Search Results (17,413)

Stroke frequently results in persistent upper limb impairments, which are often accompanied by compensatory movement strategies that are not fully captured by conventional clinical assessment scales. Quantitative kinematic analyses may provide more objective and sensitive measures of motor dysfunction. In this study, we propose a probabilistic, distribution-based analysis of upper limb kinematics to quantify motor disability in post-stroke patients. We analyzed reaching movement data acquired with a markerless Kinect V2 system from 36 post-stroke patients and age-matched healthy controls. Wrist velocity profiles were characterized using distribution metrics, including variance, skewness, kurtosis, and entropy, and divergence measures (Hellinger distance, Kullback–Leibler divergence, and Jensen–Shannon divergence). Group differences between patients and controls, as well as across impairment levels stratified by the Fugl-Meyer (FM) score, were evaluated. Several distribution metrics significantly discriminated patients from controls and scaled with motor impairment severity. In particular, divergence-based measures showed a strong association with FM scores, indicating increasing deviation from normative movement patterns with greater impairment. These findings demonstrate that distribution-based metrics focusing on kinematic analysis provide a clinically meaningful, objective descriptor of motor dysfunction and complement conventional biomechanical assessments, offering a sensitive framework for quantifying motor disability after stroke. Full article

Introduction: University students often describe their academic years as a period of continuous personal change, which may increase vulnerability to unhealthy lifestyle habits. These habits can influence body composition and are associated with both physical conditions (e.g., overweight, sedentary behavior) and psychological well-being, including suicide risk. Method: A quantitative, non-experimental, cross-sectional, descriptive–comparative–correlational design was employed, using a non-probabilistic intentional sample of 174 university students. Data were collected using the OMRON 514C body composition monitor, the Difficulties in Emotional Regulation Scale (DERS-E), and Plutchik’s Suicide Risk Scale. Statistical analyses included descriptive statistics, independent samples t-tests for gender comparisons, Pearson’s correlation analyses, and multiple linear regression analyses to examine whether the observed bivariate associations remained significant after controlling for gender. Results: Descriptive analyses showed variability in body composition, emotional dysregulation, and suicide risk. Gender comparisons indicated that men presented higher weight, height, skeletal muscle mass, visceral fat level, and basal metabolic rate, whereas women reported higher body fat percentage, greater emotional dysregulation, and higher suicide risk. Correlation analyses revealed that suicide risk was negatively associated with skeletal muscle mass (r = −0.24, p = 0.002), basal metabolic rate (r = −0.21, p = 0.006), height (r = −0.27, p < 0.001), emotional rejection (r = −0.24, p = 0.001), and emotional confusion (r = −0.22, p = 0.004). Multiple regression analyses, controlling for gender, indicated that the associations between body composition indicators (skeletal muscle and basal metabolism) and suicide risk did not remain statistically significant (p > 0.05). In contrast, emotional dysregulation dimensions, particularly emotional rejection, maintained significant associations with suicide risk after adjustment for gender. Additionally, negative associations were found between BMI and emotional dysregulation, and between height and emotional clarity, even after controlling for gender. Discussion: The findings highlight emotional dysregulation as a central and robust factor associated with suicide risk in university students, whereas body composition indicators appear to play a more limited and gender-dependent role. The fact that associations between physical markers (skeletal muscle, basal metabolism) and suicide risk were mediated by gender underscores the importance of considering sociodemographic factors when interpreting body–mental health relationships. These results support the need for integrated biopsychosocial prevention strategies that address emotional regulation within the university context, while considering the differential impact of gender on both physical and psychological risk factors. Full article

This cross-sectional study explores the connections between resilience, work engagement, proactive strategies and personal resources among Italian kindergarten and primary school teachers. It specifically seeks to determine if and how personal resources can foster teachers’ work engagement, resilience, and proactive strategies at work. The study was conducted using a sample of 183 full-time, in-service kindergarten and primary teachers at public schools in Italy. Data were collected through self-report questionnaires, including the Brief Resilience Scale, the Ultra-Short Measure for Work Engagement, the Proactive Strategy scale, the Self-Compassion Scale, the Life Orientation Test-Revised, the Experienced compassion at work scale. Data were analyzed using a path analysis model. Results indicated that teachers’ self-compassion was positively associated with the use of proactive strategies and perceived received compassion was strongly related to work engagement. Moreover, higher levels of self-compassion were linked to greater work engagement. Teachers’ optimism and self-compassion were both positively associated with resilience, whereas self-criticism showed a significant negative association. Our research supports the need for educational policymakers and school leaders to focus on personal resources and work-related well-being. Full article

