Search Results (3,457)

This paper investigates the multi-objective siting and sizing problem of a transformer–energy storage deeply integrated system (TES-DIS) that serves as a grid-side common interest entity. This study is motivated by the critical role of energy storage systems in generation–grid–load–storage resource allocation and the superior capability of artificial intelligence algorithms in addressing multi-dimensional, multi-constrained optimization challenges. A multi-objective optimization model is first formulated with dual objectives: minimizing voltage deviation levels and comprehensive economic costs. To overcome the limitations of conventional methods in complex power systems—particularly regarding solution quality and convergence speed—an enhanced Q-learning with hybrid guidance algorithm is proposed. The improved algorithm demonstrates strengthened local search capability and accelerated late-stage convergence performance. Validation using a real-world urban power grid in China confirms the method’s effectiveness. Compared to traditional approaches, the proposed solution achieves optimal TES-DIS planning through autonomous learning, demonstrating (1) 70.73% cost reduction and (2) 89.85% faster computational efficiency. These results verify the method’s capability for intelligent, simplified power system planning with superior optimization performance. Full article

Urbanization imposes complex environmental gradients that threaten plant diversity and urban ecosystem integrity. Understanding the multifactorial drivers that govern species distribution in urban contexts is essential for biodiversity conservation and sustainable landscape planning. This study addresses this challenge by examining the environmental determinants of urban flora in a rapidly developing city. We integrated data from 397 floristic sampling sites and 13 environmental monitoring locations across Düzce, Türkiye. A multidimensional suite of environmental predictors—including microclimatic variables (soil temperature, moisture, light), edaphic properties (pH, EC (Electrical Conductivity), texture, carbonate content), precipitation chemistry (pH and major ions), macroclimatic parameters (CHELSA bioclimatic variables), and spatial metrics (elevation, proximity to urban and natural features)—was analyzed using nonlinear regression models and machine learning algorithms (RF (Random Forest), XGBoost, and SVR (Support Vector Regression)). Shannon diversity exhibited strong variation across land cover types, with the highest values in broad-leaved forests and pastures (>3.0) and lowest in construction and mining zones (<2.3). Species richness and evenness followed similar spatial trends. Evenness peaked in semi-natural habitats such as agricultural and riparian areas (~0.85). Random Forest outperformed other models in predictive accuracy. Elevation was the most influential predictor of Shannon diversity, while proximity to riparian zones best explained richness and evenness. Chloride concentrations in rainfall were also linked to species composition. When the models were recalibrated using only native species, they exhibited consistent patterns and maintained high predictive performance (Shannon R² ≈ 0.937474; Richness R² ≈ 0.855305; Evenness R² ≈ 0.631796). Full article

Consumer choices are largely influenced by sustainability, necessitating SMEs from the agri-food sector to strategically address sustainability and innovate their business models. Nonetheless, the challenge for such sustainable leadership lies in maintaining an equilibrium between innovation, sustainability, and financial performance. This study examined how strategic leadership fosters sustainability-oriented innovation within SMEs exemplified by the wine industry. A survey involving 354 German wineries served to analyze a multi-dimensional concept of innovation clusters (early adopters, pragmatists, pioneers, skeptics, conservatives), type of innovation, sustainability orientation, strategic ambitions, and business performance. Exploring the adoption of fungus-resistant grape varieties (FRV) allowed investigating how sustainability transitions to meet EU Green Deal targets are shaped by strategic groups involving strategic positioning and innovation clusters. There was a correlation between stronger sustainability orientation with greater innovation (Means up to 4.39). As per the findings, it was observed that high scores (p < 0.001, η² = 0.144–0.160) in market and process innovation were obtained by early adopters and pioneers. These innovation champions excel in economic and social sustainability (p < 0.001) but nonetheless were found to be financially underperforming (Means 1.97–2.18). Innovations that were applied enhanced innovation scores (η² = 0.128) but did not improve immediate performance. The strongest performance (Mean 2.60) was reported by skeptics though they fared poor in terms of sustainability and innovation. It was also noted that early adopters and pioneers (44–45%) were leading in FRV adoption, while a lag was observed within premium-oriented organizations. These insights may motivate SMEs in their quest for strategic sustainability and allow fine-tuning political and societal measures to achieve a sustainable transition and quantified Green Deal ambitions. It was concluded that long-term positioning was improved by sustainability-driven innovation, however, it would involve short-term performance trade-offs for SMEs. Political support should motivate the sustainable leadership champions to also safeguard profitability. Full article

