జర్నల్ ఆఫ్ అప్లైడ్ & కంప్యూటేషనల్ మ్యాథమెటిక్స్

Sleep is a fundamental component of human health, influencing various aspects of physical and mental well-being. Despite its importance, sleep disorders are pervasive, affecting millions of individuals globally. Traditional approaches to sleep medicine often follow a one-size-fits-all model, which may not adequately address the unique needs of each patient. However, the advent of personalized sleep medicine marks a significant shift in how sleep disorders are understood and treated, offering tailored solutions based on an individual's genetic, environmental, and lifestyle factors.

Fractional Lévy Stable Motion (FLSM) is an extension of the Lévy Stable Motion (LSM) that incorporates the concept of fractional dynamics, offering a rich framework for modeling various complex phenomena in fields such as finance, physics, and signal processing. Unlike Brownian motion, which is characterized by Gaussian increments, Lévy Stable Motion allows for heavy-tailed distributions, making it a more versatile tool for capturing real-world anomalies and extreme events. Fractional Lévy Stable Motion further extends this by introducing memory and long-range dependence, described by the Hurst parameter.

Fuel cells have emerged as a promising technology for clean energy production, converting chemical energy directly into electrical energy with high efficiency and low environmental impact. Among various types of fuel cells, polymer electrolyte membrane fuel cells have garnered significant attention due to their high power density, operational efficiency, and suitability for a wide range of applications, from portable electronics to transportation and stationary power generation. Central to the performance of PEMFCs is the membrane, which conducts protons while acting as a barrier to electrons and reactant gases.

Neural networks have revolutionized the field of artificial intelligence, offering powerful tools for pattern recognition, classification, and regression tasks. Among the various types of neural networks, Product Unit Neural Networks (PUNNs) stand out due to their unique architecture, which enables them to model complex, non-linear relationships more effectively than traditional networks. A crucial aspect of understanding and optimizing these networks involves the analysis of their fitness landscapes. This mini review explores the concept of fitness landscape analysis in the context of PUNNs, examining its implications for network design, training efficiency, and overall performance.