To address the challenge of model selection in large-scale traffic flow prediction tasks, this paper proposes a dynamic multi-model selection framework based on Deep Contextual Bandits (DCB). Centered on the optimal combination of sub-models, the framework leverages contextual information of road segments to select dynamically among candidate predictors, achieving more efficient and accurate traffic flow prediction. Several mechanisms are introduced to improve strategy learning and convergence, including a baseline network, experience replay, double-model estimation, and prioritized experience sampling. A clustering-based strategy is further designed to reduce the search space and enhance the generalization and transferability. Experiments on real-world traffic datasets demonstrate that the proposed framework significantly outperforms traditional static fusion methods, reinforcement learning (RL) baselines, and mainstream spatiotemporal prediction models. In particular, the framework yields a 1.0% improvement in R² and a 3.2% reduction in MAE compared to state-of-the-art baselines, while reducing inference time by 43.1%. Moreover, the proposed framework shows strong capability in adaptive model selection under varying contexts, with ablation studies confirming the effectiveness of its key components. Full article

Intensive pig production systems in China face dual challenges of heavy metal (HM) contamination and nutrient overloading from manure. However, stage-specific quantitative relationships between diet and excretion remain poorly characterized, hindering targeted mitigation. To address this, we conducted a comprehensive farm survey in the southern water network region—a major pig production hub in China—collecting 93 paired feed and manure samples from piglets, finishing pigs, and sows across 32 large-, medium-, and small-scale farms. The results revealed that essential trace elements (Cu, Zn, Fe, Mn) in feed exceeded safety guidelines by 3–19-fold, while toxic metals (As, Hg, Pb, Cd, Cr) remained below hygienic limits. Notably, Cu and Zn concentrations in manure significantly surpassed organic fertilizer standards, with piglet manure showing the highest exceedance rates (69–91%). Strong linear correlations (Pearson’s r = 0.360–0.766) were found between feed additives (Cu, Zn, As, Pb, Cd, Cr) and their excretion in manure, with Cu and Zn exhibiting the strongest relationships, especially in piglets. Feed crude protein (CP) and phosphorus (P) levels positively influenced nitrogen (N) and P excretion (r = 0.389–0.860), particularly in finishing pigs. Scenario analysis demonstrated that aligning Cu and Zn supplementation with safety guidelines could reduce HM excretion by 50–67%, while low-CP diets and precision P feeding lowered N and P losses by 10.2–10.8% and reduced feed costs by 4.1%. These findings highlight the potential of dietary interventions to mitigate environmental risks without compromising productivity, offering actionable strategies for sustainable pig production and revised feed regulations. This study provides quantitative, stage-specific evidence linking feed formulation to excretion patterns, addressing critical knowledge gaps in feed-to-manure transfer mechanisms and supporting the development of precision feeding standards and integrated manure management systems to decouple livestock intensification from environmental degradation. Full article

Hypertension is highly prevalent among patients with chronic kidney disease (CKD), contributing significantly to cardiovascular morbidity and progressive renal decline. This overview explores the intricate pathophysiologic mechanisms driving hypertension in renal insufficiency, including volume overload, renin–angiotensin–aldosterone system (RAAS) activation, sympathetic overactivity, and vascular dysfunction. Diagnostic challenges such as white-coat hypertension and the underuse of ambulatory monitoring are discussed, along with the importance of volume assessment and target organ evaluation. We also emphasize individualized management strategies combining lifestyle modification, pharmacotherapy—including RAAS inhibitors, diuretics, and novel agents—and the growing role of device-based interventions. In particular, renal denervation (RDN) has emerged as a potential adjunctive option for selected patients with resistant hypertension in CKD, with preliminary evidence suggesting blood pressure reduction in selected and carefully studied populations, including dialysis-dependent patients. Special considerations for transplant recipients, elderly individuals, and those on dialysis are highlighted, underscoring the need for nuanced, patient-centered care. Misconceptions surrounding RAAS blockade, dialysis hypotension, and therapeutic inertia are critically appraised. Finally, future directions point to biomarker-driven approaches, digital health integration, and large-scale trials on RDN to refine treatment paradigms. This comprehensive synthesis offers a pragmatic framework for clinicians managing hypertension in CKD, aligning mechanistic insights with emerging evidence and clinical realities. Full article

