Search Results (9,964)

Optimizing the “production–living–ecological” space (PLES) is critical for achieving the UN Sustainable Development Goals (SDGs), particularly in ecologically sensitive mountainous border regions. This study investigates the spatial patterns and dynamic evolution of PLES in Lincang City (2010–2020) to reveal the trade-offs between development and conservation. Methodologically, we proposed a coupling-coordination-based grid-level PLES identification framework. This framework integrates the coupling coordination degree model (CCDM) directly into the functional classification process at a 600 m grid scale—a resolution selected to balance the capture of spatial heterogeneity with the maintenance of functional integrity in complex terrains. Spatiotemporal dynamics were further quantified using transition matrices and a dimension-based landscape metric system. The results reveal that (a) ecological space and production–living–ecological space represent the predominant categories in the study area. During the study period, ecological space continued to decrease, while production–living space increased steadily, and other PLES categories showed only marginal variations. (b) Mutual transitions among PLES types primarily occurred among ecological space, production–ecological space, and production–living–ecological space. These transitions intensified markedly between 2015 and 2020 compared to the 2010–2015 period. (c) From 2010 to 2020, the landscape in Lincang evolved towards lower ecological risk yet higher fragmentation. High fragmentation values, often associated with grassland, cropland, and forested areas, were evenly distributed across northeastern and northwestern regions. Likewise, high landscape dominance and isolation appeared in these regions as well as in the southeast. Conversely, landscape disturbance remained relatively uniform throughout the city, with lower values detected in forested land. Full article

The Ego-Centric Sampling Method (ECM) leverages individual-level reports about peers to estimate population proportions within social networks, offering strong privacy protection without requiring full network data. However, the conventional ECM estimator is unbiased only under the restrictive assumption of a homogeneous network, where node degrees are uniform and uncorrelated with attributes. To overcome this limitation, we introduce the Activity Ratio Corrected ECM estimator (${\mathrm{ECM}}_{\mathrm{ac}}$), which exploits network reciprocity to recast the population–proportion problem into an equivalent formulation in edge space. This reformulation relies solely on ego–peer data and explicitly corrects for degree–attribute dependencies, yielding unbiased and stable estimates even in highly heterogeneous networks. Simulations and analyses on real-world networks show that ${\mathrm{ECM}}_{\mathrm{ac}}$ reduces estimation error by up to 70% compared with the conventional ECM. Our results establish a theoretically grounded and practically scalable framework for unbiased inference in network-based sampling designs. Full article

Symbiotic radio (SR) has recently emerged as a promising paradigm for enabling spectrum- and energy-efficient massive connectivity in low-power Internet-of-Things (IoT) networks. By allowing passive backscatter devices (BDs) to coexist with active primary link transmissions, SR significantly improves spectrum utilization without requiring dedicated spectrum resources. However, most existing studies on multi-tag multiple-input multiple-output (MIMO) SR systems assume homogeneous traffic demands among BDs and primarily focus on rate-based performance metrics, while neglecting system-level task completion time (TCT) optimization under heterogeneous data requirements. In this paper, we investigate a joint performance optimization framework for a multi-tag MIMO symbiotic radio network. We first formulate a weighted sum-rate (WSR) maximization problem for the secondary backscatter links. The original non-convex WSR maximization problem is transformed into an equivalent weighted minimum mean square error (WMMSE) problem, and then solved by a block coordinate descent (BCD) approach, where the transmit precoding matrix, decoding filters, backscatter reflection coefficients are alternatively optimized. Second, to address the transmission delay imbalance caused by heterogeneous data sizes among BDs, we further propose a rate weight adaptive task TCT minimization scheme, which dynamically updates the rate weight of each BD to minimize the overall TCT. Simulation results demonstrate that the proposed framework significantly improves the WSR of the secondary system without degrading the primary link performance, and achieves substantial TCT reduction in multi-tag heterogeneous traffic scenarios, validating its effectiveness and robustness for MIMO symbiotic radio networks. Full article

