Search Results (17,452)

The rapid deployment of autonomous haulage systems (AHSs) in open-pit mining has significantly altered haul road geometric design requirements, as autonomous trucks operate under strict kinematic constraints related to turning radius, gradient, and braking performance. Since haulage accounts for 50–60% of total mining costs, optimizing haul road geometry is critical for improving operational efficiency, energy consumption, and safety. This study presents a systematic review of 50 highly relevant studies selected from 81 candidate publications published between 2003 and 2025 through structured database searches and citation chaining. The review synthesizes current developments in haul road layout optimization, turning radius accommodation, gradient design, and safety integration for autonomous mining systems. The findings indicate that GIS-based and integrated optimization approaches consistently improve haulage performance, with reported productivity gains of 5–20%. Turning radius constraints emerged as the primary factor governing kinematic feasibility, while Hybrid A* and its advanced variants represent the dominant path-planning approaches. Recommended gradient limits of 8–12% remain important for balancing efficiency and safety, although emerging AHS-specific models suggest opportunities for controlled relaxation. The review identifies key research gaps in adaptive road design, integrated safety–geometry optimization, and field validation, providing a consolidated foundation for future AHS-compatible haul road design research. Full article

Land use planning in the Black Soil Region of Northeast China must be sustainable, taking into account food security, water use, GHG emissions, and economic returns. Current crop suitability mapping and single-objective optimization studies tend to analyze crop occurrence, crop structure, and spatial allocation independently, which is of little value in spatial planning. In this study, a three-stage integrated approach is proposed, involving deep learning crop occurrence mapping, multi-objective crop structure optimization, and suitability-guided spatial allocation. During Stage I, a lightweight U-Net semantic segmentation model, BlackSoilCropNet, is developed to provide per-pixel occurrence probabilities of rice, maize, soybean, and other types of crops based on Sentinel-2 time series and auxiliary environmental predictors. In stage II, NSGA II will optimize the area structure of the crops and reduce water consumption and GHG emissions with the maximum profit under the constraints of the cropland, water, and production. Selected Pareto optimal solutions are transformed to crop allocation maps and transition hotspot outputs in Stage III. The framework resulted in three viable planning options. The economic priority scenario resulted in the highest profit (USD 27.9 billion), with higher water consumption and emissions. The environmental-priority scenario resulted in a reduction in water use to 118.2 × 10⁹ m³ and emissions to 50.9 MtCO₂e, but at the cost of lower production and profits. There was a balance between economic stability and an improved environment in the balanced scenario. The framework provides a reproducible, geospatial decision support approach for sustainable farming planning and black soil conservation overall. Full article

Wildfires pose serious risks to ecosystems, air quality, and human health. Effective wildfire monitoring requires accurate detection and timely validation, but current approaches are often constrained by fragmented data sources, false alarms, and delays in field verification. This study presents WildfireGO, a multi-source wildfire detection and validation system that integrates crowdsourced observations, satellite hotspot data, and image-based classification in a geospatial monitoring environment. The system combines user-submitted images, Sentinel-2 imagery, and Moderate Resolution Imaging Spectroradiometer (MODIS) hotspot data processed through Google Earth Engine (GEE) to support wildfire detection and verification. Four classification models, namely Convolutional Neural Network (CNN), Random Forest (RF), K-Nearest Neighbors (KNN), and Gradient Boosting (GB), were evaluated using 10-fold cross-validation and an independent test dataset of 800 wildfire-related images. The CNN model produced the best result, with an accuracy of 97.5% on the independent test dataset. By combining image-based classification with crowdsourced reporting, the system helps screen user-submitted wildfire information and reduce false detections. Satellite-derived hotspot data provide spatial evidence for cross-checking reported events and improving spatial situational awareness for wildfire monitoring and response planning. WildfireGO supports near real-time data submission, automated processing, and interactive map-based visualization through a web-based interface. The findings indicate that combining crowdsourced reports, satellite observations, and image classification in a single geospatial system has the potential to support more reliable wildfire detection and provide practical support for environmental monitoring, disaster response, and spatial decision-making. Full article

