Search Results (1,049)

Accurate and spatially consistent rainfall estimation is essential for hydrological modeling and flood risk mitigation, especially in mountainous tropical regions with sparse observational networks and highly heterogeneous rainfall. This study presents a comparative analysis of six radar–gauge merging methods, including three statistical approaches—Quantile Adaptive Gaussian (QAG), Empirical Quantile Mapping (EQM), and radial basis function (RBF)—and three geostatistical approaches—external drift kriging (EDK), Bayesian Kriging (BAK), and Residual Kriging (REK). The evaluation was conducted over the Huong River Basin in Central Vietnam, a region characterized by steep terrain, monsoonal climate, and frequent hydrometeorological extremes. Two observational scenarios were established: Scenario S1 utilized 13 gauges for merging and 7 for independent validation, while Scenario S2 employed all 20 stations. Hourly radar and gauge data from peak rainy months were used for the evaluation. Each method was assessed using continuous metrics (RMSE, MAE, CC, NSE, and KGE), categorical metrics (POD and CSI), and spatial consistency indicators. Results indicate that all merging methods significantly improved the accuracy of rainfall estimates compared to raw radar data. Among them, RBF consistently achieved the highest accuracy, with the lowest RMSE (1.24 mm/h), highest NSE (0.954), and strongest spatial correlation (CC = 0.978) in Scenario S2. RBF also maintained high classification skills across all rainfall categories, including very heavy rain. EDK and BAK performed better with denser gauge input but required recalibration of variogram parameters. EQM and REK yielded moderate performance and had limitations near basin boundaries where gauge coverage was sparse. The results highlight trade-offs between method complexity, spatial accuracy, and robustness. While complex methods like EDK and BAK offer detailed spatial outputs, they require more calibration. Simpler methods are easier to apply across different conditions. RBF emerged as the most practical and transferable option, offering strong generalization, minimal calibration needs, and computational efficiency. These findings provide useful guidance for integrating radar and gauge data in flood-prone, data-scarce regions. Full article

Public service innovation research often focuses on the private or general public sectors, leaving the distinct challenges government entities face unexplored. An empirical study was carried out to bridge this gap using survey results from the United Arab Emirates (UAE) government entities. This study built on that research by further analyzing the relationships among these challenges through a social network approach, visualizing and analyzing the connections between them by utilizing betweenness centrality and eigenvector centrality as key metrics. Based on this analysis, the challenges were classified into different categories; 8 out of 22 challenges were identified as critical due to their high values in both metrics. Addressing these critical challenges is expected to create a cascading impact, helping to resolve many others. Targeted strategies are proposed, and leveraging open innovation is highlighted as an effective and versatile solution to address and mitigate these challenges. This study is one of the few to adopt a social network analysis perspective to visualize and analyze the relationships among challenges, enabling the identification of critical ones. This research offers novel and valuable insights that could assist decision-makers in UAE government entities and countries with similar contexts with actionable strategies to advance public service innovation. Full article

Agriculture, livestock, and fisheries significantly impact socioeconomic, environmental, and health dimensions at global level, ensuring food supply for growing populations whilst promoting economic welfare through international trade, employment, and income. Considering that bilateral food exchanges between countries represent exchanges of natural resources involved in food production (i.e., food imports are equivalent to savings of natural resources), the purpose of the study is to investigate the evolution of carbon and water footprints corresponding to the global food trade networks between 1986 and 2020. The research aims to identify potential associations between carbon and water footprints embedded in food trade and countries’ economic welfare. Complex network analysis was used to map countries’ positions within annual food trade networks, and countries’ metrics within networks were used to identify connections between participation in global trade of carbon and water footprints and economic welfare. The findings of the study show an increase in carbon and water footprints linked to global food exchanges between countries during the period. Furthermore, a country’s centrality within the network was linked to economic welfare, showing that countries with higher imports of carbon and water through global food trade derive economic benefits from participating in global trade. Global efforts towards transformations of food systems should prioritize sustainable development standards to ensure continued access to healthy sustainable diets for populations worldwide. Full article