Human–Robot Collaboration (HRC) in construction projects promises enhanced productivity, safety, and quality, yet realizing these benefits requires understanding the multifaceted human and robotic factors that influence team performance. This study develops and validates a multidimensional framework that links key human abilities (operational skill, decision-making ability, and learning ability) and robot capacities (functionality and operability) to HRC team performance, with task complexity considered as contextual influence. A field survey of construction practitioners (n = 548) was analyzed using partial least squares structural equation modeling (PLS-SEM) to test direct effects and human–robot synergies. Results reveal that all five main effects are positive and significant, indicating that both human abilities and robot capacities have significant contribution. Moreover, every hypothesized two-way interaction is supported, evidencing strong interaction effects. Three-way moderation analyses further reveal that task complexity significantly strengthened the interactions of human abilities with robot functionality, whereas its interactions with robot operability were not significant. The study contributes an integrated and theory-driven model of HRC team performance that accounts for human abilities and robot capacities under varying task complexity, and validated constructs that can be used to diagnose and predict performance. The findings offer actionable guidance for project managers by recommending that they prioritize user-friendly robot operability to translate worker expertise into performance across a wide range of tasks, invest in training to strengthen operators’ skills and decision-making, and, for complex tasks, pair highly skilled workers with high-functionality robots to maximize performance gains. Full article

This study aimed to examine what makes a teacher resilient by investigating the psychological and contextual characteristics that distinguish more resilient educators from their peers. Specifically, it explored the relationships of psychological resilience with emotional intelligence, meaning in life, burnout, and self-efficacy among primary and secondary school teachers. Drawing on data from two independent but methodologically aligned studies (N = 222 and N = 407, respectively), cluster analyses identified two distinct teacher profiles in each study: high-resilience and lower-resilience. Teachers in the high-resilience group consistently reported higher emotional intelligence (in Study 1), greater self-efficacy, and lower levels of burnout (in Study 2). Interestingly, while the presence of meaning in life did not differ significantly between groups, high-resilience teachers were more actively engaged in the search for meaning (in Study 1). Analyses of teachers’ demographics revealed modest associations between resilience and gender or marital status, with women and partnered individuals more frequently represented in the high-resilience profile. No significant differences were observed concerning age, experience, or educational background. These findings support theoretical models that conceptualize resilience as a dynamic, multidimensional construct shaped by emotional, motivational, and social resources rather than fixed demographic traits. Implications for teacher training, institutional policy, and future resilience research are discussed. Full article

Suboptimal placement of charging infrastructure is a major barrier to the transition to sustainable transportation, even with the growing popularity of electric vehicles (EVs). The research addresses this challenge by proposing a novel hybrid genetic algorithm (GA) to solve the NP-hard Multiple-Choice Multidimensional Knapsack Problem (MMKP) for computationally derived optimal charging station placement and configurations in Sapporo, Japan. The methodology leverages high-granularity human flow data to identify charging demand and a Traveling Salesperson Problem (TSP)-based encoding to prioritize potential station locations. A greedy heuristic then decodes this prioritization, selecting charger configurations that maximize service capacity within a defined budget. The results reveal that as the budget increases, the network evolves through distinct phases of concentrated deployment, expansion, and saturation, with a nonlinear increase in covered demand, indicating diminishing returns on investment. The findings demonstrate the efficacy of the proposed model in providing a strategic roadmap for urban planners and policymakers to make cost-effective decisions that maximize charging demand coverage and accelerate EV adoption. Full article

