Department of Gastroenterology, Weifang People's Hospital, The First Affiliated Hospital of Shandong Second Medical University, 151 Guangwen Street, Weifang, Shandong, 261000, China.
Background: Current scoring systems for hypertriglyceridaemia-induced acute pancreatitis (HTG-AP) severity are few and lack reliability. The present work focused on screening predicting factors for HTG-SAP, then constructing and validating the visualization model of HTG-AP severity by combining relevant metabolic indexes.
Methods: Between January 2020 and December 2024, retrospective clinical information for HTG-AP inpatients from Weifang People's Hospital was examined.
Nat Microbiol
September 2025
Division of Computational Pathology, Brigham and Women's Hospital, Boston, MA, USA.
Although dynamical systems models are a powerful tool for analysing microbial ecosystems, challenges in learning these models from complex microbiome datasets and interpreting their outputs limit use. We introduce the Microbial Dynamical Systems Inference Engine 2 (MDSINE2), a Bayesian method that learns compact and interpretable ecosystems-scale dynamical systems models from microbiome timeseries data. Microbial dynamics are modelled as stochastic processes driven by interaction modules, or groups of microbes with similar interaction structure and responses to perturbations, and additionally, noise characteristics of data are modelled.
View Article and Find Full Text PDFEur J Neurosci
September 2025
The Tampa Human Neurophysiology Lab, Department of Neurosurgery, Brain and Spine, Morsani College of Medicine, University of South Florida, Tampa, Florida, USA.
Sensory areas exhibit modular selectivity to stimuli, but they can also respond to features outside of their basic modality. Several studies have shown cross-modal plastic modifications between visual and auditory cortices; however, the exact mechanisms of these modifications are yet not completely known. To this aim, we investigated the effect of 12 min of visual versus sound adaptation (referring to forceful application of an optimal/nonoptimal stimulus to a neuron[s] under observation) on the infragranular and supragranular primary visual neurons (V1) of the cat (Felis catus).
View Article and Find Full Text PDFMed Eng Phys
October 2025
Biomedical Device Technology, Istanbul Aydın University, Istanbul, 34093, Istanbul, Turkey. Electronic address:
Deep learning approaches have improved disease diagnosis efficiency. However, AI-based decision systems lack sufficient transparency and interpretability. This study aims to enhance the explainability and training performance of deep learning models using explainable artificial intelligence (XAI) techniques for brain tumor detection.
View Article and Find Full Text PDFInt J Pharm
September 2025
Department of Pharmaceutical Sciences, Via del Liceo 1, 06123 Perugia, Italy. Electronic address:
Indole-3-carboxaldehyde (I3A), a microbial tryptophan metabolite, exhibits significant immunomodulatory activity at the host-microbial interface. However, its rapid transformation into metabolites like indole-3-carboxylic acid (I3CA) raises questions about their therapeutic potential. This study aimed to evaluate the pharmacological contributions of I3CA through the development of a proper delivery strategy.
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