The increased interdependency among the renewable energy source, energy storage systems, and computer-based control systems has transformed the power grid of today into a high-tech, adaptive network, but now increasingly susceptible to stochastic fluctuation and cascading uncertainties. This paper presents a next-generation Uncertainty Quantification and MultiObjective Optimization (UQ-MOO) approach to improving Integrated Renewable Energy Systems' resilience, adaptability, and efficiency. The study provides an answer to a relevant question: How can uncertainty, instead of being an obstacle, be utilized intentionally to enhance the adaptive intelligence and resilience of renewable power grids? The envisioned framework combines state-of-the-art probabilistic analysis, such as Bayesian inference, Monte Carlo simulation, and Polynomial Chaos Expansion, with multi-objective evolutionary optimization and reinforcement learning-based decision intelligence to support real-time, data-driven operation management. When brought to hybrid solar–wind–hydrogen microgrid systems, the approach registered a 36% system reliability improvement, 29% gain in energy efficiency, and 41% loss reduction in volatility-tolerant performance over traditional optimization algorithms. Aside from computational gain, the result sets a new standard in renewable energy research: uncertainty is no longer addressed as a constraint but as a dynamic facilitator of resilience and intelligent adaptation. Finally, this book is leading the way in smart, self-adjusting energy systems, with uncertainty science, optimization theory, and green system engineering coming together to create an unbreakable net-zero energy future.
Peace Chinonyerem Ike, Adjoa Okezie Okuma, Odunmorayo David Adedokun et al.· International Journal of Eng...· 0 citations
Cancer cachexia, a multidimensional, complex metabolic syndrome of involuntary weight loss, muscle wasting, and systemic inflammation, continues to threaten cancer therapy outcomes and survival and quality of life. Traditional physiotherapy and nutrition care often employ nonindividualized methods, disregarding metabolic heterogeneity at the individual level. This study introduces a novel Integrative Physiotherapy Framework (IPF) that integrates metabolomicsguided phenotyping with personalized rehabilitation and nutritional programs to enhance metabolic resilience in cancer cachexia patients. Metabolic signatures defining impaired energy flux, mitochondrial perturbation, and inflammatory dysregulation were defined through untargeted plasma and skeletal muscle metabolomic profiling. These evidence-based individualized physiotherapy treatment regimens consist of resistance and aerobic exercise with omega-3 fatty acid-fortified dietary preparations, branchedchain amino acids, and antiinflammatory plant phytochemicals. In a 12week pilot cohort, the IPF elicited striking increases in lean body mass, muscle strength, and fatigue indices, as well as normalization of important metabolites like lactate, kynurenine, and βhydroxybutyrate. The results show that modulation of individual metabolic phenotypes by physiotherapy-facilitated metabolic modulation can reduce cachexia onset and restore functional capacity. By incorporating metabolomic understanding into clinical rehabilitation, the paradigm provides a precision-medicine treatment model for oncology physiotherapy, integrating molecular science and functional rehabilitation in a synthesis of supportive cancer care that redefines supportive cancer care.
Peace Chinonyerem Ike, Collins Atuahene, Y. Owusu et al.· International Journal of Med...· 0 citations
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