Search Results (162)

Due to the recent significant increase in the scale of both domestic and international cargo transportation, the transport sector is becoming an important factor in the country’s economic development. This implies the need to improve all links in the cargo transportation chain. A key role in it is played by logistics centers, which in their activities must meet both state (CO₂ emissions, reduction in road load, increase in transportation safety, etc.) and commercial (cargo transportation in the shortest time and at the lowest cost) requirements. The objective of the paper is freight transportation from China to European countries, reflecting issues of CO₂ emissions, reduction in road load, and increase in transportation safety. Transport operations from the manufacturer to the logistics center are especially important in this chain, since the efficiency of transportation largely depends on the decisions made by its employees. They select the appropriate types of transport (air, sea, rail, and road transport) and routes for a specific situation. In methodology, the analyzed problem can be presented as a dynamic multi-criteria decision model. It is assumed that the decision-maker—the manager responsible for planning transportation operations—is interested in achieving three basic goals: financial goal minimizing total delivery costs from factories to the logistics center, environmental goal minimizing the negative impact of supply chain operations on the environment, and high level of customer service goal minimizing delivery times from factories to the logistics center. The proposed methodology allows one to reduce the total carbon dioxide emission by 1.1 percent and the average duration of cargo transportation by 1.47 percent. On the other hand, the total cost of their delivery increases by 1.25 percent. By combining these, it is possible to create optimal transportation options, effectively use vehicles, reduce air pollution, and increase the quality of customer service. All this would significantly contribute to the country’s socio-economic development. It is proposed to solve this complex problem based on a dynamic multi-criteria model. In this paper, the problem of constructing a schedule of transport operations from factories to a logistics center is considered. The analyzed problem can be presented as a dynamic multi-criteria decision model. Linear programming and the AHP method were used to solve it. Full article

The challenges of globalization and urbanization increasingly impact the Historic Urban Landscape (HUL), yet fine-grained and quantitative methods for evaluating HUL remain limited. Adopting a human-centered perspective, this study introduces a novel framework to quantitatively evaluate HUL through the lens of Historical Appearance Integrity (HAI). An evaluation system comprising four key dimensions (building materials, building colors, decorative details, and streetscape morphology) was constructed using the Analytic Hierarchy Process (AHP). An Elo rating system was subsequently applied to quantify the scores of the indicators. A prediction model was developed based on transfer learning and feature fusion to estimate the scores of the indicators. The model achieved accuracies above 93% and loss values below 0.2 for all four indicators. The framework was applied to the Inner Qinhuai Historical Character Area in Nanjing for validation. Results show that the spatial distribution of HAI in the area exhibits significant spatial heterogeneity. On a 0–100 scale, the average HAI scores were 23.17 for primary roads, 27.73 for secondary roads, and 46.93 for branch roads. This study offers a fine-grained, automated approach to evaluate HAI along urban streets and provides a quantitative reference for heritage conservation and urban renewal strategies. Full article

The northern foothills of the Qinling Mountains, a critical ecological barrier and urban–rural transition zone in China, face intensifying rainstorm–flood disasters under climate extremes and rapid urbanization. This study pioneers a remote sensing-driven, dynamically coupled framework by integrating multi-source satellite data, system resilience theory, and spatial modeling to develop a novel “risk identification–resilience assessment–scenario simulation” chain. This framework quantitatively evaluates the nonlinear response mechanisms of town–village systems to flood disasters, emphasizing the synergistic effects of spatial scale, morphology, and functional organization. The proposed framework uniquely integrates three innovative modules: (1) a hybrid risk identification engine combining normalized difference vegetation index (NDVI) temporal anomaly detection and spatiotemporal hotspot analysis; (2) a morpho-functional resilience quantification model featuring a newly developed spatial morphological resilience index (SMRI) that synergizes landscape compactness, land-use diversity, and ecological connectivity through the entropy-weighted analytic hierarchy process (AHP); and (3) a dynamic scenario simulator embedding rainfall projections into a coupled hydrodynamic model. Key advancements over existing methods include the multi-temporal SMRI and the introduction of a nonlinear threshold response function to quantify “safe-fail” adaptation capacities. Scenario simulations reveal a reduction in flood losses under ecological priority strategies, outperforming conventional engineering-based solutions by resilience gain. The proposed zoning strategy prioritizing ecological restoration, infrastructure hardening, and community-based resilience units provides a scalable framework for disaster-adaptive spatial planning, underpinned by remote sensing-driven dynamic risk mapping. This work advances the application of satellite-aided geospatial analytics in balancing ecological security and socioeconomic resilience across complex terrains. Full article

