Search Results (1,435)

Accurate modeling and parameter estimation of photovoltaic (PV) systems are vital for advancing energy sustainability and achieving global decarbonization goals. Reliable PV models enable better integration of solar resources into smart grids, improve system efficiency, and reduce maintenance costs. This aligns with the vision of sustainable energy systems that combine intelligent optimization with environmental responsibility. The recently introduced Black-Winged Kite Algorithm (BWKA) has shown promise by emulating the predatory and migratory behaviors of black-winged kites; however, it still suffers from issues of slow convergence, limited population diversity, and imbalance between exploration and exploitation. To address these limitations, this paper proposes an Improved Black-Winged Kite Algorithm (IBWKA) that integrates two novel strategies: (i) a Soft-Rime Search (SRS) modulation in the attacking phase, which introduces a smoothly decaying nonlinear factor to adaptively balance global exploration and local exploitation, and (ii) a Quadratic Interpolation (QI) refinement mechanism, applied to a subset of elite individuals, that accelerates local search by fitting a parabola through representative candidate solutions and guiding the search toward promising minima. These dual enhancements reinforce both global diversity and local accuracy, preventing premature convergence and improving convergence speed. The effectiveness of the proposed IBWKA in contrast to the standard BWKA is validated through a comprehensive experimental study for accurate parameter identification of PV models, including single-, double-, and three-diode equivalents, using standard datasets (RTC France and STM6_40_36). The findings show that IBWKA delivers higher accuracy and faster convergence than existing methods, with its improvements confirmed through statistical analysis. Compared to BWKA and others, it proves to be more robust, reliable, and consistent. By combining adaptive exploration, strong diversity maintenance, and refined local search, IBWKA emerges as a versatile optimization tool. Full article

A well-designed layout for urban parks plays a crucial role in constructing livable cities and enhancing residents’ well-being. The provision of age-friendly park access is fundamental to building an elderly-friendly city. However, previous studies have lacked comprehensive analyses that integrate the distribution of the elderly population, park accessibility, park quality, environmental characteristics, and social equity within a unified framework. Specifically, the supply–demand imbalance mechanism underlying the spatial variations in accessibility has not been adequately addressed. This study employs an improved two-step floating catchment area (2SFCA) method, combined with Lorenz curves and urban park-adapted Gini coefficients, to examine the supply–demand relationship and allocation differences between the elderly population and parks at the neighborhood and community levels. The analysis highlights issues related to equity and accessibility and explores their spatial disparity and influencing factors. The key findings are as follows: (1) The classic 2SFCA model exhibits significant biases in evaluating park supply–demand relationships, accessibility, and equity at a fine-grained scale, indicating the necessity of high-precision modeling. (2) Park accessibility in the Old City of Nanjing follows a dual-ring pattern of high accessibility, contrasted with clustered areas of low accessibility, while accessibility equity shows a central–peripheral gradient. Overall equity is relatively low, with good walking accessibility within only about one-third of communities. (3) Park supply levels, neighborhood construction year, and plot ratios are the primary factors influencing park accessibility for elderly residents. The comprehensive aging index is positively correlated with the equity in park layout, whereas housing prices and neighborhood size do not exhibit a simple linear relationship with park accessibility or equity for elderly residents. These findings provide a comprehensive and realistic perspective for understanding elderly park accessibility and equity, offering decision-making references for enhancing urban livability, managing an aging society, and formulating spatial equity policies in the future. Full article

