Search Results (60,930)

The rapid adoption of electric vehicles (EVs) has driven a strong need for optimizing locations of electric vehicle charging stations (EVCSs). Previous methods for locating EVCSs rely on statistical and optimization models, but these methods have limitations in capturing complex nonlinear relationships and spatial dependencies among factors influencing EVCS locations. To address this research gap and better understand the spatial impacts of urban activities on EVCS placement, this study presents a spatially aware machine learning (SAML) method that combines a multi-layer perceptron (MLP) model with a spatial loss function to optimize EVCS sites. Additionally, the method uses the Shapley additive explanation (SHAP) technique to investigate nonlinear relationships embedded in EVCS placement. Using the city of Wuhan as a case study, the SAML method reveals that parking site (PS), road density (RD), population density (PD), and commercial residential (CR) areas are key factors in determining optimal EVCS sites. The SAML model classifies these grid cells into no EVCS demand (0 EVCS), low EVCS demand (from 1 to 3 EVCSs), and high EVCS demand (4+ EVCSs) classes. The model performs well in predicting EVCS demand. Findings from ablation tests also indicate that the inclusion of spatial correlations in the model’s loss function significantly enhances the model’s performance. Additionally, results from case studies validate that the model is effective in predicting EVCSs in other metropolitan cities. Full article

Design Thinking (DT) has emerged as a relevant methodology for addressing global challenges aligned with the United Nations Sustainable Development Goals (SDGs). This study presents a systematic literature review, conducted following PRISMA 2020 guidelines, which analyzes 42 peer-reviewed publications from 2013 to 2023. Through inductive content analysis, 10 core DT principles—such as empathy, iteration, user-centeredness, and systems thinking—I identified and thematically mapped to specific SDGs, including goals related to health, education, innovation, and climate action. The study also presents five real-world cases from diverse sectors such as technology, healthcare, and urban planning, illustrating how DT has been applied to address practical challenges aligned with the SDGs. However, the review identifies persistent gaps in the field: the lack of standardized evaluation frameworks, limited integration across SDG domains, and weak adaptation of ethical and contextual considerations, particularly in vulnerable communities. As a response, this paper recommends the adoption of structured impact assessment tools (e.g., Cities2030, Responsible Design Thinking), integration of design justice principles, and the development of participatory, iterative ecosystems for innovation. By offering both conceptual synthesis and applied insights, this article positions Design Thinking as a strategic and systemic approach for driving sustainable transformation aligned with the 2030 Agenda. Full article

The rapid growth of e-commerce and advances in information and communication technologies have placed increasing pressure on last-mile delivery companies to enhance operational productivity. As investments in logistics infrastructure require long-term planning, maximizing the efficiency of existing terminal operations has become a critical priority. This study proposes a mathematical model for inbound truck scheduling that simultaneously minimizes truck waiting times and balances workload across temporary inventory storage located at outbound chutes in cross-docking terminals. The model incorporates a dynamic rescheduling strategy that updates the assignment of inbound trucks in real time, based on the latest terminal conditions. Numerical experiments, based on real operational data, demonstrate that the proposed approach significantly outperforms conventional strategies such as First-In First-Out (FIFO) and Random assignment in terms of both load balancing and truck turnaround efficiency. In particular, the proposed model improves workload balance by approximately 10% and 12% compared to the FIFO and Random strategies, respectively, and it reduces average truck waiting time by 17% and 18%, thereby contributing to more efficient workflow and alleviating bottlenecks. The findings highlight the practical potential of the proposed strategy for improving the responsiveness and efficiency of parcel distribution centers operating under fixed infrastructure constraints. Future research may extend the proposed approach by incorporating realistic operational factors, such as cargo heterogeneity, uncertain arrivals, and terminal shutdowns due to limited chute storage. Full article

Background: Disruptions in freight transportation—such as service delays, infrastructure failures, and labor strikes—pose significant challenges to the reliability and efficiency of intermodal networks. To address these challenges, this study introduces Adaptive Intermodal Transportation (AIT), a resilient and flexible planning framework that enhances Synchromodal Freight Transport (SFT) by integrating real-time disruption management. Methods: Building on recent advances, we propose two novel strategies: (1) Reassign with Delay Buffer, which enables dynamic rerouting of shipments within a user-defined delay tolerance, and (2) (De)Consolidation, which allows splitting or merging of shipments across services depending on available capacity. These strategies are incorporated into a re-planning module that complements a baseline optimization model and a continuous disruption-monitoring system. Numerical experiments conducted on a Great Lakes-based case study evaluate the performance of the proposed strategies against a benchmark approach. Results: Results show that under moderate and high-disruption conditions, the proposed strategies reduce delay and disruption-incurred costs while increasing the percentage of matched shipments. The Reassign with Delay Buffer strategy offers controlled flexibility, while (De)Consolidation improves resource utilization in constrained environments. Conclusions: Overall, the AIT framework demonstrates strong potential for improving operational resilience in intermodal freight systems by enabling adaptive, disruption-aware planning decisions. Full article

