Search Results (4,687)

In the context of the rapid development of e-commerce, the selection of optimal land plots for the construction of warehouse complexes that meet environmental, technical, and political requirements has become increasingly relevant. This task requires a comprehensive approach that accounts for a wide range of factors, including transportation accessibility, environmental conditions, geographic features, legal constraints, and more. Such an approach enhances the efficiency and sustainability of decision-making processes. This article presents a solution to the aforementioned problem that employs the use of land suitability maps generated by aggregating multiple evaluation criteria. These criteria represent the degree to which each land plot satisfies the requirements of various stakeholders and are expressed as suitability functions based on attribute values. Attributes describe different characteristics of the land plots and are represented as layers on a digital terrain map. The criteria and their corresponding attributes are classified as either quantitative or binary. Binary criteria are aggregated using the minimum operator, which filters out plots that violate any constraints by assigning them a suitability score of zero. Quantitative criteria are aggregated using the second-order Choquet integral, a method that accounts for interdependencies among criteria while maintaining computational simplicity. The criteria were developed based on statistical and environmental data obtained from an analysis of the Samara region in Russia. The resulting suitability maps are visualized as gradient maps, where land plots are categorized according to their degree of suitability—from completely unsuitable to highly suitable. This visual representation facilitates intuitive interpretation and comparison of different location options. These maps serve as an effective tool for planners and stakeholders, providing comprehensive and objective insights into the potential of land plots while incorporating all relevant factors. The proposed approach supports spatial analysis and land use planning by integrating mathematical modeling with modern information technologies to address pressing challenges in sustainable development. Full article

To address the logistical challenges of traffic congestion and environmental concerns associated with carbon emissions in last-mile delivery, this paper explores the potential of vehicle–drone cooperative delivery. The existing studies are predominantly confined to single-drone scenarios, failing to simultaneously consider the constraints of drone payload capacity and endurance. This limitation leads to task allocation imbalance in large-scale customer deliveries and low distribution efficiency. Firstly, a mathematical model for vehicle–multi-drone collaborative delivery with payload and endurance constraint (VMDCD-PEC) is proposed. Secondly, an improved genetic algorithm (IGA) is developed, as follows: 1. designing a hybrid selection strategy to achieve symmetrical equilibrium between exploration and exploitation by adjusting the weights of dynamic fitness–distance balance, greedy selection, and random selection; and 2. introducing the local search operator composed of gene sequence reversal, single-gene slide-down, and random half-swap to improve the neighborhood quality solution mining efficiency. Finally, the experimental results show that compared with a traditional genetic algorithm (GA) and adaptive large neighborhood search (ALNS), the IGA requires less time to find solutions in various test cases and reduces the average cost of the optimal solution by up to 30%. In addition, an analysis of drone payload sensitivity showed that drone payload capacity is negatively correlated with delivery time, and that larger customer sizes corresponded to higher sensitivity. Full article

Water reservoirs are an important source of drinking water in Slovakia and are managed under the control of regional water utility enterprises. These enterprises face increasing challenges due to ecological pressures, land use conflicts, and technological constraints. This paper investigates the external factors that influence the management of drinking water reservoirs and their implications for water quality. Using the PESTLE framework (Political, Economic, Social, Technological, Legal, and Environmental), we analyse case studies from four Slovak reservoirs: Málinec, Turček, Nová Bystrica, and Hriňová. The data was collected in 2025 using structured interviews with representatives of four water management enterprises and forest managers (three respondents from Málinec, Turček and Hriňová, two respondents from Nová Bystrica), whose forests surround the chosen water reservoirs. The analysis reveals that forest management, stakeholder relations, extreme climate events, and outdated infrastructure significantly affect water treatment efficiency and operational costs. While national water policy provides a uniform regulatory framework, the case studies demonstrate that local conditions and governance dynamics strongly impacts the water management utilities and its performance in drinking water treatment Full article