Understanding how environmental conditions shape the functional composition of ecological communities is a central goal in community ecology. In this study, we apply this framework to the reptile and amphibian assemblages within Greece’s Mount Chelmos protected area. Based on comprehensive field surveys (2018–2021) across 168 sampling stations, we compiled species trait databases and quantified functional diversity using a corrected Rao’s Q index. We modeled the response of functional diversity to climate, land cover, topography (altitude, slope, aspect), geographic location, and taxonomic diversity, using Generalized Additive Models (GAMs). Additionally, we examined traitspace structure via PCA and evaluated environmental drivers of trait composition with multivariate GAMs. For reptiles, functional diversity was significantly affected by altitude, climate, and aspect, with higher values predicted in water-associated marginal zones surrounding the mountain massif. Traitspace analysis revealed clear ecological structuring along axes related to locomotion, body size, reproductive mode, foraging strategy, and substrate use, shaped by distinct combinations of environmental filters. In amphibians, environmental effects on functional diversity were not statistically significant; however, traitspace showed discernible responses to land cover, climate, and aspect, suggesting weaker—though detectable—filtering processes. Collectively, our findings indicate that Mount Chelmos functions as a system that modulates diversity, with environmental filters operating at fine-to-medium spatial scales to shape the functional composition and diversity of its herpetofauna. Full article

As a key technology in the intelligent interpretation of remote sensing, remote sensing image change detection aims to automatically identify surface changes from images of the same area acquired at different times. Although vision foundation models have demonstrated outstanding capabilities in image feature representation, their inherent patch-based processing and global attention mechanisms limit their effectiveness in perceiving multi-scale targets. To address this, we propose a multi-scale remote sensing image change detection network based on a vision foundation model, termed SAM-MSCD. This network integrates an efficient parameter fine-tuning strategy with a cross-temporal multi-scale feature fusion mechanism, significantly improving change perception accuracy in complex scenarios. Specifically, the Low-Rank Adaptation mechanism is adopted for parameter-efficient fine-tuning of the Segment Anything Model (SAM) image encoder, adapting it for the remote sensing change detection task. A bi-temporal feature interaction module(BIM) is designed to enhance the semantic alignment and the modeling of change relationships between feature maps from different time phases. Furthermore, a change feature enhancement module (CFEM) is proposed to fuse and highlight differential information from different levels, achieving precise capture of multi-scale changes. Comprehensive experimental results on four public remote sensing change detection datasets, namely LEVIR-CD, WHU-CD, NJDS, and MSRS-CD, demonstrate that SAM-MSCD surpasses current state-of-the-art (SOTA) methods on several key evaluation metrics, including the F1-score and Intersection over Union(IoU), indicating its broad prospects for practical application. Full article

Understanding the coupling mechanisms between vegetation phenology and carbon productivity is essential for assessing ecosystem responses to climate change and guiding sustainable grassland management. This study focuses on stable alpine grasslands on the southern slope of the Qilian Mountains from 2001 to 2020, a climatically sensitive but relatively under-investigated transition zone on the northeastern Tibetan Plateau. We utilized MODIS NDVI time-series (MOD13Q1) and the latest PML V2 gross primary productivity (GPP) product at 500 m resolution to quantify changes in the start (SOS), end (EOS), and length (LOS) of the growing season. A pixel-wise linear regression approach was applied to evaluate the sensitivity of GPP to phenological metrics, explicitly characterizing how much GPP changes in response to unit shifts in SOS, EOS and LOS. Compared with previous studies that mainly described large-scale correlations between phenology and GPP or relied on coarser GPP products, this study provides a pixel-level, sensitivity-based assessment of phenology–carbon coupling in alpine grasslands using a long-term, phenology–GPP dataset tailored to the Qilian alpine region. The results revealed trends of earlier SOS, delayed EOS, and extended LOS, accompanied by a gradual increase in GPP. However, phenology–GPP coupling exhibited notable spatial heterogeneity. In mid- and low-altitude areas, extended growing seasons enhanced GPP, whereas high-altitude zones showed limited or even negative responses, likely due to climatic constraints such as cold stress and thermal–moisture mismatches. To better understand these spatial differences, we constructed a three-dimensional phenology–GPP sensitivity space and applied k-means clustering to delineate three ecological functional zones: (1) high carbon sink potential, (2) ecologically fragile regions, and (3) neutral buffers. This sensitivity-based functional zonation moves beyond traditional correlation analyses and provides a process-oriented and spatially explicit framework for ecosystem service assessment, carbon sink enhancement and adaptive land-use strategies in sensitive mountain environments. Full article