Decarbonization of the Greek economy requires significant investments in clean technologies. This will boost demand for goods and services and will create multiplier effects on output value added and employment, though reliance on imported technologies might increase the trade deficit. This study employs input–output analysis to estimate the direct, indirect, and multiplier effects of green transition investments on Greek output, value added, employment, and imports across five-year intervals from 2025 to 2050. Two scenarios are considered: the former is based on the National Energy and Climate Plan (NECP), driven by a large-scale exploitation of RES and technologies promoting electrification of final demand, while the latter (developed in the context of the CLEVER project) prioritizes energy sufficiency and efficiency interventions to reduce final energy demand. In the NECP scenario, GDP increases by 3–10% (relative to 2023), and employment increases by 4–11%. The CLEVER scenario yields smaller direct effects—owing to lower investment levels—but larger induced impacts, since energy savings boost household disposable income. The consideration of three sub-scenarios adopting different levels of import-substitution rates in key manufacturing sectors exhibits pronounced divergence, indicating that targeted industrial policies can significantly amplify the domestic economic benefits of the green transition. Full article

To enable intelligent inspection of underground cable systems, this study presents a gecko-inspired quadruped robot that integrates multi-degree-of-freedom motion with a deep learning-based visual detection system. Inspired by the gecko’s flexible spine and leg structure, the robot exhibits strong adaptability to confined and uneven tunnel environments. The motion system is modeled using the standard Denavit–Hartenberg (D–H) method, with both forward and inverse kinematics derived analytically. A zero-impact foot trajectory is employed to achieve stable gait planning. For defect detection, the robot incorporates a binocular vision module and an enhanced YOLOv8 framework. The key improvements include a lightweight feature fusion structure (SlimNeck), a multidimensional coordinate attention (MCA) mechanism, and a refined MPDIoU loss function, which collectively improve the detection accuracy of subtle defects such as insulation aging, micro-cracks, and surface contamination. A variety of data augmentation techniques—such as brightness adjustment, Gaussian noise, and occlusion simulation—are applied to enhance robustness under complex lighting and environmental conditions. The experimental results validate the effectiveness of the proposed system in both kinematic control and vision-based defect recognition. This work demonstrates the potential of integrating bio-inspired mechanical design with intelligent visual perception to support practical, efficient cable inspection in confined underground environments. Full article

Background: In high-risk and frail patients with multivessel coronary artery disease (MV CAD), guidelines indicated complete revascularization with or without the use of cardiopulmonary bypass (CPB) bears a high morbidity and mortality risk. In cases where catheter interventions were deemed unsuitable and conventional coronary artery bypass grafting (CABG) posed an unacceptable perioperative risk, patients were scheduled for minimally invasive direct coronary artery bypass (MIDCAB) grafting or minimally invasive multivessel coronary artery bypass grafting (MICS-CABG). We called this approach “palliative revascularization.” This study assesses the safety and impact of palliative revascularization on clinical outcomes and overall survival. Methods: A consecutive series of 57 patients undergoing MIDCAB or MICS-CABG as a palliative surgery between 2008 and 2018 was included. The decision for palliative surgery was met in heart team after carefully assessing each case. The patients underwent single or double-vessel revascularization using the left internal thoracic artery and rarely radial artery/saphenous vein segments, both endoscopically harvested. Inpatient data could be completed for all 57 patients. The mean follow-up interval was 4.2 ± 3.7 years, with a follow-up rate of 91.2%. Results: Mean patient age was 79.7 ± 7.4 years. Overall, 46 patients (80.7%) were male, 26 (45.6%) had a history of atrial fibrillation and 25 (43.9%) of chronic kidney disease. In total, 13 patients exhibited a moderate EuroSCORE II, while 27 were classified as high risk, with a EuroSCORE II exceeding 5%. Additionally, 40 patients (70.2%) presented with three-vessel disease, 17 (29.8%) suffered an acute myocardial infarction within three weeks prior to surgery and 50.9% presented an impaired ejection fraction. There were 48 MIDCAB and nine MICS CABG with no conversions either to sternotomy or to CPB. Eight cases were planned as hybrid procedures and only 15 patients (26.3%) were completely revascularized. During the first 30 days, four patients (7%) died. A myocardial infarction occurred in only one case, no patient necessitated immediate reoperation. The one-, three- and five-year survival rates were 83%, 67% and 61%, respectively. Conclusions: MIDCAB and MICS CABG can be successfully conducted as less invasive palliative surgery in high-risk multimorbid patients with MV CAD. The early and mid-term results were better than predicted. A higher rate of hybrid procedures could improve long-term outcome in selected cases. Full article