For multiple unmanned underwater vehicles (UUVs) systems, obstacle avoidance during cooperative operation in complex marine environments remains a challenging issue. Recent studies demonstrate the effectiveness of deep reinforcement learning (DRL) for obstacle avoidance in unknown marine environments. However, existing methods struggle in marine environments with complex non-convex obstacles, especially during multi-UUV cooperative operation, as they typically simplify environmental obstacles to convex shapes with sparse distributions and ignore the dynamic coupling between cooperative operation and collision avoidance. To address these limitations, we propose a centralized training with decentralized execution framework with a novel multi-agent dynamic encoder based on an efficient self-attention mechanism. The framework, to our knowledge, is the first to dynamically process observations from an arbitrary number of neighbors that effectively addresses multi-UUV collision avoidance in marine environments with complex non-convex obstacles while satisfying additional constraints derived from cooperative operation. Experimental results show that the proposed method effectively avoids obstacles and satisfies cooperative constraints in both simulated and real-world scenarios with complex non-convex obstacles. Our method outperforms typical collision avoidance baselines and enables policy transfer from simulation to real-world scenarios without additional training, demonstrating practical application potential. Full article

As energy systems transition toward high shares of variable renewable generation, local energy communities (ECs) are increasingly relevant for enabling demand-side flexibility and self-sufficiency. This shift is particularly evident in the residential sector, where the deployment of photovoltaic (PV) systems is rapidly growing. While mixed-integer linear programming (MILP) remains the standard for operational optimization and demand response in such systems, its computational burden limits scalability and responsiveness under real-time or uncertain conditions. Reinforcement learning (RL), by contrast, offers a model-free, adaptive alternative. However, its application to real-world energy system operation remains limited. This study explores the application of a Deep Q-Network (DQN) to a real residential EC, which has received limited attention in prior work. The system comprises three single-family homes sharing a centralized heating system with a thermal energy storage (TES), a PV installation, and a grid connection. We compare the performance of MILP and RL controllers across economic and environmental metrics. Relative to a reference scenario without TES, MILP and RL reduce energy costs by 10.06% and 8.78%, respectively, and both approaches yield lower total energy consumption and CO${}_2$-equivalent emissions. Notably, the trained RL agent achieves a near-optimal outcome while requiring only 22% of the MILP’s computation time. These results demonstrate that DQNs can offer a computationally efficient and practically viable alternative to MILP for real-time control in residential energy systems. Full article

The rapid growth of e-commerce has created substantial environmental impacts, driving the need for advanced optimization models to enhance supply chain sustainability. As consumer preferences shift toward environmental responsibility, organizations must adopt robust quantitative methods to reduce ecological footprints while ensuring operational efficiency. This study develops a novel hybrid multi-criteria decision-making (MCDM) model to evaluate and prioritize green suppliers under uncertainty, integrating the rough-Dombi best–worst method (BWM) and an improved Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The proposed model addresses two key challenges: (1) inconsistency in expert judgments through rough set theory and Dombi aggregation operators and (2) ranking instability via an enhanced TOPSIS formulation that mitigates rank reversal. Mathematically, the rough-Dombi BWM leverages interval-valued rough numbers to model subjective expert preferences, while the Dombi operator ensures flexible and precise weight aggregation. The modified TOPSIS incorporates a dynamic distance metric to strengthen ranking robustness. A case study of five e-commerce suppliers validates the model’s effectiveness, with results identifying cost, green competitiveness, and external environmental management as the dominant evaluation dimensions. Key indicators—such as product price, pollution control, and green design—are rigorously prioritized using the proposed framework. Theoretical contributions include (1) a new rough-Dombi fusion for criteria weighting under uncertainty and (2) a stabilized TOPSIS variant with reduced sensitivity to data perturbations. Practically, the model provides e-commerce enterprises with a computationally efficient tool for sustainable supplier selection, enhancing resource allocation and green innovation. This study advances the intersection of uncertainty modeling, operational research, and sustainability analytics, offering scalable methodologies for mathematical decision-making in supply chain contexts. Full article

Most of the failures during the actual operation of equipment are caused by improper human operation, tools, spare parts, and environmental factors. These faults are routine. Conventional faults have been validated during equipment development, testing, identification, and maintenance processes, with clear definitions and clear fault tree analysis (FTA) conclusions. Digital twins can offer rapid and interactive diagnostic capabilities for routine equipment failures. To enhance the efficiency of routine fault diagnosis and the interactive experience of the diagnosis process, this paper proposes a digital twin-based equipment routine fault diagnosis model. On this basis, considering the excellent interactivity of the Interactive Electronic Technical Manual (IETM), a conventional equipment fault diagnosis scheme based on twin data and IETM is designed. This scheme converts the equipment fault tree into an IETM fault data model (DM), which is structured and stored in a database to form a fault database. Using real-time twin data of equipment as input, the FTA method is adopted to perform step-by-step fault diagnosis and isolation guidance operation through the IETM process DM combined with fault, while providing maintenance operation guidance. When the real-time twin data of the equipment is not completely consistent with the fault information in the fault library, the case analysis method is used to calculate the similarity between the real-time twin data of the equipment and the clearly defined fault symptom information in the fault library. Based on the set similarity threshold, IETM pushes fault DMs above the threshold for corresponding fault diagnosis isolation guidance. Full article