Accurately evaluating how aero-engine performance supports upper-level capability remains a challenging issue in the digital planning, demonstration, and design of complex equipment systems-of-systems. Existing studies mainly rely on two-level analyses at the subsystem and system-of-systems levels, which are insufficient to characterize the cross-level transmission relationships among the aero-engine, aircraft performance, and overall capability. To address this limitation, this paper proposes a multilayer network-based contribution evaluation method for aero-engines oriented toward digital equipment planning and demonstration. First, a three-layer evaluation index system is constructed, including the overall capability layer, the aircraft performance layer, and the aero-engine performance layer, based on the OODA loop concept and aviation physical constraints. This provides a structured and traceable basis for cross-level requirement decomposition and scheme evaluation. Second, by integrating expert prior judgment with mechanism-based sensitivity analysis, the interrelationships among indicators at different layers are quantified, and a multilayer evaluation index network is established. Third, topological structure analysis is employed to identify key indicators in the aero-engine layer, and a cascading propagation model is introduced to evaluate the supporting roles and contribution rates of both individual indicators and the overall aero-engine layer with respect to the overall capability layer. Simulation results show that the proposed method can effectively reveal the structural characteristics, propagation paths, and dynamic influence patterns of aero-engine-layer indicators within the multilayer network. The proposed method provides methodological support for digital equipment planning, scheme demonstration, design optimization, and capability-oriented decision-making of aero-engines. Full article

While the influence of the urban built environment on public emotions has garnered extensive attention, existing studies predominantly focus on temperate climates or warmer seasons. As a result, they rarely extend their scope to winter-specific emotions in cold-region cities, thereby overlooking the complex human–environment emotional interactions under extreme climates. To bridge this seasonal research gap, this study develops an innovative analytical framework integrating Large Language Models (LLMs) with Multiscale Geographically Weighted Regression (MGWR). Drawing on social media data, this framework leverages the powerful zero-shot reasoning capabilities of LLMs to precisely quantify the two-dimensional emotional characteristics of Valence and Arousal. Concurrently, by incorporating the multi-scale spatial modeling strengths of MGWR, it thoroughly investigates the spatial patterns and driving mechanisms of public emotions within the winter context of typical cold-region cities. The results indicate that, first, extreme climates do not lead to urban emotional suppression; instead, frozen rivers transform into vibrant emotional corridors, with the public demonstrating a high degree of thermal-psychological adaptability. Second, by incorporating winter-specific environmental variables, the research reveals a cold-region paradox of emotional valence. Specifically, under snow cover, lower winter Land Surface Temperature (LST) and winter Normalized Difference Vegetation Index (NDVI) paradoxically evoke positive emotions by reconstructing the aesthetic experience of ice-snow landscapes. Furthermore, the impact of urban service facilities on emotional arousal exhibits a significant pattern of diminishing marginal utility. Overall, the LLMs-MGWR framework achieves a closed loop of high-throughput, multi-dimensional semantic decoding and multi-scale spatial interpretation, demonstrating exceptional cross-regional generalizability. Ultimately, this study not only provides a novel paradigm for understanding human–environment interactions in complex environments but also offers transferable planning guidelines for microclimate design, facility decentralization, and the reshaping of winter blue-green infrastructure in global cold-region cities. Full article

(1) Background: This study develops a hybrid assessment matrix for the early identification of material-related risks in adaptive reuse projects, with a particular focus on material conservation. The Material Conservation for Adaptive Reuse Risk Evaluation Matrix (M-CARE) integrates UNESCO Tools, the ICCROM ABC Method and its agents of deterioration, and the material damage classifications of the MDCS Damage Atlas. (2) Methods: The framework was tested through a case study of Issız Han, an Early Ottoman caravanserai. Risk mechanisms identified through M-CARE were compared with deterioration patterns documented after 18 years of post-reuse operation. The comparison focused on the degree of correspondence between predicted deterioration mechanisms and observed deterioration patterns. (3) Results: The findings indicate a high degree of correspondence between the deterioration mechanisms identified through M-CARE and the deterioration patterns documented during field surveys. In particular, moisture-related deterioration patterns showed substantial correspondence with the risks identified during the assessment stage. The results also highlight the influence of cumulative microclimatic factors and the value of complementary analytical approaches for evaluating long-term deterioration processes. (4) Conclusions: M-CARE provides a practical and rapidly applicable decision-support framework for the early identification and classification of material-related risks in adaptive reuse projects, supporting more informed conservation planning and proactive heritage management. Full article