Urban public transportation systems face increasing pressure from shifting travel patterns, rising peak-hour demand, and the need for equitable and resilient service delivery. While complex network theory has been widely applied to analyze transit systems, limited attention has been paid to behavioral segmentation within such networks. This study introduces a frequency-based framework that differentiates high-frequency (HF) and low-frequency (LF) passengers to examine how distinct user groups shape network structure, congestion vulnerability, and robustness. Using over 20 million smart-card records from Beijing’s multimodal transit system, we construct and analyze directed weighted networks for HF and LF users, integrating topological metrics, temporal comparisons, and community detection. Results reveal that HF networks are densely connected but structurally fragile, exhibiting lower modularity and significantly greater efficiency loss during peak periods. In contrast, LF networks are more spatially dispersed yet resilient, maintaining stronger intracommunity stability. Peak-hour simulation shows a 70% drop in efficiency and a 99% decrease in clustering, with HF networks experiencing higher vulnerability. Based on these findings, we propose differentiated policy strategies for each user group and outline a future optimization framework constrained by budget and equity considerations. This study contributes a scalable, data-driven approach to integrating passenger behavior with network science, offering actionable insights for resilient and inclusive transit planning. Full article

The objective of this study is to create an integrated and sustainability-centered framework to identify optimal locations for wind energy projects in Indonesia. This research employs a novel two-phase multi-criteria decision-making (MCDM) framework that combines the strengths of Data Envelopment Analysis (DEA), Fuzzy Analytic Hierarchy Process (FAHP), and Fuzzy Combined Compromise Solution (F-CoCoSo). Initially, DEA is utilized to pinpoint the most promising sites based on a variety of quantitative factors. Subsequently, these sites are evaluated against qualitative criteria such as technical, economic, environmental, and socio-political considerations using FAHP for criteria weighting and F-CoCoSo for ranking the sites. Comprehensive sensitivity analysis of the criteria weights and a comparative assessment of methodologies substantiate the robustness of the proposed framework. The results converge on consistent rankings across methods, highlighting the effectiveness of the integrated approach. Notably, the results consistently identify Lampung, Aceh, and Riau as the top-ranked provinces, showcasing their strategic suitability for wind plant development. This framework provides a systematic approach for enhancing resource efficiency and strategic planning in Indonesia’s renewable energy sector. Full article

Due to the complex spatial structure, dense tree distribution, and narrow passages in orchard environments, traditional path planning algorithms often struggle with large path deviations, frequent turning, and reduced navigational safety. In order to overcome these challenges, this paper proposes a hybrid path planning algorithm based on improved D* Lite for narrow forest orchard environments. The proposed approach enhances path feasibility and improves the robustness of the navigation system. The algorithm begins by constructing a 2D grid map reflecting the orchard layout and inflates the tree regions to create safety buffers for reliable path planning. For global path planning, an enhanced D* Lite algorithm is used with a cost function that jointly considers centerline proximity, turning angle smoothness, and directional consistency. This guides the path to remain close to the orchard row centerline, improving structural adaptability and path rationality. Narrow passages along the initial path are detected, and local replanning is performed using a Hybrid A* algorithm that accounts for the kinematic constraints of a differential tracked robot. This generates curvature-continuous and directionally stable segments that replace the original narrow-path portions. Finally, a gradient descent method is applied to smooth the overall path, improving trajectory continuity and execution stability. Field experiments in representative orchard environments demonstrate that the proposed hybrid algorithm significantly outperforms traditional D* Lite and KD* Lite-B methods in terms of path accuracy and navigational safety. The average deviation from the centerline is only 0.06 m, representing reductions of 75.55% and 38.27% compared to traditional D* Lite and KD* Lite-B, respectively, thereby enabling high-precision centerline tracking. Moreover, the number of hazardous nodes, defined as path points near obstacles, was reduced to five, marking decreases of 92.86% and 68.75%, respectively, and substantially enhancing navigation safety. These results confirm the method’s strong applicability in complex, constrained orchard environments and its potential as a foundation for efficient, safe, and fully autonomous agricultural robot operation. Full article

Automated cherry harvesting presents a significant opportunity to overcome the high costs and inefficiencies of manual labor in modern agriculture. However, robotic harvesting in dense canopies requires sophisticated path planning to navigate cluttered branches and selectively pick target fruits. This paper introduces a complete robotic harvesting solution centered on a novel path-planning algorithm: the Multi-Strategy Integrated RRT for Continuous Harvesting Path (MSI-RRTCHP) algorithm. Our system first employs a machine vision system to identify and locate mature cherries, distinguishing them from unripe fruits, leaves, and branches, which are treated as obstacles. Based on this visual data, the MSI-RRTCHP algorithm generates an optimal picking trajectory. Its core innovation is a synergistic strategy that enables intelligent navigation by combining probability-guided exploration, goal-oriented sampling, and adaptive step size adjustments based on the obstacle’s density. To optimize the picking sequence for multiple targets, we introduce an enhanced traversal algorithm ($\sigma$-TSP) that accounts for obstacle interference. Field experiments demonstrate that our integrated system achieved a 90% picking success rate. Compared with established algorithms, the MSI-RRTCHP algorithm reduced the path length by up to 25.47% and the planning time by up to 39.06%. This work provides a practical and efficient framework for robotic cherry harvesting, showcasing a significant step toward intelligent agricultural automation. Full article

