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The event-triggered sliding-mode control (SMC) for discrete-time networked Markov jumping systems (MJSs) with channel fading is investigated by means of a genetic algorithm. In order to reduce resource consumption in the transmission process, an event-triggered protocol is adopted for networked MJSs. A key feature is that the signal transmission is inevitably affected by fading phenomenon due to delay, random noise, and amplitude attenuation in a networked environment. With the aid of a common sliding surface, an event-triggered SMC law is designed by adjusting the system network mode. Under the framework of stochastic Lyapunov stability, sufficient conditions are constructed to ensure the mean-square stability of the closed-loop networked MJSs, and the sliding region is reached around the specified sliding surface. Moreover, based on the iteration optimizing accessibility of objective function, an effective SMC approach under genetic algorithm is proposed to minimize the convergence region around the sliding surface. Finally, the effectiveness of the proposed method is proved by the F-404 aircraft model.
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http://dx.doi.org/10.1109/TCYB.2023.3253701 | DOI Listing |
Clin Kidney J
September 2025
Department of Nephrology. University Clinical Hospital, INCLIVA, Valencia. RICORS Renal Instituto de salud Carlos III, Valencia. Spain.
Metabolic dysfunction-associated steatotic liver disease (MASLD) has emerged as a major contributor to systemic metabolic dysfunction and is increasingly recognized as a risk enhancer for both cardiovascular disease (CVD) and chronic kidney disease (CKD). This review explores the complex interconnections between MASLD, CVD, and CKD, with emphasis on shared pathophysiological mechanisms and the clinical implications for risk assessment and management. We describe the crosstalk among the liver, heart, and kidneys, focusing on insulin resistance, chronic inflammation, and progressive fibrosis as key mediators.
View Article and Find Full Text PDFPatterns (N Y)
July 2025
Cedars-Sinai Medical Center, Los Angeles, CA, USA.
The tree-based pipeline optimization tool (TPOT) is one of the earliest automated machine learning (ML) frameworks developed for optimizing ML pipelines, with an emphasis on addressing the complexities of biomedical research. TPOT uses genetic programming to explore a diverse space of pipeline structures and hyperparameter configurations in search of optimal pipelines. Here, we provide a comparative overview of the conceptual similarities and implementation differences between the previous and latest versions of TPOT, focusing on two key aspects: (1) the representation of ML pipelines and (2) the underlying algorithm driving pipeline optimization.
View Article and Find Full Text PDFSci Rep
September 2025
Fukushima Renewable Energy Institute, Koriyama, Japan.
Ultra-fast charging stations (UFCS) present a significant challenge due to their high power demand and reliance on grid electricity. This paper proposes an optimization framework that integrates deep learning-based solar forecasting with a Genetic Algorithm (GA) for optimal sizing of photovoltaic (PV) and battery energy storage systems (BESS). A Gated Recurrent Unit (GRU) model is employed to forecast PV output, while the GA maximizes the Net Present Value (NPV) by selecting optimal PV and BESS sizes tailored to weekday and weekend demand profiles.
View Article and Find Full Text PDFAm J Trop Med Hyg
September 2025
Rickettsial Zoonoses Branch, Centers for Disease Control and Prevention, Atlanta, Georgia.
Haemaphysalis leporispalustris (the rabbit tick) is one of the most broadly distributed hard tick species in the Americas. In 2018, investigators amplified DNA from a spotted fever group Rickettsia (SFGR) species found in host-seeking larvae and nymphs of H. leporispalustris collected in northern California and proposed the name Candidatus "Rickettsia lanei" using results obtained via multilocus sequence typing.
View Article and Find Full Text PDFIntroduction: Genetic analysis is essential for diagnosing, treating, and predicting complications in neonatal diabetes mellitus (NDM) but is unavailable in some regions. Sulfonylureas are effective for NDM caused by KCNJ11 or ABCC8 mutations, which are among the most common genetic causes, therefore they are often given before genetic testing. Unfortunately, in certain ethnicities, this mutation rarely occurs.
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