Search Results (1,366)

This study examines the organization of offshore bunkering operations with the aim of improving their economic and logistical efficiency. A mathematical model is proposed that minimizes the total cost of fleet refueling while accounting for technical limitations of vessels, service time windows, and external operational constraints. The formulation extends classical vehicle routing approaches by incorporating fixed and variable costs as well as penalties for delays. A case study based on the Sea of Okhotsk fleet illustrates the application of the model to ten client vessels and four bunkering ships. Using mixed-integer programming combined with heuristic route construction, optimal routing solutions were obtained and tested under varying fuel prices, demand volumes, and fleet sizes. In a stylized one-day case study with ten client vessels located within a 100 km radius around Magadan, the results indicate that reducing the number of active bunkering vessels from four to three can lower overall operating costs while maintaining service quality, yielding indicative savings of approximately 12–18% relative to a simple sequential baseline policy in which bunkering vessels serve customers in a fixed order and the client set is partitioned roughly equally among vessels. The proposed approach provides a practical framework for decision-makers to enhance planning, resource allocation, and operational reliability in marine fuel supply chains. Full article

Many countries around the world repeatedly suffer from natural disasters such as earthquakes, tsunamis, floods, and hurricanes due to geographical factors, including plate boundaries, tropical cyclone zones, and coastal regions. Representative examples include Hurricane Katrina, which struck the United States in 2005, and the Great East Japan Earthquake in 2011. Both were large-scale disasters that occurred in developed countries and caused enormous human and economic losses regardless of disaster type or location. As the occurrence of such catastrophic events remains inevitable, establishing effective preparedness and rapid response systems for large-scale disasters has become an urgent global challenge. One of the critical issues in disaster response is the rapid estimation of the number of affected individuals required for effective rescue operations. During large-scale disasters, terrestrial communication infrastructure is often rendered unusable, which severely hampers the collection of situational information. If the population within a disaster-affected area can be estimated without relying on ground-based communication networks, rescue resources can be more appropriately allocated based on the estimated number of people in need, thereby accelerating rescue operations and potentially reducing casualties. In this study, we propose a population-estimation system that remotely senses radio signals emitted from smartphones in disaster areas using Low Earth Orbit (LEO) satellites. Through numerical analysis conducted in MATLAB R2023b, the feasibility of the proposed system is examined. The numerical results demonstrate that, under ideal conditions, the proposed system can estimate the number of smartphones within the observation area with an average error of 2.254 devices. Furthermore, an additional evaluation incorporating a 3D urban model demonstrates that the proposed system can estimate the number of smartphones with an average error of 19.03 devices. To the best of our knowledge, this is the first attempt to estimate post-disaster population using wireless LAN signals sensed by LEO satellites, offering a novel remote-sensing-based approach for rapid disaster response. Full article

In mass-casualty incidents, emergency responders require access to accurate and timely information to support informed decision-making and ensure the efficient allocation of resources. This article presents a dataset derived from a case study conducted in Mexico City (CDMX) based on the earthquake of 19 September 2017. The dataset presents hypothetical scenarios involving multiple demand points and large numbers of victims, making it suitable for analysis using optimization techniques. It integrates voluntary collaborative geographic information, open government data sources, and historical records, and details the data collection, cleaning, and preprocessing stages. The accompanying Python 3 source code enables users to update the original data for consistent analysis and processing. Researchers can adapt this dataset to other cities with similar risk characteristics, such as Santiago (Chile), Los Angeles (USA), or Tokyo (Japan), and extend it to other types of catastrophic events, including floods, landslides, or epidemics, to support emergency response and resource allocation planning. Full article

In the digital economy, data has emerged a pivotal driver for optimizing resource allocation, enhancing productivity, and accelerating the transition toward environmentally sustainable development. Exploring how the marketization of data elements affects corporate green innovation is of considerable theoretical and practical significance. Using the establishment of data trading platforms in China as a quasi-natural experiment, this study constructs a multi-period difference-in-differences (DID) model based on panel data of A-share listed firms between 2009 and 2022 to investigate the impact of data element marketization on corporate green innovation. The empirical results demonstrate that the marketization of data elements significantly promotes corporate green innovation, and this conclusion remains consistent across a series of robustness checks. Further exploration of the underlying mechanisms reveals that the marketization of data elements fosters green innovation by alleviating financing constraints, improving the structure of human capital, and facilitating collaborative innovation. These mechanisms highlight the role of data markets in strengthening corporate innovation capacity while reinforcing environmental responsibility. Moreover, heterogeneity analyses indicate that the promoting effect is particularly pronounced among firms located in the eastern China, regions equipped with advanced digital infrastructure, industries with lower pollution level, and non-state-owned enterprises. By linking reforms in data governance with green development objectives, this research enriches the growing literature on digital institutional transformation and corporate environmental innovation. The findings provide new empirical evidence that the establishment of data markets constitutes an effective institutional mechanism for advancing green and low-carbon development, offering valuable policy insights for integrating digital economy progress with ecological sustainability. Full article

