Search Results (27,981)

Soil fungi are key drivers of terrestrial ecosystem functioning, contributing to organic matter decomposition, nutrient cycling, and plant–microorganism interactions. Despite their importance, the global distribution and structural biases of public sequence records for soil fungi remain incompletely characterized. In this study, we analyzed soil-associated fungal DNA sequences retrieved from the NCBI GenBank database using a custom R-based bioinformatics pipeline. Following filtering and metadata standardization, 544,554 filtered sequence records were obtained. The taxonomic composition of the dataset consisted primarily of Ascomycota (69.62%), followed by Basidiomycota, Glomeromycota, and Mucoromycota, with Trichoderma, Penicillium, and Aspergillus representing the most frequent genera. The geographic distribution revealed strong sampling bias, with China and the United States accounting for over one-third of all records. Ecological metadata indicated that rhizospheric and forest soils were the most common sources of the deposited sequences. At the same time, gene marker analyses confirmed the widespread use of the ITS region as the primary fungal barcode. Sequence diversity analyses revealed continental variation, with Europe and Asia showing higher medians, while the ordination highlighted clustering of sequence profiles, particularly among records from extreme environments. This study demonstrates the potential of public sequence databases for large-scale biodiversity assessments while highlighting the influence of sampling bias and the limitations of metadata. Full article

PM_2.5 is a major air pollutant that threatens urban air quality and public health. Its concentration is influenced by both meteorological conditions and air pollutants, exhibiting complex nonlinear and temporal characteristics. Traditional statistical methods are limited in their ability to model complex relationships among environmental variables, while machine learning models still require improvements in hyperparameter optimization and interpretability. Therefore, developing an accurate and interpretable PM_2.5 estimation model remains an important research objective. This study used daily air-quality and meteorological data collected in Wuhan from 2016 to 2025 to develop six machine learning models: Decision Tree (DT), Random Forest (RF), XGBoost, LightGBM, Support Vector Machine (SVM), and Multilayer Perceptron (MLP). The Particle Swarm Optimization (PSO) algorithm was employed to optimize the hyperparameters of these models. By comparing the root mean square error (RMSE), coefficient of determination (R²), and mean absolute error (MAE) of each model on both the training and test sets, the PSO-MLP model was identified as the best-performing model. Furthermore, the Shapley Additive Explanations (SHAP) method was applied to perform both global and local interpretation analyses of the best-performing model. The results indicate that the PSO-MLP model achieved the highest estimation performance among all evaluated models, with an R² value of 0.746 on the test set. SHAP analysis revealed that CO, Temperature (Temp), and NO₂ were the most influential predictors, while all variables exhibited distinct nonlinear relationships with PM_2.5 concentration. These findings may contribute to PM_2.5 concentration estimation, air-quality management, and environmental decision-making. Full article

Reliable outdoor localization is essential for autonomous mobile robots, where the Global Navigation Satellite System (GNSS) is widely used to provide global positioning information. However, GNSS signals are often degraded in real-world environments due to occlusions, multipath effects, and environmental interference, leading to unstable localization and degraded navigation performance. This paper proposes an adaptive multi-sensor fusion framework for robust outdoor localization and path tracking under weak GNSS conditions. The proposed system integrates GNSS, LiDAR, wheel odometry, and inertial measurement unit (IMU) measurements within an Extended Kalman Filter (EKF) framework. To address the limitations of GNSS, an adaptive weighting mechanism is introduced to dynamically adjust the influence of GNSS observations based on signal quality indicators. Furthermore, a GNSS quality-aware mode-switching strategy is developed, enabling seamless transition between GNSS-dominant localization and multi-sensor fusion-based localization. In the fusion mode, LiDAR, odometry, and IMU jointly provide robust pose estimation, while GNSS acts as a weak global constraint. The IMU further enhances heading estimation, improving orientation stability and path tracking performance. The estimated pose is then used for trajectory tracking using a path-following controller. Experimental results conducted in outdoor environments demonstrate that the proposed framework significantly improves localization robustness and path tracking performance under degraded GNSS conditions. Compared with raw GNSS localization, the proposed method reduces the mean localization error by 47.2% and decreases the root mean square localization error by 55.5%, while maintaining smoother and more continuous trajectory estimation in weak GNSS environments. Full article

