Search Results (29)

This study investigates an intermodal routing problem for emergency materials in the early stage of post-disaster recovery, in which the rapid transportation of emergency materials is formulated as the objective. To achieve reliable transportation that can avoid transportation interruption, this study formulates the uncertainty of both emergency materials’ demand and the network capacity by LR triangular fuzzy numbers, and thus explores a reliable routing problem for transporting emergency materials that is further formulated by a fuzzy linear programming model. Considering the decision makers’ cautious attitude on the transportation of emergency materials to avoid transportation interruption, this study adopts chance-constrained programming based on necessity measure to build a solvable reformulation of the proposed model. A numerical case study is carried out to reveal the conflicting relationship between improving the reliability and reducing the time of transporting emergency materials. The decision-makers of the emergency materials transportation organization should select a reasonable confidence level based on the actual decision-making scenario to plan the reliable intermodal route for emergency materials. By comparing with deterministic modeling, this study verifies the feasibility of the modeling the uncertainty of both demand and capacity in avoiding unreliable transportation and enhancing the flexibility of the intermodal routing for emergency materials. By comparing with chance-constrained programming using possibility measure, this study demonstrates the feasibility of the necessity measure in planning the reliable intermodal route. This study further analyzes how the capacity level of the intermodal network, demand level of the emergency materials and stability of the LR triangular fuzzy parameters influence the optimization results. Accordingly, this study emphasizes the importance of objectively evaluating the uncertain demand for emergency materials, and reveals that the enhancement of the capacity level of the intermodal network and stability of LR triangular fuzzy parameters is able to reduce the transportation time of emergency materials and meanwhile maintain a high reliability. Full article

In response to the coexistence of multi-objective conflicts and environmental complexity in the renewal of contemporary urban industrial heritage, this study develops a simulation and decision-making methodology for architectural and environmental renewal based on a digital twin framework. Using the Xi’an Old Steel Plant Industrial Heritage Park as a case study, a community-scale digital twin model integrating multiple dimensions—architecture, environment, population, and energy systems—was constructed to enable dynamic integration of multi-source data and cross-scale response analysis. The proposed methodology comprises four core components: (1) integration of multi-source baseline datasets—including typical meteorological year data, industry standards, and open geospatial information—through BIM, GIS, and parametric modeling, to establish a unified data environment for methodological validation; (2) development of a high-performance dynamic simulation system integrating ENVI-met for microclimate and thermal comfort modeling, EnergyPlus for building energy and carbon emission assessment, and AnyLogic for multi-agent spatial behavior simulation; (3) establishment of a comprehensive performance evaluation model based on Multi-Criteria Decision Analysis (MCDA) and the Analytic Hierarchy Process (AHP); (4) implementation of a visual interactive platform for design feedback and scheme optimization. The results demonstrate that under parameter-calibrated simulation conditions, the digital twin system accurately reflects environmental variations and crowd behavioral dynamics within the industrial heritage site. Under the optimized renewal scheme, the annual carbon emissions of the park decrease relative to the baseline scenario, while the Universal Thermal Climate Index (UTCI) and spatial vitality index both show significant improvement. The findings confirm that digital twin-driven design interventions can substantially enhance environmental performance, energy efficiency, and social vitality in industrial heritage renewal. This approach marks a shift from experience-driven to evidence-based design, providing a replicable technological pathway and decision-support framework for the intelligent, adaptive, and sustainable renewal of post-industrial urban spaces. The digital twin framework proposed in this study establishes a validated paradigm for model coupling and decision-making processes, laying a methodological foundation for future integration of comprehensive real-world data and dynamic precision mapping. Full article

Agrivoltaic (AV) systems are increasingly recognized as a strategy to enhance sustainable land management, yet their application in post-conflict settings remains underexplored. This study addresses this gap by evaluating AV deployment in two Colombian municipalities located in PDET/ZOMAC regions, using an integrated framework that expands the conventional Water–Energy–Food (WEF) nexus into the Water–Energy–Food–Soil–Climate–Communities (WEFSCC) nexus. The research combined GIS-based site characterization, crop yield and water balance modeling (contrasting traditional irrigation with hydroponics), and photovoltaic performance simulations for 30 kW systems, under conservative and moderate scenarios. Economic analyses included Net Present Value (NPV), Internal Rate of Return (IRR), and Free Cash Flow (FCL), with sensitivity tests for crop prices, yields, tariffs, and costs. Results indicate that AV can reduce crop irrigation demand by up to 40%, while generating 17 MWh/month of electricity per site. Cabrera exhibited higher profitability than Pisba, explained by yield differences and site-specific energy outputs. Comparative analysis confirmed consistency with experiences in Africa and Europe, while emphasizing local socio-environmental benefits. Conclusions highlight AV systems as resilient tools for sustainable land management in Colombia’s post-conflict regions, with actionable implications for land-use regulation, fiscal incentives, and international cooperation programs targeting rural development. Full article

