Search Results (396)

Aircraft type classification plays a crucial role in various applications, including remote sensing, surveillance, and aviation management. Since the development of deep learning techniques, nearly all related methods are based on neural networks, achieving excellent classification results. However, existing classification networks primarily focus on optimizing single-branch architectures, often overlooking the underlying factors driving recognition performance. Our analysis suggests that color and shape are two important and complementary visual cues for aircraft classification, with their relative importance varying across datasets and imaging scenarios. Motivated by this insight, we propose a novel dual-branch network architecture that separately processes shape and color cues, allowing each branch to emphasize one type of visual information before adaptive fusion. Specifically, we designed two dedicated modules: a Shape Feature Module (SFM) and a Color Feature Module (CFM), tailored for extracting shape and color information independently. Furthermore, we introduced a Color–Shape Cross-Attention-based Fusion Module (CSCAFM) to integrate these features. Within CSCAFM, the separated shape and color features are adaptively fused through a cross-attention mechanism, enabling the network to dynamically weigh the contributions of shape and color. Experimental results on benchmark datasets demonstrate the effectiveness of our approach. Full article

Revegetation serves as a critical ecological safeguard, while these interventions have added complexity to the evapotranspiration processes and water balance. Dalinor Lake basin (DLB), located in the southeast of Inner Mongolia Plateau, serves as a vital habitat for migratory birds and plays an important role in the ecological security of northern China. To enhance biodiversity, numerous ecological restoration projects have been carried out in this area in recent years. Dalinor Lake, a large inland lake within the basin, has experienced persistent shrinkage. Although existing studies have explored its driving factors, the potential influence of revegetation activities on lake shrinkage remains unclear. In this study, we used remote sensing imagery, combined with supervised classification and visual interpretation methods, to extract changes in the surface areas of lakes within the DLB (i.e., Dalinor Lake and Ganggeng Lake), and analyzed the effects of total terrestrial evapotranspiration (ET_t), precipitation (PPT), runoff, soil moisture content, and the vapor pressure deficit on these changes. Results showed that the Dalinor Lake’s area decreased by 18.68% from 2000 to 2020, and was mainly influenced by ET_t, with the Normalized Difference Vegetation Index (NDVI) contributing the most to ET_t (54.02%). In contrast, Ganggeng Lake expanded by 5.68% and was strongly driven by PPT. Compared with Ganggeng Lake, there have been more revegetation activities around Dalinor Lake, resulting in significant increases in NDVI and ET_t, together with widespread declines in soil moisture in its surrounding areas, suggesting that revegetation exerted non-negligible water pressure on Dalinor Lake. These findings can provide valuable information for policymakers to balance large-scale ecological restoration with sustainable water management in semi-arid regions. Full article

Road safety is a global priority, and driver behavioral factors are among its most critical determinants. Although the literature has advanced in characterizing driving styles using psychometric instruments such as the Multidimensional Driving Style Inventory (MDSI), an empirical gap persists in the simultaneous integration of latent behavioral profiles, driving exposure, and road safety outcomes, particularly in Latin American contexts and across generational extremes. This study examined the relationship between latent driving style profiles and road safety outcomes among young (18–25 years) and older (≥65 years) licensed drivers in Ecuador, while evaluating the moderating role of driving exposure. A structured survey based on the MDSI was administered to 833 active drivers, and data were analyzed using Latent Profile Analysis (LPA) and binary logistic regression. The six-profile solution was selected according to the Bayesian Information Criterion (BIC = 11,655.07), with acceptable classification quality (entropy = 0.860; minimum posterior probability = 0.808); for inferential parsimony, these profiles were subsequently consolidated into three analytically interpretable categories: Predominantly Careful, Predominantly Risky, and Distress-Reduction. The Predominantly Risky profile was significantly associated with higher odds of traffic accident involvement (OR = 2.76, 95% CI [1.55, 4.93]), whereas the Distress-Reduction profile showed substantially higher odds of receiving traffic infraction fines (OR = 4.74, 95% CI [1.69, 13.34]). The composite driving exposure index was a robust predictor across both models (accident model: OR = 2.82, 95% CI [1.60, 5.14]; fine model: OR = 1.87, 95% CI [1.29, 2.74]). In addition, a significant interaction was observed between the Predominantly Risky profile and driving exposure in the model predicting traffic infraction fines, suggesting that exposure amplified sanction risk within this behavioral category. Older drivers showed a substantially higher representation of the Distress-Reduction profile than young drivers. These findings underscore the utility of person-centered approaches for identifying heterogeneous driver configurations and for designing profile-differentiated road safety interventions; from a practical perspective, these results support the development of targeted road safety programs that integrate behavioral profile screening with exposure-based risk management for young and older drivers. Full article