The analysis of dynamic remote sensing scenes from unmanned aerial vehicles (UAVs) is shifting from reactive processing to proactive, predictive intelligence. Central to this evolution is video prediction—forecasting future imagery from past observations—which enables critical remote sensing applications like persistent environmental monitoring, occlusion-robust object tracking, and infrastructure anomaly detection under challenging aerial conditions. Yet, a systematic review of video prediction models tailored for the unique constraints of aerial remote sensing has been lacking. Existing taxonomies often obscure key design choices, especially for emerging operators like state-space models (SSMs). We address this gap by proposing a unified, multi-dimensional taxonomy with three orthogonal axes: (i) operator architecture; (ii) generative nature; and (iii) training/inference regime. Through this lens, we analyze recent methods, clarifying their trade-offs for deployment on UAV platforms that demand processing of high-resolution, long-horizon video streams under tight resource constraints. Our review assesses the utility of these models for key applications like proactive infrastructure inspection and wildlife tracking. We then identify open problems—from the scarcity of annotated aerial video data to evaluation beyond pixel-level metrics—and chart future directions. We highlight a convergence toward scalable dynamic world models for geospatial intelligence, which leverage physics-informed learning, multimodal fusion, and action-conditioning, powered by efficient operators like SSMs. Full article

In slab continuous casting, achieving uniform cooling in the secondary cooling zone is essential for ensuring both surface integrity and internal quality. To optimize the process for ship plate steel, a solidification heat transfer model was developed, incorporating radiation, water film evaporation, spray impingement, and roll contact. The influence of secondary cooling water flow on slab temperature distribution was systematically investigated from multiple perspectives. The results show that a weak cooling strategy is crucial for maintaining higher surface temperatures and aligning the solidification endpoint with the soft reduction zone. Along the casting direction, a “strong-to-weak” cooling pattern effectively prevents abrupt temperature fluctuations, while reducing the inner-to-outer arc water ratio from 1.0 to 0.74 mitigates transverse thermal gradients. In addition, shutting off selected nozzles in the later stage of secondary cooling at medium and low casting speeds increases the slab corner temperature in the straightening zone by approximately 50 °C, thereby avoiding brittle temperature ranges. Overall, the proposed multi-dimensional uniform cooling strategy reduces temperature fluctuations and significantly improves slab quality, demonstrating strong potential for industrial application. Full article

The simulation of multidimensional wave propagation with variable material parameters is a computationally intensive task, with applications from seismology to electromagnetics. While quantum computers offer a promising path forward, their algorithms are often analyzed in the abstract oracle model, which can mask the high gate-level complexity of implementing those oracles. We present a framework for constructing a quantum algorithm for the multidimensional wave equation with a variable speed profile. The core of our method is a decomposition of the system Hamiltonian into sets of mutually commuting Pauli strings, paired with a dedicated diagonalization procedure that uses Clifford gates to minimize simulation cost. Within this framework, we derive explicit bounds on the number of quantum gates required for Trotter–Suzuki-based simulation. Our analysis reveals significant computational savings for structured block-model speed profiles compared to general cases. Numerical experiments in three dimensions confirm the practical viability and performance of our approach. Beyond providing a concrete, gate-level algorithm for an important class of wave problems, the techniques introduced here for Hamiltonian decomposition and diagonalization enrich the general toolbox of quantum simulation. Full article

As an energy-intensive sector, China’s iron and steel industry is crucial for achieving “Dual Carbon” goals. This study fills the research gap in systematically comparing the synergistic effects of multiple policies by evaluating five key measures (2020–2023) in ultra-low-emission retrofits and clean energy alternatives. Using public macro-data at the national level, this study quantified cumulative reductions in air pollutants (SO₂, NOx, PM, VOCs) and CO₂. A synergistic control effect coordinate system and a normalized synergistic emission reduction equivalent (AP_eq) model were employed. The results reveal significant differences: Sintering machine desulfurization and denitrification (SDD) showed the highest AP_eq but increased CO₂ emissions in 2023. Dust removal equipment upgrades (DRE) and unorganized emission control (UEC) demonstrated stable co-reduction effects. While electric furnace short-process steelmaking (ES) and hydrogen metallurgy (HM) showed limited current benefits, they represent crucial deep decarbonization pathways. The framework provides multi-dimensional policy insights beyond simple ranking, suggesting balancing short-term pollution control with long-term transition by prioritizing clean alternatives. Full article