The present article focuses on the assessment of the potential advantages and disadvantages of the utilisation of modern building inventory technologies in crisis situations, using a case study of Ukraine, currently engulfed in armed conflict. The following methods are described in detail: laser scanning, 360-degree camera images, and photo series. The authors conducted an in-depth SWOT/TOWS analysis, adapted to the specifics of the post-conflict environment, with a view to the future reconstruction of damaged buildings. The originality of the study lies in the use of a modified, quantitative version of the conventional SWOT analysis, supplemented with a weighting and rating system, which allowed for a more accurate assessment of the effectiveness of various technologies, including laser scanning. While the study focuses on the Ukrainian context, the authors emphasise that the developed methodology is universal and can be successfully applied to other critical areas, such as regions affected by earthquakes, floods, fires, or technological disasters. A modified SWOT/TOWS analysis can serve as a valuable tool in crisis management and infrastructure reconstruction during emergencies, providing the data necessary for making rational and effective decisions regarding the use of modern technologies in construction. The analysis revealed that, of the analysed inventory strategies, only laser scanning technology fits the so-called “maxi-maxi” strategy, a scenario in which both internal resources and external capabilities are maximised. The remaining two strategies were designated as “maxi-mini,” signifying that their implementation is associated with elevated levels of risk despite their inherent advantages. It is imperative to acknowledge the existence of substantial external threats that persist. Nevertheless, this does not constitute a complete rejection of the concept. This study examines armed conflict as a research context for a selection of buildings in Ukraine. The analysis was constrained to the three most prevalent methods: The use of TLS, SfM, and 360-degree cameras is also a key component of the methodology. Full article

In high-density urban areas where ecological protection constraints are increasingly stringent, transportation infrastructure layout must balance service efficiency and environmental preservation. From an ecological-prioritization perspective, this study proposes a three-stage multi-objective optimization strategy for siting underground smart parking facilities using the NSGA-III algorithm, with Haidian District, Beijing, as a case study. First, spatial identification and screening are conducted using GIS, integrating urban fringe-space extraction with POI, AOI, population, and transportation network data to determine candidate locations. Second, a multi-objective model is constructed to minimize green space occupation, walking distance, and construction cost while maximizing service coverage, and is solved with NSGA-III. Third, under the ecological-prioritization strategy, the solution with the lowest land occupation is selected, and marginal benefit analysis is applied to identify the optimal trade-off between ecological and economic objectives, forming a flexible decision-making framework. The findings show that several feasible schemes can achieve zero green-space occupation while maintaining high service coverage, and marginal benefit analysis identifies a cost-effective solution serving about 20,000 residents with an investment of 7 billion CNY. These results confirm that ecological protection and urban service efficiency can be reconciled through quantitative optimization, offering practical guidance for sustainable infrastructure planning. The proposed methodology integrates spatial analysis, multi-objective optimization, and post-Pareto analysis into a unified framework, addressing diverse infrastructure planning problems with conflicting objectives and ecological constraints. It offers both theoretical significance and practical applicability, supporting sustainable urban development under multiple scenarios. Full article