Land-use and land cover (LULC) change is a key driver of environmental dynamics in semi-arid Mediterranean watersheds, strongly influencing hydrological processes, soil degradation, and ecosystem stability. In this context, understanding and predicting spatial–temporal land transformations is essential for sustainable watershed management. This study proposes a nonlinear spatial–temporal modeling framework integrating a hybrid Artificial Neural Network (ANN), Cellular Automata (CA), and Markov chain approach to simulate LULC dynamics in the Sebdou watershed, northwestern Algeria. Multi-temporal Landsat imagery (1985, 2005, and 2025), combined with topographic, socio-economic, and accessibility variables (slope, population density, distance to roads, and hydrographic network), was used to reconstruct historical land-use patterns and identify key driving forces of change. A supervised Maximum Likelihood classification achieved high accuracies, with overall accuracy ranging from 92.87% to 96.26% and Kappa coefficients between 0.85 and 0.91. The ANN model was trained to estimate nonlinear transition potentials, while the CA component incorporated spatial neighborhood effects to simulate land allocation processes. Markov chain analysis provided temporal transition probabilities, enabling the construction of a coupled ANN–CA–Markov framework for scenario-based prediction. Model validation against observed 2025 LULC maps indicated strong agreement in quantity distribution (Kappa histogram = 0.767), while spatial agreement (Kappa = 0.3566) reflected inherent spatial displacement typical of CA-based stochastic allocation. Simulation results for 2045 indicate continued urban expansion along major transport corridors, progressive decline of dense forest cover, and increasing bare soil areas, while agricultural land remains dominant but increasingly fragmented. These trends highlight the growing influence of anthropogenic pressure and accessibility factors on landscape restructuring in semi-arid environments. The proposed hybrid framework provides a robust decision-support tool for anticipating land-use dynamics and assessing future environmental pressures in Mediterranean drylands. Its integration with hydrological and erosion models can further support sustainable watershed planning under combined socio-economic and climatic changes. Full article