Objective: This study examined the disparities in the prevalence of obesity among children and adolescents from 1990 to 2022. Methods: Crude prevalence estimates were obtained from the World Health Organization’s (WHO) Global Health Observatory, accessible through the WHO Health Equity Assessment Toolkit (WHO HEAT). The study population comprised children and adolescents aged 5 to 19 years. Obesity was defined as a body mass index (BMI) exceeding two standard deviations above the mean, in accordance with the WHO Growth Reference. Descriptive analysis was employed to examine longitudinal trends and disparities in crude obesity prevalence. The dimensions of age (5–9 and 10–19 years) and sex (female and male) were utilised to assess disparities related to obesity. Absolute and relative inequalities were evaluated using difference (D) and ratio (R) summary measures, respectively. Results: In 1990, the crude prevalence of obesity was higher among female children and adolescents (1.45%; confidence interval [CI] 0.38–3.41) compared to their male counterparts (1.07%; CI 0.14–3.40). However, by 2022, the prevalence was higher among males (8.20%; CI 5.15–12.01) compared to females (5.78%; CI 3.57–8.48). Regarding age, the prevalence of obesity in 1990 was 2.24% among 5–9-year-olds, compared with 0.59% among 10–19-year-olds. Both age groups saw an increase in crude obesity prevalence over time, and by 2022, the prevalence of obesity was 12.10% among 5–9-year-olds, compared with 4.04% among 10–19-year-olds. In 1990, the difference and ratio estimates were 0.38 and 1.36, respectively, indicating a higher prevalence among females than males. Concurrently, the ratio decreased from 1.36 in 1990 to 0.71 in 2022, further confirming the shift towards a higher prevalence of male obesity in later years. The difference in obesity prevalence (5–9 years minus 10–19 years) stayed positive throughout the study period. In 2022, the age difference in crude obesity prevalence was +8.07 percentage points, and the ratio was 3.00, indicating that the younger group had a prevalence three times that of the older group. Conclusions: The prevalence of childhood and adolescent obesity increased significantly from 1990 to 2022, with a shift from females to males and a disproportionate impact on younger children. These trends underscore the necessity for targeted public health interventions that address age- and sex-specific disparities. Full article

Political violence poses a significant challenge to global stability, underscoring the need for comparative analytical models that support analytical interpretation of structured conflict data. This paper presents a comparative evaluation of 12 machine learning approaches, including traditional supervised models, deep learning architectures, and zero-shot large language models, for the classification of political violence events using the Armed Conflict Location and Event Data Project (ACLED) dataset (2010–2020, over 40,000 events). The results demonstrate that, on short structured event text represented via TF-IDF, fine-tuned traditional machine learning models achieve stronger performance than zero-shot LLM approaches and deep learning models on structured event data. We further introduce a multilingual classification framework for English and Urdu news content, illustrating cross-lingual transfer robustness using machine-translated Urdu data; results reflect translation-based evaluation conditions and should not be interpreted as performance on naturally occurring low-resource Urdu political-event text. As an exploratory extension, the framework is applied to 57,700 tweets related to the Article 370 crisis in Kashmir to illustrate applicability to unstructured social media text; given that the best Twitter model (55% accuracy) falls below the 69% majority-class baseline, these results should be interpreted solely as coarse discourse indicators and not as a validated classification component. Unlike prior work, this study systematically combines multilingual evaluation with zero-shot LLM analysis for political event classification. Geographic out-of-sample validation (leave-one-country-out or leave-one-region-out) was not conducted; the reported performance should therefore not be interpreted as evidence of cross-regional generalizability without further experimentation. The findings highlight practical considerations for designing data-driven analytical frameworks for conflict monitoring and analytical decision support. Full article