Background/Objective: The rising prevalence of Type 2 Diabetes Mellitus (T2DM), coupled with sedentary behavior and an increase in obesity rates in South Asian countries, calls for effective management strategies. We aimed to assess the efficacy of lifestyle interventions on glycemic control among adults with T2DM in South Asian countries. Methods: A systematic review and meta-analysis of randomized controlled trials (RCTs) were conducted to assess the effectiveness of lifestyle interventions on glycemic control in adults diagnosed with T2DM in South Asia. We conducted a comprehensive search in CINAHL, Embase, PubMed, Cochrane Library, Web of Science (WoS), and Scopus to identify related studies published from 2000 to 13 June 2024. We assessed the risk of bias using the ROB 2.0 tool and calculated the pooled mean differences in HbA1c and FBG levels under a random-effects model. We conducted subgroup and leave-one-out sensitivity analyses to assess and explore sources of heterogeneity. PROSPERO Registration: CRD42024552286. Results: We included 16 RCTs with a total of 1499 participants. Lifestyle interventions reduced HbA1c levels by 0.86% (95% CI: −1.30 to −0.42, p < 0.01) and FBG levels by 22.49 mg/dL (95% CI: −32.88 to −12.10, p < 0.01). We observed substantial heterogeneity (I² = 98% for HbA1c and I² = 87% for FBG). Subgroup analyses indicated larger HbA1c reductions in long-term (−1.44%) than short-term trials (−0.62%), and greater FBG decreases in long-term (−23.7 mg/dL) versus short-term studies (−22.5 mg/dL). Physical activity interventions had the largest improvements (HbA1c −0.99%; FBG −26.1 mg/dL), followed by dietary (HbA1c −0.59%; FBG −15.8 mg/dL) and combined programs (HbA1c −0.55%). Participants aged >50 years achieved greater glycemic improvements (HbA1c −0.92%; FBG −24.0 mg/dL) compared to younger adults (HbA1c −0.60%; FBG −21.3 mg/dL). Despite high heterogeneity, sensitivity analyses confirmed the robustness of the overall findings. Conclusions: Lifestyle modifications yielded a clinically significant reduction in HbA1c and FBG in adults with T2DM in South Asia. Although heterogeneity of the included studies was substantial, the direction of the effects was uniformly consistent across subgroups. To further validate these findings and assess their long-term effects, large-scale and standardized RCTs conducted for longer durations are necessary. Full article

Background: Honeybees sustain vital ecological roles through foraging behavior, which provides pollination services and is likely regulated by dopamine signaling coupled to brain energy metabolism. However, the genetic and metabolic mechanisms underlying this regulation remain unclear. Methods: We treated honeybee workers with the dopamine receptor agonist bromocriptine and employed an integrative approach, combining liquid chromatography–mass spectrometry (LC–MS) metabolomics with single-nucleus RNA sequencing (snRNA-seq). Results: Metabolomics revealed increased levels of N6-carboxymethyllysine (CML) and a coordinated shift in central carbon metabolites, including higher glucose, pyruvate, and lactate within glycolysis, and ribose-5-phosphate in the pentose phosphate pathway (PPP). Integration with transcriptomics showed heterogeneous responses: glial cells exhibited higher glycolysis pathway scores and upregulated hexokinase expression compared to neurons, whereas major PPP enzymes were upregulated in both glial and neuronal subsets. Conclusions: These findings suggest that dopamine receptor activation is associated with altered whole-brain metabolic profiles and concurrent, cell-type-specific upregulation of glycolytic and PPP enzyme genes, particularly in glia. This study characterizes these neuro-metabolic associations, offering insights into the cellular and metabolic basis of foraging behavior in worker bees. Full article