Heart failure (HF) is a complex and heterogeneous clinical syndrome defined by progressive structural, functional, and molecular alterations in the myocardium, representing a significant global health challenge. Beyond haemodynamic compromise, HF arises from intricate interactions among neurohormonal activation, chronic inflammation, oxidative stress, mitochondrial dysfunction, impaired calcium handling, and extracellular matrix remodelling. These processes drive maladaptive cardiac remodelling and progressive functional decline across multiple HF phenotypes, including HF with reduced (HFrEF), mildly reduced (HFmrEF), and preserved ejection fraction (HFpEF). Recent advances in molecular biology have highlighted the critical roles of genomic, epigenetic, and transcriptomic mechanisms in the progression of HF. DNA methylation, histone modifications, chromatin remodelling, and non-coding RNAs regulate gene expression in response to environmental and metabolic stimuli, thereby connecting systemic risk factors to cardiac dysfunction. Proteomic and post-translational modifications, such as phosphorylation, acetylation, and redox signalling, modulate protein function and contribute to contractile impairment and metabolic dysregulation. Metabolomic studies have revealed significant changes in myocardial energy metabolism, including reduced oxidative capacity, decreased metabolic flexibility, and limited bioenergetic reserves. The integration of multi-omics approaches—including genomics, transcriptomics, proteomics, metabolomics, and epigenomics—has provided unprecedented insight into the biological heterogeneity of HF, facilitating the identification of distinct molecular subtypes and novel therapeutic targets. Systems biology and network-based analyses, supported by artificial intelligence and machine learning, enable the synthesis of complex datasets and enhance risk classification, prognosis, and personalised treatment approaches. This narrative review synthesises the current understanding of the molecular mechanisms underlying HF, with particular emphasis on the interplay between metabolic and epigenetic regulation in disease progression. It also highlights emerging translational opportunities, including omics-based biomarkers, targeted therapies, and precision medicine approaches. Despite significant advances, challenges remain in translating these findings into clinical practice, underscoring the need for standardised methodologies, extensive validation, and integrative frameworks. Ultimately, a systems-level, multi-omics perspective is crucial for redefining HF as a biologically stratified condition in the landscape of advancing tailored cardiovascular medicine. Full article

Autonomous driving in complex urban environments remains challenging due to perception uncertainty, dynamic multi-agent interactions, and control instability under adverse conditions. Despite advances in individual components, systematic evaluations of fully integrated modular pipelines under compounded urban disturbances remain scarce. This work presents a modular autonomous driving framework in CARLA Town10HD, integrating Convolutional Neural Network (CNN)-based perception using ResNet-18, global path planning via A* algorithm, and two control strategies: a classical Proportional–Integral–Derivative (PID) controller and a Deep Q-Network (DQN) agent with adaptive geometric steering assistance. A structured protocol assessed robustness across five scenarios: Heavy Rain, Dense Fog, Nighttime Driving, Dense Traffic, and Combined Extreme Conditions. The perception module achieved F1-scores close to 0.99 for traffic-sign, pedestrian, and lane classification; results reflect synthetic CARLA data and should not be interpreted as real-world generalization. The PID controller produced smoother trajectories with lower steering oscillations, while the DQN agent achieved faster traversal times at the cost of higher control variability. Route efficiency remained around 0.96 under isolated disturbances and decreased to 0.52 under compounded conditions, confirming sensitivity to multi-factor complexity. This study contributes a reproducible multi-scenario benchmark quantifying stability–adaptability trade-offs between classical and learning-based control, identifying scenario generalization and simulation-to-reality transfer as key future directions. Full article

Identifying and optimizing core factor configurations for anchorage operational safety under typhoon scenarios is critical to enhancing anchorage operational resilience and sustainable port development. This study develops a complementary hybrid SEM–fsQCA framework: key factors are identified via literature review and expert interviews; SEM quantifies factor correlations and contribution weights and corrects expert-evaluated anchorage capacity; six core factors are extracted, three typhoon types (heavy-rainfall, strong-wind, complex-track) are defined, and a coupled anchorage–typhoon case dataset is constructed. Subsequently, fsQCA performs necessary condition analysis and identifies causal configurations driving safety effectiveness. Based on these configurations, we establish a dynamic three-tier risk classification framework for refined anchorage management. Validated using 36 coupled cases (12 anchorages × 3 typhoon types) from Huizhou Port, a core hub in the Guangdong–Hong Kong–Macao Greater Bay Area, this framework enables adaptive vessel traffic scheduling throughout the entire typhoon cycle through dynamic tiered management. The proposed “identification-intervention-feedback” closed-loop governance model delivers theoretical rigor and operational implementation ability for coastal port typhoon risk mitigation. Full article

Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a complex, multi-system disease characterized by a multitude of symptoms across various organ systems. Diagnosis has relied heavily on heterogeneous clinical symptom presentation and evolving case definitions, with treatment focused on addressing presenting symptoms due to the paucity of validated biomarkers. Meanwhile, advances have been made in understanding the underlying pathophysiology through strong epidemiologic, clinical, and basic science studies. This narrative review synthesizes recent advances that are likely to drive a shift in understanding from symptom-based classification toward a molecularly defined understanding of the disease. This shift in understanding will likely provide the foundation for future research efforts focused on targeting diagnosis and treatment more effectively. Specifically, we reference the identification of rare genetic risk variants through the HEAL2 deep learning framework, the large-scale DecodeME genome-wide association study, and dynamic epigenetic markers of disease state. In addition, the findings revealed the downstream consequences of this genetic and epigenetic priming: chronic innate immune activation, CD8+ T cell exhaustion characterized by upregulation of the exhaustion-driving transcription factors Thymocyte Selection-Associated HMG Box (TOX) and Eomesodermin (EOMES), and a cellular energy crisis centered on mitochondrial dysfunction. Furthermore, results of recent studies have revealed sex-specific transcriptomic and proteomic signatures of maladaptive recovery. We also highlight the role of machine learning and artificial intelligence integrations in translating high-dimensional multi-omics data into actionable biological insights, including the identification of monocyte subsets via Positive Unlabeled Learning, circulating cell-free RNA diagnostic signatures, and integrated multi-modal disease models such as BioMapAI. The combination of these findings, which highlight multiple identifiable mechanisms of molecular activity, support the feasibility of molecular subtyping, precision diagnostics, and targeted therapeutic strategies for ME/CFS. Full article

Blue–green spaces serve as the core carriers of urban ecosystems, and their conservation and optimization have emerged as pivotal issues in territorial spatial planning and ecological governance. Taking Guilin, a national innovation demonstration zone for China’s Sustainable Development Agenda, as the study area, a deep learning-based DBDTAF-Net classification model is constructed using 2020 Sentinel-2 remote sensing imagery and AW3D30 Digital Surface Model (DSM) data. The model achieves a mean Intersection-over-Union (mIoU) of 86.05% on the test set and an IoU of 94.67% for rocky desertification areas. Based on the classification results, 21 derived indicators (including landscape patterns of BGSs) and six meteorological and topographic factors, alongside three core ecosystem service indicators—Aboveground Biomass (AGB), Net Primary Productivity (NPP), and soil conservation—are extracted to characterize their spatial patterns. The XGBoost-SHAP framework is employed to quantify the driving effects and threshold responses of BGS patterns on ecosystem services. The results indicate that (1) BGSs in Guilin display a spatial pattern of “green-dominated, blue-supplemented, generally contiguous yet locally fragmented,” and all three ecosystem services exhibit significant spatial clustering. (2) Landscape pattern factors of green spaces constitute the dominant influencing factors, with contribution rates ranging from 22.3% to 28.6%. Specifically, green space_COHESION demonstrates a stable linear positive effect. A green space ratio below 45% suppresses AGB, whereas exceeding 45% shifts to a positive effect and represents an efficient enhancement interval for NPP while exerting a continuously positive influence on soil conservation. A cultivated land proportion below 30% leads to a strongly increasing inhibitory effect on AGB and soil conservation, whereas its inhibition on NPP weakens beyond 20%. A construction land proportion exceeding 10% significantly suppresses NPP, and the inhibitory effect stabilizes above 20%. Green space patch density below 0.8 shows a pronounced negative effect, which diminishes above 0.8. Blue space factors exert relatively weak effects. (3) The ecosystem service supply capacity varies across functional zones in Guilin, with the ecological barrier zone performing the best, the modern agricultural zone performing moderately, and the six central urban districts of the Shanshui Metropolis Area exhibiting the lowest levels. This study provides a technical framework for high-precision extraction of urban BGSs and quantitative analysis of factors influencing ecosystem services, offers decision support for ecological conservation and restoration in Guilin, and furthermore proposes insights for the coordinated development of rational land resource utilization and ecosystem service enhancement in other karst cities. Full article