We propose a high-speed all-optical logic gate design based on silicon racetrack and ring resonators patterned on a silica substrate. The architecture features racetrack resonators at both the input and output, with a central ring resonator enabling the required phase-sensitive interference for logic processing. Logic operations are achieved through the interplay of constructive and destructive interference induced by phase-shifted input beams. Using the finite-difference time-domain (FDTD) method in Lumerical software, we simulate and demonstrate seven fundamental Boolean logic functions, namely XOR, AND, OR, NOT, NOR, NAND, and XNOR, at an operating wavelength of 1.33 µm. The system supports a data rate of 47.94 Gb/s, suitable for ultrafast optical computing. The performance is quantitatively evaluated using the contrast ratio (CR) as the reference metric, with more than acceptable values of 13.09 dB (XOR), 13.84 dB (AND), 13.14 dB (OR), 13.80 dB (NOT), 14.53 dB (NOR), 13.80 dB (NAND), and 14.67 dB (XNOR), confirming strong logic level discrimination. Comparative analysis with existing optical gate designs underscores the advantages of our compact silicon-on-silica structure in terms of speed, CR performance, and integration potential. This study validates the effectiveness of racetrack–ring configurations for next-generation all-optical logic circuits. Full article

Alzheimer’s disease (AD) involves the accumulation of amyloid-β (Aβ) plaques, whose quantification plays a central role in understanding disease progression. Automated segmentation of Aβ deposits in histopathological micrographs enables large-scale analyses but is hindered by the high cost of detailed pixel-level annotations. Weakly supervised learning offers a promising alternative by leveraging coarse or indirect labels to reduce the annotation burden. We evaluated a weakly supervised approach to segment and analyze thioflavin-S-positive parenchymal amyloid pathology in AD and age-matched brains. Our pipeline integrates three key components, each designed to operate under weak supervision. First, robust preprocessing (including retrospective multi-image illumination correction and gradient-based background estimation) was applied to enhance image fidelity and support training, as models rely more on image features. Second, class activation maps (CAMs), generated by a compact deep classifier SqueezeNet, were used to identify, and coarsely localize amyloid-rich parenchymal regions from patch-wise image labels, serving as spatial priors for subsequent refinement without requiring dense pixel-level annotations. Third, a patch-based convolutional neural network, U-Net, was trained on synthetic data generated from micrographs based on CAM-derived pseudo-labels via an extensive object-level augmentation strategy, enabling refined whole-image semantic segmentation and generalization across diverse spatial configurations. To ensure robustness and unbiased evaluation, we assessed the segmentation performance of the entire framework using patient-wise group k-fold cross-validation, explicitly modeling generalization across unseen individuals, critical in clinical scenarios. Despite relying on weak labels, the integrated pipeline achieved strong segmentation performance with an average Dice similarity coefficient (≈0.763) and Jaccard index (≈0.639), widely accepted metrics for assessing segmentation quality in medical image analysis. The resulting segmentations were also visually coherent, demonstrating that weakly supervised segmentation is a viable alternative in histopathology, where acquiring dense annotations is prohibitively labor-intensive and time-consuming. Subsequent morphometric analyses on automatically segmented Aβ deposits revealed size-, structural complexity-, and global geometry-related differences across brain regions and cognitive status. These findings confirm that deposit architecture exhibits region-specific patterns and reflects underlying neurodegenerative processes, thereby highlighting the biological relevance and practical applicability of the proposed image-processing pipeline for morphometric analysis. Full article