Flood hazards caused by intense short-term precipitation have led to significant social and economic losses and pose serious threats to human life and property. Accurate disaster risk assessment plays a critical role in verifying disaster statistics and supporting disaster recovery and reconstruction processes. In this study, a novel Large-Scale Flood Risk Assessment Model (LS-FRAM) is proposed, incorporating the dimensions of hazard, exposure, vulnerability, and coping capacity. Multi-source heterogeneous data are utilized for evaluating the flood risks. Soil erosion modeling is incorporated into the assessment framework to better understand the interactions between flood intensity and land surface degradation. An index system comprising 12 secondary indicators is constructed and screened using Pearson correlation analysis to minimize redundancy. Subsequently, the Analytic Hierarchy Process (AHP) is utilized to determine the weights of the primary-level indicators, while the entropy weight method, Fuzzy Analytic Hierarchy Process (FAHP), and an integrated weighting approach are combined to calculate the weights of the secondary-level indicators. This model addresses the complexity of large-scale flood risk assessment and management by incorporating multiple perspectives and leveraging diverse data sources. The experimental results demonstrate that the flood risk assessment model, utilizing multi-source data, achieves an overall accuracy of 88.49%. Specifically, the proportions of areas classified as high and very high flood risk are 54.11% in Henan, 31.74% in Shaanxi, and 18.2% in Shanxi. These results provide valuable scientific support for enhancing flood control, disaster relief capabilities, and risk management in the middle and lower reaches of the Yellow River. Furthermore, they can furnish the necessary data support for post-disaster reconstruction efforts in impacted areas. Full article

In today’s rapidly evolving global landscape, the need to cultivate innovation-ready, globally competent talent has become a strategic imperative. This study critically investigates how sustainable open innovation strategies—particularly within non-profit organizations and university alliances—can serve as a catalyst for global talent development. Responding to the growing demand for interdisciplinary, cross-sectoral collaboration, the research employs a robust mixed-methods approach, integrating the Analytic Hierarchy Process (AHP) and Fuzzy Analytic Hierarchy Process (FAHP) to evaluate and prioritize key strategic factors. The findings reveal that initiatives such as international internship programs, operational funding mechanisms, joint research ventures, and technology transfer are essential drivers in creating environments that nurture and scale global talent. Building on these insights, this study introduces a structured, sustainable innovation model that categorizes strategies into three tiers—collaborative, interactive, and foundational service-oriented actions—providing a practical roadmap for resource optimization and strategic planning. More than a theoretical exercise, this research offers actionable guidance for non-profit leaders, academic administrators, and corporate partners. It highlights the reciprocal value of multi-sector collaboration and contributes to a broader understanding of how mission-driven innovation ecosystems can foster resilient, future-ready workforces. By positioning non-profit–academic partnerships at the center of global talent strategies, the study sets a foundation for rethinking how institutions can co-create value in addressing pressing global challenges. Full article

Slope-related geological disasters occur frequently in various countries, posing significant threats to surrounding infrastructure, ecosystems, and human lives and property. Traditional manual monitoring methods for slope hazards are inefficient and have limited coverage. To enhance the monitoring and analysis of geological hazards, a study was conducted on the legacy slopes of an abandoned quarry in Jinan, Shandong Province, China. High-resolution images of the slopes were captured using unmanned aerial vehicle (UAV) phase tilt photogrammetry, and three-dimensional models were subsequently constructed. Software tools, including LiDAR360 5.2 and ArcMap 10.8, were employed to extract slope geological information, identify disaster-prone areas, and conduct stability analyses. The Analytic Hierarchy Process (AHP) was employed to further evaluate the stability of hazardous slopes. The results reveal the presence of two geohazard-prone areas in the study area. Geological analysis shows that both areas exhibit instability, with a high susceptibility to small-scale rockfalls and landslides. The integration of UAV remote sensing technology with AHP represents a novel approach, and the combination of multiple analytical methods enhances the accuracy of slope stability assessments. Full article