Agrivoltaic (AV) systems offer a promising solution to global challenges, such as land scarcity, food insecurity, and increasing energy demand, by enabling the simultaneous production of photovoltaic (PV) electricity and agricultural outputs on the same land. This review synthesizes more than two decades of interdisciplinary research on solar–agriculture integration, including agrivoltaic systems, biomass-based approaches, and greenhouse-integrated photovoltaic technologies, with particular emphasis on their relevance to arid and semi-arid environments, such as those found in the Middle East. The impacts of different PV configurations (such as semi-transparent, bifacial, vertical, and sun-tracking modules) on crop productivity, microclimatic conditions, and land-use efficiency are critically examined. The findings indicate that AV systems, particularly in water-scarce, high-irradiance regions, can enhance climate resilience, reduce competition for land, and improve both energy and water-use efficiency. Recent advances in crop selection strategies, adaptive PV system designs, and smart irrigation technologies further strengthen the feasibility of these systems for Middle Eastern agricultural systems. Nevertheless, key challenges remain, including the need for region-specific design optimization, improved understanding of crop light requirements, and robust assessments of economic viability under diverse policy and market conditions. Overall, life cycle assessments and techno-economic analyses confirm the environmental and economic benefits of AV systems, especially for sustainable irrigation, agricultural productivity, and rural development in the Middle East context. This review provides strategic insights to support the sustainable deployment and scaling of agrivoltaic systems across Middle Eastern agricultural landscapes, informed by global experience. A dedicated regional assessment summarizes existing agrivoltaic pilots and feasibility studies across the Middle East and North Africa, highlighting technology choices, crop compatibility, and policy drivers. Full article

Energy efficiency retrofits are central to climate policy, yet their implications for indoor environmental quality (IEQ) and occupant health remain underexplored. This study investigates IEQ outcomes following staged retrofits in Irish social housing, where achieving Building Energy Rating (BER) targets is the primary performance metric. Four dwellings, three retrofitted and one control, were monitored over six weeks during the heating season. Built in the 1980s, these homes represent the typical social and private housing stock of that era. Continuous measurements of carbon dioxide, temperature, relative humidity, and thermal performance were complemented by analyses of vapour pressure excess and ventilation rates. While all retrofitted homes achieved BER improvement targets, persistent IEQ challenges were identified. Elevated pollutant concentrations and increased condensation/mould risk occurred in the presence of inadequate ventilation. Thermal anomalies and cold bridging were associated with cavity wall insulation, whereas external wall insulation provided more stable surface temperatures and reduced moisture-related risks. These results underscore the complex interplay between retrofit measures, occupancy patterns, and ventilation performance. The study highlights the need for retrofit strategies that integrate energy efficiency with occupant health objectives. At scale, retrofit programmes risk embedding systemic vulnerabilities unless ventilation and moisture control are prioritised, with implications that extend to health, wellbeing, and long-term building resilience. Full article

The recovery of extracellular polymeric substances (EPS) from activated sludge (AS) represents a promising strategy to transform wastewater treatment plants (WWTPs) into resource recovery facilities within a circular economy framework. In this study, EPS was extracted from an AS process in a full-scale WWTP, highlighting its catalytic and bioflocculant properties, which represent an innovation in the valorization of this biopolymer. The EPS was subsequently characterized in terms of polysaccharides, proteins, and enzymatic activities (amylase and lipase). The bioflocculation performance of the EPS was evaluated using activated sludge mixed liquor. Results showed that EPS recovery yields using 50 °C and 80 °C were 196.3 ± 38.2 mg EPS/g sludge and 283.5 ± 85.4 mg EPS/g sludge, respectively. Enzymatic assays confirmed amylase activity ranging from 100 to 350 U/g sludge according to the extraction temperature. Lipolytic activity (20 U/g sludge) was comparable to values reported in the literature for EPS from biological sludge. The addition of EPS significantly improved the sludge settling velocity (from 0.86 to 4.48 m/h) and the sludge volume index (from 118.6 to 35.5). However, EPS application also increased the resistance to filtration by 50% and reduced cellular respiration by approximately 40%. Overall, the findings demonstrate that EPS from activated sludge acts as an effective bioflocculant with relevant catalytic properties, highlighting its potential as a high-value biotechnological product while also pointing to operational challenges that require further optimization. Full article