Energy crises and environmental pollution continue to constrain sustainable development of the automotive industry. Large-scale deployment of electric vehicles (EVs) provides an effective pathway to reduce energy consumption and emissions. Regenerative braking technology plays a central role in improving energy utilization and extending EV driving range has sustained research attention. This study examines the operating principles, control strategies, and energy performance characteristics of regenerative braking systems (RBS). The historical development of brake-by-wire systems is reviewed, including system classification, structural configuration, and operating mechanisms, with an emphasis on their application in electric vehicles. On this basis, the working principles of regenerative braking systems are analyzed. A systematic review of regenerative braking control strategies is conducted across four categories: conventional control, fuzzy control, neural network control, and intelligent optimization algorithms. The analysis focuses on optimization methods for improving energy recovery efficiency and on the key factors governing energy transfer performance. Technical challenges associated with integrating regenerative braking systems into electric vehicles are further examined to provide a reference for future research and engineering development. Full article

To improve risk control at coal mining faces and reduce accident risks, this study first extracts high–frequency risk factors from 171 valid coal mining face accident cases (2020–2023) and integrates synthesis of the literature to establish a 21–factor risk indicator system covering human–machine–environment–management dimensions, and invites 10 senior experts in coal mine safety–covering mining engineering, safety science and engineering, mine ventilation, geological disaster prevention and coal mine safety management–for evaluation. Secondly, a hierarchical structure of factors is developed based on adversarial interpretive structural modeling (AISM), and the driving force and dependence of each factor are analyzed using the matrix impact cross–reference multiplication applied to a classification (MICMAC). A fuzzy Bayesian network (FBN) model is then constructed with the AISM structure as a topological constraint to clarify factor relationships and quantify the risk propagation uncertainty. Finally, an empirical analysis is conducted using the X Coal Mine. The results indicate that the “illegal and irregular organization of production” is the root control factor. The risk probability of the mining face is 86.1%, with “inadequate specialized prevention and control” having a high occurrence probability, and “illegal operation” and “illegal command” showing the most significant probability changes. Sensitivity analysis identifies “inadequate specialized prevention and control” as the most sensitive factor, which, together with the environmental factors, falls into the Level I unacceptable risk category. This research determines risk control priorities and provides a theoretical basis for coal mine safety management. Full article

Under the strategic context of building a transportation powerhouse in China, the transportation sector faces the dual challenge of reducing emissions while improving efficiency. This study constructs a two-dimensional regional classification framework based on the “economic-carbon” dimension and systematically investigates the coordinated evolution of the development level (TD) and carbon emission intensity (TCEI) of the transportation systems in 31 provinces of China from 2014 to 2023, using methods such as entropy weight TOPSIS, the coupling coordination degree (CCD) model, kernel density estimation (KDE), spatial autocorrelation analysis, and the XGBoost-SHAP explainable machine learning framework based on transfer learning. The study finds that (1) TD shows a fluctuating upward trend, while TCEI continues to decline, with regional imbalances; (2) in terms of time, CCD shows a general upward trend with an N-shaped evolution; spatially, CCD presents a pattern of stronger coordination in the east and weaker in the west, with sustained regional heterogeneity, forming a development pattern of “Region I leading, Region II breaking through, Region III maintaining, Region IV catching up”; and (3) regarding the driving factors, freight volume, transport industry output value, and passenger turnover are the core driving factors of CCD, with significant regional heterogeneity in their mechanisms. This study provides a systematic analytical framework and differentiated policy tools for promoting coordinated regional development of green transportation. Full article