Rapid urbanization in China has reshaped retail spatial structures, creating challenges of accessibility and service equity. This study employs a Geographic Information Systems (GIS)-based analytical framework to examine the spatial distribution and driving mechanisms of retail outlets in Taiyuan, a resource-based transition city in central China. Using 2023 Point of Interest (POI) data and a 2 km × 2 km grid system, kernel density estimation (KDE), Average Nearest Neighbor (ANN) Analysis, Location Quotient (LQ), and spatial autocorrelation were applied to identify clustering patterns and functional specialization. The GeoDetector (Word version, downloaded 2025) model further quantified the explanatory power of twelve natural, social, economic, and transportation variables. Results reveal a polycentric retail structure, with high-density clusters in Yingze and Xiaodian districts and under-supply in Jiancaoping and Jinyuan. Population density, nighttime light (NTL) intensity, and school distribution emerged as the strongest drivers, while topography constrained expansion. By integrating GIS-based spatial statistics with GeoDetector, the study demonstrates a transferable framework for analyzing urban retail spatial patterns. The findings extend retail geography to transition cities and provide practical guidance for optimizing retail allocation, enhancing service equity, and supporting spatial decision-making for sustainable urban development. Full article

Against the backdrop of China’s “East-West Computing Resource Transfer” and “Digital-Real Integration” national strategies, computing power has emerged as a core engine driving the digital economy. However, existing research lacks in-depth exploration of the micro-level mechanisms through which computing power operates as a strategic digital resource at the firm level and transforms into competitive advantages. This study examines a sample of manufacturing firms listed on China’s A-share markets from 2011 to 2022, treating the establishment of intelligent computing centers by firms as a quasi-natural experiment. Employing a staggered difference-in-differences model combined with causal inference strategies such as double machine learning, we empirically test the impact of computing power investment on firms’ new quality productivity. The findings reveal that computing power investment significantly enhances new quality productivity, primarily through enabling dynamic capabilities: it strengthens risk perception capabilities by improving information environments, enabling intelligent risk monitoring, and enhancing decision-making resilience; it elevates innovation opportunity-capturing capabilities by expanding the scope of innovation search, accelerating innovation iteration, and facilitating cross-domain knowledge integration; and it achieves data element reconstruction through constructing data infrastructure capabilities, improving data operational efficiency, and optimizing data ecosystem collaboration. Further analysis demonstrates that this promotional effect is more pronounced in firms with strong executive digital cognition and intense market competition, and is more significant among non-heavily polluting, high-tech firms with high absorptive capacity, those located in eastern regions, and those with superior digital endowments. Extended analysis also reveals that the new quality productivity gains from computing power investment drive optimal allocation of human capital while potentially inducing strategic information concealment behaviors as firms seek to protect competitive advantages. By conceptualizing computing power as a contestable strategic resource at the micro level, this study unveils the micro-mechanisms of digital transformation through a dynamic capability framework, offering important implications for firms and governments in optimizing digital strategies. Full article

Cities need emergency-shelter systems that are computationally efficient, socially fair, and consistent with long-term goals for sustainable urban development. This paper proposes a GIS-integrated, two-stage location–allocation framework for urban earthquakes that jointly optimizes shelter siting and evacuee assignment under time-varying demand. The model incorporates equity constraints that cap extreme travel burdens for vulnerable groups and robust capacity safeguards against demand uncertainty, helping prevent over- or under-investment in shelter infrastructure and promoting efficient use of land and public resources. A customized Phased Nested Local Search (PNLS) heuristic enables city-scale application and is benchmarked against a mixed-integer programming baseline solved by CPLEX. In a district-level case study of Chengdu, China, the framework reduces total assignment distance by 12.3% and the 95th-percentile travel burden by 15.8% while maintaining feasibility during the peak demand window. The results show that integrating equity, robustness, and spatial efficiency in shelter planning can strengthen urban resilience and directly support SDG 11 on sustainable cities and communities. Full article