As social media becomes increasingly embedded in daily life, understanding the psychological mechanisms that shape users’ digital experiences is essential for promoting healthy, sustainable digital behavior. This study examines two motivational life-orientation strategies—prioritizing meaning and prioritizing positivity—and investigates how they are differentially associated with patterns of social media use, digital flourishing, and psychological well-being. A sample of 414 adults completed validated measures of digital and psychological outcomes, including a Hebrew adaptation of the Digital Flourishing Scale (DFS), and measures of prioritizing meaning and prioritizing positivity. Across seven regression models, prioritizing meaning was consistently associated with more adaptive digital outcomes, including lower screen time, reduced FoMO, higher digital flourishing, greater self-compassion, and lower psychopathology. In contrast, prioritizing positivity was associated with greater social media engagement and elevated social comparison but showed no associations with digital flourishing or psychological well-being. These findings support a dual-pathway framework, suggesting that meaning-oriented individuals tend to interact with digital platforms more intentionally and resiliently, whereas positivity-oriented individuals engage in more affect-driven and evaluative patterns that do not translate into well-being benefits. The study advances current understanding of digital behavior by identifying motivational factors that shape how users navigate online environments and highlights implications for designing digital well-being interventions and platform features. Full article

Aim: As the global population is aging rapidly, promoting physical activity in later life is increasingly seen as a good strategy to enhance and sustain the social and psychological well-being of older adults from a positive aging perspective. This study explored how social capital influences the relationship between playing football and happiness in veteran footballers. Specifically, it aimed to understand if social capital acts as a mediator between these two factors. Materials and Methods: This cross-sectional study was performed on a convenience sample of 423 veteran footballers aged from 38 to 59 years who completed a survey at 35th Sakarya Veterans Football Tournament in Sakarya in north-west Turkey. Football participation was assessed using the Serious Leisure Inventory. Social capital was assessed in both cognitive and structural aspects. A single-item scale assessed general happiness. Data were analyzed with Pearson correlation coefficients and were calculated using SPSS (version 24) to assess the direction and strength of the links between the investigated variables. Path coefficients were calculated through regression analyses. For testing mediation effects, the study utilized Hayes’ bootstrapping method, executed with the Version 4.2 Beta of the PROCESS macro. Results: According to the research findings, the direct effect of football participation on happiness was determined to be 0.43 (p < 0.01). While community involvement played a significant mediating role with a coefficient of 0.11 (95% CI [0.05, 0.15]), the social trust and neighborhood dimensions of social capital did not yield statistically significant effects. Collectively, the model explains 24% of the variance in happiness (R² = 0.24), with a total effect of football participation calculated at 0.57 (p = 0.000). Conclusions: it is believed that social environments that include participation in sport can contribute to successful and comfortable aging by greatly enhancing the overall well-being and happiness of older athletes/adults. Full article

This paper details the development of innovative grading techniques for 3D-printed biopolymer composites that utilize locally sourced, cellulose-based fibre streams to produce architectural-scale components. It examines the design considerations, methodologies, and fabrication strategies that are necessitated by the utilisation of biopolymers for architectural applications, and which underlie key processes of designing for and with variable materials. The presented research interrogates the methodological challenges of formulating new approaches that actively engage architects and designers with the ecological implications of their design choices. It outlines new methods for material grading that enable targeted compositional variation through three interlinked contributions: a gradable recipe, a design-interfaced specification process for grading, and an infrastructure for large-scale 3D printing of biopolymer composites. The paper presents the Rhizaerial demonstrator as an implementation of these contributions. Rhizaerial is a full-scale interior ceiling vault system, whose curved components are printed as a 3D porous lattice structure that creates an interplay of light, visual transparency, and colour, while maintaining structural integrity. We detail the gradable biopolymer composite recipe, and the residual and regenerative material streams it combines. We outline the implicit modelling pipeline, which includes methods for locally specifying lattice structures for 3D printing, as well as assigning continuous grading specifications to print paths. Finally, we describe the fabrication infrastructure and tooling for robotic printing of large-scale graded biopolymer composites. Full article