This paper proposes a low-cost measurement system that can be used to perform simultaneous blood pressure (BP) and breathing (BR) measurements. Regarding BP measurements, the main parameters that are accessed include systolic blood pressure (SBP), diastolic blood pressure (DBP), mean arterial pressure blood pressure (MAP), and heartbeat rate (HR). Concerning BR measurements, the main parameters that are accessed include the inspiration period and amplitude (IPA), the expiration period and amplitude (EPA), and the breathing rate (BR), as well as the statistical and standard deviation of all these parameters. The dual measurement capability of the proposed measurement system is very important since blood pressure and breathing parameters are not statistically independent and it is possible to obtain additional and valuable clinical information from the information provided by both biomedical variables when measured simultaneously. The analysis of the correlation between these variables is particularly important after performing intensive physical exercises, since it enables cardiac rehabilitation assessment, pre-surgical risk evaluation, detection of silent ischemia, and monitoring of chronic diseases recovery, among others. Regarding the performance evaluation of the proposed biomedical device, a prototype of the measurement system was developed, tested, and calibrated. Several experimental tests were carried out to evaluate the performance of the proposed measurement system and to obtain the correlation coefficients between different blood pressure and breathing parameters. The tests were based on a statistically significant number of measurements that were performed with a population that integrated twenty students in two groups with different habits of physical exercise practice but subjected to a set of common physical exercises, with graduated intensity levels. Full article

Background: Metabolically associated fatty liver disease (MASLD) constitutes a major burden. Glucagon-like peptide-1 agonists (GLP-1) could improve hepatic steatosis as well as weight loss. However, the effect of GLP-1 agonists on patients with and without diabetes and the effect of newer drugs (dual and triple agonists) are unclear. Objective: To investigate the effect of GLP-1 agonists, including dual and triple agonists, in patients with metabolic-associated liver steatosis and steatohepatitis, while exploring their effect on patients with and without type 2 diabetes. Methods: We searched PubMed, Scopus, and Web of Science in October 2025 for randomized parallel controlled trials that investigated the effect of GLP-1 agonists in patients with MASLD or metabolic-associated steatohepatitis (MASH). We assessed the quality of the included studies using Cochrane ROB2. We performed the analysis using RevMan 5.4. We performed subgroup analysis based on the status of diabetes, the control group, and the class of GLP-1 agonist (single, dual, or triple). Results: We included twenty studies. Compared to the control group, GLP-1 agonists were associated with a statistically significant increase in the resolution of MASH without worsening fibrosis (RR 3.03, p < 0.0001) and at least one stage of liver fibrosis without the worsening of MASH compared to the control group (RR: 1.45, p < 0.00001). GLP-1 agonists were associated with a statistically significant weight reduction (SMD −1.11, p < 0.0001), glycosylated hemoglobin (SMD −0.81, p < 0.00001), levels of aspartate aminotransferase (SMD −0.48, p = 0.008), and alanine aminotransferase (SMD −0.54, p = 0.008). However, in patients without type 2 diabetes, GLP-1 agonists had no significant effect on weight loss (SMD −0.97, p = 0.12) or improvement in fibrosis (RR 1.54, p = 0.24). There was a statistically significant increase in the overall adverse events (RR 1.10, p < 0.00001), while there was no significant difference in serious adverse events (p = 0.35). Conclusions: GLP-1 agonists improved liver fibrosis, steatohepatitis, weight loss, HbA1c, and liver enzymes in patients with MASLD or MASH. Overall, GLP-1 agonists were associated with a significantly higher risk of adverse events compared to the control, while serious adverse events were comparable between both groups. There was no significant effect on weight loss or improvement in fibrosis in patients without type 2 diabetes. However, there was a limited number of studies in this population. Thus, further research is needed before recommendations can be made for this subgroup. Full article

Population activity drives urban development, and high-spatiotemporal-resolution population distribution provides critical insights for refined urban management and social services. However, mixed population activity patterns and spatial heterogeneity make simultaneous high-temporal- and -spatial-resolution estimation difficult. Therefore, we propose the High-Spatiotemporal-Resolution Population Distribution Estimation Based on the Strong and Weak Perception of Population Activity Patterns (SWPP-HSTPE) method to estimate hourly population distribution at the building scale. During the weak-perception period, we construct a Modified Dual-Environment Feature Fusion model using building features within small-scale grids to estimate stable nighttime populations. During the strong-perception period, we incorporate activity characteristics of weakly perceived activity populations (minors and older people). Then, the Self-Organizing Map algorithm and spatial environment function purity are used to decompose mixed patterns of strongly perceived activity populations (young and middle-aged) and to extract fundamental patterns, combined with building types, for population calculation. Results demonstrated that the SWPP-HSTPE method achieved high-spatiotemporal-resolution population distribution estimation. During the weak-perception period, the estimated population correlated strongly with actual household counts (r = 0.72) and outperformed WorldPop and GHS-POP by 0.157 and 0.133, respectively. During the strong-perception period, the SWPP-HSTPE model achieves a correlation with hourly population estimates that is approximately 4% higher than that of the baseline model, while reducing estimation errors by nearly 2%. By jointly accounting for temporal dynamics and population activity patterns, this study provides valuable data support and methodological insights for fine-grained urban management. Full article