Understanding the environmental drivers of radial growth in the Pinus massoniana (lamb.) is essential for improving forest productivity and carbon sequestration in subtropical ecosystems. This study used the basal area increment (BAI) as an indicator of radial growth to investigate the main factors affecting the radial growth rate of P. massoniana and the changes in BAI with these factors. A total of 58 high quality tree ring series were analyzed. Six common methods were used to comprehensively analyze the importance of nine factor variables on the BAI, including tree age, competition index, average temperature, and so on. Generalized additive models (GAMs) were developed to explore the nonlinear relationships between each selected variable and the BAI. The results revealed the following: (1) Age and Competition Index was identified as the primary driving force; (2) BAI increased with Age when tree age was below 69 years; (3) from the overall trend, the BAI of P. massoniana decreased with the increase in the Competition Index. These findings provide a scientific basis for developing management plans for P. massoniana forests. Full article

Public transport companies are aware that the success of their operations largely depends on the proper sizing and optimization of their processes. Among the key activities are the maintenance and repair of the vehicle fleet. This paper presents the application of mathematical optimization methods from the field of operations research to improve the efficiency of service workshops for bus maintenance and repair. Based on an analysis of collected data using queueing theory, the authors assessed the current system performance and found that the queueing system still has spare capacity and could be downsized, which aligns with the company’s management goals. Specifically, the company plans to reduce the number of bus repair service stations (servers in a queueing system). The main question is whether the system will continue to function effectively after this reduction. Three specific downsizing solutions were proposed and evaluated using queueing theory methods: extending the daily operating hours of the workshops, reducing the number of arriving buses, and increasing the productivity of a service station (server). The results show that, under high system load, only those solutions that increase the productivity of individual service stations (servers) in the queueing system provide optimal outcomes. Other solutions merely result in longer queues and associated losses due to buses waiting for service, preventing them from performing their intended function and causing financial loss to the company. Full article

With increasing life expectancy, the number of older people living with comorbid diabetes and frailty is increasing. The development of frailty accelerates diabetes-related adverse outcomes. Frailty is a multidimensional syndrome with physical, mental and social aspects which is associated with increased risk of hypoglycaemia, dementia and hospitalisation. Therefore, regular screening for all aspects of frailty should be an integrated part of the care plans of older people with diabetes. In addition, every effort should be made for prevention, which includes adequate nutrition combined with regular resistance exercise training. In already frail older people with diabetes, metabolic targets should be relaxed and hypoglycaemic agents should be of low hypoglycaemic risk potential. Furthermore, the metabolic phenotype of frailty should be considered when choosing hypoglycaemic agents and determining targets. With increasing severity of frailty, proactive chronological plans of de-escalation, palliation and end-of-life care should be considered. These plans should be undertaken in a shared decision-making manner which involves patients and their families. This ensures that patients’ views, wishes and preferences are in the heart of these plans. Full article

A just and sustainable energy transition in historically coal-dependent regions like Kentucky requires more than the adoption of new technologies and market-based solutions. This study uses a stated preferences approach to evaluate public support for various attributes of energy transition programs, revealing broad backing for moving away from coal, as indicated by a negative willingness to pay (WTP) for the status quo (–USD 4.63). Key findings show strong bipartisan support for solar energy, with Democrats showing the highest WTP at USD 8.29, followed closely by Independents/Others at USD 8.22, and Republicans at USD 8.08. Wind energy also garnered support, particularly among Republicans (USD 4.04), who may view it as more industry-compatible and less ideologically polarizing. Job creation was a dominant priority across political affiliations, especially for Independents (USD 9.07), indicating a preference for tangible, near-term economic benefits. Similarly, preserving cultural values tied to coal received support among Independents/Others (USD 4.98), emphasizing the importance of place-based identity in shaping preferences. In contrast, social support programs (e.g., job retraining) and certain post-mining land uses (e.g., recreation and conservation) were less favored, possibly due to their abstract nature, delayed benefits, and political framing. Findings from Kentucky offer insights for other coal-reliant states like Wyoming, West Virginia, Pennsylvania, Indiana, and Illinois. Ultimately, equitable transitions must integrate local voices, address cultural and economic realities, and ensure community-driven planning and investment. Full article