Introduction: Sleep disturbances are common as we age and have been linked to poor cognition and increased cognitive decline. Objective: We aimed to examine cross-sectional and longitudinal associations between objective sleep measures and cognition in middle-aged and older adults, including cognitively healthy (CH) individuals and those with mild cognitive impairment (MCI). Methods: Participants from the Aiginition Longitudinal Biomarker Investigation Of Neurodegeneration (ALBION) study (age > 40) underwent 7-day wrist actigraphy (Actiwatch 2). Sleep exposures included sleep duration, sleep efficiency, sleep variability, sleep onset latency, wake after sleep onset (WASO), and number of awakenings. A neuropsychological battery was administered examining memory, executive function, visuospatial ability, language, attention speed, and a global composite score. Cross-sectional associations were tested using generalized linear models (adjusted for age, sex, education). Longitudinal associations with cognitive trajectories were examined with linear mixed-effect models. Results: In total (N = 184; 65% women; mean age 65 years), average sleep duration was 7.2 h and mean sleep efficiency was at 80%. Cross-sectionally, more nightly awakenings were associated with poor memory and attention speed. In a 1.5-year follow-up, (n = 93), higher baseline sleep efficiency was associated with better memory and language performance, while longer WASO, more awakenings, and longer sleep onset latency showed nominal associations with less favorable cognitive trajectories, although these associations did not remain statistically significant after FDR correction. Time-varying analyses indicated that sleep variability showed robust non-linear associations with poorer memory trajectories over follow-up and remained significant after FDR adjustment; significant mean change in awakenings and variability appeared to intensify in later follow-up phases. The association between sleep characteristics and cognitive decline varied across follow-up time, with stronger adverse changes observed during later follow-up phases. Discussion: Objective indicators of sleep continuity, especially sleep variability, were most consistently related to domain-specific cognitive outcomes, with strongest evidence for memory over time. Sleep fragmentation and irregular sleep patterns may represent potentially modifiable targets for future strategies aimed at preserving cognitive health during aging. Full article

Background/Objectives: Childhood overweight and obesity represent a major global public health challenge, with increasing prevalence and significant long-term metabolic, cardiovascular, and psychosocial consequences. Standard pediatric weight-management strategies based on lifestyle modification often achieve modest and variable results, highlighting the need for more personalized and scalable approaches. Artificial intelligence (AI) has emerged as a promising tool to enhance prevention, early risk stratification, and management of pediatric overweight and obesity. Methods: This narrative review was conducted through a structured search of PubMed, Scopus, and Web of Science for English-language studies published up to January 2026. The main search terms included “artificial intelligence”, “machine learning”, and “deep learning”, combined with “child”, “adolescent”, “pediatric”, “childhood obesity”, “pediatric overweight”, “body mass index”, “weight management”, “nutrition”, “diet”, “physical activity”, “lifestyle”, and “behavior change”. After title/abstract and full-text screening according to predefined eligibility criteria, the included studies were qualitatively synthesized and grouped by main application domains. The initial database search identified 412 records. After removal of 96 duplicates, 316 records were screened by title and abstract. Full-text assessment was subsequently performed for 175 potentially eligible articles. Following this evaluation, 51 studies met the eligibility criteria and were retained from the database search. Additional relevant articles were identified through manual screening of reference lists and related reviews, resulting in the final set of studies included in the narrative synthesis. Results: The review identified five main domains of AI application in pediatric weight management: risk assessment and prediction, dietary assessment and nutritional support, physical activity and lifestyle monitoring, behavioral and psychological support, and clinical decision support. Across the included literature, AI-based approaches were most frequently applied to predictive modeling using longitudinal BMI or growth trajectories, birth characteristics, parental BMI, sleep duration, physical activity, sedentary behavior, and family or socioeconomic factors. However, the evidence base was largely composed of observational and predictive-modeling studies, whereas interventional studies, real-world implementation studies, and long-term pediatric weight-outcome data remained limited. Conclusions: This narrative review indicates that AI has potential as a complementary tool within multidisciplinary, family-centered pediatric weight-management pathways, particularly for early risk stratification, personalized monitoring, and behavioral support. However, the findings also highlight that current evidence remains mainly exploratory and predictive rather than interventional. Further longitudinal, real-world, and ethically grounded research is required to confirm effectiveness, safety, clinical usefulness, and equitable implementation in pediatric populations. Full article