Local energy systems (LESs) are becoming larger and more heterogeneous as distributed energy resources, electrified loads, and active prosumers proliferate, increasing the need for reliable coordination of operation, markets, and community governance. This Perspective synthesizes recent literature to map how artificial intelligence (AI) supports forecasting and situational awareness, optimization, and real-time control of distributed assets, and community-oriented markets and engagement, while arguing that adoption is limited by system-level credibility rather than model accuracy alone. The analysis highlights interlocking deployment barriers, such as governance-integrated explainability, distributional equity, privacy and data governance, robustness under non-stationarity, and the computational footprint of AI. Building on this diagnosis, the paper proposes principles-as-constraints for sustainable, trustworthy LES AI and a deployment-oriented validation and reporting framework. It recommends evaluating LES AI with deployment-ready evidence, including stress testing under shift and rare events, calibrated uncertainty, constraint-violation and safe-fallback behavior, distributional impact metrics, audit-ready documentation, edge feasibility, and transparent energy/carbon accounting. Progress should be judged by measurable system benefits delivered under verifiable safeguards. Full article

Objectives: Polycystic ovary syndrome (PCOS) is a heterogeneous disorder with diverse pathogenetic mechanisms and clinical manifestations. Phenotype D PCOS is characterized by oligomenorrhoea and polycystic ovaries without hyperandrogenism. Altered adipokine profiles may contribute to reproductive and metabolic disturbances. Chemerin is an adipokine involved in inflammatory and metabolic processes. It remains unclear whether altered chemerin levels in PCOS reflect metabolic dysfunction alone or are directly associated with hyperandrogenism. The aim of this study was to compare serum chemerin levels in women with normoandrogenic PCOS and a control group. Methods: This cross-sectional preliminary study included 49 women with phenotype D PCOS and 40 healthy, age- and body mass index (BMI)-matched controls. Anthropometric, biochemical, hormonal parameters, and serum chemerin concentrations were assessed. Results: Serum chemerin concentrations did not differ significantly between the groups. In the PCOS group, the 95% confidence interval ranged from 198.61 to 234.37, while in the controls, it ranged from 187.13 to 216.21. In women with PCOS, chemerin showed significant positive correlations with weight, BMI, waist and hip circumference, total adipose tissue, and both gynoid and android fat content. Positive correlations were also observed with highly sensitive C-reactive protein (hs-CRP), insulin, glucose, triglycerides, and Homeostasis Model Assessment of Insulin Resistance (HOMA-IR), and a negative correlation was found with high-density lipoprotein (HDL) cholesterol. Chemerin was weakly negatively correlated with sex hormone binding globulin (SHBG) and positively correlated with the free androgen index (FAI). In the control group, chemerin correlated positively with CRP, insulin, triglycerides, total and gynoid adipose tissue, and negatively correlated with HDL cholesterol and SHBG. Conclusions Although chemerin levels did not differ from controls, chemerin was associated with metabolic and inflammatory markers in both groups. These findings should be considered preliminary due to the limited sample size. Chemerin may reflect metabolic and inflammatory status rather than hyperandrogenism in normoandrogenic PCOS. Full article

Against the escalating challenges of global climate change and intensifying resource-environment constraints, exploring the green effects of industrial spatial organization has become crucial. Utilizing panel data from the Yangtze River Delta cities spanning 2011–2023, this study empirically examines the nonlinear impact of manufacturing-producer services co-agglomeration on urban green efficiency. The results reveal a significant U-shaped relationship: co-agglomeration initially suppresses efficiency due to coordination costs and congestion effects, but after crossing a specific threshold, the resulting scale economies and knowledge spillovers dominate and begin to promote green enhancement. Mechanism tests indicate that industrial upgrading serves as a direct mediating channel, while the mediating effect of green technological innovation exhibits a time lag. Further heterogeneity analysis shows that this U-shaped pattern is particularly pronounced in cities with low agglomeration levels, those not designated as low-carbon pilots, and non-resource-based cities. This study uncovers the nonlinear dynamics and key boundary conditions of the green effects arising from industrial co-agglomeration, providing an empirical basis for implementing differentiated regional spatial coordination policies. Full article

