Search Results (8,237)

Accurately identifying critical nodes in complex networks and applying targeted protection strategies significantly enhances network security. Traditional importance metrics rely on a single topological feature and cannot fully capture node influence. Existing multi-attribute fusion methods integrate multiple structural sources but typically use fixed weights or predefined rules, failing to adaptively adjust attribute contributions based on local and global network characteristics, which limits their generalization across diverse networks. To address this, we propose the CRITIC-based Objective Weighting and Multi-Metric Fusion Method (COWMF). COWMF first builds a Graph Attention Network with Virtual Global–Local Integration (GAT-VGL), taking four low-complexity topological metrics, degree centrality (DC), H-index, degree and neighborhood information centrality (DNC), and k-shell, as input. Through a learnable attention mechanism, GAT-VGL adaptively aggregates multi-hop neighborhood information and explicitly incorporates global structural information via a virtual node to achieve whole-graph topological awareness, generating a global influence score with good discriminative power and high computational efficiency. This score is then integrated with DC and DNC into an improved CRITIC-based objective weighting fusion scheme, enabling adaptive synergy among local connectivity, semi-local radiation, and global structure. Experiments on six real-world networks of varying types and scales show that COWMF demonstrates relatively stable and competitive performance in both simulated attack and susceptible-infected-recovered (SIR) spreading simulations, two complementary experiments, demonstrating satisfactory disruptive capability and propagation influence. Its importance scores exhibit high monotonicity across all networks, with good discriminative power. Full article

Maritime transportation, handling over 80% of global trade, is critical to the world economy. Automatic Identification System (AIS) data provides extensive static and dynamic information of vessels, enabling trajectory reconstruction and vessel behavior analysis. Recently, trajectory clustering has become a key method for analyzing maritime traffic, offering valuable insights to improve traffic management and operational efficiency. This research aims to investigate how to effectively cluster ship sub-trajectories derived from AIS data by comparing two machine learning clustering algorithms, Density-based spatial clustering of applications with noise (DBSCAN) and spectral clustering, with a focus on improving data quality, extracting key dynamic features, and evaluating the effect of dimensionality reduction on clustering performance. Clustering sub-trajectories can help reveal localized navigation patterns and movement behaviors. The study implemented the proposed methods for tankers and cargo ships (with AIS data from 2022) in a Y-shaped channel in the Sabine-Neches Waterway (SNWW) in Southeast Texas, where the busiest docks are located. Finally, clustering performance was evaluated with the silhouette coefficient (SC), Davies–Bouldin Index (DBI), and Joint Performance Index (JPI), respectively. Experimental results show that DBSCAN effectively identifies dense, overlapping trajectory clusters and labels noise, while the spectral clustering algorithm detects subtle behavioral differences but struggles with less cohesive clusters, and does not explicitly handle noise. Full article

Software code testing remains a critically important but labour-intensive process in software quality assurance. Existing research evaluates large language model (LLM)-based unit test generation using various quality metrics, such as correctness, coverage, mutation score, and test code smells. However, these single metrics do not reflect the trustworthiness of the unit test generation process. Therefore, this research formalises the trustworthiness of LLM-based unit test generation as a multidimensional index comprising reliability, hallucination resistance, maintainability, functional completeness, and human-reference alignment. In this research, we investigate the effect of prompt engineering strategies on the trustworthiness of LLM-generated unit tests and compare them with human-written tests for the same focal methods. Each dimension is fed by a distinct artefact-level measurement and grounded in dependability theory and ISO/IEC 25010:2023. A centralised multi-agent system generates, builds, repairs, and measures the tests, so that all inputs are collected automatically. The index is evaluated on real-world C# focal methods across 18 model × prompt configurations and a paired human-written baseline. The human baseline achieves the highest T-UTG value (0.904), and the best configuration, Combined × Gemini, achieves 0.788. Entropy weighting identifies maintainability and hallucination resistance as the most discriminating dimensions, and a rank-acceptability analysis over the whole weight simplex confirms that this ordering does not depend on the chosen weighting scheme. Full article

