Search Results (962)

This study addresses the problems of poor dynamic stability, high vibration coupling, and inefficient energy use in large-farm manure handling machines. A profiling wheel-based multi-disciplinary approach is proposed in the study. With the rocker arm prototype, double-ball heads, and a hydraulic damping system, a parametric design is built that includes vibration and energy consumption. The simulation results in EDEM2022 and ANSYS2022 prove the structure viability and motion compensation capability, while NSGA-II optimizes the damping parameters (k₁ = 380 kN/m, C = 1200 Ns/m). The results show a 14.7% σ_Fc reduction, 14.3% α_RMS decrease, resonance avoidance (14–18 Hz), Δx (horizontal offset of the frame) < 5 mm, 18% power loss to 12.5%, and 62% stability improvement. The new research includes constructing a dynamic model by combining the Hertz contact theory with the modal decoupling method, while interacting with an automatic algorithm of adaptive damping and a mechanical-hydraulic-control-oriented optimization platform. Future work could integrate lightweight materials and multi-machine collaboration for smarter, greener manure cleaning. Full article

Chazence is a subsidiary of Zence Object Technology Company in the Greater Bay Area of China. It is a sustainable enterprise that combines tea industry consumables (tea residue) with fiber composite technology to replace traditional materials and conduct product practices. Their core philosophy aligns with the United Nations’ Sustainable Development Goals (SDGs), particularly Goals 9 and 12, emphasizing industrial innovation, the sustainable management of natural resources, and the promotion of sustainable consumption and production patterns. However, the current system of tea recycling is extensive and requires precise data management and back-end human resource allocation to ensure efficient collaboration between professionals and grassroots staff. Currently, the system does not have a user-friendly interface for human resource allocation, data management, resource management, and visual information. Therefore, we optimized the interface and functional design of the warehouse system to improve the efficiency of resource management of Chazence by understanding its approach to tea recycling. Through surveys and interviews, employee needs and user experiences were analyzed, and the results guide the design of a sustainable enterprise management system from a user experience (UX) perspective. Full article

With the expansion of vehicle-to-everything (V2X) networks and the rising demand for intelligent services, vehicle edge computing encounters heightened requirements for more efficient task offloading. This study proposes a task offloading technique that utilizes federated collaboration and multi-agent deep reinforcement learning to reduce system latency and energy consumption. The task offloading issue is formulated as a Markov decision process (MDP), and a framework utilizing the Multi-Agent Dueling Double Deep Q-Network (MAD3QN) is developed to facilitate agents in making optimal offloading decisions inside intricate environments. Secondly, Federated Learning (FL) is implemented during the training phase, leveraging local training outcomes from many vehicles to enhance the global model, thus augmenting the learning efficiency of the agents. Experimental results indicate that, compared to conventional baseline algorithms, the proposed method decreases latency and energy consumption by at least 10% and 9%, respectively, while enhancing the average reward by at least 21%. Full article

In the past 25 years, the Qinghai–Tibet Plateau has experienced a significant climate transition, which directly triggers vegetation degradation. Vegetation degradation also aggravated the water erosion process in the Qinghai–Tibet Plateau. The accelerated warming from 2011 led to the emergence of degraded patches in the central region. The spatial heterogeneity of erosion intensity in the degraded area of Northwest China is significantly enhanced by the extreme climate events after 2021. In recent years, under the influence of human activities, vegetation degradation has aggravated the water erosion phenomenon. Based on the above content, this study analyzes the literature on the impact of vegetation on water erosion in the Qinghai–Tibet Plateau under climate change from 2008 to 2025 from the perspective of bibliometrics. CiteSpace software v.6.3.R1 was used to visualize the knowledge map of the 206 selected articles, and the research hotspots, topics, and development process in this field were analyzed. The results show that the main research hotspots in this field are climate change, basin, CO₂ consumption, etc., which can be divided into eight main research topics; after three stages of development, the research relationship between climate–vegetation–water erosion has gradually become clear. By identifying research gaps, future research can consider three aspects: cross-scale multi-dimensional analysis, technical method innovation, and policy collaborative research to address the dual challenges of vegetation degradation and water erosion in the Qinghai–Tibet Plateau under the dual pressures of climate change and human activities. Full article

This paper explores the feasibility and implications of developing a privacy-preserving, data-driven cloud service for predicting the energy consumption of industrial robots. Using machine learning, we evaluated three neural network architectures—dense, LSTM, and convolutional–LSTM hybrids—to model energy usage based on robot trajectory data. Our results show that models incorporating manually engineered features (angles, velocities, and accelerations) significantly improve prediction accuracy. To ensure secure collaboration in industrial environments where data confidentiality is critical, we integrate privacy-preserving machine learning (ppML) techniques based on secure multi-party computation (SMPC). This allows energy inference to be performed without exposing proprietary model weights or confidential input trajectories. We analyze the performance impact of SMPC on different network types and evaluate two optimization strategies, using public model weights through permutation and evaluating activation functions in plaintext, to reduce inference overhead. The results highlight that network architecture plays a larger role in encrypted inference efficiency than feature dimensionality, with dense networks being the most SMPC-efficient. In addition to model development, we identify and discuss specific stages in the MLOps workflow—particularly model serving and monitoring—that require adaptation to support ppML. These insights are useful for integrating ppML into modern machine learning pipelines. Full article