Fine-grained feature extraction and affective semantic mapping remain significant challenges in product form analysis. To address these issues, this study proposes a contrastive learning-based cross-modal fusion approach for product form imagery recognition, using the front-end design of new energy vehicles (NEVs) as a case study. The proposed method first employs the Biterm Topic Model (BTM) and Analytic Hierarchy Process (AHP) to extract thematic patterns and compute weight distributions from consumer review texts, thereby identifying key imagery style labels. These labels are then leveraged for image annotation, facilitating the construction of a multimodal dataset. Next, ResNet-50 and Transformer architectures serve as the image and text encoders, respectively, to extract and represent multimodal features. To ensure effective alignment and deep fusion of textual and visual representations in a shared embedding space, a contrastive learning mechanism is introduced, optimizing cosine similarity between positive and negative sample pairs. Finally, a fully connected multilayer network is integrated at the output of the Transformer and ResNet with Contrastive Learning (TRCL) model to enhance classification accuracy and reliability. Comparative experiments against various deep convolutional neural networks (DCNNs) demonstrate that the TRCL model effectively integrates semantic and visual information, significantly improving the accuracy and robustness of complex product form imagery recognition. These findings suggest that the proposed method holds substantial potential for large-scale product appearance evaluation and affective cognition research. Moreover, this data-driven fusion underpins sustainable product form design by streamlining evaluation and optimizing resource use. Full article

Habitat suitability assessment for forest and grassland ecosystems is a critical component of ecological restoration and land use planning in the Loess Plateau, aiming to advance soil and water conservation and foster sustainable ecological environment development. Despite progress in vegetation restoration, systematic evaluations of habitat suitability in complex geomorphic regions like the Loess Plateau remain scarce, particularly in balancing hydrological and ecological trade-offs. The Yanhe River Basin (7725 km²), a sediment-prone tributary of the Yellow River, exemplifies the challenges of soil erosion and semi-arid climatic constraints, making it a critical case for evaluating restoration strategies. This study employed a comprehensive approach utilizing Analytic Hierarchy Process (AHP), Geographic Detector, mathematical statistics, and other methods. An evaluation indicator system and methodology were established to assess the suitability of forest and grassland habitats in the Yanhe River Basin, evaluating the suitability and quality improvement potential under the current land use conditions. The results indicate: (1) The dominant factors influencing the suitable distribution of forests include photosynthetically active radiation (PAR), soil total phosphorus content, annual precipitation, and elevation. For grasslands, the dominant factors include photosynthetically active radiation, annual average temperature, elevation, and annual precipitation. (2) In the watershed, forestland and grassland areas classified as moderately suitable or higher cover 1064.9 km² and 4196.9 km², accounting for 91.9% and 94.7% of their total respective areas, indicating a generally rational spatial allocation of forest and grassland ecosystems. (3) The improvable area for forests measures 366 km² (34.4% of moderately or higher suitability zones), with most already meeting coverage thresholds. In contrast, grasslands have an improvable area of 2491.6 km² (59.4% of moderately or higher suitability zones), where over half of the area remains below coverage thresholds corresponding to their habitat conditions. (4) Forests can adopt natural restoration-focused low-intensity interventions through strengthened closure management, while grasslands require spatially tailored measures—such as precipitation interception and enhanced stewardship—targeting suitability-based potential grades, collectively achieving overall improvement in grassland vegetation coverage. This study represents the first systematic evaluation of forest–grassland habitat suitability in the Yanhe River Basin, elucidating its spatial distribution patterns and providing critical insights for watershed-scale ecological restoration. Full article