In the context of urban regeneration and the redevelopment of existing urban land and built assets, industrial heritage has become a cross-sectoral policy issue involving heritage conservation, spatial reuse, land governance and public cultural uses. Existing studies have primarily examined individual adaptive reuse projects and spatial strategies, whereas the long-term evolution of policy texts has received less systematic attention. Taking Shanghai as a case study, this paper constructs a clause-level corpus of industrial heritage-related policies issued between 2006 and 2025. The corpus comprises 524 clauses extracted from 86 policy documents covering heritage conservation, historic building conservation, cultural and creative industries, land use, planning, urban renewal and industrial tourism. Overall and stage-based Latent Dirichlet Allocation (LDA) models are combined with cross-period topic alignment to identify the structure and evolution of policy themes. The results show that Shanghai’s industrial heritage policies have been shaped not only by heritage conservation concerns, but also by industrial land governance, the transformation of underused industrial land, the regeneration of existing industrial spaces (EIS), industrial culture, tourism and public service provision. Four stages are identified: initial exploration, regulatory consolidation, revitalisation and renewal, and integrated consolidation. Across these stages, four major evolutionary pathways can be observed: industrial land supply and governance, renewal of EIS and old industrial areas (OIA), industrial heritage conservation and value recognition and the expansion of industrial culture, tourism and public services. The paper provides clause-level evidence for understanding industrial heritage governance in China’s urban regeneration context and highlights the need for stronger coordination between heritage, land, planning, industry, culture and tourism policies. Full article

This paper focuses on the analysis of long-term land surface temperature (LST) dynamics and land-use changes in the city of Košice, Slovakia, during the period 1985–2025. The analysis is based on multi-temporal Landsat satellite imagery processed within a geographic information system (GIS) environment. Non-parametric statistical methods, including the Mann–Kendall trend test and the Theil–Sen slope estimator, were applied at the pixel level to identify the direction, magnitude, and statistical significance of long-term trends. Land-use changes were evaluated using CORINE Land Cover data together with the NDVI and NDBI spectral indices. The results revealed a statistically significant increase in land surface temperature across almost the entire urban area, with the mean LST increasing by 5.83 °C between 1985 and 2025. The analysis also confirmed a strong positive correlation between built-up areas and LST values, whereas vegetation cover exhibited a significant cooling effect represented by a strong negative correlation with surface temperature. Spatial analysis identified pronounced warming hotspots concentrated mainly in industrial and newly urbanized areas, while vegetation-stabilized zones showed lower warming intensity or localized cooling trends. The findings highlight the dominant influence of urbanization processes on the city’s thermal regime and emphasize the importance of urban vegetation as a key adaptation element for mitigating the surface urban heat island effect. The study also illustrates the added value of integrating remote sensing data, GIS tools, and pixel-based trend analysis in the assessment of long-term changes in the urban thermal environment of medium-sized Central European cities. The results provide a spatial basis for climate adaptation planning and future assessments of urban thermal comfort and environmental quality. Full article

Due to the rising share of renewable energy sources within distribution systems, there is a need for planning methods that can accommodate wind uncertainty. This paper introduces a holistic bi-level optimization approach for the optimal planning of wind-based distributed generation (WBDG) and battery energy storage system (BESS). At the higher level, the optimal location and size of WBDG and BESS are selected based on minimizing power loss, improving voltage stability, and minimizing the cost of electricity production. Meanwhile, at the lower level, the BESS charging–discharging operations and power transmission between different wind situations are scheduled. Wind uncertainty is considered in the model by applying the Weibull probability density function in combination with the Two-Point Estimation Method (2m-PEM). The resulting complicated bi-level optimization issue is addressed by creating a new Improved Cheetah Optimizer (ICO) that incorporates four enhancements to the Cheetah Optimizer (CO) to improve its explorative and exploitative capabilities. Simulations conducted on the IEEE 33-bus system show the superiority of the proposed method compared to other methods. The ICO outperforms particle swarm optimization (PSO), genetic algorithm (GA), and the basic CO by achieving up to 69.07% daily energy savings, raising the lowest bus voltage from 0.9440 per unit to 0.9610 per unit, and providing an expected operating cost of $5449.92. Full article