This paper examines the evolving role of social economy organisations (SEOs) in advancing sustainability and contributing to the green transition. While traditionally focused on social inclusion and local development, SEOs are increasingly integrating environmental objectives into their operations, particularly through circular economy (CE) practices, decarbonisation strategies, and short food supply chains (SFSCs). Based on qualitative research and the analysis of 16 good practices from five European countries, the study demonstrates how SEOs create blended social and environmental value by combining economic, social, and ecological goals. The findings show that SEOs foster environmental sustainability by reducing resource consumption and carbon emissions, creating green jobs, strengthening local cooperation, and raising environmental awareness within communities. Importantly, SEOs emerge not only as service providers but also as innovators and agents of change in local ecosystems. The paper concludes with policy recommendations to enhance the role of SEOs in the green transition and identifies directions for future research, particularly regarding the measurement of their long-term environmental impact and the conditions enabling effective collaboration with public and private sector actors. Full article

The rapid economic growth in major coal-consuming countries has often come at the cost of environmental quality and social well-being. This study is urgently needed to provide empirical evidence on how such growth impacts sustainable development, helping policymakers balance economic progress with environmental protection and social welfare in an era of increasing climate concerns. Despite growing attention on sustainability, few studies have examined how key economic-environmental transformation drivers, such as coal consumption, financial development, globalization, urbanization, and economic growth, affect social sustainability. This study addresses this gap by analyzing the impact of these drivers on social sustainability in the world’s leading coal-consuming countries, as classified by Global Firepower. Using data from ten major coal-consuming nations between 1991 and 2022, sourced from the International Monetary Fund (IMF), KOF Swiss Economic Institute, the BP Statistical Review of World Energy, the World Bank’s World Development Indicators (WDIs), and the United Nations Development Programme (UNDP), the study applies advanced estimation techniques, including the Augmented Mean Group (AMG) and Feasible Generalized Least Squares (FGLS), to address cross-sectional dependence and slope heterogeneity. The results indicate that coal consumption has a negative and significant effect on social sustainability. In contrast, financial development, globalization, urbanization, and economic growth all show positive and significant effects. These findings highlight the urgent need for deliberate policy reforms to support a socially inclusive energy transition. Policymakers in major coal-consuming countries should invest in clean energy, fund worker retraining and community health, promote green innovation, and encourage private sector and stakeholder collaboration for a just, sustainable transition. Such measures are vital for coal-dependent countries to balance economic progress with social well-being. This study is the first to quantify social sustainability using the HDI, addressing a gap in the literature concerning the relationship between coal consumption and social development, thereby providing a quantitative basis for formulating policies that balance equity and decarbonization. Full article

This study develops a three-dimensional evaluation framework integrating policy instruments, policy themes, and policy subjects to analyze China’s ESG (Environmental, Social, and Governance) policies. Based on 82 central government policy documents issued between 2007 and 2024, it employs content analysis, Latent Dirichlet Allocation (LDA) topic modeling, and social network analysis. The findings reveal a structural imbalance in policy instruments, with overreliance on environmental instruments and insufficient application of supply side and demand side mechanisms. Four major policy themes are identified: environmental governance, corporate responsibility and disclosure, technological innovation, and financial development. These themes show evolving priorities aligned with national strategies. Social network analysis shows weak coordination among stakeholders, with only a few central agencies driving most policies. This research contributes a systematic and quantitative approach to ESG policy evaluation, offering insights into structural shortcomings and governance fragmentation. It provides actionable recommendations for optimizing instrument use, enhancing thematic design, and improving multi-agency collaboration in ESG policymaking. This study contributes to the achievement of the United Nations Sustainable Development Goals (SDGs), particularly SDG 12 (Responsible Consumption and Production) and SDG 13 (Climate Action), by evaluating China’s ESG policies and proposing a more balanced and pragmatic policy framework. Full article

As global awareness of environmental and social challenges continues to rise, companies are increasingly re-evaluating how they connect with consumers. This study investigates the role of digital marketing in promoting more sustainable consumer behaviours. Based on a systematic review of peer-reviewed literature retrieved from the Scopus database, and conducted following the PRISMA framework, this research analysed 84 academic publications. The findings highlight that strategies such as personalised messaging, social media engagement, influencer collaborations, and eco-conscious branding are significantly influencing purchasing decisions. Approaches rooted in transparency, emotional storytelling, and ethical data practices appear to enhance consumer trust and strengthen brand relationships. Although the field is technically well developed, it remains underexplored in areas such as digital accessibility and ethical governance. Overall, this study suggests that, when aligned with sustainable values, digital marketing becomes more than a promotional tool—it emerges as a key driver of responsible consumption and the cultivation of long-term, value-based connections between consumers and brands. 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