With the adoption of more ambitious emission reduction strategies in the shipping industry by the International Maritime Organization and the resulting stricter greenhouse gas emission reduction requirements, it is particularly important for all stakeholders in the global maritime shipping industry to assess the energy efficiency of shipping vessels. Forming predictive capabilities for ship fuel consumption and Carbon Intensity Indicator (CII) annual ratings, for example, are two important works. This article adopted 14 different algorithms in three categories of data-driven approaches, i.e., statistics, machine learning and deep learning, including polynomial regression, ridge regression, adaptive boosting, categorical boosting, elastic net, etc., and built the ship fuel consumption prediction model using ship noon report as the data source. The prediction accuracy and computational efficiency of model training were compared based on metrics of coefficient of determination, mean absolute percentage error and floating-point operations per amount of training data. Cross-validations were performed for all 14 algorithms to analyze their sensitivities to their respective tuned parameters. Comparisons indicated that algorithms of the statistics approach were sensitive to the quality of the data source, compared with the machine learning and the deep learning approaches. The accuracy of the elastic net algorithm was sensitive to the tuned parameters. Two algorithms, light gradient boosting machine and random forest, were selected based on their performances of prediction accuracy and computational efficiency of model training. Then, the selected algorithms were separately combined with long short-term memory as the time-series prediction algorithm to form their respective coupled framework. Both of the coupled frameworks achieved successful prediction of the CII annual discriminant and rating of the studied ships. The prediction accuracy was validated to be sufficient. Full article

Recent advancements in structural engineering have driven the development of sophisticated damping mechanisms aimed at reducing the detrimental effects of structural vibrations. As a result, accurate numerical modeling and analytical evaluation have become essential for assessing structural stability and enhancing seismic resilience. This study introduces a model-updating framework to develop analytical constitutive models for structural damping systems. The proposed approach employs a genetic algorithm (GA) to calibrate model parameters by minimizing the discrepancy between analytical predictions and experimental responses. Experimental force–displacement hysteresis data and displacement time-history records are used at both the element and system levels for model calibration. The methodology is applied to a rubber isolator, a 10-story structure equipped with Pall friction dampers, and a 6-story structure with friction dampers to evaluate its performance under different dynamic characteristics and damping mechanisms. The results indicate that the proposed approach achieves very high accuracy, with prediction errors reduced to negligible levels for both force and displacement responses in all cases. Consistent performance is observed using both global and local displacement measures in friction-damped systems, indicating the robustness of the proposed method. Overall, the findings indicate that the GA-based model-updating framework provides an efficient and reliable tool for improving the predictive capability of analytical models of structures with nonlinear damping devices and is suitable for practical structural engineering applications. Full article

As the complexity, scale, and nonlinearity of modern engineering optimization problems continue to increase, traditional optimization algorithms face significant challenges in achieving high solution accuracy, fast convergence, and robust performance. To address these issues, this paper proposes a Rural Tourism Migration-based Improved Equilibrium Optimizer (RTM-IEO), aiming to enhance the global search capability and adaptive balance between exploration and exploitation. Specifically, an adaptive lens imaging opposition-based learning strategy is introduced to effectively expand the search space and maintain population diversity. A dynamic elite-guided elimination mechanism is designed to strengthen exploitation capability and accelerate convergence by reconstructing inferior individuals using high-quality solutions. In addition, a multi-stage rural tourism migration strategy is developed to dynamically regulate the search behavior across different optimization phases, enabling a more flexible and efficient search process. The effectiveness of the proposed algorithm is comprehensively validated on the CEC2021 and CEC2022 benchmark suites, where RTM-IEO demonstrates superior performance in terms of convergence accuracy, convergence speed, and robustness compared with several representative state-of-the-art algorithms. The statistical superiority of the proposed method is further confirmed through Friedman mean ranking and Wilcoxon rank-sum tests. To further evaluate its practical applicability, RTM-IEO is applied to the sustainable economic dispatch problem of a microgrid integrating renewable energy sources, including wind power and photovoltaic generation, along with energy storage systems and controllable units. The optimization objective simultaneously considers economic cost minimization and sustainable operation requirements, such as improving renewable energy utilization and reducing dependence on fossil-fuel-based generation. Experimental results indicate that the proposed method achieves a significant reduction in daily operating cost (exceeding 52% compared with benchmark algorithms), while effectively promoting low-carbon energy utilization and enhancing overall system sustainability. Overall, the proposed RTM-IEO provides an efficient and reliable optimization framework for addressing complex global optimization problems, particularly in scenarios requiring a coordinated balance between economic performance and sustainable development. Full article