Multi-target path planning for Automated Guided Vehicle (AGV) in complex logistics environments requires balancing planning efficiency, obstacle avoidance capability, and trajectory smoothness. To address these challenges, this paper proposes a bi-level collaborative framework integrating Particle Swarm Optimization (PSO) with the Bidirectional Rapidly Exploring Random Tree (Bi-RRT). The framework unifies adaptive sampling, online parameter optimization, and trajectory smoothing within a single planning architecture. Specifically, the framework constructs a five-dimensional particle encoding that includes the expansion step size and multi-level strategy switching thresholds. During the Bi-RRT expansion process, an expansion-failure-driven adaptive sampling mechanism is introduced to enhance search performance in cluttered environments, while local-density-based suppression and directional dispersion are employed to reduce redundant exploration. In addition, a lightweight PSO-based monitoring mechanism enables online adaptive parameter adjustment. For multi-target scheduling, a greedy heuristic based on a hybrid weighted graph determines the visitation sequence. Trajectory smoothness is further improved using cubic B-spline interpolation combined with bounded perturbation optimization. Experimental results demonstrate that the proposed framework improves planning efficiency while maintaining stable performance across environments with different obstacle densities. These results demonstrate the effectiveness of the proposed framework for multi-target AGV path planning in complex warehouse environments. Full article

The relationship between financial literacy and capital market participation remains a central focus of both theoretical and empirical research in behavioral finance. However, existing research has predominantly relied on direct-effect, mediation, or moderation frameworks, thereby offering only a partial understanding of how individuals make investment decisions under uncertainty. To address this limitation, this study develops a sequential cognitive–affective framework by integrating the Theory of Planned Behavior (TPB) and the Risk-as-Feelings Hypothesis (RFH). Within this framework, investment intention is conceptualized as the outcome of cognitive evaluations and affective responses, with financial literacy influencing these processes by shaping perceived risk and institutional trust. Utilizing a multistage sampling strategy, survey data were collected from 449 individual investors and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indicate that financial literacy is positively associated with investment intention, both directly and indirectly through a sequential mediation pathway. Specifically, higher financial literacy is associated with lower perceived risk, which subsequently strengthens trust in financial institutions and ultimately increases investment intention. These findings suggest that financial literacy functions not only as a cognitive resource but also as a psychological mechanism that influences how individuals interpret and respond to financial uncertainty. By validating a sequential cognition–affect pathway, this study provides a more comprehensive behavioral explanation for the inconsistent findings reported in prior research. The findings further suggest that financial literacy initiatives designed to address risk perceptions and institutional trust may be more effective in promoting capital market participation than programs focused solely on information provision. Full article

Digitalization is transforming urban mobility and logistics, changing behaviors and the way demand is anticipated and managed. This paper frames both research and practice in this area. Through a systematic review of reviews in Scopus and Web of Science, following PRISMA 2020, a corpus of 21 documents was compiled. The analysis organizes findings into five interrelated dimensions: technology; operation and service design; society, user, and equity; institution, regulation, and governance; and economics and scalability. These are interpreted through two cross-cutting axes: behavioral change among users and operators, and the anticipation and management of demand supported by big data and predictive models. By integrating urban mobility and logistics, usually analyzed separately, this study compares barriers and enablers. The two axes help identify gaps: limited coverage of medium-sized cities, low-density environments, and developing countries; fragmented treatment of user diversity; weak integration between mobility and logistics data; and the lag between the sophistication of predictive models and the institutional capacity to incorporate them into planning. This paper proposes an integrative framework for analyzing the digitalization of urban mobility and logistics as part of a single urban transition. Full article