This study investigates the valuation dynamics of 30 publicly listed fintech firms across six Asian economies from January 2021 to December 2025. It examines how intrinsic firm-level scale (market capitalization) and extrinsic macroeconomic conditions (GDP growth) jointly influence fintech valuation ratios, as reflected in price-to-earnings (P/E), price-to-book (P/B), and price-to-sales (P/S) measures. It also identifies significant structural heterogeneity and distributional asymmetries in valuation outcomes by implementing a multi-method empirical strategy that includes a Panel Autoregressive Distributed Lag (ARDL) framework, two-way fixed-effects models with interaction terms, and quantile regression. The findings reveal a robust, positive long-run relationship between market capitalization and valuation multiples across all ratios, confirming that firm-level scale as reflected in market capitalization is the primary driver of market value. Critically, the analysis identifies a dual-regime landscape in the Asian fintech sector: developed markets (South Korea, Japan, and Singapore) are fundamentally firm-scale driven, where intrinsic scale is the superior predictor of valuation. In contrast, developing markets (China, India, and Indonesia) are primarily macro-growth driven, exhibiting high sensitivity to GDP growth as a macroeconomic indicator of market expansion. The quantile regression results demonstrate a winner-takes-all effect, where the impact of scale on valuation is significantly more pronounced for highly valued firms in the 75th percentile. These results challenge the efficacy of universal valuation models and provide a context-dependent navigational framework for investors, analysts, and policymakers to distinguish between structural scale and cyclical growth in the rapidly evolving Asian fintech ecosystem. Full article

End–edge–cloud collaboration enables significant improvements in system resource utilization by integrating heterogeneous resources while ensuring application-level quality of service (QoS). However, achieving efficient collaborative decision-making in such architectures poses critical challenges within dynamic and complex computing network environments, including dynamic resource allocation, incentive alignment between cloud and edge entities, and multi-objective optimization. To address these issues, this paper proposes a dynamic resource optimization framework for complex cloud–edge collaborative networks, decomposing the problem into two hierarchical decision schemes: cloud-level coordination and edge-side coordination, thereby achieving adaptive resource orchestration across the End–edge–cloud continuum. Furthermore, leveraging differential game theory, we model the dynamic resource allocation and cooperation incentives between cloud and edge nodes, and derive a feedback Nash equilibrium to maximize the overall system utility, effectively resolving the inherent conflicts of interest in cloud–edge collaboration. Additionally, we formulate a joint optimization model for energy consumption and latency, and propose an Improved Discrete Artificial Hummingbird Algorithm (IDAHA) to achieve an optimal trade-off between these competing objectives, addressing the challenge of multi-objective coordination from the user perspective. Extensive simulation results demonstrate that the proposed methods exhibit superior performance in multi-objective optimization, incentive alignment, and dynamic resource decision-making, significantly enhancing the adaptability and collaborative efficiency of complex cloud–edge networks. Full article

Background: The vestibular schwannoma (VS) is the most common cerebellopontine angle tumor in adults, exhibiting a highly variable natural history, from stability to rapid growth. Accurate, the non-invasive prediction of tumor behavior is essential to guide personalized management and avoid overtreatment or delayed intervention. Objective: To systematically review and synthesize the evidence on MRI-based biomarkers for predicting VS growth and treatment responses. Methods: We conducted a PRISMA-compliant search of PubMed, EMBASE, and Cochrane databases for studies published between 1 January 2000 and 1 January 2025, addressing MRI predictors of VS growth. Cohort studies evaluating texture features, signal intensity ratios, perfusion parameters, and apparent diffusion coefficient (ADC) metrics were included. Study quality was assessed using the NOS (Newcastle–Ottawa Scale) score, GRADE (Grading of Recommendations, Assessment, Development and Evaluation), and ROBIS (Risk of Bias in Systematic reviews) tool. Data on diagnostic performance, including the area under the receiver operating characteristic (ROC) curve (AUC), sensitivity, specificity, and p value, were extracted and descriptively analyzed. Results: Ten cohort studies (five retrospective, five prospective, total n = 525 patients) met the inclusion criteria. Texture analysis metrics, such as kurtosis and gray-level co-occurrence matrix (GLCM) features, yielded AUCs of 0.65–0.99 for predicting volumetric or linear growth thresholds. Signal intensity ratios on gadolinium-enhanced T1-weighted images for tumor/temporalis muscle achieved a 100% sensitivity and 93.75% specificity. Perfusion MRI parameters (K^trans, v_e, ASL, and DSC derived blood-flow metrics) differentiated growing from stable tumors with AUCs up to 0.85. ADC changes post-gamma knife surgery predicted a favorable response, though the baseline ADC had limited value for natural growth prediction. The heterogeneity in growth definitions, MRI protocols, and retrospective designs remains a key limitation. Conclusions: MRI-based biomarkers may provide exploratory signals associated with VS growth and treatment responses. However, substantial heterogeneity in growth definitions and MRI protocols, small single-center cohorts, and the absence of external validation currently limit clinical implementation. Full article