Degraded industrial sites in arid and semi-arid regions often suffer from loose surface substrates, weak water-retention capacity, high wind-erosion risk, and poor early vegetation establishment. Combining photovoltaic (PV) deployment with ecological utilization may improve near-surface habitats by shading, reducing wind speed, and regulating soil heat and moisture. This study investigated an ash storage yard of a coal-fired power plant in Ordos, Inner Mongolia, China, by comparing soil temperature, soil moisture, and near-surface wind-speed responses under three representative fixed PV tilt angles of 36°, 43°, and 50°, together with the corresponding early plant-growth suitability. A multi-physics model coupling near-surface airflow, water-vapor transport, and porous-media hydrothermal migration was established. A Gaussian suitability function combined with AHP-CRITIC weighting was used to construct a model-based comprehensive growth index (CGI) from soil temperature and moisture, while short-term field monitoring was used to validate afternoon soil hydrothermal trends. Among the three scenarios, the 36° configuration produced the widest horizontal heat–moisture-affected zone and the highest CGI values for alfalfa and Elymus nutans, reaching 0.7741 and 0.6875, respectively. Relative to the outside reference area, the rear PV zone reduced the near-surface wind speed by 33–40% and increased the plant heights of alfalfa and Elymus nutans by 49.4% and 37.8%, respectively. A first-order PVsyst assessment showed that the 43° configuration achieved the highest specific energy yield of 1814 kWh kWp⁻¹ year⁻¹, whereas the annual grid-connected output at 36° was only 0.59% lower. These findings indicate that the 36° configuration may provide a favorable compromise between early vegetation establishment and photovoltaic electricity generation among the tested scenarios. By linking renewable-energy production with microenvironment regulation and early vegetation establishment, the proposed framework provides a decision basis for the multifunctional and sustainable reuse of degraded industrial land. Nevertheless, the results represent a site-specific, single-season assessment and should not be interpreted as a universal optimum. Full article

Approach channels are affected by sedimentation and scour, resulting in continuous changes in underwater topography. Such processes tend to generate shallow spots and inadequate navigable dimensions, posing safety hazards that undermine both waterway resilience and navigation capacity. To address these issues, this paper proposes a multi-level grid-based spatiotemporal indexing method for bathymetric data, aiming to support resilience-oriented management by improving the effectiveness of bathymetric data management. First, a channel-segment-section partitioning strategy is designed to construct hierarchical progressive grids for the efficient organization of massive bathymetric data. Second, a multi-dimensional spatiotemporal integrated query method is developed to meet diverse analytical and retrieval requirements. Third, a digital depth model (DDM) construction method is introduced that integrates boundary-constrained terrain reconstruction with efficient mesh optimization, enabling underwater terrain representation that adapts to the elongated and irregular morphology of approach channels. The contribution of this work lies not in proposing new individual algorithms but in the tailored integration of these techniques to address the specific challenges of approach-channel bathymetric data. Experimental results demonstrate that the proposed method achieves high construction efficiency across different storage and query schemes. The method enhances the retrieval and analytical capabilities of bathymetric data in representative application scenarios, such as shallow spot identification, critical section analysis, dredging analysis, and erosion–deposition evolution. Consequently, these improvements provide technical support for resilience-oriented channel management and ensure navigational safety. Full article

Provincial cold-chain investment decisions must reconcile food-loss prevention, digital visibility, logistics capability and the environmental burden of freight-intensive growth. This study develops a sustainability-oriented readiness–intensity framework for 31 provincial-level regions in mainland China. The baseline model uses 14 auditable public-data criteria and combines Criteria Importance Through Intercriteria Correlation (CRITIC) with the standard Combined Compromise Solution (CoCoSo) algorithm. Because the observations combine 2024 statistics, a 2023 digital-finance index and the cumulative 2020–2025 cold-chain-base list, the design is described as an asynchronous cross-sectional snapshot rather than a single-year panel. Municipal sewage and green-space variables are interpreted as regional enabling capacity, not direct cold-chain environmental performance; road freight turnover relative to gross domestic product is treated as a cost-type freight-intensity transition-pressure proxy. A separate diagnostic replaces the earlier inverse-size term with logistics residuals conditional on agri-food output. Shandong, Guangdong, Jiangsu, Henan and Zhejiang form the leading demonstration-readiness group. Equal-weight CoCoSo closely matches the CRITIC result (Spearman ρ = 0.996), while TOPSIS and VIKOR retain the broad ordering but expose local method sensitivity. Dropping either digital criterion, removing the three indirect green proxies, winsorizing the normalization range, varying the CoCoSo compromise parameter and substituting 2022 digital data do not alter the leading pattern. Under an assumed 5% indicator-error perturbation, Shandong and Guangdong remain within ranks 1–2, whereas the ordering of several adjacent provinces is less secure. The framework supports sequenced investment packages rather than a deterministic league table and distinguishes demonstration-ready, scale-led, intensity-led and coverage-building contexts. Full article