To address the challenges of high computational costs, susceptibility to local optima, and heavy reliance on manual intervention in multi-physics parameter optimization for symmetric acoustic metamaterials, an enhanced Chimp Optimization Algorithm (DADCOA) is proposed in this paper. This algorithm integrates the double chaotic initialization strategy (DCS), adaptive multimodal convergence mechanism (AMC), and dual-weight pinhole imaging update operator (DWPI). It employs a Logistic–Tent composite chaotic mapping strategy for population initialization, significantly enhancing distribution uniformity within high-dimensional parameter spaces. An AMC factor is then introduced to dynamically balance global exploration and local exploitation based on the real-time evolutionary state of the population. A dual-weight population update mechanism, incorporating distance and historical contributions, is integrated with a pinhole imaging opposition-based learning strategy to improve population diversity. Additionally, a composite single objective error feedback local differential mutation operation is introduced to improve optimization accuracy for coupled multi-physics objectives. Experimental validation based on the CEC 2022 test function suite and an acoustic metamaterial parameter optimization model demonstrates that compared to the standard COA algorithm and existing improved algorithms, the DADCOA algorithm reduces simulation time by 28.46% to 60.76% while maintaining high accuracy. This approach effectively addresses the challenges of high computational cost, stringent accuracy requirements, and composite single objective coupling in COMSOL physical parameter optimization, providing an effective solution for the design of acoustic metamaterials based on symmetric structures. Full article

Airborne iodine-131 plays a pivotal role in both nuclear medicine and nuclear safety due to its radiotoxicity, volatility, and affinity for the thyroid gland. Although the total exhaled activity after medical I-131 therapy is minimal, over 95% of this activity appears in volatile organic forms, which evade standard filtration and reflect metabolic pathways of iodine turnover. Our experimental work in patients and mice confirms the metabolic origin of these species, modulated by thyroidal function. In nuclear reactor environments, both under routine operation and during accidents, organic iodides such as [¹³¹I]CH₃I have also been identified as major airborne components, often termed “penetrating iodine” due to their low adsorption to conventional filters. This review compares the molecular speciation, environmental persistence, and dosimetric impact of airborne I-131 across clinical, technical, and accidental release scenarios. While routine reactor emissions yield negligible doses (<0.1 µSv/year), severe nuclear incidents like Chernobyl and Fukushima have resulted in significant thyroid exposures. Doses from these events ranged from tens of millisieverts to several Sieverts, particularly in children. We argue that a deeper understanding of chemical forms is essential for effective risk assessment, filtration technology, and emergency preparedness. Iodine-131 exemplifies the dual nature of radioactive substances: in nuclear medicine its radiotoxicity is therapeutically harnessed, whereas in industrial or reactor contexts it represents an unwanted hazard. The same physicochemical properties that enable therapeutic efficacy also determine, in the event of uncontrolled release, the range, persistence, and the potential for unwanted radiotoxic exposure in the general population. In nuclear medicine, exhaled activity after radioiodine therapy is minute but largely organically bound, reflecting enzymatic and metabolic methylation processes. During normal reactor operation, airborne iodine levels are negligible and dominated by inorganic vapors efficiently captured by filtration systems. In contrast, major accidents released large fractions of volatile iodine, primarily as elemental [¹³¹I]I₂ and organically bound iodine species like [¹³¹I]CH₃I. The chemical nature of these compounds defined their atmospheric lifetime, transport distance, and deposition pattern, thereby governing the thyroid dose to exposed populations. Chemical speciation is the key determinant across all scenarios. Exhaled iodine in medicine is predominantly organic; routine reactor releases are negligible; severe accidents predominantly release elemental and organic iodine that drive environmental transport and exposure. Integrating these domains shows how chemical speciation governs volatility, mobility, and bioavailability. The novelty of this review lies not in introducing new iodine chemistry, but in the systematic comparative synthesis of airborne radioiodine speciation across medical therapy, routine nuclear operation, and severe accident scenarios, identifying chemical form as the unifying determinant of volatility, environmental transport, and dose. Full article

