Search Results (510)

Agricultural plastic waste is a growing global concern, as the widespread use of plastics in farming paired with limited waste management infrastructure has led to environmental pollution, resource inefficiency, and practical challenges in rural communities. This review systematically analyzes international policy frameworks and technological advancements aimed at improving agricultural plastic waste management, drawing on peer-reviewed literature and policy documents identified through targeted database searches and screened by transparent inclusion criteria. Comparative analysis of national strategies, such as extended producer responsibility, regional management models, and technology-driven incentives, is combined with a critical evaluation of recycling and biodegradable innovations. The results reveal that while integrated policies can enhance collectthion efficiency and funding stability, their implementation often encounters high costs, logistical barriers, and variability in stakeholder commitment. Advanced recycling methods and emerging biodegradable materials demonstrate technical promise, but face challenges related to field performance, cost-effectiveness, and scalability. The review concludes that sustainable management of agricultural plastics requires a multi-faceted approach, combining robust regulation, economic incentives, technological innovation, and ongoing empirical assessment. These findings emphasize the importance of adapting strategies to local contexts and suggest that the successful transition to circular management models will depend on continued collaboration across policy, technology, and stakeholder domains. Full article

To enhance the overall operational efficiency of heavy haul railway port stations, which serve as critical hubs in rail–water intermodal transportation systems, this study develops a novel scheduling optimization method that integrates operation plans and resource allocation. By analyzing the operational processes of heavy haul trains and shunting operation modes within a hybrid unloading system, we establish an integrated scheduling optimization model. To solve the model efficiently, a dual-agent advantage actor–critic with Pareto reward shaping (DAA2C-PRS) algorithm framework is proposed, which captures the matching relationship between operations and resources through joint actions taken by the train agent and the shunting agent to depict the scheduling decision process. Convolutional neural networks (CNNs) are employed to extract features from a multi-channel matrix containing real-time scheduling data. Considering the objective function and resource allocation with capacity, we design knowledge-based composite dispatching rules. Regarding the communication among agents, a shared experience replay buffer and Pareto reward shaping mechanism are implemented to enhance the level of strategic collaboration and learning efficiency. Based on this algorithm framework, we conduct experimental verification at H port station, and the results demonstrate that the proposed algorithm exhibits a superior solution quality and convergence performance compared with other methods for all tested instances. Full article

In the context of the coordinated operation of microgrids and community energy storage systems, achieving optimal resource allocation under complex and uncertain conditions has emerged as a prominent research focus. This study proposes a robust collaborative optimization model for microgrids and community energy storage systems under a game-theoretic environment where potential fraudulent behavior is considered. A multi-energy collaborative system model is first constructed, integrating multiple uncertainties in source-load pricing, and a max-min robust optimization strategy is employed to improve scheduling resilience. Secondly, a game-theoretic model is introduced to identify and suppress manipulative behaviors by dishonest microgrids in energy transactions, based on a Nash bargaining mechanism. Finally, a distributed collaborative solution framework is developed using the Alternating Direction Method of Multipliers and Column-and-Constraint Generation to enable efficient parallel computation. Simulation results indicate that the framework reduces the alliance’s total cost from CNY 66,319.37 to CNY 57,924.89, saving CNY 8394.48. Specifically, the operational costs of MG1, MG2, and MG3 were reduced by CNY 742.60, CNY 1069.92, and CNY 1451.40, respectively, while CES achieved an additional revenue of CNY 5130.56 through peak shaving and valley filling operations. Furthermore, this distributed algorithm converges within 6–15 iterations and demonstrates high computational efficiency and robustness across various uncertain scenarios. Full article

China boasts abundant geoheritage, including numerous paleontological geosites; however, many of these geosites are currently at high risk of degradation and face considerable challenges in protection and management. Using Liaoning Province as a case study, this study employs Geographic Information Systems (GIS) and spatial analysis to conduct the systematic data mining of provincial paleontological geosites. We quantitatively examine their spatiotemporal distribution patterns, identify key natural and socio-economic factors influencing their spatial occurrence, and pinpoint areas at high risk of degradation. Results reveal that the distribution of paleontological geosites across prefectural-city, regional, and geological time scales is highly uneven, leading to significant disparities in scientific research, resource allocation, and geotourism development. Significant spatial correlations are observed between the locations of these geosites and natural parameters as well as socio-economic indicators, providing a theoretical foundation for designing targeted conservation measures and precise management strategies. Based on these findings, the study proposes a multi-scale geoheritage conservation framework for Liaoning, which systematically addresses protection strategies across three distinct dimensions: at the prefectural-level city scale, through precise basic management, systematic investigation, and differentiated protection measures; at the regional scale, by enhancing collaborative mechanisms and establishing an integrated conservation network; and at the geological time scale, by deepening value recognition and promoting forward-looking conservation initiatives. This study not only offers tailored recommendations for conserving paleontological heritage in Liaoning, but also presents a transferable research model for other regions rich in paleontological resources worldwide, thereby bridging the gap between geoheritage conservation needs and practical solutions. Full article

