Due to its NP-hard nature, the electric vehicle charging scheduling problem requires a robust optimization algorithm capable of producing accurate solutions within a reasonable computational cost. Existing algorithms reviewed in the literature often suffer from either slow convergence speed or an inability to escape local optima. Therefore, this study proposes a novel evolutionary algorithm, termed FL-SHADE, designed to address these limitations by achieving a better balance between exploration and exploitation. This algorithm combines the FGO algorithm with the adaptive L-SHADE algorithm to present a new variant, namely FL-SHADE. The FGO algorithm has robust exploration that helps escape from local optima but suffers from a poor exploitation operator, leading to slow convergence. In contrast, the AL-SHADE algorithm is known for its effective exploitation but limited exploration. By leveraging the complementary strengths of both algorithms, FL-SHADE features strong abilities to avoid stagnation in local optima and accelerate convergence toward high-quality solutions. FL-SHADE is initially assessed using the CEC2017 benchmark and compared with several competing algorithms based on several performance indicators to evaluate its stability and effectiveness. According to the experimental results, FL-SHADE can outperform all algorithms on 15 of 29 test functions, be competitive on 12, and perform worse on only 2, demonstrating that it is a robust alternative for addressing continuous optimization challenges. Subsequently, FL-SHADE is evaluated on 12 charge scheduling problems under four different penetration levels and three scenarios to assess performance at small, medium, and large scales. In addition, it is compared with several high-performing and recently proposed optimization algorithms to validate its effectiveness and stability. The experimental results indicate that FL-SHADE outperforms competing algorithms in eight test cases, whereas AL-SHADE performs better in the remaining cases, suggesting that both algorithms are strong candidates for solving electric vehicle charge scheduling problems.
This comprehensive review presents a thorough examination of recent advances in nanoemulsion (NE) green technology, focusing on biomass-assisted synthesis, characterization, and the diverse biomedical implications of these nanoscale emulsions. NEs, characterized by their minute droplet sizes and kinetic stability, have garnered considerable attention due to their potential applications across various biomedical fields. This review presents a comprehensive analysis of state-of-the-art synthesis methods, including mini-emulsion polymerization, NE–solvent evaporation, spontaneous emulsification, sol–gel techniques, and innovative strategies for producing complex multicomponent materials. Emphasis is placed on the evolution of synthetic approaches, offering insights into the current landscape of NE production. In exploring the biomedical applications, the study categorizes nanocarriers formed within NEs, distinguishing between polymeric, inorganic, and hybrid nanocarriers based on their chemical composition. Noteworthy advancements in synthetic strategies are outlined for each category, showcasing the dynamic nature of NEs technology. A key highlight is the discussion of emerging trends in biomedical applications, spanning medicine, food, agriculture, cosmetics, and environmental science. Specific attention is given to the role of NEs in nanofiltration, elucidating their effectiveness in removing diverse pharmaceuticals through polyamide nano-filters. Moreover, the manuscript delves into the pivotal role of NEs in bioremediation, addressing hazardous substances such as PFASs through adsorption, photo-degradation/defluorination, and other innovative mechanisms. This review aims to provide a contemporary overview of green NE technologies, offering valuable insights for researchers, scientists, and practitioners in nanotechnology, pharmaceuticals, and biomedical sciences.
This review explores the potential of microalgae as a sustainable feed ingredient for shrimp cultivation. Microalgae, used as nutritional supplements, offer several advantages as protein sources, including rich nutrient profiles and bioactive compounds. As shrimp production in the aquaculture sector grows and infectious diseases become more prevalent, microalgae can serve as a safe feed additive and an alternative to antibiotic overuse. This report reviews the prominent microalgae species used as feed ingredients for shrimp, examining their nutritional value, as they are widely utilized in the aquaculture industry. The focus is on the potential of microalgae as a substitute for fishmeal and fish oil in shrimp feed within a circular bioeconomic approach. Additionally, it examines how the inclusion of dietary microalgae influences shrimp growth performance, feed conversion efficiency, antioxidant activity, immune response, and disease resistance. The review also emphasizes the use of microalgae to improve water quality in shrimp culture systems. Furthermore, it aims to demonstrate effective methods of delivering vaccines to shrimp via transgenic microalgae, especially through oral vaccination, to combat pathogens. Overall, this comprehensive review provides an overview of current applications and prospects of microalgae as a sustainable feed ingredient to enhance shrimp aquaculture productivity and sustainability.
Metal complexes constitute a key component of modern chemistry due to their structural diversity, adjustable electronic properties, and broad applicability in catalysis, materials science, and biomedicine. In recent years, artificial intelligence (AI) has increasingly contributed to accelerating the discovery and development of metal complexes by supporting several stages of research, including molecular design, synthesis, characterization, and functional optimization. These advances have improved the efficiency of discovery processes while also enhancing sustainability and the reliability of predictive models. This review first considers traditional coordination chemistry approaches alongside recently developed environmentally sustainable methods for the synthesis of metal complexes. Particular attention is given to ongoing challenges associated with reaction optimization, scalability, and reproducibility. In addition to experimental methodologies, machine learning methods are increasingly employed to complement conventional strategies by enabling a rapid estimate of physicochemical properties and catalytic activity. In the biomedical field, particular focus is placed on platinum- and ruthenium-based anticancer complexes. In this area, AI-assisted drug discovery strategies, computational molecular design, and predictive modeling have supported the development of next-generation metal-based therapeutics characterized by improved selectivity and reduced toxicity. The review also examines catalytic applications of metal complexes, including cross-coupling reactions, hydrogenation, transfer hydrogenation, electrocatalysis, photoredox catalysis, carbon dioxide activation, and asymmetric catalysis. Recent developments in hybrid photoelectrodes, supported catalytic systems, redox-active ligands, and rational molecular catalyst design are also considered. Furthermore, emerging AI-driven approaches for catalyst discovery, such as generative models, inverse design strategies, closed-loop automated experimentation, and high-throughput virtual screening, are presented as effective tools for accelerating catalyst development. Finally, the review discusses current limitations associated with AI-based methodologies, particularly those related to data availability, model interpretability, and generalizability. Future directions emphasize the importance of integrating explainable AI with mechanistic understanding in coordination chemistry in order to improve the reliability and interpretability of catalyst and drug discovery processes.