Efficient and intelligent spectrum access is crucial for meeting the diverse Quality of Service (QoS) demands of Vehicular Internet of Things (V-IoT) systems in next-generation cellular networks. This work proposes a novel reinforcement learning (RL)-based priority-aware spectrum management (RL-PASM) framework, a centralized self-learning priority-aware spectrum management framework operating through Roadside Units (RSUs). RL-PASM dynamically allocates spectrum resources across three traffic classes: high-priority (HP), low-priority (LP), and best-effort (BE), utilizing reinforcement learning (RL). This work compares four RL algorithms: Q-Learning, Double Q-Learning, Deep Q-Network (DQN), and Actor-Critic (AC) methods. The environment is modeled as a discrete-time Markov Decision Process (MDP), and a context-sensitive reward function guides fairness-preserving decisions for access, preemption, coexistence, and hand-off. Extensive simulations conducted under realistic vehicular load conditions evaluate the performance across key metrics, including throughput, delay, energy efficiency, fairness, blocking, and interruption probability. Unlike prior approaches, RL-PASM introduces a unified multi-objective reward formulation and centralized RSU-based control to support adaptive priority-aware access for dynamic vehicular environments. Simulation results confirm that RL-PASM balances throughput, latency, fairness, and energy efficiency, demonstrating its suitability for scalable and resource-constrained deployments. The results also demonstrate that DQN achieves the highest average throughput, followed by vanilla QL. DQL and AC maintain fairness at high levels and low average interruption probability. QL demonstrates the lowest average delay and the highest energy efficiency, making it a suitable candidate for edge-constrained vehicular deployments. Selecting the appropriate RL method, RL-PASM offers a robust and adaptable solution for scalable, intelligent, and priority-aware spectrum access in vehicular communication infrastructures. Full article

Understanding the detailed spatiotemporal variations in soil organic carbon (SOC) stocks is essential for assessing soil carbon sequestration potential. However, most existing studies predominantly focus on topsoil SOC stocks, leaving significant knowledge gaps regarding critical zones, depth-dependent variations, and key influencing factors associated with deeper SOC stock dynamics. This study adopted a comprehensive methodology that integrates random forest modeling, equal-area soil profile analysis, and space-for-time substitution to predict depth-specific SOC stock dynamics under climate warming in Northeast China’s forest ecosystems. By combining these techniques, the approach effectively addresses existing research limitations and provides robust projections of soil carbon changes across various depth intervals. The analysis utilized 63 comprehensive soil profiles and 12 environmental predictors encompassing climatic, topographic, biological, and soil property variables. The model’s predictive accuracy was assessed using 10-fold cross-validation with four evaluation metrics: MAE, RMSE, R², and LCCC, ensuring comprehensive performance evaluation. Validation results demonstrated the model’s robust predictive capability across all soil layers, achieving high accuracy with minimized MAE and RMSE values while maintaining elevated R² and LCCC scores. Three-dimensional spatial projections revealed distinct SOC distribution patterns, with higher stocks concentrated in central regions and lower stocks prevalent in northern areas. Under simulated warming conditions (1.5 °C, 2 °C, and 4 °C increases), both topsoil (0–30 cm) and deep-layer (100 cm) SOC stocks exhibited consistent declining trends, with the most pronounced reductions observed under the 4 °C warming scenario. Additionally, the study identified mean annual temperature (MAT) and normalized difference vegetation index (NDVI) as dominant environmental drivers controlling three-dimensional SOC spatial variability. These findings underscore the importance of depth-resolved SOC stock assessments and suggest that precise three-dimensional mapping of SOC distribution under various climate change projections can inform more effective land management strategies, ultimately enhancing regional soil carbon storage capacity in forest ecosystems. Full article

As global climate change intensifies and the transition to clean energy accelerates, lithium-ion batteries—critical components of electric vehicles—are becoming increasingly vital in international trade networks. This study investigates the structural evolution and determinants of the electric vehicle lithium-ion battery trade network among European Union (EU) member states from 2012 to 2023, employing social network analysis and the multiple regression quadratic assignment procedure method. The findings demonstrate the transformation of the network from a centralized and loosely connected structure, with Germany as the dominant hub, to a more interconnected and decentralized system in which Poland and Hungary emerge as the leading players. Key network metrics, such as the density, clustering coefficients, and average path lengths, reveal increased regional trade connectivity and enhanced supply chain efficiency. The analysis identifies geographic and economic proximity, logistics performance, labor cost differentials, energy resource availability, and venture capital investment as significant drivers of trade flows, highlighting the interaction among spatial, economic, and infrastructural factors in shaping the network. Based on these findings, this study underscores the need for targeted policy measures to support Central and Eastern European countries, including investment in logistics infrastructure, technological innovation, and regional cooperation initiatives, to strengthen their integration into the supply chain and bolster their export capacity. Furthermore, fostering balanced inter-regional collaborations is essential in building a resilient trade network. Continued investment in transportation infrastructure and innovation is recommended to sustain the EU’s competitive advantage in the global electric vehicle lithium-ion battery supply chain. Full article