Urban networks are a key force in reshaping regional resilience patterns. However, existing research has not yet systematically elucidated, from a physical–virtual integration perspective, the underlying mechanisms through which composite urban networks shape multidimensional urban resilience in regions confronted with severe environmental and infrastructural challenges. Northwest China, characterized by its extreme arid climate, pronounced core–periphery structure, and heavy reliance on overland transportation, provides an important empirical context for examining the unique relationship between network centrality and the mechanisms of resilience formation. Based on the panel data of 33 prefecture-level cities in northwest China from 2011 to 2023, this article empirically examines the impact of the composite urban network constructed by traffic and information flows on urban resilience from the perspective of network node centrality using a two-way fixed-effects model. It is found that (1) the spatial evolution of urban resilience in northwest China is characterized by “core leadership—gradient agglomeration”: provincial capitals demonstrate significantly the highest resilience levels, while non-provincial cities are predominantly characterized by medium resilience and contiguous distribution, and the growth rate of low-resilience cities is faster, which pushes down the relative gap in the region, but the absolute gap persists; (2) the urban network in this region is characterized by a highly centralized topology, which improves the efficiency of resource allocation yet simultaneously introduces systemic vulnerability due to its over-reliance on a limited number of core hubs; (3) urban network centrality exerts a significant positive impact on resilience enhancement (β = 0.002, p < 0.01) and the core nodes of the city through the control of resources to strengthen the economic, ecological, social, and infrastructural resilience; (4) multi-dimensional factors synergistically drive the resilience, with the financial development level, economic density, and informationization level as a positive pillar. The population size and rough water utilization significantly inhibit the resilience of the region. Accordingly, the optimization path of “multi-center resilience network reconstruction, classified measures to break resource constraints, regional wisdom, and collaborative governance” is proposed to provide theoretical support and a practical paradigm for the construction of resilient cities in northwest China. Full article

Rapid urbanization has intensified the systemic inequities of resources and infrastructure distribution in informal settlements, particularly in the Global South. Digital Twin Modeling (DTM), as an effective data-driven representation, enables real-time analysis, scenario simulation, and design optimization, making it a promising tool to support urban resilience. This study introduces the Integrated Modification Methodology (IMM), developed by Politecnico di Milano (Italy), to explore how DTM can be systematically structured and transformed into an active instrument, linking theories with practical application. Focusing on Paranoá (Brasília), a case study developed under the NBSouth project in collaboration with the Politecnico di Milano and the University of Brasília, this research integrates advanced spatial mapping with comprehensive key performance indicators (KPIs) analysis to address developmental and environmental challenges during the regeneration process. Key metrics—Green Space Diversity, Ecosystem Service Proximity, and Green Space Continuity—were analyzed by a Geographic Information System (GIS) platform on 30 m by 30 m sampling grids. Additional KPIs across urban structural, environmental, and mobility layers were calculated to support the decision-making process for strategic mapping. This study contributes to theoretical advancements in DTM and broader discourse on urban regeneration under climate stress, offering a systemic and practical approach for multi-dimensional digitalization of urban structure and performance, supporting a more adaptive, data-based, and transferable planning process in the Global South. Full article

With the upgrading of consumption driving the transformation of the home furnishing industry towards personalized customization, panel furniture enterprises are confronted with a core contradiction between large-scale production and individualized demands: The traditional production management model is unable to cope with the chaos in production scheduling, resource waste, and low collaborative efficiency caused by small-batch and multi-variety orders. This paper proposes an intelligent production scheduling system that integrates Enterprise Resource Planning (ERP), Manufacturing Execution System (MES), Advanced Planning and Scheduling (APS), and Warehouse Management System (WMS), and elaborates on its data processing methods and specific application processes in each production stage. Compared with the traditional model, it effectively overcomes limitations such as coarse-grained planning, delayed execution, and information islands in middle-level systems, achieving deep collaboration between planning, workshop execution, and warehouse logistics. Empirical studies show that this system not only can effectively reduce the production costs of customized panel furniture manufacturers, enhance their market competitiveness, but also provides a digital transformation framework for the entire customized panel furniture manufacturing industry, with significant theoretical and practical value. Full article

