Search Results (13,072)

The frequent occurrence of fires has prompted China to accelerate the development of community fire prevention and emergency management systems. Language, serving both communicative and affective functions by facilitating the flow of information and fostering mutual understanding, runs through the entire process of community fire emergency management. In response to the early-stage nature of this field and the lack of a systematic framework, this study constructs a dynamic capability evaluation system for urban community fire-related emergency language services (FELS) by integrating multi-source and heterogeneous data. First, by adopting a hybrid approach combining dynamic capability theory and text mining, a three-level indicator system is established. Second, based on domain knowledge, quantitative methods and scoring rules are designed for the third-level qualitative indicators to provide standardized input for the model. Third, a weighting and integration framework is developed that simultaneously considers the internal mechanism characteristics and statistical properties of indicators. Specifically, a knowledge-driven weighting approach combining FAHP and fuzzy DEMATEL is employed to characterize indicator importance and interrelationships, while the CRITIC method is used to extract Data-Driven weights based on data dispersion and information content. These knowledge-driven and Data-Driven weights are then integrated through a multi-feature fusion weighting approach. Finally, a linear weighting model is applied to combine the normalized indicator values with the integrated weights, enabling a systematic evaluation of the dynamic capabilities of community FELS. To validate the proposed framework,, application tests were conducted in four representative types of urban communities, including internationally developed, aging and vulnerable, newly developed, and economically diverse communities, using fire emergency scenarios as the entry point. The external validity and internal robustness of the proposed model were verified through these tests. The results indicate that the evaluation system provides accurate, objective, and adaptive assessments of dynamic capabilities in FELS across different community contexts, offering a governance-oriented quantitative tool to support grassroots fire prevention and to enhance community resilience. Full article

The current paper determined water resource classes and Resource Quality Objectives (RQOs) for significant water resources in the Mzimvubu to Tsitsikamma Water Management Area 7 (WMA7) to facilitate sustainable use of the water resources while maintaining ecological integrity. A novel stepwise quantitative and qualitative method was developed to ensure water resource protection in the study area. The methodological approach is proposed as a model framework that could be adopted as guideline and transferable to other catchments in the implementation of Resource Directed Measures (RDMs). The method used water quality and quality components of water resources to determine the classes and RQOs. The study’s major findings were that nineteen Integrated Units of Analysis (IUAs) were delineated, and ninety-five Resource Units were identified and prioritized for both surface and groundwater. Driving water quality variables (nutrients, electrical conductivity, and Escherichia coli) were observed and primary water users (irrigation, settlements, and wastewater treatment works) were identified per Integrated Units of Analysis. Five water resource scenarios were developed and evaluated to capture a likely water resource condition for the present and future. The scenario analysis showed impact is expected under any of the operational scenarios assessed at selected reaches. The water resource classes were determined, with eleven IUAs classified as Class lll, seven IUAs as Class ll, and one IUA as Class l. Water quality and quantity RQOs were set to ensure that both river and groundwater resources are compliant and protected. Therefore, the study recommends that this methodological framework, where classes and RQOs were determined, needs to be implemented and tested. Full article

Accelerating the integration of wind and solar power is essential for achieving China’s “Dual Carbon” goals, but their inherent intermittency poses significant challenges for grid stability and renewable energy utilization. This study addresses these challenges by proposing a comprehensive economic benefit optimization model for a combined gas–wind–solar power generation system under a natural gas peaking mode. The model systematically incorporates multidimensional economic indicators—including generation revenue, green certificate revenue, curtailment losses, and carbon emission costs—while accounting for operational constraints and the fluctuating nature of renewables. Simulation results show that the hybrid system achieves a total economic benefit of 9.97 million yuan, with operating costs at 20% of income and curtailment plus carbon penalty costs below 2%. Compared to single-source generation, the hybrid approach reduces wind and solar curtailment by over 90%, and maintains high channel utilization. Sensitivity analysis reveals that lower gas prices and higher green certificate prices significantly enhance both renewable energy integration and economic returns, while balanced output scenarios maximize system benefits. This research provides a quantitative assessment of the economic and environmental outcomes of a gas–wind–solar complementary system, offering practical insights to maximize renewable energy utilization and support China’s low-carbon energy transition. Full article