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

Solid waste for incineration and wastewater from the country’s largest city, Mexico City (CDMX), is transported to the southern region of Valle del Mezquital (MV). This area also hosts an oil refinery, a thermoelectric plant (PEMEX-CFE), cement factories, industrial corridors, and mining operations, all of which harm environmental and public health. From a Political Ecology (PE) perspective, we examine the mechanisms of accumulation, emphasizing the allocation of property titles and the extraction of rent as an environmental reservoir. We also explore the power of socio-environmental movements to provide a comprehensive understanding of environmental conflict. Based on economic power structures, we identify a geopolitical configuration that deepens the spatial divisions between labor in the MV and consumption in CDMX, exacerbating health disparities. We conclude that an unequal geography has been built that has produced capitalist and rentier landowners who are exempt from the externalities that have produced a sacrifice zone. The Mexican State is a key stakeholder, collaborating with the industrial elite in both legal and illegal spheres. Within this sacrifice zone, the inhabitants of the MV have resisted pollution and industrial accidents for over 50 years. Despite publicizing their struggle internationally and collaborating with academics, members of the movement have been assassinated. Full article

The built environment represents a significant portion of global resource consumption and waste generation, underscoring the pressing necessity for innovative circular economy approaches in architecture. This paper presents the findings of a systematic literature review on six critical areas: circular economy, circularity indicators, design for adaptability, design for disassembly, life cycle assessment, and material and component reuse. The analysis revealed the emergent aspects of circular economy practices in architecture, emphasizing the preeminence of life cycle assessment (LCA) and material reuse. However, the authors observe a relative scarcity of focus on design-for-adaptability and circularity indicators, highlighting a gap to be addressed. The findings underline the need for unified assessment tools, supportive regulations, and collaborative frameworks that can enable the full potential of circular architecture. By harnessing innovative reuse strategies from deconstruction projects, the circular economy offers a transformative pathway towards reducing emissions and fostering regenerative practices that can enhance material and component recovery and significantly contribute to decarbonization and the realization of sustainable development goals. Full article

The Internet of Things (IoT) has promoted emerging applications that require massive device collaboration, heavy computation, and stringent latency. Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) systems can provide flexible services for user devices (UDs) with wide coverage. The optimization of both latency and energy consumption remains a critical yet challenging task due to the inherent trade-off between them. Joint association, offloading, and computing resource allocation are essential to achieving satisfying system performance. However, these processes are difficult due to the highly dynamic environment and the exponentially increasing complexity of large-scale networks. To address these challenges, we introduce a carefully designed cost function to balance the latency and the energy consumption, formulate the joint problem into a partially observable Markov decision process, and propose two multi-agent deep-reinforcement-learning-based schemes to tackle the long-term problem. Specifically, the multi-agent proximal policy optimization (MAPPO)-based scheme uses centralized learning and decentralized execution, while the closed-form enhanced multi-armed bandit (CF-MAB)-based scheme decouples association from offloading and computing resource allocation. In both schemes, UDs act as independent agents that learn from environmental interactions and historic decisions, make decision to maximize its individual reward function, and achieve implicit collaboration through the reward mechanism. The numerical results validate the effectiveness and show the superiority of our proposed schemes. The MAPPO-based scheme enables collaborative agent decisions for high performance in complex dynamic environments, while the CF-MAB-based scheme supports independent rapid response decisions. Full article

In high-voltage switchgear, partial discharge (PD) detection using six-degree-of-freedom (6-DOF) manipulators presents challenges. However, these involve inverse kinematics (IK) solution redundancy and the lack of synergistic optimization between end-effector positioning accuracy and energy consumption. To address these issues, a dual-layer adaptive optimization model integrating multiple algorithms is proposed. In the first layer, a spatio-temporal correlation particle memory-based particle swarm optimization BP neural network (STPSO-BP) is employed. It replaces traditional IK, while long short-term memory (LSTM) predicts particle movement trends, and trajectory similarity penalties constrain search trajectories. Thereby, positioning accuracy and adaptability are enhanced. In the second layer, a chaotic mapping-based simulated annealing (CM-SA) algorithm is utilized. Chaotic joint angle constraints, dynamic weight adjustment, and dynamic temperature regulation are incorporated. This approach achieves collaborative optimization of energy consumption and positioning error, utilizing cubic spline interpolation to smooth the joint trajectory. Specifically, the positioning error decreases by 68.9% compared with the traditional BP neural network algorithm. Energy consumption is reduced by 60.18% in contrast to the pre-optimization state. Overall, the model achieves significant optimization. An innovative solution for synergistic accuracy–energy control in 6-DOF manipulators for PD detection is offered. Full article