This paper introduces KG-FLoc, a knowledge graph-enhanced framework for secondary circuit fault localization in intelligent substations. The proposed KG-FLoc innovatively formalizes secondary components (e.g., circuit breakers, disconnectors) as graph nodes and their multi-dimensional relationships (e.g., electrical connections, control logic) as edges, constructing the first comprehensive knowledge graph (KG) to structurally and operationally model secondary circuits. By reframing fault localization as a knowledge graph link prediction task, KG-FLoc identifies missing or abnormal connections (edges) as fault indicators. To address dynamic topologies and sparse fault samples, KG-FLoc integrates two core innovations: (1) a relation-aware gated unit (RGU) that dynamically regulates information flow through adaptive gating mechanisms, and (2) a hierarchical graph isomorphism network (GIN) architecture for multi-scale feature extraction. Evaluated on real-world datasets from 110 kV/220 kV substations, KG-FLoc achieves 97.2% accuracy in single-fault scenarios and 93.9% accuracy in triple-fault scenarios, surpassing SVM, RF, MLP, and standard GNN baselines by 12.4–31.6%. Beyond enhancing substation reliability, KG-FLoc establishes a knowledge-aware paradigm for fault diagnosis in industrial systems, enabling precise reasoning over complex interdependencies. Full article

Farmers’ perceptions of environmental change are a key trigger for livelihood behaviors. However, it remains unclear how these perceptions become specific livelihood strategies through internal psychological processes. To address this, this study constructs an analytical framework. It integrates multidimensional environmental perceptions, risk aversion, and livelihood strategies. Particular focus is given to the mediating role of risk aversion in the link between perception of environmental change and livelihood strategy. The proposed mechanism is tested using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings indicate that farmers pursue both adaptive and defensive livelihood strategies. They balance security with development opportunities. Perceptions of ecological transition and market volatility significantly affect both adaptive and defensive strategies. Perception of social dynamics mainly influences adaptive strategies. The perception of policy adjustment has no significant effect. Risk aversion mediates these relationships. It strengthens defensive behaviors while promoting adaptive actions, showing its dual function in risk management and proactive adaptation. These findings underscore the complexity of decision-making in rural areas. They elucidate how environmental perceptions shape risk awareness and responses to livelihoods. This offers insights for policies aimed at enhancing rural resilience. Full article

Underemployment in Australia represents a critical facet of precarious work, shaped not only by insufficient hours and skill underutilisation but also by care responsibilities and financial insecurity. Using data from the Household, Income and Labour Dynamics in Australia (HILDA) Survey, this study employed latent class analysis (LCA), a person-centred, model-based clustering method, to uncover hidden subgroups within the underemployed population. Previous studies identify different profiles, but few embed care burden and financial stress as core latent dimensions. This study extends latent class approaches by integrating multidimensional vulnerabilities into subgroup analysis. The LCA analysis revealed four distinct classes. These findings confirmed three hypotheses: (H1) Care burden is a core latent dimension of underemployment (Classes 1 and 2), (H2) economic insecurity is a second defining dimension (Class 3), and (H3) a mental health/social isolation subgroup exists (Class 4). Class 1 exhibits dual care burdens and high representation from culturally and linguistically diverse (CALD) backgrounds. Class 2, Parents with Children, forms the largest group and is defined by intensive childrearing and caregiving roles. Class 3, Financially Strained Undereducated, includes individuals with low educational attainment experiencing pronounced financial hardship. Class 4, Socially Isolated with Poor Mental Health, represents the smallest yet most disadvantaged group, characterised by severe psychological distress, lack of social support, and acute financial vulnerability. Together, these findings highlight the need for tailored policy responses for diverse experiences among the underemployed and reveal intersecting social and economic disadvantages. Full article