The integration of sustainability into strategic management (SSM) has drawn increased academic interest, yet the literature is conceptually fragmented and lacks a cohesive framework that systematically describes the integration of SSM. This study seeks to fill this gap and uncover the essential strategic dimensions, driving forces, and influencing variables that shape the integration of SSM and planning. This study conducts a systematic literature review (SLR) of peer-reviewed articles indexed in the Web of Science and Scopus databases. Through the application of established search parameters and content analysis methodologies, 30 relevant studies are identified and examined. Under a management theory lens, this study synthesizes the literature using a systematic search method and thematic classification approach. The results show that the interaction between internal capabilities and external pressures leads to the formation of sustainable integration. Stakeholder participation, operational integration, governance and leadership commitment, strategy alignment, and sustainability performance evaluation are important factors. The findings also point to important enabling and limiting variables, including the lack of defined measures, regulatory uncertainties, and resource constraints. This study proposes a structured conceptual framework that connects organizational integration mechanisms, strategic drivers, and sustainable results based on these discoveries. This work contributes to the literature on sustainability-oriented strategic management by offering a theory-driven synthesis and highlighting important boundary conditions. It also provides practical implications for practitioners and researchers alike. Full article

Urban–suburban PM_2.5 differences are widely used to characterize spatial disparities in air pollution, yet their long-term trends may depend on urban definitions. For China during 2013–2020, this study used nationwide ground PM_2.5 monitoring data and 1 km × 1 km gridded population density data to analyze the sensitivity of urban–suburban PM_2.5 trends to spatial structure-based and population-density-based classification (300, 1500, 2200, 2500 people km⁻²) at national, Eastern and Western China scales. Results showed significant national PM_2.5 decline, with urban reduction rates of −3.1 to −3.3 µg m⁻³ yr⁻¹ in summer and −6.0 to −6.3 µg m⁻³ yr⁻¹ in winter, and faster air quality improvement in winter. Urban–suburban PM_2.5 differences were highly sensitive to classification methods: the spatial structure-based framework showed minimal differences (0.09 µg m⁻³ in summer, 5 µg m⁻³ in winter), while the 300 people km⁻² threshold yielded much larger ones (11 µg m⁻³ in summer, 29 µg m⁻³ in winter) with faster urban declines. Higher population density thresholds narrowed such differences and converged trends with the spatial structure-based results. Pronounced spatial heterogeneity existed: Eastern China had larger PM_2.5 declines with consistent response patterns to national trends, while Western China showed weaker declines, with urban–suburban differences highly sensitive to classification methods and opposite temporal evolution trends. This study confirms that urban definition is a critical methodological factor for interpreting China’s long-term urban–suburban PM_2.5 trends, as different methods cause notable inferential deviations. Future air pollution spatial heterogeneity studies should carefully select and specify urban classification methods to ensure comparable, scientifically rigorous findings. Full article

Precipitation Use Efficiency (PUE) is a key ecological indicator for evaluating how vegetation converts precipitation into biomass or productivity. A thorough analysis of its quantification methods and driving mechanisms is of great significance for improving regional precipitation use efficiency and ensuring agricultural and ecological water security. In this study, we conducted a comprehensive literature search without time restrictions in the Web of Science and China National Knowledge Infrastructure (CNKI) databases, using “Precipitation Use Efficiency” and “PUE” as core keywords. After retrieval, a strict “independent dual-screening plus cross-checking” procedure was adopted with unified inclusion and exclusion criteria to ensure literature quality. Only highly relevant and methodologically rigorous studies were retained, resulting in a final set of 80 eligible publications. Key information was systematically extracted using content analysis, followed by integrated summarization and inductive analysis. This paper systematically illustrates the ecological connotation of PUE, compares diverse quantification and research methods with their applicable conditions, analyzes spatiotemporal differentiation characteristics and multidimensional driving mechanisms, summarizes practical approaches for PUE improvement, and reviews current research limitations. It represents a systematic integration and refinement of the research framework of precipitation use efficiency. The results can provide targeted theoretical support for revealing the driving mechanisms of PUE and promoting the efficient utilization of precipitation resources. Full article