Managing social diversity and fostering social cohesion have historically been vital to the nation-building processes in decolonized states. While the significance of education in addressing social diversity is well recognized, there is a limited research base on how education fosters social cohesion. This case study examines how Singapore utilized its education system as a tool to manage social diversity and to promote social cohesion. Thematic analysis of interviews conducted with educators, policymakers, and practitioners revealed several educational factors that supported the development of social cohesion. The primary data was supplemented by an analysis of relevant policies and education reform documents. Education was consistently prioritized as a strategic goal, supported by a clear political vision and robust leadership. The policy of multilingualism, multiculturalism, and meritocracy shaped its education system. A ‘realist-pragmatist’ philosophy shaped educational reforms, with a dual focus on securing economic stability and fostering social integration. Consequently, bilingual policies, technical and vocational education (TVET), and citizenship education emerged as central tools for advancing social cohesion. With limited natural resources, visionary leadership recognized education as the nation’s most valuable resource for progress. Singapore’s educational strategy exemplifies that deliberate, well-formulated policies can effectively integrate diverse populations and foster social unity. The educational reform experiences in Singapore provide valuable insights for multiethnic societies worldwide, emphasizing the importance of visionary leadership, pragmatic policy development, and viewing education as a strategic investment rather than merely an expenditure in nation-building. Full article

Neurodevelopmental disorders (NDDs) with ataxia are genetically heterogeneous and remain a diagnostic challenge. Recent advances in genomic technologies have facilitated the identification of rare, potentially causative variants in genes not traditionally associated with classic NDD phenotypes. The DNAH14 gene, encoding a dynein axonemal heavy chain involved in ciliary motility, has recently emerged as a novel candidate in neurological syndromes. Here, we report two Turkish siblings presenting with late-onset balance disorder, progressive ataxia, and cognitive impairment. Initial genetic analysis revealed that both siblings also harbor FXN GAA repeat expansions consistent with pathogenic Friedreich’s ataxia (FRDA). To elucidate the molecular basis of the patients’ cognitive impairment, whole-exome sequencing was performed. This analysis identified a novel homozygous frameshift variant in the DNAH14 gene, located within the conserved linker domain upstream of the motor core, which is critical for ATP hydrolysis and microtubule interactions. The variant is absent from population databases, predicted to be deleterious by multiple in silico algorithms, and segregates in the family in a manner consistent with autosomal recessive inheritance. The coexistence of FRDA expansions and a truncating DNAH14 variant suggests a potential dual genetic contribution to the observed phenotype, in which FRDA-associated pathology likely underlies the ataxia, while DNAH14 disruption may contribute to additional neurodevelopmental features. This is the first report describing the co-occurrence of FRDA and a homozygous truncating DNAH14 variant in the same individuals, broadening our understanding of overlapping neurogenetic mechanisms. Our findings expand the phenotypic spectrum of DNAH14-related disorders and highlight the importance of considering multilocus pathogenic variants in patients with complex or atypical ataxia presentations. Full article