This study investigates the empirical links between climatic/socio-economic factors and Consumer Price Food Indices (CPFIs) across 47 advanced and emerging economies from 2001 to 2022. Utilizing a balanced panel dataset compiled from the World Bank’s World Development Indicators, FAO databases, and UNDP Human Development Reports, the analysis applies fixed-effects and random-effects panel data models to evaluate the drivers of food price dynamics. The empirical results reveal that socio-economic variables, specifically the Human Development Index (HDI), food price inflation, and agricultural productivity, are consistently associated with food indices, underscoring the critical role of structural development and nominal inflationary pressures. Climatic factors, particularly temperature anomalies, also exert a significant impact: a one-degree Celsius increase in temperature anomalies is associated with a 0.82-unit rise in the food price index level, whereas expansions in forest area and agricultural land are linked to price reductions. These findings indicate that food price dynamics are shaped by the intricate interplay of climate variability and socio-economic conditions. Consequently, policy recommendations emphasize targeted investments in socio-economic development, climate adaptation measures (including support for climate-resilient agriculture), and sustainable land management (forest conservation and optimized land use) to enhance food system resilience and support long-term food security. Full article

Sweet potato (Ipomoea batatas (L.) Lam) is an important global food crop, but its production is threatened by numerous viral pathogens. More than 30 RNA and DNA viruses have been reported worldwide, making rapid and accurate detection essential for disease management, epidemiological surveillance, germplasm exchange, and resistance breeding. Although previous reviews have addressed sweet potato viruses and individual diagnostic methods, a comprehensive synthesis of emerging molecular technologies remains limited. This review addresses that gap by critically integrating recent advances from PCR-based and isothermal assays to high-throughput sequencing, CRISPR-based diagnostics, biosensors, nanotechnology, and artificial intelligence-driven detection platforms. Conventional approaches, including symptom observation, biological indexing, electron microscopy, and ELISA, have contributed to early virus identification but often lack the sensitivity, specificity, and speed needed for modern diagnostics. Molecular and isothermal techniques have substantially improved detection accuracy and enabled rapid identification and field-deployable diagnostics of diverse and mixed infections, while sequencing, CRISPR, biosensors, and AI-based platforms offer greater capacity for detecting novel and emerging viruses. This review discusses the comparative evaluation of molecular technologies for sweet potato virus detection in terms of diagnostic performance, cost-effectiveness, speed, and suitability for both laboratory and field applications, while highlighting future priorities for next-generation virus diagnostics. Integrating portable and high-throughput diagnostic platforms will strengthen virus surveillance, support virus-free planting material production, and promote sustainable sweet potato production worldwide. Full article

Background: Despite the WHO’s recognition of Gaming Disorder in the ICD-11, epidemiological data on its prevalence and neuropsychiatric correlates among Saudi Arabian university students remain limited. Such data are critical for calibrating campus mental health resources and developing evidence-based screening protocols. This study aimed to estimate the prevalence of Internet Gaming Disorder (IGD) in a sample of Saudi university student gamers and evaluate the clinical utility of impulsivity and resilience as case-detection tools. Methods: In a cross-sectional survey of students who engaged in electronic gaming for at least one hour per week (N = 207; 58.9% male), participants completed the IGDS9-SF, CD-RISC-10, and S-UPPS-P scales. Data were analyzed using binary logistic regression and Receiver Operating Characteristic (ROC) curves to identify predictors and compare the discriminative validity of the constructs. Results: Among these student gamers, the overall IGD prevalence was 18.4%, with significantly higher rates in males (24.6%) than females (9.4%; χ²(1) = 7.71, p = 0.006). Significant predictors of IGD included sex, daily gaming hours, higher impulsivity, and lower resilience. ROC analysis revealed that a composite impulsivity-resilience index (AUC = 0.80) outperformed single-construct screening using only impulsivity (AUC = 0.71) or resilience (AUC = 0.68). Cases exhibited markedly higher impulsivity and lower resilience than non-cases. Conclusions: Among Saudi university student gamers, IGD prevalence is clinically significant, particularly among heavier and male gamers. A composite impulsivity-resilience screening index showed superior detection accuracy in this sample and warrants further validation for integration into university mental health assessment protocols. Full article