Wave energy technology needs to be reliable, efficient, and environmentally sustainable. Therefore, life cycle assessment (LCA) is a critical tool in the design of marine renewable energy devices. However, LCA studies of floating type wave cycloidal rotors remain limited. This study builds on previous work by assessing the cradle-to-grave environmental impacts of a cycloidal rotor wave farm, incorporating updated material inventories, site-dependent energy production, and lifetime extension scenarios. The farm with the steel cyclorotor configuration exhibits a carbon intensity of 21.4 g ${\mathrm{CO}}_2$ eq/kWh and an energy intensity of 344 kJ/kWh, which makes it a competitive technology compared to other wave energy converters. Alternative materials, such as aluminium and carbon fibre, yield mass reductions but incur higher embodied emissions. Site deployment strongly influences performance, with global warming potential reduced by up to 50% in high-power-density sites, while extending the operational lifetime from 25 to 30 years further reduces the impact by 17%. Overall, the results highlight the competitive environmental performance of floating wave cycloidal rotors and emphasize the importance of material selection, site selection, and lifetime extension strategies in reducing life cycle impacts. Full article

This study was conducted to address the demand for interpretable intelligent recognition of fruit tree diseases in smart horticultural environments. A KAD-Former framework integrating an agricultural knowledge graph with a visual Transformer was proposed and systematically validated through extensive cross-regional, multi-variety, and multi-disease experiments. The primary objective of this work was to overcome the limitations of conventional deep models, including insufficient interpretability, unstable recognition of weak disease features, and poor cross-regional generalization. In the experimental evaluation, the model achieved significant advantages across multiple representative tasks: in the overall performance comparison, KAD-Former reached an accuracy of $0.946$, an F1-score of $0.933$, and a mAP of $0.938$, outperforming classical models such as ResNet50, EfficientNet, and Swin-T. In the cross-regional generalization assessment, a DGS of $0.933$ was obtained, notably surpassing competing models. In terms of explainability consistency, a Consistency@5 score of $0.826$ indicated strong alignment between the model’s attention regions and expert annotations. The ablation experiments further demonstrated that the three core modules—AKG (agricultural knowledge graph), SAM (semantic alignment module), and KGA (knowledge-guided attention)—each contributed substantially to final performance, with the complete model exhibiting the best results. These findings collectively demonstrate the comprehensive advantages of KAD-Former in disease classification, symptom localization, model interpretability, and cross-domain transfer. The proposed method not only achieved state-of-the-art performance in pure visual tasks but also advanced knowledge-enhanced and interpretable reasoning by emulating the diagnostic logic employed by agricultural experts in real orchard scenarios. Through the integration of the agricultural knowledge graph, semantic alignment, and knowledge-guided attention, the model maintained stable performance under challenging conditions such as complex illumination, background noise, and weak lesion features, while exhibiting strong robustness in cross-region and cross-variety transfer tests. Furthermore, the experimental results indicated that the approach enhanced fine-grained recognition capabilities for various fruit tree diseases, including apple ring rot, brown spot, powdery mildew, and downy mildew. Full article

On-demand public transport systems are increasingly adopted to improve service flexibility, reduce operating costs, and meet emerging mobility needs. Evaluating their performance under realistic demand and operational conditions, however, remains a complex challenge. This study presents a hybrid simulation framework that integrates deep learning-based demand forecasting, behavioural survey data, and agent-based simulation to assess system performance. A BiLSTM neural network trained on real-world smartcard data forecasts short-term passenger demand, which is embedded into an agent-based model simulating vehicle dispatch, routing, and passenger interactions. The framework is applied to a case study in Melbourne, Australia, comparing a baseline fixed-route service with two on-demand scenarios. Results show that the most flexible scenario reduces the average passenger trip time by 32%, decreases the average wait time by 34%, increases vehicle occupancy from 12.1 to 18.6 passengers per vehicle, lowers emissions per passenger trip by 72%, and cuts the service cost per trip from AUD 6.82 to AUD 4.73. These findings demonstrate the potential of hybrid on-demand services to improve operational efficiency, passenger experience, and environmental outcomes. The study presents a novel, integrated methodology for scenario-based evaluation of on-demand public transportation using real-world transportation data. Full article