This paper contributes to the field of urban waste collection systems, which are crucial for advancing sustainability, urban cleanliness, and the aesthetic quality of cities. Specifically, it introduces a novel framework designed to support planners and decision makers in the design of efficient and responsive textile waste collection systems, aligned with both environmental objectives and citizen engagement. To this end, the framework exploits a hybrid simulation platform that realistically models the logistics infrastructure in a spatially explicit environment. Also, within the framework, citizens are represented as adaptive agents whose environmental attitudes evolve through personal experience, social influence, and perceived service quality. The behavioural layer is the core element of the framework. It enables dynamic analysis of the two-way feedback between citizen participation and service effectiveness to underscore the often-overlooked role of citizen behaviour in shaping overall system performance. The model was tested in a representative urban scenario under varying operational conditions. The results highlight how policy incentives and smart collection infrastructure can significantly boost participation, while social segregation may hinder the adoption of sustainable practices. The framework ultimately offers a generalisable decision-support tool to explore the behavioural dimension of circular economy initiatives and develop robust, scenario-based strategies. Full article

In recent years, there has been a fast expansion in the usage of renewable energy sources (RESs) in power distribution systems. Numerous advantages result from this advancement, such as environmental friendliness, cost-effective power generation, easier maintenance, and energy sustainability and reliability. Reducing reliance on fossil fuels, which are of significant environmental concern, and increasing energy efficiency are two benefits of integrating RESs into maritime systems, such as port microgrids. As a result, ports are implementing several programs to increase energy efficiency using various RESs that are supported by power electronic converters. To highlight the most recent developments in seaport electrification and infrastructure, this work conducts a systematic review. It addresses important issues like energy efficiency enhancements, environmental concerns, the integration of renewable energy sources, the Internet of Things (IoT), and regulatory and legal compliance. The study also discusses technology strategies like digitization, electrification, onshore power supply systems, and port energy storage options. Operational tactics, including peak-shaving methods and energy-efficient operations, are also covered. Additionally, an infrastructure framework—which includes port microgrids and smart seaport microgrids—that is intended to enhance energy efficiency in contemporary ports is examined. Full article

An advanced gasification module (AGM) for green hydrogen production involves a small-scale biomass gasification process owing to the low energy density of biomass. Therefore, significant heat loss and the endothermic nature of gasification system require additional fossil fuel heat, increasing CO₂ emissions. This study focuses on bioenergy conversion with carbon capture and utilization (BECCU), where carbon-neutral CO₂ from biomass gasification is captured and reused as a gasifying agent to reduce the greenhouse gas intensity of green hydrogen. BECCU is expected to achieve negative emissions and enhance gasification efficiency by promoting conversion of char and tar through CO₂ gasification. To evaluate the effectiveness of BECCU in the AGM, we conducted a sensitivity analysis of the reformer temperature and S/C ratio using process simulation combined with life cycle assessment. In both sensitivity analyses, the GWP for CO₂ capture was lower compared with conventional conditions, considering recovered CO₂ from purification and the additional steam generated through heat recovery. This suggests improved hydrogen yields from enhanced char and tar conversion. Consequently, the GWP was reduced by more than 50%, demonstrating BECCU’s effectiveness in the AGM. This represents a step toward operating biomass gasification systems with lower environmental impact and contributing to sustainable energy production. Full article