The landscape visual sensitivity (LVS) assessment is recognized as a critical tool for identifying areas most sensitive to landscape changes and for informing multi-resource optimization and allocation strategies. However, conventional large-scale LVS assessment criteria and methodologies developed for natural landscapes do not satisfy the precision-oriented assessment requirements of streetscape visual sensitivity (SVS) in historic districts, nor do they facilitate the operational linkage between assessment outcomes and planning applications. This study proposes an innovative SVS–PAP assessment methodology, which is a systematic integration of the SVS assessment and public esthetic perception (PAP) evaluation. The SVS assessment criteria framework was first improved through the integration of enriched multi-modal datasets. Subjective weights were obtained via the analytic hierarchy process (AHP), incorporating expert and public judgments, while objective weights were derived through the entropy weight method (EWM) based on data information entropy. The integration of both approaches enhances the methodological rigor and scientific validity of SVS weight determination. An SVS–PAP analytical matrix was subsequently constructed through integration of SVS assessments and PAP-based scenic beauty estimation (SBE), enabling the derivation of planning strategies. An empirical validation conducted in Anshandao Historic District yielded four key findings: (1) The SVS–PAP methodology, which integrates subjective–objective evaluation factors and incorporates broad public participation, demonstrates strong scientific validity and reliability, establishing a novel paradigm for SVS assessment and strategic planning; (2) The technical framework—leveraging multi-modal data and GIS spatial analysis techniques—improves assessment precision, operability, and replicability; (3) The planning and management strategies formulated by the SVS–PAP analytical matrix were verified as reasonable, demonstrating effective planning-transition capability; (4) Notably, historical and cultural influences showed significantly higher weighting coefficients across assessment criteria compared to non-historic streetscape assessments. Overall, these research results address the persistent undervaluation of the esthetic and spiritual values of historic landscapes in multi-resource value trade-off and decision-making processes, demonstrating both theoretical and practical significance through a systematic methodological advancement. Full article

A business-continuity plan is crucial in providing an organization with the ability to maintain operations against possible risks. Therefore, companies should consider holistic risk management to sustain their activities and enhance their capabilities. Also, sustainability is able to eliminate the number of adverse environmental effects and increase the financial and social performance of a company. The purpose of this paper is to evaluate the sustainability and risk performance pillars for logistics networks, including a business-continuity plan. For this particular aim, this study considers the ten main criteria and sixty-six sub-criteria to evaluate sustainability and risk performances in logistics operations when dealing with a business-continuity plan under uncertainty. A novel and innovative four-phased integrated procedure involving a fuzzy-based AHP method with novel linguistic scales and operators is proposed. The TOPSIS technique, part of the integrated technique, is also presented to rank the alternative cities for an urban logistics network analysis. Moreover, the criteria of transportation and information infrastructures are analyzed for logistics operations. A case study of the thirty metropolitan cities in Türkiye is conducted to determine the best logistics center for a logistics firm. Several scenario analyses are performed, and a comparison study is also carried out from the literature. This study comprehensively analyzes the problem, including sustainability, risks, renewable energy and social aspects. Based on the results from the fuzzy-based AHP method, economic, safety and hazard risk are the top three main criteria. Moreover, Istanbul, Konya and Ankara are the top three alternatives for logistic networks from the results of the TOPSIS technique. Finally, managerial and policy implications are presented for policy-makers who should pay attention to the main criteria and sub-criteria in this paper for successful logistics operations dealing with the business-continuity plan when achieving Sustainable Development Goals. Full article

It is well known that the safety concerns surrounding lithium-ion batteries (LIBs), such as fire and explosion, are currently a bottleneck problem for the large-scale usage of energy storage power stations. The study of water-based fire-extinguishing agents used for LIBs is a promising direction. How to choose a suitable water-based fire-extinguishing agent is a significant scientific problem. In this study, a comprehensive evaluation model, including four primary indexes and eleven secondary indexes was established, which was used in the scenario of an electrochemical energy storage power station. The model is only suitable for assessing water-based fire extinguishing for suppressing lithium iron phosphate battery fire. Based on the comprehensive evaluation index system and extinguishing experiment data, the analytic hierarchy process (AHP) combined with fuzzy TOPSIS was used to evaluate the performances of the three kinds of water-based fire-extinguishing agents. According to the results of the fuzzy binary contrast method, the three kinds of fire-extinguishing agents could be ranked as follows: YS1000 > F-500 additive > pure water. The study provided a method for choosing and preparing a suitable fire-extinguishing agent for lithium iron phosphate batteries. Full article

The integration of multi-criteria decision analysis (MCDA) and Artificial Intelligence (AI) is revolutionizing the governance of reverse supply chains for solid waste (RSCSW) within a circular economy framework. However, the existing literature lacks a systematic assessment of the effectiveness of these methods compared to traditional waste management practices. This study conducts a systematic literature review (SLR), following PRISMA guidelines and the P.I.C.O. framework, to investigate how MCDA and AI can optimize governance, operational efficiency, and the sustainability of RSCSW. After collecting 1139 articles, 22 were selected and used for analysis. The results indicate that hybrid MCDA-AI models, employing techniques, such as TOPSIS, AHP, neural networks, and genetic algorithms, enhance decision-making automation, reduce costs, and improve waste traceability. Nevertheless, regulatory barriers and technological challenges still hinder large-scale adoption. This study proposes an innovative framework to address these gaps and drive evidence-based public policies. The findings provide guidelines for policymakers and managers, contributing to the Sustainable Development Goals (SDGs) agenda and advancements in circular economy governance. Full article