Rising carbon emissions from international road freight transport in the EU—increasing from 29.4% in 2023 to 31.4% in 2025 under the With Existing Measures (WEM) Road Transport scenario—necessitate the implementation of additional measures within the framework of the EU Carbon Border Adjustment Mechanism (CBAM). For Ukraine, operating under martial law and pursuing a post-war green recovery of its transport and trade sectors, the adoption of EU experience in distributed generation (DG) from renewable energy sources (RESs) is particularly critical. This study evaluates the synergy between energy-efficient and low-carbon management in logistics chains for road freight transportation in Ukraine, drawing on EU evidence of DG based on RESs. To this end, a decoupling analysis was conducted to identify the factors influencing low-carbon and energy-efficient management of logistics chains in Ukraine’s freight transport sector. Under wartime conditions, the EU practice of utilising electric vehicles (EVs) as an auxiliary source of renewable energy for distributed electricity generation within microgrids—through Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) technologies—was modelled. The results confirm the relevance of RES-based DG and the integration of EVs as a means of enhancing energy resilience in resource-constrained and conflict-affected regions. The scientific novelty of this research lies in identifying the conditions for achieving energy-efficient and low-carbon effects in the design of logistics chains through RES-based distributed generation, grounded in circular and inclusive economic development. The practical significance of the findings lies in formulating a replicable model for diversifying low-carbon fuel sources via the development of distributed generation of electricity based on renewable resources, providing a scalable paradigm for energy-limited and conflict-affected areas. Future research should focus on developing innovative logistics chain models that integrate DG and renewable energy use into Ukraine’s transport system. Full article

The growing influence of social media on political processes extends beyond electoral campaigns and is rapidly transforming the communication practices of incumbent leaders. We address the gap between populist practices in electoral marketing and the implementation of the Ecuadorian president’s discursive strategies from a geopolitical perspective, with a special focus on the use of two platforms: Instagram and TikTok. While existing scholarship has generally analyzed populist discourse on social media, this article applies theoretical and methodological tools to analyze the grammar of war and the performative strategies used to build leadership in contexts of high social unrest. Grounded in contemporary perspectives. This article reveals how populist leaders mobilize emotions through narratives on digital platforms to frame political crises. Using qualitative critical discourse analysis with multimodal and semiotic tools, we examined 156 posts from the official TikTok and Instagram accounts of Ecuadorian President Daniel Noboa, published between January and July 2024. The findings highlight the strategic use of patriotic symbolism, personalization, and emotional appeals to legitimize executive actions and disseminate polarizing narratives. The proposed framework demonstrates how social media communication simplifies complex crisis scenarios into affect-laden “good versus evil” narratives. This model is transferable to other geopolitical and digital contexts, offering both conceptual and methodological tools for analyzing conflict-driven political communication. Full article

Background/Objectives: Workplace conflict and aggression pose significant psychosocial risks across diverse professional sectors. This protocol outlines a novel, university-based educational intervention. Developed by a multidisciplinary team from the University Hospital of Pisa, Italy, including occupational physicians and a psychiatrist specializing in work and organizational psychology, its primary purpose is to enhance conflict prevention and crisis management skills. While initially developed and tested within the veterinary sector due to its identified vulnerabilities, the intervention is inherently generalizable to any high-stress professional environment characterized by intense client, customer, or public interactions. Methods: The intervention integrates didactic instruction with active, immersive learning through tailored role-playing scenarios simulating real-world challenging encounters. This study protocol details the structured methodology for evaluating the immediate effectiveness of this training. We are using a specifically developed efficacy scale to assess outcomes. Results: The results demonstrate a significant improvement in all assessed skills from the pre-training to the post-training evaluation. For every item on the scale, the median scores increased, indicating a positive shift in overall group performance. The p-value for each item was <0.001, confirming that the observed improvements were statistically significant. These results demonstrate enhanced conflict resolution skills, improved communication, and an increased sense of self-efficacy among participants. Conclusions: This protocol offers a comprehensive and generalizable approach to addressing workplace psychosocial risks through an innovative educational intervention. A key future goal involves advancing this training methodology by integrating virtual reality (VR) environments with AI-driven avatars for role-playing, aiming to achieve a more realistic and impactful learning experience and sustained behavioral change. Full article

Between 2019 and 2023, left-turn crashes accounted for 4.5% of traffic accidents in Japan, with 36% of injuries involving cyclists and 66% at signalized intersections. This study quantifies conflict situations between left-turning vehicles and straight-moving bicycles in real-world traffic environments and provides a foundation for determining appropriate timing of future in-vehicle early warning systems. Trajectories reconstructed from seven hours of camera footage yielded six spatio-temporal and behavioral indicators for 37 events with a post-encroachment time (PET) ≤ 3 s. Indicators—PET, time-to-crossing (TTC), right-of-way, urgent braking, deceleration to avoid a crash, and Kalman-based trajectory variance—were statistically related to a composite risk index, R. Approximately 80% of events fell within PETs of 2–3 s, while urgent braking occurred in 50% of cases with PETs of ≤2 s. Each 1 s reduction in PET increased R by 0.18 (R² = 0.55). PETs ≤ 2.5 s or TTCs ≤ 1.5 s flagged 95% of high-risk events 0.5 s in advance. Joint thresholds involving urgent braking and high variance raised coverage to 100%, with lead times of 0–1.4 s and a false alarm rate of 8%. These findings provide an innovative multi-indicator framework based on real-world trajectories, offering quantitative scenario-specific thresholds for effective in-vehicle warnings at urban intersections. Full article