Feature selection and continuous optimization are fundamental yet challenging tasks in machine learning and engineering design. To address premature convergence and insufficient population diversity in Student Psychology-Based Optimization (SPBO), this paper proposes a Multi-Strategy-Enhanced Student Psychology-Based Optimizer (MESPBO). The proposed method incorporates three complementary strategies: (i) a hybrid heuristic initialization scheme based on Latin Hypercube Sampling and Gaussian perturbation; (ii) an adaptive dual-learning position update mechanism to dynamically balance exploration and exploitation; (iii) a hybrid opposition-based reflective boundary control strategy to enhance search stability. Extensive experiments on the CEC2017 benchmark suite with 10, 30, and 50 dimensions demonstrate that MESPBO consistently outperforms 11 state-of-the-art metaheuristic algorithms. Specifically, MESPBO achieves the best Friedman mean ranks of 2.00, 1.67, and 1.67 under 10D, 30D, and 50D settings, respectively, indicating superior convergence accuracy, robustness, and scalability. In real-world feature selection tasks conducted on 10 benchmark datasets, MESPBO achieves the highest average classification accuracy on 9 datasets, reaching 100% accuracy on several datasets, while maintaining competitive performance on the remaining one. Moreover, MESPBO selects the smallest feature subsets on 7 datasets, typically retaining only 2–4 features without sacrificing classification accuracy. Compared with the original SPBO, MESPBO further reduces the fitness values on 7 out of 10 datasets, achieving an average improvement of approximately 10%. These results verify that MESPBO provides an effective trade-off between optimization accuracy and feature compactness, demonstrating strong adaptability and generalization capability for both global optimization and feature selection problems. Full article

To cope with the highly stochastic nature of wind speed, we explored the development of a predictive methodology. Considering an absence of studies pertaining to wind speed prediction that utilise state-of-the-art fuzzy models, the proposed approach adopted a novel higher-order Takagi–Sugeno–Kang fuzzy model intermixed with variational mode decomposition. The novelty of the predictive fuzzy model arises from the enhancement of rule consequents to include generalised terms and the incorporation of model complexity into the training scheme. To optimise the model, two approaches are considered: an adaptive differential evolution and a surrogate-based optimisation algorithm. The evolutionary approach employed two populations and a dual mutation scheme. The surrogate-based optimisation employed a Bayesian framework by fitting a Gaussian process model to the objective function. The latter approach yielded accurate predictive results while rapidly reducing the training time of the fuzzy model. A sequential wrapper-based algorithm was developed to effectively determine the feature space. The variational mode decomposed wind speed data were predicted individually, using an associated optimised fuzzy model. The proposed method was applied to a real-world wind speed dataset with exceptional approximation results. Comparisons with several artificial intelligence models highlighted the effectiveness and statistical significance of the methodology. Full article

Migratory birds have been implicated in the spread of diverse emerging infectious pathogens, including West Nile virus, Usutu virus, Avian influenza viruses, Salmonella, Campylobacter, antimicrobial-resistant (AMR) bacteria, and antibiotic resistance genes (ARGs). Beyond their roles as vectors and reservoirs, migratory birds are also susceptible hosts whose own health may be compromised by these infections, reflecting their dual position in the ecology of pathogens. As facilitators of pathogen transmission during their long-distance migrations, often spanning thousands of kilometres and connecting ecosystems across continents, these birds can easily cross-national borders and circumvent traditional biosecurity measures, thereby acting as primary or secondary vectors in the transmission of cross-species diseases among wildlife, livestock, and humans. Africa occupies a pivotal position in global migratory bird networks, yet comprehensive data on pathogen carriage remain limited. Gaps in knowledge of pathogen diversity constrain current surveillance systems, resulting in insufficient genomic monitoring of pathogen evolution and a weak integration of avian ecology with veterinary and human health. These limitations hinder early detection of novel pathogens and reduce the continent’s preparedness to manage outbreaks. Therefore, this review provides a holistic assessment of these challenges by consolidating existing knowledge concerning the pathogens transmitted by migratory birds in Africa, while recognizing the adverse effect of pathogens, which potentiates population decline, extinction, and ecological imbalance. It further advocates for the adoption of a comprehensive One Health-omics approach that not only strengthens surveillance and technological capacity but also prioritizes the protection of avian health as an integral component of ecosystem and public health. Full article