Under global warming, understanding regional drought evolution is essential for drought early warning, food security, climate adaptation, and sustainable water-resource management. This study analyzed meteorological drought in Jiangxi Province during 1961–2022 using SPI-12, SPEI-12, and EDDI-12 from the CHM_Drought high-resolution multi-index dataset. The Mann–Kendall (MK) test, Theil–Sen slope estimator, three-threshold run theory, Morlet wavelet analysis, wavelet coherence (WTC), and cross-wavelet transform (XWT) were used to examine drought trends, event characteristics, periodicity, and inter-index relationships. Results showed a widespread drying tendency. EDDI-12 exhibited a highly significant increase in 99.86% of valid resampled raster pixels, indicating enhanced atmospheric evaporative demand, while SPEI-12 and SPI-12 showed significant decreasing trends in 97.96% and 93.24% of valid pixels, respectively. Stronger drying signals were mainly distributed in central and northern Jiangxi. Run-theory analysis indicated longer-duration cumulative droughts in southern mountainous areas and frequent short-duration drought events in the Poyang Lake Plain and central-northern Jiangxi. Wavelet analysis identified a dominant interdecadal periodicity of approximately 20–21 years. WTC and XWT revealed strong in-phase coherence between SPI and SPEI, whereas SPI/SPEI and EDDI mainly showed anti-phase statistical phase relationships. From a sustainability perspective, these findings provide scientific support for multi-index drought monitoring, adaptive agricultural water allocation, drought early warning, and climate-resilient water-resource management in humid monsoon regions. Full article

This study investigated the impact of recreational use on trails in the Atlantic Forest (Ubatuba Municipality, São Paulo State, Brazil) using physical, chemical and geochemical indicators. Five trails with different morphological characteristics were selected, and paired samples were collected from the trail surface (TR) and trail-side slope (TA). The statistical approach combined local analyses for each trail with global clustering (n = 19) using Student’s t-test, along with multivariate modeling through Principal Component Analysis (PCA) and Pearson correlation. The analysis included physical attributes (bulk density, particle size and porosity), chemical attributes (pH, organic matter and macronutrients) and geochemical compositions (major oxides and trace elements determined by XRF). The overall results reveal systematic compaction in the trail surface (TR), with bulk density increasing from 1.32 g/cm³ (TA) to 1.37 g/cm³ (TR) (p = 0.038), and total porosity decreasing from 47.26% to 45.34% (p = 0.016). In contrast, the geochemical oxide composition (SiO₂, Al₂O₃, Fe₂O₃) remained stable (p > 0.05), indicating the resilience of the mineral matrix. However, significant local dynamics (p < 0.05) in K₂O and MgO were observed in more preserved trails, associated with surface compaction and fragmentation of the litter layer, and phosphorus showed strong dependence on organic matter (r = 0.85). Multivariate analysis indicates that degradation is predominantly physical and micromorphological at the local scale, with bulk density and porosity being the most sensitive indicators for environmental monitoring. Full article