Port seaside scheduling, involving berth allocation, quay crane, and tugboat scheduling, is central to intelligent port operations. This survey reviews and statistically analyzes 152 academic publications from 2000 to 2025 that focus on optimization techniques for port seaside scheduling. The reviewed methods span mathematical modeling and exact algorithms, heuristic and simulation-based approaches, and agent-based and learning-driven techniques. Findings show deterministic models remain mainstream (77% of studies), with uncertainty-aware models accounting for 23%. Heuristic and simulation approaches are most commonly used (60.5%), followed by exact algorithms (21.7%) and agent-based methods (12.5%). While berth and quay crane scheduling have historically been the primary focus, there is growing research interest in tugboat operations, pilot assignment, and vessel routing under navigational constraints. The review traces a clear evolution from static, single-resource optimization to dynamic, multi-resource coordination enabled by intelligent modeling. Finally, emerging trends such as the integration of large language models, green scheduling strategies, and human–machine collaboration are discussed, providing insights and directions for future research and practical implementations. Full article

In recent years, digital twin technology has demonstrated remarkable potential in intelligent transportation systems, leveraging its capabilities of high-precision virtual mapping and real-time dynamic simulation of physical entities. By integrating multi-source data, it constructs virtual replicas of vehicles, roads, and infrastructure, enabling in-depth analysis and optimal decision-making for traffic scenarios. In vehicular networks, existing information caching and transmission systems suffer from low real-time information update and serious transmission delay accumulation due to outdated storage mechanism and insufficient interference coordination, thus leading to a high age of information (AoI). In response to this issue, we focus on pairwise road side unit (RSU) collaboration and propose a digital twin-integrated framework to jointly optimize information caching and communication power allocation. We model the tradeoff between information freshness and resource utilization to formulate an AoI-minimization problem with energy consumption and communication rate constraints, which is solved through deep reinforcement learning within digital twin systems. Simulation results show that our approach reduces the AoI by more than 12 percent compared with baseline methods, validating its effectiveness in balancing information freshness and communication efficiency. Full article

China frequently experiences natural disasters, making emergency language services a key link in information transmission, cross-lingual communication, and resource coordination during disaster relief. Traditional contingency plans rely on manual experience, which results in low efficiency, limited coverage, and insufficient dynamic adaptability. Large language models (LLMs), with their advantages in semantic understanding, multilingual adaptation, and scalability, provide new technical approaches for emergency language services. Our study establishes the country’s first generative evaluation index system for emergency language service contingency plans, covering eight major dimensions. Through an evaluation of 11 mainstream large language models, including Deepseek, we find that these models perform excellently in precise service stratification and resource network stereoscopic coordination but show significant shortcomings in legal/regulatory frameworks and mechanisms for dynamic evolution. It is recommended to construct a more comprehensive emergency language service system by means of targeted data augmentation, multi-model collaboration, and human–machine integration so as to improve cross-linguistic communication efficiency in emergencies and reduce secondary risks caused by information transmission barriers. Full article

This study aims to systematically reveal, from the perspective of organizational scale, the differences between large and small architectural design organizations in China in terms of characteristics of production activities, technological capabilities and innovation levels, resource integration capabilities, and client groups, and to quantify the priority order of clients’ attention to architectural design products, thereby providing a reference for industry structure optimization and strategic decision making. This research combines case analysis and comparative study to construct a four-dimensional comparative framework. The results show that large design organizations, leveraging their advantages in technological research and development as well as resource integration, focus on large-scale complex projects, technology-driven projects, cultural landmark projects, and multi-stakeholder collaborative projects, primarily serving government agencies and large enterprises. In contrast, small design organizations excel in flexibility, concentrating on small-scale simple projects, specialized niche projects, localized projects, and short-cycle, low-budget projects, serving individual owners and small businesses. Furthermore, this study adopts the Analytic Hierarchy Process (AHP) to establish an evaluation model. Twenty experts from architectural design organizations, construction organizations, and research institutions were invited to score the survey questionnaires, and quantitative weight analysis was performed. The research findings provide support for the optimization of the industry. Full article

Global ambitions for net-zero emissions and resource circularity are propelling industry from linear “make-use-dispose”models toward closed-loop value creation. Remanufacturing, which aims to restore end-of-life products to a “like-new” condition, plays a central role in this transition. However, its stochastic inputs and complex, multi-stage processes pose significant challenges to traditional production planning methods. This study delivers an integrated overview of remanufacturing scheduling by combining a systematic bibliometric review of 190 publications (2005–2025) with a critical synthesis of modelling approaches and enabling technologies. The bibliometric results reveal five thematic clusters and a 14% annual growth rate, highlighting a shift from deterministic, shop-floor-focused models to uncertainty-aware, sustainability-oriented frameworks. The scheduling problems are formalised to capture features arising from variable core quality, multi-phase precedence, and carbon reduction goals, in both centralised and cloud-based systems. Advances in human–robot disassembly, vision-based inspection, hybrid repair, and digital testing demonstrate feedback-rich environments that increasingly integrate planning and execution. A comparative analysis shows that, while mixed-integer programming and metaheuristics perform well in small static settings, dynamic and large-scale contexts benefit from reinforcement learning and hybrid decomposition models. Finally, future directions for dynamic, collaborative, carbon-conscious, and digital-twin-driven scheduling are outlined and investigated. Full article