The relationship between ecosystem services (ESs) and human well-being (HWB) is a central issue of sustainable development. However, current research often relies on qualitative frameworks or indicator-based assessments, limiting a comprehensive understanding of the relationship between natural environment and human acquisition, which still needs to be strengthened. As an element transferred in the natural–society coupling system, carbon can assist in characterizing the dynamic interactions within coupled human–natural systems. Carbon, as a fundamental element transferred across ecological and social spheres, offers a powerful lens to characterize these linkages. This study develops and applies a novel analytical framework that integrates carbon flow as a unifying metric to quantitatively assess the spatiotemporal dynamics of the land use and land cover change (LUCC)–ESs–HWB nexus in Guizhou Province, China, from 2000 to 2020. The results show that: (1) Ecosystem services in Guizhou showed distinct trends from 2000 to 2020: supporting and regulating services declined and then recovered, and provisioning services steadily increased, while cultural services remained stable but varied across cities. (2) Human well-being generally improved over time, with health remaining stable and the HSI rising across most cities, although security levels fluctuated and remained low in some areas. (3) The contribution of ecosystem services to human well-being peaked in 2010–2015, followed by declines in central and northern regions, while southern and western areas maintained or improved their levels. (4) Supporting and regulating services were positively correlated with HWB security, while cultural services showed mixed effects, with strong synergies between culture and health in cities like Liupanshui and Qiandongnan. Overall, this study quantified the coupled dynamics between ecosystem services and human well-being through a carbon flow framework, which not only offers a unified metric for cross-dimensional analysis but also reduces subjective bias in evaluation. This integrated approach provides critical insights for crafting spatially explicit land management policies in Guizhou and offers a replicable methodology for exploring sustainable development pathways in other ecologically fragile karst regions worldwide. Compared with conventional ecosystem service frameworks, the carbon flow approach provides a process-based, dynamic mediator that quantifies biogeochemical linkages in LUCC–ESs–HWB systems, which is particularly important in fragile karst regions. However, we acknowledge that further empirical comparison with traditional ESs metrics could strengthen the framework’s generalizability. Full article

The rapid growth in large-scale distributed video conferencing, remote education, and real-time broadcasting poses significant challenges to traditional centralized streaming systems, particularly regarding scalability, cost, and reliability under high concurrency. Centralized approaches often encounter bottlenecks, increased bandwidth expenses, and diminished fault tolerance. This paper proposes a novel decentralized real-time broadcasting system employing a peer-to-peer (P2P) chain topology based on IPv6 networking and the Secure Reliable Transport (SRT) protocol. By exploiting the global addressing capability of IPv6, our solution simplifies direct node interconnections, effectively eliminating complexities associated with Network Address Translation (NAT). Furthermore, we introduce an innovative chain-relay transmission method combined with distributed node management strategies, substantially reducing reliance on central servers and minimizing deployment complexity. Leveraging SRT’s low-latency UDP transmission, packet retransmission, congestion control, and AES-128/256 encryption, the proposed system ensures robust security and high video stream quality across wide-area networks. Additionally, a WebSocket-based real-time fault detection algorithm coupled with a rapid fallback self-healing mechanism is developed, enabling millisecond-level fault detection and swift restoration of disrupted links. Extensive performance evaluations using Video Multi-Resolution Fidelity (VMRF) metrics across geographically diverse and heterogeneous environments confirm significant performance gains. Specifically, our approach achieves substantial improvements in latency, video quality stability, and fault tolerance over existing P2P methods, along with over tenfold enhancements in frame rates compared with conventional RTMP-based solutions, thereby demonstrating its efficacy, scalability, and cost-effectiveness for real-time video streaming applications. Full article