Background and Objectives: Brain metastases frequently evolve over time in multiple waves, especially in patients with prolonged survival. Despite repeated imaging and targeted therapies, lesion-level continuity is fragmented in clinical practice, as follow-up is typically limited to pairwise MRI comparisons. The aim of the study is to assess the ability of routine narrative MRI follow-up reports to preserve longitudinal lesion identity and to reconstruct a coherent trajectory of disease evolution. Materials and Methods: We conducted a single-center, retrospective, observational study of all brain MRI examinations performed between June 2024 and June 2025 (n = 731 scans, 616 patients). All imaging reviews and longitudinal lesion tracking were performed by one board-certified neuroradiologist. Adult patients with confirmed brain metastases and at least three MRI examinations (including external studies) were included. We assessed the concordance of routine narrative MRI follow-up reports against a longitudinal review of all available MRIs and treatment timelines, which served as the reference standard. Lesion identity was considered preserved when reports explicitly recognized and linked lesions across time points, and lost when identity was omitted or ambiguous in at least one report. Results: The final cohort comprised 73 patients (477 tracked lesions). More than half of monitored lesions disappeared (42.9%) or evolved into post-treatment sequelae (9.9%), and were omitted from narrative reports, limiting retrospective recognition without prior imaging. The ability of routine reports to preserve lesion identity declined as cases became more complex. Concordance was higher in uniform evolution patterns (≈60%) but dropped to 18.2% in mixed evolution. A similar decline was seen with sequential metastatic waves, defined as new metastases appearing at distinct time points: 65.2% (1 wave), 46.7% (2 waves), 18.2% (3 waves), and complete loss of continuity when >3 waves occurred. Conclusions: Routine narrative MRI follow-up reports generally provide adequate information in simple cases with uniform lesion behavior, but tend to lose critical details as disease trajectories become more complex, particularly in heterogeneous or multi-wave disease. Even when individual lesions are identified across examinations, documentation remains fragmented and reflects only a snapshot of the disease course rather than an integrated longitudinal perspective. These findings highlight a critical vulnerability in current follow-up practices. Improving lesion-level continuity, potentially through AI-assisted tools, may enhance the accuracy, consistency, and clinical utility of MRI surveillance in patients with brain metastases. Full article

Background/Objectives: Emotional dysregulation (ED) is emerging as a major contributor to functional impairment in Autism Spectrum Disorder (ASD). Although effective behavioral interventions exist, pharmacological treatments remain constrained by side effects and variable tolerability. Given their neurobiological roles that include neurotransmission, inflammation, and neuroplasticity, vitamin D and omega-3 polyunsaturated fatty acids (PUFAs) have been identified as promising candidates for modulating emotional and behavioral dysregulation. This systematic review aimed to evaluate the efficacy of combined vitamin D and omega-3 supplementation in improving emotional and behavioral regulation in individuals with ASD. Methods: This review was conducted in accordance with PRISMA guidelines. Included studies were English peer-reviewed studies involving participants with ASD that assessed combined vitamin D and omega-3 suppleupplementation with outcomes related to emotional or behavioral dysregulation. The search was restricted to 2015–2025 to ensure inclusion of recent, methodologically consistent studies and to minimize heterogeneity in diagnostic criteria and supplementation protocols. Results: Of 649 records initially screened, 3 studies met inclusion criteria: one randomized controlled trial, one observational study, and one case report, involving participants ranging from early childhood to young adulthood. Across studies, combined supplementation was associated with improvements in irritability, hyperactivity, agitation, and self-injurious behaviors. These clinical effects were accompanied by specific biochemical changes, including reductions in the AA/EPA ratio, increases in serum 25(OH)D and omega-3 indices, and decreased urinary levels of HVA and VMA. Conclusions: This review indicates that co-supplementation with vitamin D and omega-3 fatty acids may exert preliminary beneficial effects on emotional and behavioral dysregulation in individuals with ASD, potentially through anti-inflammatory and neuroregulatory mechanisms. However, the available evidence remains limited due to a small number of studies, their modest sample size, and methodological heterogeneity. Further, biomarker-driven randomized studies are needed to confirm efficacy and delineate optimal dosing strategies for application in clinics. Full article