Background: Undergraduate nursing students (UNSs) often enter clinical training just as they are still mastering the emotional labor of the profession. In Nepal, where teaching hierarchies discourage upward dialogue and hospitals routinely struggle with overcrowding, supply shortages, and outward nurse migration, these learners confront a distinct, under-documented burden of psychological distress. Objective: This study examines how UNSs interpret, negotiate, and cope with the mental health challenges that arise at the intersection of cultural deference, resource scarcity, and migration-fueled uncertainty. Methods: A qualitative design employing reflexive thematic analysis (RTA), guided by the Reflexive Thematic Analysis Reporting Guidelines (RTARG), was used. Fifteen second-, third-, and fourth-year Bachelor of Science in Nursing students at a major urban tertiary institution in Nepal were purposively recruited via on-campus digital flyers and brief in-class announcements that directed students (by QR code) to a secure sign-up form. Participants then completed semi-structured interviews; audio files were transcribed verbatim and iteratively analyzed through an inductive, reflexive coding process to ensure methodological rigor. Results: Four themes portray a continuum from silenced struggle to systemic constraint. First, Shrouded Voices, Quiet Connections captures how students confide only in trusted peers, fearing that formal disclosure could be perceived as weakness or incompetence. Second, Performing Resilience: Masking Authentic Struggles describes the institutional narratives of “strong nurses” that drive students to suppress anxiety, adopting scripted positivity to satisfy assessment expectations. Third, Power, Hierarchy, and the Weight of Tradition reveals that strict authority gradients inhibit questions in classrooms and clinical placements, leaving stress unvoiced and unaddressed. Finally, Overshadowed by Systemic Realities shows how chronic understaffing, equipment shortages, and patient poverty compel students to prioritize patients’ hardships, normalizing self-neglect. Conclusions: Psychological distress among Nepalese UNSs is not an individual failing but a product of structural silence and resource poverty. Educators and policymakers must move beyond resilience-only rhetoric toward concrete reforms that dismantle punitive hierarchies, create confidential support avenues, and embed collaborative pedagogy. Institutional accountability—through regulated workloads, faculty-endorsed wellbeing forums, and systematic mentoring—can shift mental health care from a private struggle to a shared professional responsibility. Multi-site studies across low- and middle-income countries are now essential for testing such system-level interventions and building a globally resilient, compassionate nursing workforce. Full article

The swift growth of the low-altitude economic ecosystem and Unmanned Aerial Vehicle (UAV) swarm applications across diverse sectors presents significant challenges for engineering managers in terms of effective performance evaluation and operational optimization. Traditional evaluation methods often struggle with the inherent complexities, dynamic nature, and multi-faceted performance criteria of UAV swarms. This study introduces a novel Bayesian Network (BN)-based multicriteria decision-making framework that systematically integrates expert intuition with real-time data. By employing variance decomposition, the framework establishes theoretically grounded, bidirectional mapping between expert-assigned weights and the network’s probabilistic parameters, creating a unified model of subjective expertise and objective data. Comprehensive validation demonstrates the framework’s efficacy in identifying critical performance drivers, including environmental awareness, communication ability, and a collaborative decision. Ultimately, our work provides engineering managers with a transparent and adaptive tool, offering actionable insights to inform resource allocation, guide technology adoption, and enhance the overall operational effectiveness of complex UAV swarm systems. Full article

In the era of digital–real integration, open-source collaboration has become a strategic pathway for accelerating the innovation catch-up of China’s industrial software. This study employs an exploratory multi-case design, focusing on the China Automotive Operating System open-source project and the FastCAE open-source domestic CAE software integrated development platform to examine how open-source strategies shape collaborative mechanisms and innovation outcomes. The analysis reveals that firms adopt both formal (behavioral and outcome coordination) and informal (relationship and empowerment coordination) strategies, fostering high-level complementary collaboration in data, technology, institution, and human resources. These mechanisms significantly enhance R&D efficiency and quality, drive technological innovation, and create new market innovation, thereby improving collaborative performance. The study contributes to theory by linking open-source-driven digital–real integration with industrial software innovation catch-up and offers practical governance recommendations for strengthening China’s industrial software autonomy and ecosystem sustainability. Full article