Background/Objectives: The role of body composition in sports performance has been widely studied, particularly in soccer. Understanding how anthropometric characteristics impact movement efficiency and neuromuscular performance is crucial for optimizing player performance. This study examined the relationship between body composition and locomotor performance in elite soccer players. Methods: Thirty-six male soccer players from the Mexican National Team participated in the study, where body composition was assessed using surface anthropometry. Players underwent tests to measure countermovement jump (CMJ) performance, sprinting speed, maximum acceleration, and distance covered during two games of the CONCACAF Nations League quarterfinals. Correlation matrices were created to identify the most significant associations, followed by generalized linear models (GLMs) to associate body composition variables with performance metrics. Results: Anthropometric profile tables were created by playing position. Higher body fat percentage (%BF) was associated with lower performance. Specifically, higher %BF was associated with slower sprint speed (B = −0.74 m/s, p < 0.001) and shorter distance covered (B = −4.86 m/min, p < 0.001). Conversely, greater muscularity, reflected by corrected girth values for the thigh and calf, was associated with improved CMJ performance. Thigh corrected girth was positively associated with concentric mean force (B = 48.85 N, p < 0.001), and calf corrected girth was positively associated with peak power (B = 240.50 W, p < 0.001). These findings underscore the importance of low body fat and high lean mass for efficient movement. Conclusions: The results highlight the critical role of body composition in enhancing soccer performance, particularly for explosive movements like jumps, sprints, and accelerations. This study suggests that monitoring and optimizing body composition should be a central focus of nutrition, training, and conditioning strategies, adapted to the specific positional demands of professional soccer. Full article

In the era of homogenized competition, brand knowledge has become a critical factor that influences consumer purchasing decisions. However, traditional single-layer network models fail to capture the multi-dimensional semantic relationships embedded in brand-related textual data. To address this gap, this study proposes a BKMN framework integrating TF-IDF and TextRank algorithms for comprehensive brand knowledge discovery. By analyzing 19,875 consumer reviews of a mobile phone brand from JD website, we constructed a tri-layer network comprising TF-IDF-derived keywords, TextRank-derived keywords, and their overlapping nodes. The model incorporates co-occurrence matrices and centrality metrics (degree, closeness, betweenness, eigenvector) to identify semantic hubs and interlayer associations. The results reveal that consumers prioritize attributes such as “camera performance”, “operational speed”, “screen quality”, and “battery life”. Notably, the overlap layer exhibits the highest node centrality, indicating convergent consumer focus across algorithms. The network demonstrates small-world characteristics (average path length = 1.627) with strong clustering (average clustering coefficient = 0.848), reflecting cohesive consumer discourse around key features. Meanwhile, this study proposes the Mul-LSTM model for sentiment analysis of reviews, achieving a 93% sentiment classification accuracy, revealing that consumers have a higher proportion of positive attitudes towards the brand’s cell phones, which provides a quantitative basis for enterprises to understand users’ emotional tendencies and optimize brand word-of-mouth management. This research advances brand knowledge modeling by synergizing heterogeneous algorithms and multilayer network analysis. Its practical implications include enabling enterprises to pinpoint competitive differentiators and optimize marketing strategies. Future work could extend the framework to incorporate sentiment dynamics and cross-domain applications in smart home or cosmetic industries. Full article