Tai Chi has evolved into a widely used mind–body practice increasingly incorporated into complementary therapy, rehabilitation, and public health. This study provides an updated global bibliometric overview, with VOSviewer mapping publication performance, co-authorship networks, and keyword-based thematic clusters. Articles and reviews with Tai Chi–related terms in the title were retrieved from Scopus, with no restrictions on language or publication year. A total of 2253 publications from 1978 to 2025 were analyzed, revealing steady growth, concentrated largely in the past decade. China led the publication output, while the United States had the highest number of citations, forming a dual-core pattern. The field is largely driven by a small group of authors and regional clusters, and its visibility in mainstream medical journals remains limited. Nine software-generated keyword clusters were manually synthesized into five themes: motor function (balance and fall prevention), musculoskeletal conditions (osteoarthritis, rheumatoid arthritis, fibromyalgia), chronic disease management (cardiovascular disease, stroke, COPD), psychological health (quality of life, depression, anxiety, mindfulness), and cognitive aging (dementia, mild cognitive impairment). Future progress requires greater methodological rigor, including mechanistic inquiry, long-term study designs, and community- or population-level applications, along with stronger international collaboration and deeper integration into clinical and public health practice. Full article

Achieving a dynamic equilibrium among feasibility, convergence, and diversity remains a fundamental challenge in Constrained Multi-objective Optimization Problems (CMOPs). To address the limitations of conventional methods in handling complex constraints and resource allocation, this paper proposes a Dual-Population Cooperative Evolutionary Algorithm based on Relaxed Feasibility Selection and Shrinking Contribution Resource Allocation (RFSCMOEA). First, a relaxed feasibility selection strategy is designed with a dynamically shrinking threshold, allowing near-feasible solutions to survive in early stages to enhance boundary exploration. Second, a dual-criterion environmental selection mechanism integrates non-dominated sorting with k-nearest neighbor density estimation to prevent premature convergence and ensure solution uniformity. Furthermore, a dynamic resource allocation model optimizes computational configuration by adjusting offspring generation ratios based on the real-time evolutionary contribution of each population. Extensive experiments on 47 benchmark functions and 12 real-world engineering problems demonstrate that RFSCMOEA significantly outperforms eight state-of-the-art algorithms in Feasibility Rate, Inverted Generational Distance, and Hypervolume. Full article

Amphibians are among the most threatened vertebrate groups globally, with over 40% of species at risk of extinction. However, a gap remains in understanding how to effectively develop and implement amphibian conservation strategies at local and global scales to minimize extinction risk. This review synthesizes multidisciplinary evidence to frame amphibian conservation as a priority not only for species preservation but for safeguarding ecosystem functioning and human well-being. Drawing on ecological, physiological, biomedical, and technological literature, we highlight the foundational roles amphibians play in various biomes: regulating invertebrate populations, mediating nutrient and energy flows, modifying physical habitats, and supporting biodiversity through trophic interactions. Their dual aquatic–terrestrial life cycles and highly permeable skin make them highly sensitive to environmental change, positioning them as bioindicators for ecosystem health. We further explore emerging tools and concepts such as environmental metabolomics, remote sensing, and citizen science for monitoring population trends and environmental stressors. Additionally, we discuss conservation challenges in relation to land-use change, climate disruption, invasive species, emerging diseases, and institutional underinvestment. We argue for the recognition of amphibians as ecological allies and the increased integration of amphibian conservation into broader frameworks such as ecosystem service valuation, climate resilience planning, and public health policy improvement. Finally, we identify key research gaps and suggest future directions to remedy these oversights, including the incorporation of traditional knowledge, socio-cultural engagement, and technological innovations for sustainable amphibian conservation. Realizing this vision will require globally coordinated, locally grounded strategies that fuse scientific insight, inclusive governance, and long-term investment—ensuring that amphibian conservation advances ecosystem stability and benefits both nature and society. Full article