The European Union’s energy transition is based on three fundamental pillars, the realisation of which is intended to achieve climate neutrality by 2050. These pillars comprise the decarbonization of the economy, the development of renewable energy sources (RES), and the improvement of energy efficiency. The prevailing decarbonization trend involves a systematic reduction in the use of fossil fuels across the economy and their replacement with energy derived from low-emission and renewable sources. These objectives pose a significant challenge, particularly for countries such as Poland, where electricity generation remains predominantly reliant on hard coal and lignite. In recent years, a substantial reduction in CO₂ emissions has been observed in the energy sector, primarily due to the increasing share of renewables in the electricity generation mix. The main energy companies, most of which are majority-owned by the State Treasury, have developed specific strategies to meet these targets. This article analyses the strategic documents of domestic energy companies together with other publicly available sources. Based on these documents, projections have been developed regarding the decommissioning of individual generating units in public power plants and combined heat and power facilities fuelled by hard coal and lignite. Scenario-based analyses were then conducted, drawing on these projections and assumptions, to assess the potential scale of CO₂ emission reductions from the domestic energy sector through to 2050. Full article

This paper presents new aggregation operators for $p,q,r$-fractional fuzzy sets based on the Frank t-norm and t-conorm. We introduce the $p,q,r$-fractional fuzzy Frank weighted average and $p,q,r$-fractional fuzzy Frank weighted geometric operators and discuss their algebraic properties, including closure, boundedness, idempotency, and monotonicity. Based on new operations, we develop a multi-criteria group decision-making framework that integrates the evaluations of multiple experts via the proposed Frank operators and ranks the alternatives under $p,q,r$-fractional fuzzy information. The model is applied to a cryptocurrency stability assessment problem, where four coins are evaluated with respect to six criteria. The results show that both aggregation operators yield consistent rankings with good discriminatory power among the alternatives. A sensitivity analysis is conducted to check the stability of the model under parameter variations. A comparative study further demonstrates the compatibility and advantages of the proposed method over several existing decision-making approaches. The proposed framework is well suited to decision-making scenarios in which multiple experts’ opinions must be integrated within a complex fuzzy information environment. Full article

Background: Passing in lacrosse is a fundamental skill essential for both offense and defense, directly influencing game flow. Although the speed–accuracy trade-off is well recognized in motor control, its features in lacrosse passing—particularly regarding directional aspects and skill differences—remain unclear. This study quantified the relationship between pass speed, accuracy, bias, and consistency and examined directional effects and skill-level differences. Methods: Twenty-two female university players (skilled: n = 9; unskilled: n = 13) executed overhand passes to a 5 cm × 5 cm target from 11 m under three effort conditions: warm-up, game intensity, and full effort. Ball speed was derived from lateral video, and landing coordinates from posterior footage. Accuracy, bias, and consistency were assessed using radial error (RE), centroid error (CE), absolute CE (|CE|), and bivariate variable error (BVE). Directional patterns were analyzed through lateral and vertical components and the 95% confidence intervals of the major and minor axes of an error ellipse. A two-way analysis of variance was performed with condition as the within-subject factor and skill level as the between-subject factor. Results: Ball speed increased significantly across conditions. RE, |CE|, and BVE increased with speed, showing directional dependence: variability expanded mainly along the major axis, while the minor axis remained stable. Skilled players showed smaller RE and BVE, with differences most evident vertically and along the major axis. CE direction stayed consistent, indicating that reduced accuracy stemmed from greater bias magnitude and lower consistency rather than shifts in the mean landing point. Conclusions: Findings confirm a speed–accuracy trade-off in lacrosse passing, characterized by directional specificity and skill-related effects. Combining RE, CE, BVE, and ellipse-axis analyses clarified error structure, showing variability concentrated along the movement axis. These results support training focused on vertical control and timing and highlight the value of directional metrics for assessing lacrosse performance. Future research should include male athletes, advanced levels, and in-game scenarios to extend generalizability. Full article