Renewable energy stands as a pivotal solution to environmental concerns, prompting substantial research and development endeavors to promote its adoption and enhance energy efficiency. Despite the recognized environmental superiority of renewable energy systems, there is a lack of globally standardized indicators specifically focused on renewable energy efficiency. This study aims to develop and apply a non-parametric data envelopment analysis (DEA) indicator, termed the Renewable Energy Indicator (REI), to measure environmental performance at the national level and to identify differences in renewable energy efficiency across countries grouped by development status and income level. The REI incorporates new factors such as agricultural methane emissions (thousand metric tons of ${\mathrm{CO}}_2$ equivalent), PM2.5 air pollution exposure (µg/m³), and aspects related to electricity, including consumption (as % of total final energy consumption), production from renewable sources, excluding hydroelectric (kWh), and accessibility in rural and urban areas (% of population with access), aligning with the emerging paradigm outlined by the United Nations. By segmenting the REI into global, developmental, and income group classifications, this study conducts the Mann–Whitney U test and the Kruskal–Wallis H tests to identify variations in renewable energy efficiency among different country groups. Our findings reveal top-performing countries globally, highlighting both developed (e.g., Sweden) and developing nations (e.g., Costa Rica, Sri Lanka). Central and North European countries demonstrate high efficiency, while those facing political and economic instability perform poorly. Agricultural-dependent nations like Australia and Argentina exhibit lower REI due to significant methane emissions. Disparities between developed and developing markets underscore the importance of understanding distinct socio-economic dynamics for effective policy formulation. Comparative analysis across income groups informs specific strategies tailored to each category. Full article

The joint optimization of fleet size and task allocation presents a critical challenge in deploying Unmanned Aerial Vehicles (UAVs) for time-sensitive missions such as emergency logistics. Conventional approaches often rely on pre-determined fleet sizes or computationally intensive centralized optimizers, which can lead to suboptimal performance. To address this gap, this paper proposes a novel two-stage hierarchical framework that integrates the Grey Wolf Optimizer (GWO) with the Consensus-Based Bundle Algorithm (CBBA). At the strategic level, the GWO determines the optimal number of UAVs by minimizing a comprehensive cost function that balances mission efficiency and operational costs. Subsequently, at the tactical level, the CBBA performs decentralized, real-time task allocation for the optimally sized fleet. We validated our GWO-CBBA framework through extensive simulations against three benchmarks: a standard CBBA with a fixed fleet, a centralized Particle Swarm Optimization (PSO) approach, and a Greedy Heuristic algorithm. The results are compelling: our framework demonstrates superior performance across all key metrics, reducing the overall scheduling cost by 13.2–36.5%, minimizing UAV mileage cost and significantly decreasing total task waiting time. This work provides a robust and efficient solution that effectively balances operational costs with service quality for dynamic multi-UAV scheduling problems. Full article

Monsoon evergreen broad-leaved forests (MEBFs) represent one of the most species-rich and structurally complex vegetation types, and one of the most widely distributed forests in Yunnan Province, Southwest China. However, they have yet to undergo a comprehensive analysis on their community diversity, spatial differentiation patterns, and underlying drivers across Yunnan. Based on extensive field surveys during 2021–2024 with 548 MEBF plots, this study employed the Unweighted Pair Group Method for forest community classification and Non-metric Multidimensional Scaling for ordination and interpretation of community–environment association. A total of 3517 vascular plant species were recorded in the plots, including 1137 tree species, 1161 shrubs, and 1219 herbs. Numerical classification divided the plots into 3 alliance groups and 24 alliances: (1) Castanopsis–Schima (Lithocarpus) Forest Alliance Group (16 alliances), predominantly distributed west of 102°E in central-south and southwest Yunnan; (2) Castanopsis–Machilus (Beilschmiedia) Forest Alliance Group (6 alliances), concentrated east of 101°E in southeast Yunnan with limited latitudinal range; (3) Castanopsis–Camellia Forest Alliance Group (2 alliances), restricted to higher-elevation mountainous areas within 103–104° E and 22.5–23° N. Climatic variation accounted for 81.1% of the species compositional variation among alliance groups, with contributions of 83.5%, 57.6%, and 62.1% to alliance-level differentiation within alliance groups 1, 2, and 3, respectively. Precipitation days in the driest quarter (PDDQ) and precipitation seasonality (PS) emerged as the strongest predictors of community differentiation at both alliance group and alliance levels. Topography and soil features significantly influenced alliance differentiation in Groups 2 and 3. Collectively, the interaction between the monsoon climate and topography dominate the spatial differentiation of MEBF communities in Yunnan. Full article