Effective forest trail planning requires objective and transparent tools to balance user accessibility, recreation quality, and environmental protection. This research explores how geospatial analysis and multi-criteria decision-making can be integrated to optimize the allocation of rest and recreation facilities within forest trail networks, where limited resources and ecological constraints often restrict development. The Mount Paiko trail system in northern Greece was analyzed using a hybrid GIS–AHP–PROMETHEE II framework. Five evaluation criteria—trail difficulty, trail class, scenic attractiveness, distance from the trailhead, and traversal time from the nearest facility—were assessed to represent both physical effort and spatial accessibility. Stakeholder-based AHP weighting identified traversal time (C5) and trail difficulty (C1) as the most influential criteria, emphasizing the importance of user fatigue and service gaps. PROMETHEE II produced a clear hierarchy of forty candidate sites, prioritizing medium-difficulty and visually appealing routes located over 10 km from the starting point. Net flow values ranged from −0.228 to +0.309, with the highest-ranked location (PTF 12) highlighting a medium-difficulty, scenic segment with one of the longest traversal times from the nearest facility. By merging quantitative network analysis with structured expert judgment, the proposed framework offers a reproducible and evidence-based decision-support tool for forest planners and policymakers, promoting sustainable trail development that maximizes accessibility while minimizing environmental disturbance. Full article

Background: Disasters generate abrupt surges in humanitarian demand, requiring response strategies that balance operational performance with vulnerability considerations. This study examines how temporary Points of Distribution (PODs) can be planned and activated to support timely and equitable resource distribution after a high-magnitude earthquake. Methods: A two-stage framework is proposed. First, a modular p-median model identifies POD locations and allocates modular capacity to minimize population-weighted distance under capacity constraints; travel-distance percentiles guide the selection of p. Second, a SMART-based multi-criteria model ranks facilities using operational metrics and vulnerability indicators, including seismic and economic conditions and the presence of at-risk groups. Results: Evaluation of p values from 3 to 30 shows substantial reductions in travel distances as PODs increase, with an elbow at $p=12$, where 50% of the residents are within 500 m, 75% within 675 m, and 95% within 1200 m. The SMART analysis forms three priority clusters: facilities 24 and 9 as highest priority; 23, 4, 12, and 22 as medium priority; and the remaining sites as lower priority. Sensitivity analysis shows that rankings are responsive to vulnerability weights, although clusters remain stable. Conclusions: The framework integrates optimization and multi-criteria decision analysis without increasing model complexity, enabling meaningful decision-maker involvement throughout the modeling process. Full article

The increasing frequency and intensity of extreme disasters, exacerbated by climate change, pose significant challenges to sustainable development by disrupting critical infrastructure and hampering relief efforts. Enhancing disaster resilience—a core objective of sustainable development—requires integrated approaches that simultaneously address infrastructure restoration and efficient resource allocation. This study proposes a sustainable optimization framework for post-disaster response, integrating road rehabilitation decisions with emergency logistics planning within a three-tier supply chain network. We develop a mathematical model that synergistically optimizes repair crew scheduling, depot location, and vehicle routing, with the objective of maximizing a comprehensive satisfaction index that balances timely delivery (time satisfaction) and fulfillment of material needs (demand satisfaction). This integrated approach directly contributes to sustainable disaster management by ensuring more reliable and equitable access to vital resources in affected communities. A tailored variable neighborhood search algorithm is designed to solve the model efficiently, as demonstrated through large-scale numerical experiments. Our findings highlight several policy-relevant insights for sustainable emergency planning: adequate budgeting is crucial for uninterrupted relief operations; strategic investments in rapid road repair capabilities or vehicle fleets significantly enhance system efficiency; and prioritizing time satisfaction (rapid response) yields greater overall benefits than merely increasing delivered quantities. Furthermore, restoring critical road infrastructure is shown to mitigate transportation uncertainties, thereby strengthening the resilience of the entire relief system. This work provides a quantifiable methodology and practical decision support tools for building more sustainable and resilient communities in the face of disasters. Full article

Although women have participated in mining activities across the world for centuries, the industry continues to be perceived as predominantly male-oriented. This perception persists largely due to the male-dominated workforce and the physically demanding nature of mining operations. This paper examines the ergonomic impacts of mining machinery on female mineworkers. The study was conducted in three underground coal mining operations located in Mpumalanga, South Africa, using a quantitative research approach. To evaluate the ergonomic demands placed on women working underground, the researchers employed the Rapid Entire Body Assessment (REBA) in combination with direct observation techniques. The findings revealed that female mineworkers experience considerable challenges when performing tasks requiring significant physical strength and endurance. The observed female mineworker recorded a final REBA score of seven, indicating a medium-risk level. Ergonomic challenges in underground coal mining are further intensified for female mineworkers due to the absence of gender-specific considerations in equipment design, task allocation, and the overall working environment. Although the risk classification was moderate, the results underscore the need for further investigation and the timely implementation of corrective measures. Addressing these issues will require the integration of inclusive ergonomic principles that account for gender diversity within the mining workforce. Full article