The transition to a low-carbon economy is profoundly reshaping European labour markets, creating both opportunities for sustainable employment and challenges for regions reliant on carbon-intensive sectors. Assessing how prepared EU Member States are for this shift remains difficult due to the lack of unified evaluation tools. This study introduces the Green Labour Market Readiness Index (GLMRI)—a composite measure assessing the adaptability of national labour markets to the energy-driven green transformation in nine EU countries: Germany, France, Sweden, Spain, Italy, Greece, Poland, Romania, and the Czech Republic. The index integrates five dimensions—education and skills, investment and infrastructure, policy and institutional quality, labour market structure, and innovation—based on harmonized data from 2010 to 2024. Panel econometric models (Fixed and Random Effects), combined with Hausman tests, are used to examine how structurally independent external energy-system characteristics, institutional capacity, and macro-structural labour-market conditions are associated with observed variation in labour-market readiness, as captured by the GLMRI composite outcome. Machine learning algorithms (Random Forest, XGBoost, LSTM) are employed to forecast readiness trajectories until 2040 under alternative policy scenarios. Results reveal persistent asymmetries between Northwestern and Southeastern Europe, showing that successful energy transition is closely associated not only with investment and innovation but also with human capital and governance quality. These associations are interpreted as diagnostic rather than causal, highlighting how external structural conditions shape the translation of energy-transition pressures into differentiated labour-market outcomes. The GLMRI provides a methodological and policy-relevant framework, helping decision-makers prioritize resources and design measures that make Europe’s energy transition sustainable, inclusive, and equitable. Full article

Rootstock selection plays a pivotal role in determining the ecophysiological performance, growth dynamics, and hydraulic functioning of grafted citrus plants. This study evaluated three citrus rootstocks—Trifoliate Orange (TO), Swingle Citrumelo (SC), and Flying Dragon (FD)—grafted with Citrus clementina cv. SRA 63 (CLM), with the aim of elucidating how the rootstock genotype influences morphological traits, dry matter allocation, hydraulic conductance, and xylem anatomical features. Plants were monitored over two years under controlled agronomic conditions, and biometric, physiological, hydraulic, and anatomical traits were assessed. The results revealed distinct rootstock-dependent patterns. CLM/TO and CLM/SC exhibited greater vegetative vigor, higher total biomass, more extensive absorbing root systems, and larger conductive xylem areas, resulting in superior theoretical hydraulic flow. In contrast, CLM/FD demonstrated reduced growth, a smaller trunk diameter, lower biomass accumulation, and elevated hydraulic resistance in both root and graft union sectors, consistent with its known dwarfing behavior. Despite its lower hydraulic efficiency, FD promoted the highest stomatal conductance, suggesting a distinct water use strategy. Overall, the findings demonstrate that the rootstock genotype markedly influences the hydraulic architecture and growth partitioning of grafted Clementine plants. These insights contribute to our understanding of scion–rootstock interactions and support more informed selections of rootstocks in citrus orchards under diverse environmental and management scenarios. Full article

The nitrogen and phosphorus pollution in water is highly related to the land use pattern in the watershed. The impacts of the land use patterns on total nitrogen (TN) and total phosphorus (TP) exports in an agricultural watershed of the Lijiang River Basin were studied using the Soil and Water Assessment Tool (SWAT). The SWAT model performed well in simulating runoff, TN, and TP exports, and the R² values were all above 0.67. The model simulation results showed that the total nitrogen (TN) and total phosphorus (TP) outputs in the wet season were 13.97 tons and 1.37 tons, respectively, approximately three times those in the dry season, highlighting that outputs of TN and TP predominantly occurred in the wet season in the basin. The correlation analysis showed that the forest land and water in the sub-basin had negative impacts on TN and TP exports, while the orchard, cultivated land, and building land had a positive correlation with TN and TP exports. Then, scenario simulations were conducted using the calibrated and validated SWAT model. A total of 55 scenarios were set up, involving five land use types with five conversion ratios (10%, 20%, 30%, 40%, and 50%), to analyze the impacts of dynamic land use changes on TN and TP exports. The results showed that the TN and TP exports significantly increased under the conversion of the other land use types into building land, cultivated land, and orchards, and the increasing rate decreased in order, while the TN and TP exports declined with the rising forest and water body area. Generally, the changing rates of TN exports under land use conversion were higher than those of TP exports, except for the orchard conversion. This study revealed that the reasonable planning of land use could alleviate nitrogen and phosphorus pollution, which was helpful for aquatic ecosystem restoration. It provided scientific references for land use planning, aquatic ecosystem restoration, and the achievement of sustainable development goals related to water environment protection in similar karst basins. Full article