Ultraviolet (UV) radiation in high-altitude regions critically accelerates asphalt aging by inducing surface oxidation, molecular chain scission, and loss of low-temperature crack resistance. However, systematic comparisons of the macro-rheological and micro-chemical evolution between base asphalt and SBS-modified asphalt under UV aging remain insufficient. In this study, two types of asphalt (virgin and SBS-modified) were subjected to three aging protocols, namely short-term thermal oxidation (RTFOT), long-term thermal oxidation (PAV), and equivalent UV radiation for 13 h, 26 h, and 37 h. Low-temperature rheological properties were evaluated using the bending beam rheometer (BBR), while atomic force microscopy (AFM) and Fourier transform infrared spectroscopy (FTIR) characterized the microstructural and chemical changes. The results show that long-term thermal oxidation causes the most severe deterioration of low-temperature rheological performance, whereas short-term thermal oxidation and 13 h UV aging exhibit comparable effects. For SBS-modified asphalt, extending UV exposure from 13 h to 37 h leads to progressive stiffening and loss of relaxation capacity at −12 °C and −18 °C. However, the m-value shows a non-monotonic response at −24 °C, indicating that the temperature dependence of UV aging is more complex at extremely low temperature. For base asphalt, aging promotes the formation and subsequent agglomeration of bee-like structures. For SBS-modified asphalt, the sulfoxide index increases monotonically, while the carbonyl index first increases and then decreases. Although 13 h UV aging and RTFOT produce similar macroscopic outcomes, their mechanisms differ fundamentally, where UV aging is hypothesized to act primarily via photon-induced bond scission, whereas thermal oxidation proceeds through radical chain reactions. Full article

Urban green spaces provide essential ecosystem services, yet national-scale economic assessments remain scarce in rapidly urbanizing countries. Here, we quantify the ecosystem service value (ESV) of China’s urban green spaces from 1996 to 2024 using national statistical data and eight key indicators: water conservation, carbon sequestration, oxygen release, dust retention, biodiversity protection, nitrogen oxide absorption, cooling effects, and leisure recreation. Valuation follows Costanza et al. and the Millennium Ecosystem Assessment. We apply the full permutation polygon graphic method (FPPGM) to derive a comprehensive service index and the Eco-Economic Harmony (EEH) index to assess ecological–economic coordination. Total ESV increased from 57.18 billion to 569.01 billion yuan, at an average annual rate of 8.55%. Cooling effects, biodiversity protection, and water conservation ranked as the top three services. The comprehensive service index improved continuously, with national sustainable development capacity progressing from Level 4 (Low) in 1996–2002 to Level 1 (High) in 2013–2024. The 2024 EEH index of 0.87 indicates moderate-to-high eco-economic coordination. Despite substantial ESV growth, urban economic expansion continues to outpace ecological gains. Strengthening legal protections for urban green spaces is critical. This study provides a transferable framework for assessing urban green infrastructure across developing countries. Full article

Background/Objectives. Human voices convey critical socio-affective information, including emotional states. Although autism has frequently been associated with difficulties in processing vocal emotional cues, findings remain inconsistent, particularly in adults. This study investigated neural and behavioral sensitivity to vocal emotion expressions in autistic adults using an objective auditory frequency-tagging EEG paradigm. Methods. Twenty-five autistic adult men and 25 age- and IQ-matched non-autistic men completed an auditory frequency-tagging EEG task and an auditory and multimodal emotion-recognition assessment. During EEG recording, neutral vocal utterances were presented at 4 Hz, with emotional utterances (fear, anger, happiness, or sadness) inserted every third stimulus, generating an oddball frequency of 1.333 Hz indexing vocal emotion discrimination. Results. No significant group differences were observed in neural or behavioral measures of emotion processing. Robust oddball EEG responses were present in both groups, indicating automatic discrimination of emotional from neutral vocalizations. Fearful and angry vocalizations elicited the strongest neural responses. On the behavioral task, autistic and non-autistic participants showed comparable performance in the auditory modality as well as in the visual and audiovisual modalities, with auditory emotion recognition being the most challenging condition for both groups. Conclusions. These findings provide converging neural and behavioral evidence for intact vocal emotion processing in autistic adult men and are consistent with the view that socio-affective processing differences may attenuate across development. Auditory frequency-tagging EEG shows promise as a sensitive tool for studying individual differences in socio-affective processing. Full article

The operational reliability of shaft systems in hydropower units has become increasingly critical as these units are frequently engaged in grid regulation under new power systems. This paper presents a sensor-driven method for operational status assessment and trend prediction of Francis turbine generator unit shaft systems using vibration and swing signals. Time domain features are extracted from the sensor-acquired signals to construct a multi-dimensional quantitative index system for characterizing the operational state, and a combined Entropy Weight–Coefficient of Variation–TOPSIS model with dynamic health thresholds is established for adaptive condition assessment. To address the nonlinear and non-stationary characteristics inherent in such signals, a decomposition–prediction–reconstruction fusion framework is developed, incorporating Variational Mode Decomposition (VMD) for signal decomposition and noise reduction, iTransformer for capturing global multi-variable interactions, and Bidirectional Long Short-Term Memory (BiLSTM) for bidirectional temporal feature extraction. The hybrid model achieves a coefficient of determination R² of 0.9845 on complex vibration and swing signals, demonstrating its superior prediction capability. Based on the prediction results, health scores and dynamic thresholds are calculated to perform trend analysis and health early warning. A case study is conducted using real-world monitoring data from a 306 MW Francis turbine unit. The results demonstrate that the proposed method effectively characterizes the shaft system operational state, achieving a closed-loop integration from condition monitoring to fault diagnosis and predictive maintenance. The operational status assessment and trend prediction analyses are in good agreement with actual operating conditions, providing reliable technical support for the intelligent health management of hydropower units. Full article