Small-scale biogas systems can play a pivotal role in sustainable energy provision, particularly in developing countries. However, their dependence on livestock manure as the only feedstock poses challenges to their adoption and long-term viability. This often leads to insufficient biogas production and plant abandonment. This study proposes co-digestion of livestock manure with other farm residues to enhance the technical sustainability of small-scale biogas systems by ensuring adequate and consistent biogas production throughout the plant’s lifespan, minimizing the risks associated with reliance on a single feedstock. A novel feedstock selection approach is developed using the Analytic Hierarchy Process (AHP), a multicriteria decision-making method, to prioritize feedstocks based on adequacy, supply consistency, and logistical ease. AHP is chosen due to its capability to handle both quantitative and qualitative evaluation criteria. This approach is applied to the Fès-Meknès region of Morocco, which offers abundant livestock and crop residues alongside product utilization pathways. The prioritization and ranking of the potential feedstocks identified in the region reveals cattle manure as the top-ranked feedstock due to its consistent supply and ease of collection, followed by straw, valued for its storability and nutrient stability. Sheep, horse, and chicken manure ranked third, fourth, and fifth, respectively, while household food waste and fruit and vegetable residues, limited by seasonality and perishability, were ranked lower. Based on these findings, co-digestion of cattle manure and straw is proposed as a sustainable strategy for small-scale biogas plants in Fès-Meknès, addressing feedstock shortages, enhancing biogas production, and reducing plant abandonment. This approach strengthens technical sustainability and promotes the broader adoption of biogas technologies in developing countries. Full article

The increasingly serious imbalance between the supply and demand of water resources necessitates the establishment of a scientific and reasonable comprehensive evaluation method for industrial water use efficiency (WUE). In this study, a general method for industrial WUE evaluation on an enterprise scale was proposed by combining the analytic hierarchy process (AHP), entropy weight method (EWM), and self-organizing map (SOM), and it was tested in several areas of Zhejiang Province, China. The results show that the composite indexes generated using the AHP and EWM were different and were employed as the input of the SOM to divide enterprises into four categories. Most enterprises were classified as Class A, with a relatively high WUE, accounting for 82.5% of the total, while those in Class D, with a relatively low WUE, only accounted for 0.5% of the total. Furthermore, the differences in WUE for industry classification and spatial distribution were also analyzed. The classification results of several industries were more diverse, especially for those industries in which water plays an important role in production. Moreover, the spatial distribution of WUE classifications also implied that the clustering of enterprises has a positive effect on the improvement in WUE. In other words, it is feasible to improve WUE through industry clustering and sub-industry management. In summary, a comprehensive, detailed evaluation of industrial WUE was conducted on an enterprise scale, which can also be applied to other areas and used as a reference for local water resource managers for formulating targeted policies. Full article

This contribution presents a new approach for assessing/ranking the vulnerability of beaches to mean and extreme sea level rise at regional (island) scales. It combines socio-economic information with beach erosion projections from morphodynamic models to rank beach vulnerability in a structured, ‘holistic’ manner. It involves the collation of various beach geo-spatial environmental and socio-economic data, which are then combined with erosion projections under different climatic scenarios. A Strengths–Weaknesses–Opportunities–Threats (SWOT) framework is employed for the indicator selection, and multi-criteria methods (Analytical Hierarchy Process—AHP, Technique for Order of Preference by Similarity to Ideal Solution—TOPSIS, Preference Ranking Organization Method for Enrichment Evaluations—PROMETHEE II) are then used to optimize indicator weights and rank beach vulnerability. Framework implementation in Lesvos and Kos has shown that there will be significant effects of the mean and (particularly) of the extreme sea levels on the carrying capacity and the capability of the beaches to buffer backshore assets, in the absence of appropriate adaptation measures. As the proposed approach relies on widely available information on many of the socio-economic indicators required to assess the beach’s significance/criticality, it can provide a reproducible and transferable methodology that can be applied at different locations and spatial scales. Full article