Background/Objectives: Artificial intelligence (AI) is increasingly being integrated into vascular surgery, particularly in diagnostic imaging, perioperative planning, intraoperative guidance, and postoperative surveillance. This literature review evaluates the current applications of artificial intelligence in vascular surgery and endovascular practice, with a particular focus on imaging technologies and their role in improving diagnostic precision, workflow efficiency, and patient outcomes. In addition, the review examines emerging AI applications in operative workflow optimization, endovascular navigation, postoperative surveillance, training platforms, and AI-assisted clinical decision support. Methods: A literature review was conducted using PubMed and Scopus with the search terms: (artificial intelligence OR AI OR neural network) AND (vascular surgery) AND (diagnosis OR treatment). Reference lists of included studies were manually screened, and additional recent studies were identified from relevant journals. Articles published in English up to April 2026 were included. Studies were assessed for their applications in vascular diagnostics, plaque characterization, endovascular workflow optimization, and postoperative surveillance. Results: AI demonstrated strong diagnostic performance across multiple imaging modalities. Deep learning systems achieved a sensitivity of 91.3% and specificity of 95.2% in peripheral arterial stenosis classification, while plaque characterization models showed accuracies up to 96% and substantial agreement with expert imaging interpretation. AI-assisted operative systems improved procedural efficiency through reductions in operative duration, radiation exposure, and contrast utilization. However, many studies were retrospective, single-center, and based on relatively small cohorts with heterogeneous endpoints. Conclusions: AI has significant potential to improve vascular surgical practice through enhanced image interpretation, procedural guidance, and individualized treatment planning. Despite promising outcomes, current evidence remains limited by methodological heterogeneity and insufficient external validation. Prospective multicenter studies and standardized evaluation frameworks are required before widespread clinical implementation can be achieved. Full article

The increasing integration of electric vehicle (EV) charging infrastructure is reshaping the operational and stability characteristics of modern power systems. Unlike conventional load growth, large-scale EV charging introduces converter-interfaced, time-varying, and controllable demand that affects the grid across multiple temporal and spatial scales. This review examines the static, dynamic, and electromagnetic interactions between EV charging infrastructure and power systems, with emphasis on stability mechanisms, converter-control effects, modeling methods, and mitigation strategies. Static impacts are reviewed in terms of voltage deviation, feeder and transformer loading, reactive power demand, phase imbalance, and hosting capacity constraints. Dynamic interactions are discussed from the perspectives of voltage stability, transient response, small-signal oscillation, and converter control coupling. Electromagnetic issues, including harmonic emission, resonance, impedance-based stability, and interoperability among heterogeneous charger topologies, are also assessed. In addition, the review summarizes key mitigation approaches such as coordinated charging, adaptive converter control, hierarchical energy management, and grid-supportive operation. Finally, major research gaps are identified in multi-timescale modeling, stability-aware planning, control co-design, and standardized technical assessment frameworks, and recommendations for future research are presented. Full article

This paper presents a detailed case study of a semi-top-down excavation carried out for the Haowang Tower project in Shaoxing, China, where thick soft clay deposits dominate the subsurface profile. The excavation, covering approximately 10,000 m² in plan area and reaching a depth of 12.35 m, posed significant challenges due to the presence of sensitive adjacent utilities and roads. In response, an integrated design–construction strategy was adopted, combining soldier pile retaining walls with a permanent first-floor slab serving as horizontal bracing. Several innovative structural features—including load-transfer beams, stress-reinforced strips, and soil molds—were introduced to address the specific demands of the semi-top-down method in soft ground. A multi-stage numerical analysis framework was implemented, employing the Hardening-Soil (HS) model within 2D and 3D finite element analyses (PLAXIS), alongside the subgrade reaction method (FRWS2006). Predicted wall deflections, ground settlements, and structural forces were systematically compared with field measurements. The 3D analysis showed good agreement for wall deflections (within 5% of the maximum measured value), validating the approach’s effectiveness. However, the analysis over-predicted ground settlements (e.g., sewage pipe settlement was over-predicted by 60%), indicating a need for more refined settlement prediction models or parameter calibration. Based on this finding, a correction factor of 0.6–0.7 is proposed for settlement prediction when using HS parameters derived from standard drained tests. The results also highlight the importance of spatial effects and the critical role of construction sequencing. This study offers both practical insights and validated numerical tools for similar deep excavations in urban soft clay environments. Full article