Soil organic carbon (SOC) stock is a key component of terrestrial ecosystems, playing a critical role in climate regulation and ecosystem productivity. Despite its economic importance, the impacts of the expansion of khat (Catha edulis) cultivation at the expense of other land uses and its intensive management practices on the depletion of soil carbon content are overlooked in Eastern Ethiopia. This study aimed to estimate and map SOC stocks using multispectral Sentinel-2 and RapidEye imagery, combined with environmental, soil, and topographic variables, across khat-dominated landscapes in the Haramaya District of Eastern Ethiopia. A total of 88 soil samples were collected and analyzed to quantify SOC stocks. Random Forest (RF) and extreme gradient boosting (XGBoost) algorithms were employed to predict SOC stocks. The dataset was stratified into training (70%) and an independent validation (30%) subset. Model development was performed using five-fold cross-validation on the training dataset, while final performance was assessed on the independent validation set using the coefficient of determination (R²), root mean square error (RMSE) and mean absolute error (MAE). Laboratory-measured SOC stocks ranged from 24.99 to 65.94 Mg C ha⁻¹, with a mean value of 36.88 Mg C ha⁻¹. The predicted spatial SOC stocks ranged from 30.4 to 50.4 Mg C ha⁻¹ using RapidEye data and from 32.8 to 51.5 Mg C ha⁻¹ using Sentinel-2, with Sentinel-2 producing slightly higher mean estimates. The lowest SOC stocks were consistently observed in bare, grass, and shrub land-use types across both datasets. RF demonstrated superior performance compared with XGBoost, achieving moderate predictive performance for both the RapidEye (R² = 0.56, RMSE = 5.91 Mg C ha⁻¹) and Sentinel-2 (R² = 0.42, RMSE = 6.90 Mg C ha⁻¹) datasets. This result indicates that RF provided greater robustness for SOC stock prediction under the heterogeneous environmental conditions of khat-dominated agricultural landscapes. Topographic and soil-related variables, particularly the Topographic Wetness Index (TWI), land surface temperature (LST), and clay content, were identified as the most influential predictors in both models. Although less consistent, remote sensing indices such as GNDVI, BSI, and NDWI also contributed to SOC prediction. While both sensors proved effective for SOC mapping, a measurable sensor-related effect was observed. The findings demonstrate the effectiveness of integrating multisource remote sensing, environmental, soil, and topographic variables with machine learning for SOC stock mapping in khat-dominated landscapes. This approach provides valuable spatial information to understand SOC variability and support sustainable land management and climate change mitigation strategies in Eastern Ethiopia. Full article

This study employs panel data from 11 provinces and municipalities along the Yangtze River Economic Belt spanning 2010–2021. It measures green transport efficiency (GTE) using principal component analysis (PCA) and the undesirable Super-SBM model while constructing an economic high-quality development index (HQEDI) through an entropy-weighted CRITIC approach. The study combines coupling coordination degree modeling with spatial autocorrelation analysis (Global Moran’s I, LISA, hotspot/coldspot detection) to empirically investigate their synergistic evolution mechanism. The findings indicate the following: (1) Multidimensional policy combinations exhibit a nonlinear threshold effect on enhancing green transport efficiency, with efficiency significantly rebounding post-2015 as low-carbon policies deepened. (2) High-quality economic development displays a dual-stage ‘convergence-divergence’ pattern, where downstream regions lead in HQEDI but mid- and upstream regions show faster growth in coordination and green dimensions. (3) The coupling coordination degree exhibits pronounced spatial spillover effects, with the global Moran’s I mean reaching 0.485. High-value clusters form in downstream regions, while upstream areas predominantly exhibit low-value clusters, revealing an ‘east-high, west-low’ regional differentiation pattern. (4) The gradient divergence mechanism stems from heterogeneity in infrastructure density, industrial structure, and policy responsiveness elasticity. Accordingly, it is recommended to establish a multi-level governance mechanism to dismantle administrative barriers and to construct a tripartite policy package integrating ‘digital transport, ecological compensation, and industrial radiation’ to advance coordinated basin development. Full article