Personality traits are well-established predictors of safety behavior in construction, yet the cognitive mechanisms through which these traits influence such behavior remain poorly understood. In particular, hazard recognition and risk perception are underexamined cognitive mediators that elucidate how personality traits shape safety behavior. Moreover, the mediating effects of these cognitive processes are likely to vary across individuals, reflecting heterogeneity in background characteristics. Neglecting these mediating processes and their differentiated effects not only limits theoretical understanding of the pathways linking personality traits to safety behavior but also undermines the effectiveness of safety interventions. To address this gap, this study develops a framework incorporating cognitive mediators to examine how personality traits influence safety behavior (safety compliance and participation). The hypothesized cognitive-mediation pathways were tested using structural equation modeling based on offline questionnaire data collected from 213 site managers and workers. The findings reveal distinct cognitive pathways through which personality traits shape safety behavior. Extraversion and openness indirectly reduced safety compliance and safety participation by weakening hazard recognition and risk perception, either independently or sequentially. In contrast, agreeableness and conscientiousness enhanced safety behavior by strengthening these same cognitive processes. Higher education levels positively moderated certain mediating effects, whereas extensive work experience exerted mixed influences on specific pathways, facilitating some and inhibiting others depending on context. These findings deepen understanding of the internal mechanisms through which personality traits influence safety behavior via risk cognition. By identifying differentiated pathways across groups, this study further refines the theoretical framework explaining construction workers’ safety behavior. In addition, the theoretical insights generated by this study offer proactive and effective directions for safety practice, including improving person–job fit, designing targeted risk cognition training, and implementing stratified safety management strategies. Full article

Background/Objectives: Low back pain (LBP) is increasingly prevalent among children and adolescents and represents a growing public health concern due to its potential persistence into adulthood. Screen-based sedentary behavior has substantially increased in pediatric populations. However, evidence regarding its association with LBP remains inconsistent, and the existence of a dose–response relationship is not well established. Methods: A systematic review and meta-analysis of observational studies was conducted in accordance with PRISMA guidelines. Studies examining the association between screen-based sedentary behavior and LBP in children and adolescents aged 6–18 years were included. Random-effects meta-analyses were used to pool continuous exposure estimates, and a multivariate random-effects dose–response meta-analysis was performed to assess changes in LBP risk across increasing levels of daily screen time. Results: A total of 30 studies were included. The pairwise meta-analysis of continuous exposure showed no statistically significant association between screen time and LBP, with OR = 1.02 (95% CI 0.65 to 1.59). In contrast, the dose–response meta-analysis demonstrated a significant positive association, with a 26% (95% CI 8% to 48%) increase in the odds of LBP for each additional hour of daily screen time. High between-study heterogeneity was observed, and most studies relied on self-reported measures of screen exposure and LBP, which may have introduced recall and misclassification bias and warrants cautious interpretation of the findings. Conclusions: Higher levels of screen-based sedentary behavior were associated with an increased risk of LBP in children and adolescents when examined using a dose–response approach, whereas pairwise meta-analyses did not identify a significant association. Nevertheless, substantial between-study heterogeneity and high risk of bias limit causal inference and require cautious interpretation. Full article