As part of the post-conflict reconstruction and recovery, the development of Kabul New City aims to bring relief to the existing capital city, Kabul, which has experienced exponential population growth, putting heavy pressure on its existing resources. Kabul New City is divided into four subsectors, and each of them is being developed and is expected to reach a target population by 2025, as defined by the master plan. The study’s objective is to determine which of the four zones are in demand and need to be prioritized for development, as per the model results. The data collection involves an online questionnaire, and the responses are collected from residents of Kabul and Herat. Agent-based modeling (ABM) is an emerging method of simulating urban dynamics. Cities are evolving continuously and are forming unique spatial patterns that result from the movement of residents in search of new locations that accommodate their needs and preferences. An agent-based model is developed using the weighted random selection process based on household size and income levels. The agents are the residents of Kabul and Herat, and the environment is the land use classification image using the Sentinel 2 image of Kabul New City. The barren class is treated as the developable area and is divided into four sub-sectors. The model simulates three alternative growth rate scenarios, i.e., ambitious, moderate, and steady. The results of the simulation reveal that the sub-sector Dehsabz South, being closer to Kabul city, is in higher demand. Barikab is another sub-sector high in demand, which has connectivity through the highway and is an upcoming industrial hub. Full article

Addressing the urgent need for sustainable energy transitions in rural development while achieving the dual carbon goals, this study focuses on resolving critical challenges in agricultural photovoltaic (PV) applications, including land-use conflicts, compound energy demands (electricity, heating, cooling), and financial constraints among farmers. To tackle these issues, a dual-mode cost–benefit analysis framework was developed, integrating two distinct investment models: self-invested construction (SIC), where farmers independently finance and manage the system, and energy performance contracting (EPC), where third-party investors fund infrastructure through shared energy-saving or revenue agreements. Then, an integrated photovoltaic-storage agricultural greenhouse (PSAG) microgrid optimization model is established, synergizing renewable energy generation, battery storage, and demand-side management while incorporating operational mode selection. The proposed model is validated through a real-world case study of a village agricultural greenhouse in Gannan, China, characterized by typical rural energy profiles and climatic conditions. Simulation results demonstrate that the optimal system configuration requires 27.91 kWh energy storage capacity and 18.67 kW peak output, with annualized post-depreciation costs of 81,083.69 yuan (SIC) and 74,216.22 yuan (EPC). The key findings reveal that energy storage integration reduces operational costs by 8.5% compared to non-storage scenarios, with the EPC model achieving 9.3% greater cost-effectiveness than SIC through shared-investment mechanisms. The findings suggest that incorporating an energy storage system reduces costs for farmers, with the EPC model offering greater cost savings. Full article

Deforestation in Colombia, driven by armed conflict and illicit crops, triggered an environmental crisis, particularly in the Caribbean region, where forest loss in areas such as the Sierra Nevada de Santa Marta degraded ecosystems, reduced carbon sequestration, and increased soil erosion, threatening biodiversity and local food security. In response, the Arhuaco Indigenous community implemented an ethnic territorial management system to restore degraded lands and safeguard their ancestral territory. This study evaluates the effectiveness of their efforts, supporting their call for territorial expansion by analyzing forest cover changes (1973–2023) in the Tucurinca and Aracataca river basins. Using Landsat imagery, remote sensing, and a maximum likelihood algorithm, we generated thematic maps and statistical vegetation change data, validated by a 91.4% accuracy rate (kappa coefficient and confusion matrices). Results demonstrate significant forest recovery, highlighting collective reforestation and Indigenous sustainable management as pivotal strategies for reversing deforestation in post-conflict scenarios. Full article