To address the issues of insufficient feature utilization in high-entropy regions (such as complex textures and edges), difficulty in detail recovery, and excessive model parameters with high computational complexity in existing remote sensing image super-resolution networks, a novel dual-branch hybrid-scale feature aggregation network (HSFAN) is proposed. The design of this network aims to achieve an optimal balance between model complexity and reconstruction quality. The main branch of the HSFAN effectively expands the receptive field through a multi-scale parallel large convolution kernel (MSPLCK) module, enhancing the ability to model global structures that contain rich information, while maintaining consistency constraints in the feature space. Meanwhile, an enhanced parallel attention (EPA) module is incorporated, optimizing feature allocation by prioritizing high-entropy feature channels and spatial locations, thereby improving the expression of key details. The auxiliary branch is designed with a multi-scale large-kernel attention (MSLA) module, employing depthwise separable convolutions to significantly reduce the computational overhead in the feature processing path, while adaptive attention weighting strengthens the capture and reconstruction of local high-frequency information. Experimental results show that, for the ×4 super-resolution task on the UC Merced dataset, the proposed algorithm achieves a PSNR of 27.91 dB and an SSIM of 0.7616, outperforming most current mainstream super-resolution algorithms, while maintaining a low computational cost and model parameter count. This provides a new research approach and technical route for remote sensing image super-resolution reconstruction. Full article

The whole-plant preferential allocation patterns of recently assimilated carbon by the source leaves of six-year-old cork oaks (Quercus suber L.) were assessed 7 days after a ¹⁴CO₂ pulse-labelling in late spring (end of May). The ¹⁴CO₂ assimilation was separately induced on attached leaves on branches located at the top-down 30% of the crown height, in the middle 40% and at the bottom-up 30% of the crown height of twelve plants. Our results showed that the top source leaves retained the highest amount (64%) of their own current produced carbohydrates compared to either lower (49%) or middle (42%) source leaves. The top source leaves preferentially export current carbohydrates to their most proximal sinks, namely, other leaves or their branches. However, lower source leaves exported the highest amount of current carbon, about 37%, preferentially to the root system. Roots displayed the greatest sink strength for the available current carbohydrates, due to their largest biomass (between 69% and 75% of the whole plant biomass), when other strong sinks, such as the annual leaves, were fully expanded. Taken together, our data revealed that carbon supply by leaves and delivery to roots are critical for maintaining root growth in cork oak under Mediterranean seasonal drought conditions. Full article

To address the challenges of climate change mitigation and operational flexibility in active distribution networks (ADNs) amid high renewable energy penetration, this paper proposes a low-carbon economic dispatch framework integrating demand-side carbon regulation and cyber–physical system (CPS)-enabled shared energy storage. First, a consumer-side emission penalty mechanism is developed by fusing a carbon emission flow (CEF) model with price elasticity coefficients. This mechanism embeds carbon costs into end-user electricity pricing, guiding users to adjust consumption patterns (e.g., reducing usage during high-carbon-intensity periods) and shifting partial carbon responsibility to the demand side. Second, a CPS-based shared energy storage mechanism is constructed, featuring a three-layer architecture (physical layer, control decision layer, security layer) that aggregates distributed energy storage (DES) resources into a unified, schedulable pool. A cooperative, game-based profit-sharing strategy using Shapley values is adopted to allocate benefits based on each DES participant’s marginal contribution, ensuring fairness and motivating resource pooling. Finally, a unified mixed-integer linear programming (MILP) optimization model is formulated for ADNs, co-optimizing locational marginal prices, DES state-of-charge trajectories, and demand curtailment to minimize operational costs and carbon emissions simultaneously. Simulations on a modified IEEE 33-bus system demonstrate that the proposed framework reduces carbon emissions by 4.5–4.7% and renewable energy curtailment by 71.1–71.3% compared to traditional dispatch methods, while lowering system operational costs by 6.6–6.8%. The results confirm its effectiveness in enhancing ADN’s low-carbon performance, renewable energy integration, and economic efficiency. Full article

Cloud resource provider deployment at random locations increases operational costs regardless of the application demand intervals. To provide adaptable load balancing under varying application traffic intervals, the auto-scaling concept has been introduced. This article introduces a Pervasive Auto-Scaling Method (PASM) for Computing Resource Allocation (CRA) to improve the application quality of service. In this auto-scaling method, deep reinforcement learning is employed to verify shared instances of up-scaling and down-scaling pervasively. The overflowing application demands are computed for their service failures and are used to train the learning network. In this process, the scaling is decided based on the maximum computing resource allocation to the demand ratio. Therefore, the learning network is also trained using scaling rates from the previous (completed) allocation intervals. This process is thus recurrent until maximum resource allocation with high sharing is achieved. The resource provider migrates to reduce the wait time based on the high-to-low demand ratio between successive computing intervals. This enhances the resource allocation rate without high wait times. The proposed method’s performance is validated using the metrics resource allocation rate, service delay, allocated wait time, allocation failures, and resource utilization. Full article