Arboreal ants occupy a thermally dynamic environment, yet the mechanisms integrating nest architecture and worker behavior to maintain colony homeostasis remain understudied. We investigated the interplay among circadian rhythm, nest homeostasis, and worker morphology in Azteca chartifex spiriti, a Neotropical arboreal species that builds large polydomous nests suspended in trees. In ten colonies, we measured internal moisture and temperature gradients in the main nest, which houses most individuals, including the reproductive female, immatures, and numerous workers. In six colonies, we assessed the polymorphism of foraging workers over a 24 h cycle in relation to external temperature variation. The results show integrated thermoregulatory mechanisms that combine passive strategies, derived from nest architecture and moisture gradients from the suspension base to the lower extremity, with active strategies linked to foraging patterns and worker polymorphism. Internal temperature (27.8 ± 2.41 °C) remained buffered relative to external fluctuations, and moisture was significantly higher at the nest’s lower extremity (p < 0.001). Worker size displayed a bimodal distribution during the day that shifted to a unimodal pattern at night, indicating behavioral adjustments to thermal and operational demands. These findings demonstrate that the interaction between physical structure and worker behavior maintains colony homeostasis, providing essential insights into how dominant canopy ants may cope with future climate change scenarios. Full article

Floodwater depth estimation is essential for disaster response and infrastructure planning yet remains challenging in urban areas with limited gage and hydrological data. This study presents a deep learning-based framework grounded in the hydrostatic equilibrium principle to estimate flood depth using a remote sensing approach. A series of ResNet architectures were trained and evaluated under two different scenarios: (a) a baseline model input using LiDAR-derived DTM and flood extent, and (b) an enhanced model incorporating additional terrain features such as slope, curvature, and Topographic Wetness Index (TWI). The results demonstrate that ResNet18 outperformed deeper models, achieving an RMSE of 0.71 ft, Huber Loss of 0.28 ft, MAE of 0.23 ft, SSIM of approximately 99% and R-Squared of approximately 94% under the enhanced scenario. Inclusion of terrain predictors led to significant improvements in prediction accuracy and spatial coherence. They improved Huber Loss by 28%, RMSE by 13%, and MAE by 21%. However, when applied to an unseen peri-urban catchment, model performance declined (RMSE = 1.95 ft), mainly due to limited and temporally misaligned ground truth data, and differences in spatial characteristics. Despite these limitations, ResNet18 generalizes well, mapping flood depth in unseen catchments, and demonstrates the potential for rapid assessments in data-scarce regions. Full article

With the growing scale of university examinations, teaching buildings are increasingly converted into temporary examination venues, raising concerns regarding crowding conditions and evacuation safety. This study proposes a comprehensive evaluation framework for assessing evacuation safety in temporary examination-room settings using Grey Relational Analysis (GRA). A multi-level indicator system covering structural conditions, evacuation facilities, organizational management, personnel characteristics, and emergency preparedness is established. Through data standardization, weight assignment, and computation of grey relational coefficients, the framework quantitatively identifies the key factors affecting evacuation performance. A case study of a university teaching building yields an overall grey relational grade of 0.5956, corresponding to a “Moderate to Relatively Safe’’ level. Although most safety measures meet baseline requirements, several localized bottlenecks remain. In particular, insufficient corridor width, long evacuation distances, and inadequate emergency lighting reduce overall performance. Scenario-based improvement analyses indicate that targeted measures—such as widening critical corridors, optimizing evacuation path layouts, and enhancing emergency lighting—can substantially raise the grey relational grade and mitigate evacuation risks. The proposed evaluation model offers a practical and scalable tool for assessing and improving evacuation safety in teaching buildings repurposed as temporary examination venues. Full article