Background/Objectives: The potential of generative artificial intelligence (GenAI) to augment clinical consultation services in clinical microbiology and infectious diseases (ID) is being evaluated. Methods: This cross-sectional study evaluated the performance of four GenAI chatbots (GPT-4.0, a Custom Chatbot based on GPT-4.0, Gemini Pro, and Claude 2) by analysing 40 unique clinical scenarios. Six specialists and resident trainees from clinical microbiology or ID units conducted randomised, blinded evaluations across factual consistency, comprehensiveness, coherence, and medical harmfulness. Results: Analysis showed that GPT-4.0 achieved significantly higher composite scores compared to Gemini Pro (p = 0.001) and Claude 2 (p = 0.006). GPT-4.0 outperformed Gemini Pro and Claude 2 in factual consistency (Gemini Pro, p = 0.02; Claude 2, p = 0.02), comprehensiveness (Gemini Pro, p = 0.04; Claude 2, p = 0.03), and the absence of medical harm (Gemini Pro, p = 0.02; Claude 2, p = 0.04). Within-group comparisons showed that specialists consistently awarded higher ratings than resident trainees across all assessed domains (p < 0.001) and overall composite scores (p < 0.001). Specialists were five times more likely to consider responses as “harmless”. Overall, fewer than two-fifths of AI-generated responses were deemed “harmless”. Post hoc analysis revealed that specialists may inadvertently disregard conflicting or inaccurate information in their assessments. Conclusions: Clinical experience and domain expertise of individual clinicians significantly shaped the interpretation of AI-generated responses. In our analysis, we have demonstrated disconcerting human vulnerabilities in safeguarding against potentially harmful outputs, which seemed to be most apparent among experienced specialists. At the current stage, none of the tested AI models should be considered safe for direct clinical deployment in the absence of human supervision. Full article

Connected autonomous vehicles (CAVs) face constraints from multiple traffic elements, such as the vehicle, road, and environmental factors. Accurately quantifying the vehicle’s operational status and driving risk level in complex traffic scenarios is crucial for enhancing the efficiency and safety of connected autonomous driving. To continuously and dynamically quantify the driving risks faced by CAVs in the road environment—arising from the front, rear, and lateral directions—this study focused s on the self-driving particle characteristics that enable CAVs to perceive their surrounding environment and make driving decisions. The vehicle-to-vehicle interaction behavior was analogized to the inter-molecular interaction relationship, and a molecular Morse potential model was applied, coupled with the vehicle dynamics theory. This approach considers the safety margin and the specificity of driving styles. A multi-layer decoder–encoder long short-term memory (LSTM) network was employed to predict vehicle trajectories and establish a risk quantification model for vehicle-to-vehicle interaction behavior. Using SUMO software (win64-1.11.0), three typical driving behavior scenarios—car-following, lane-changing, and yielding—were modeled. A comparative analysis was conducted between the risk field quantification method and existing risk quantification indicators such as post-encroachment time (PET), deceleration rate to avoid crash (DRAC), modified time to collision (MTTC), and safety potential fields (SPFs). The evaluation results demonstrate that the risk field quantification method has the advantage of continuously quantifying risk, addressing the limitations of traditional risk indicators, which may yield discontinuous results when conflict points disappear. Furthermore, when the half-life parameter is reasonably set, the method exhibits more stable evaluation performance. This research provides a theoretical basis for the dynamic equilibrium control of driving risks in connected autonomous vehicle fleets within mixed-traffic environments, offering insights and references for collision avoidance design. Full article

Background: Youth violence that takes place within school settings exposes youth to serious social, mental and physical consequences that affect education performance, and life opportunities. Previous work shows positive youth development frameworks can promote social-emotional learning by enhancing empathy and building problem-solving and conflict management skills. Theater-based interventions have been shown to enhance social emotional development by privileging youth voices, and building youth capacities and strengths. The current manuscript presents the evaluation of an arts-based and public health framework conducted to assess the development, implementation and impact of a forum theater production, Say Something, Do Something (SSDS) in St. Louis, Missouri. Methods: An iterative mixed methods approach was used, starting with observations of productions. Using convenience sampling, we then conducted post interviews of the theater team (n = 8) and school personnel (n = 10). Results: Respondents highlighted that as a result of engagement of school personnel in program development, the language and scenarios presented were relevant to students. Data indicated that SSDS increased student knowledge and changed attitudes, developed student conflict management and problem-solving skills, and improved interpersonal behavior. SSDS also raised awareness of the importance of, and created the foundation for, additional system and policy changes in the schools. Conclusion and implications: Forum theater is an approach that can enhance socio-emotional learning and conflict management among youth. Collaborative initiatives between public health and the arts are poised to uniquely engage community partners, animate interventions, and impact critical public health issues including youth violence prevention. Full article