The expansion of conventional agricultural models in the Colombian Amazon has caused deforestation, biodiversity loss, and socio-environmental degradation. In response, agroecology and bioeconomy are emerging as key strategies to regenerate landscapes and foster sustainable production systems. We evaluated the agroecological performance of 25 farms in the Andean–Amazon transition zone of Colombia using FAO’s Tool for Agroecology Performance Evaluation (TAPE). The analysis included land cover dynamics (2002–2024), characterization of the agroecological transition based on the 10 Elements of Agroecology, and 23 economic, environmental, and social indicators. Four farm typologies were identified; among them, Mixed Family Farms (MFF) achieved the highest transition score (CAET = 60.5%) and excelled in crop diversity (64%), soil health (SHI = 4.24), productive autonomy (VA/GVP = 0.69), and household empowerment (FMEF= 85%). Correlation analyses showed strong links between agroecological practices, economic efficiency, and social cohesion. Land cover dynamics revealed a continuous decline in forest cover (12.9% in 2002 to 7.1% in 2024) and an increase in secondary vegetation, underscoring the urgent need for restorative approaches. Overall, farms further along the agroecological transition were more productive, autonomous, and socially cohesive, strengthening territorial resilience. The application of TAPE proved robust multidimensional evidence to support agroecological monitoring and decision-making, with direct implications for land use planning, rural development strategies, and sustainability policies in the Amazon. At the same time, its sensitivity to high baseline biodiversity and to the complex socio-ecological dynamics of the Colombian Amazon underscores the need to refine the methodology in future applications. By addressing these challenges, the study contributes to the broader international debate on agroecological transitions, offering insights relevant for other tropical frontiers and biodiversity-rich regions facing similar pressures. Full article

Background: Cardiovascular diseases (CVDs) remain the primary cause of global mortality, with arterial hypertension, ischemic heart disease (IHD), and cerebrovascular accident (CVA) forming a progressive continuum from early risk factors to severe outcomes. While numerous studies focus on isolated biomarkers, few integrate multidimensional visualization with artificial intelligence to reveal hidden, clinically relevant patterns. Methods: We conducted a comprehensive analysis of 106 patients using an integrated framework that combined clinical, biochemical, and lifestyle data. Parameters included renal function (glomerular filtration rate, cystatin C), inflammatory markers, lipid profile, enzymatic activity, and behavioral factors. After normalization and imputation, we applied correlation analysis, parallel coordinates visualization, t-distributed stochastic neighbor embedding (t-SNE) with k-means clustering, principal component analysis (PCA), and Random Forest modeling with SHAP (SHapley Additive exPlanations) interpretation. Bootstrap resampling was used to estimate 95% confidence intervals for mean absolute SHAP values, assessing feature stability. Results: Consistent patterns across outcomes revealed impaired renal function, reduced physical activity, and high hypertension prevalence in IHD and CVA. t-SNE clustering achieved complete separation of a high-risk group (100% CVD-positive) from a predominantly low-risk group (7.8% CVD rate), demonstrating unsupervised validation of biomarker discriminative power. PCA confirmed multidimensional structure, while Random Forest identified renal function, hypertension status, and physical activity as dominant predictors, achieving robust performance (Accuracy 0.818; AUC-ROC 0.854). SHAP analysis identified arterial hypertension, BMI, and physical inactivity as dominant predictors, complemented by renal biomarkers (GFR, cystatin) and NT-proBNP. Conclusions: This study pioneers the integration of multidimensional visualization and AI-driven analysis for CVD risk profiling, enabling interpretable, data-driven identification of high- and low-risk clusters. Despite the limited single-center cohort (n = 106) and cross-sectional design, the findings highlight the potential of interpretable models for precision prevention and transparent decision support in cardiovascular aging research. Full article