The integration of collaborative robots into industrial environments requires rigorous safety validation under the Power and Force Limiting (PFL) regime. This review article systematically maps the technological and normative development of certified Pressure and Force Measurement Devices (PFMDs) and experimental biofidelic instruments for Physical Human–Robot Interaction (pHRI) between the years 2011 and 2026. A quantitative screening of 68 studies revealed a publication peak in impact metrology in 2021. This peak occurred with a five-year latency after the release of the ISO/TS 15066 technical specification. Although global interest in collaborative robotics steadily grows, the publication trend indicates a gradual shift in scientific focus from reactive testing toward proactive prevention. A methodological deconstruction of four Research Questions (RQs) identifies persistent limitations in safety evaluation. The findings demonstrate that the internal structure of conventional sensors induces nonlinear shock filtering and parasitic oscillations (RQ1). Furthermore, the rigid fixation of test stands generates unrealistic pressure spikes. This physical limitation forces a transition to flexible and pendulum-based configurations (RQ2). Commercial flat films physically fail due to sensor saturation and introduced stiffness. Such failures accelerate the development of conformable electronic skins (e-skins) and multimodal test manikins (RQ3). To ensure interlaboratory reproducibility within the current ISO 10218-2:2025 standard, the text defines imperative metrological parameters. These parameters strictly include frequency response, calibration protocols, and volumetric mapping of inertial masses (RQ4). Furthermore, the analysed publications were systematically stratified into distinct technological categories, strictly reflecting their primary engineering domains, ranging from empirical metrological evaluation and sensor hardware design to advanced numerical modeling. Finally, the vision for future research anticipates a definitive shift toward proactive anti-collision technologies, encompassing Artificial Intelligence (AI), machine vision, and Augmented Reality/Virtual Reality/Mixed reality (AR/VR/MR). Future methodologies must also consider demographic anisotropies and the cognitive fatigue of the human operator. Full article

With the global rise in sustainable investment concepts, environmental, social, and governance (ESG) factors have increasingly become important criteria influencing investment decisions. Although institutional investors are paying greater attention to corporate ESG performance, limited evidence exists regarding its impact within the Chinese A-share market. Using panel data from Chinese listed firms during the period 2010–2023, this study employs fixed-effects models with clustered standard errors as the baseline estimation method. To improve the robustness of the findings, Tobit regression, Logit regression, lagged-variable models, heterogeneity analysis, and Hausman tests are further conducted. The empirical findings indicate that the overall ESG score and the individual environmental (E), social (S), and governance (G) dimensions do not exhibit statistically significant effects on institutional ownership in the baseline fixed-effects regressions. The results suggest that ESG performance has not yet become a dominant determinant of institutional investment decisions in China’s capital market. However, the robustness tests based on Tobit and Logit models provide limited evidence that ESG performance may still influence institutional investor behavior under alternative empirical specifications. Furthermore, the heterogeneity analysis reveals that the relationship between ESG dimensions and institutional ownership differs across environmentally related and non-environmentally related firms, although the effects are generally weak and statistically limited. The study contributes to the ESG and institutional investment literature in three important ways. First, it provides updated evidence from the Chinese A-share market over the 2010–2023 period, reflecting the evolving stage of ESG development in emerging economies. Second, it comparatively examines the differentiated roles of environmental, social, and governance dimensions rather than relying solely on aggregated ESG indicators. Third, it highlights the limited and transitional nature of ESG integration among institutional investors in China, where traditional financial indicators continue to play a more important role in investment decisions. The findings provide important implications for policymakers, listed firms, and institutional investors seeking to promote sustainable finance development and improve the effectiveness of ESG disclosure practices in emerging markets. Full article

Reaction time (RT) is widely used as a fundamental indicator of central nervous system processing speed. Numerous studies have shown that RT increases with age, generally interpreted as a decline in information processing efficiency. However, most previous studies have focused on absolute RT values, and it remains unclear whether aging also alters the relative relationships between responses under different task conditions. The present study investigated whether aging affects the relative difference between inside and outside pedal reaction times in a Foot Psychomotor Vigilance Test (Foot PVT). A total of 44 participants were analyzed, including 20 younger adults (24 ± 3 years) and 24 older adults (73 ± 5 years). Participants responded to visual stimuli by pressing either the left or right pedal with the right foot. The difference between inside and outside RT (dRT) was calculated for each participant as an index of relative response structure. Group comparisons and correlation analyses were conducted to examine associations with age, height, physical activity level (PAL), and sleep-related factors. As expected, RTs were consistently longer in older adults across conditions. In contrast, dRT did not differ significantly between younger and older groups, with negligible effect sizes (|d| < 0.1). Furthermore, dRT showed no significant correlations with height, PAL, or sleep-related indices. These findings indicate that while aging affects the overall speed of motor responses, the relative temporal structure between response conditions is preserved. This dissociation between global slowing and stable response structure may represent a fundamental characteristic of neuromotor aging. Full article