With the advancement of refined urban governance and the construction of high-quality public spaces, street furniture design and usage face multiple challenges, including insufficient public participation and a neglect of actual user experience. These issues highlight the urgent need to establish a scientifically grounded user evaluation framework to inform design practices. This study focuses on Yangzhou Ecological Science and Technology New Town and, drawing on field investigation, grounded theory, and the Delphi method, develops a street furniture design evaluation framework encompassing three core dimensions: planning and configuration, environmental coordination, and operational management. Building on this framework, the Analytic Hierarchy Process and Fuzzy Comprehensive Evaluation method are employed to conduct a holistic assessment of the street furniture and to identify critical design deficiencies. The results demonstrate that the proposed framework effectively identifies the strengths and weaknesses of street furniture and provides robust support for formulating targeted optimization strategies. The results reveal significant variations in the perceived importance of design factors among different user groups. Residents primarily emphasize practicality and convenience in daily use. Tourists value aesthetic expression and cultural resonance, whereas government officials focus on construction standardization and maintenance efficiency. In terms of satisfaction, all three groups reported relatively low scores, with the ranking as follows: “planning and configuration” > “management and operations” > “environmental coordination.” Based on these findings, the study proposes targeted design guidelines for future practice. The evaluation framework has been adopted by local authorities, incorporated into official street furniture design guidelines, and implemented in pilot projects—demonstrating its practical applicability and value. This research contributes to the theoretical advancement of street furniture design and provides empirical and methodological support for applications in other emerging urban areas and new town developments. Full article

As the demand for effective wastewater treatment continues to rise, the application of three-dimensional (3D) electrochemical particle electrodes for the removal of organic compounds from industrial wastewater has emerged as a promising solution. This approach offers significant advantages, including high treatment efficiency, operational flexibility, high current efficiency, low energy consumption, and the ability to degrade non-biodegradable organic pollutants, ultimately mineralizing them. This review provides a comprehensive and systematic exploration of the research and development of particle electrodes for use in 3D electrochemical reactors in wastewater treatment. The pivotal role of particle electrodes in removing organic contaminants from wastewater was highlighted, with most materials used as particle electrodes characterized by a specific surface area and well-defined porous structure, both of which were thoroughly discussed. Through the synergistic mechanism of adsorption, the particle electrode aids in the breakdown of organic contaminants, demonstrating the 3D particle electrode’s effectiveness in facilitating multiple oxidation mechanisms for organic wastewater treatment. Furthermore, this review categorized various particle electrode types used in 3D electrochemical wastewater treatment based on their primary components or carriers and the presence or absence of catalysts. Finally, the current status and prospects for the development and enhancement of 3D electrode particles were presented. This review offers valuable insights into the application of the 3D electrode process for environmental water treatment. Full article

This paper addresses the core issues of slow coverage rate growth and high repeated detection rates in multi-UAV cooperative search operations within unknown areas. A distributed cooperative search algorithm based on the maximum entropy mechanism is proposed to resolve these challenges. It innovatively integrates the entropy gradient decision framework with DMPC-OODA (Distributed Model Predictive Control-Observe, Orient, Decide, Act) rolling optimization: environmental uncertainty is quantified through an exponential decay entropy model to drive UAVs to migrate toward high-entropy regions; element-wise product operations are employed to efficiently update environmental maps; and a dynamic weight function is designed to adaptively adjust the weights of coverage gain and entropy gain, thereby balancing “rapid coverage” and “accurate exploration”. Through multiple independent repeated experiments, the algorithm demonstrates significant improvements in coverage efficiency—by 6.95%, 12.22%, and 59.49%, respectively—compared with the Search Intent Interaction (SII) mode, non-entropy mode, and random mode, which effectively enhances resource utilization. Full article

The biochemical conversion of biomass waste and organic slurries into clean methane is a valuable strategy for both reducing environmental pollution and advancing alternative energy sources to support energy security. Anaerobic digestion (AD), a mature renewable technology operated in high-performance bioreactors, continues to attract attention for improvements in energy efficiency, profitability, and long-term sustainability at scale. Recent efforts focus on optimizing biochemical reactions throughout all phases of the anaerobic process while mitigating the production of inhibitory compounds that reduce biodegradation efficiency and, consequently, economic viability. A relatively underexplored but promising strategy involves supplementing fermentation substrates with nanoscale additives to boost biomethane yield. Laboratory-scale studies suggest that nanoparticles (NPs) can enhance process stability, improve biogas yield and quality, and positively influence the value of by-products. This paper presents a comprehensive overview of recent advancements in the application of nanoparticles in catalyzing anaerobic digestion, considering both biochemical and economic perspectives. It evaluates the influence of NPs on bioconversion efficiency at various stages of the process, explores specific metabolic pathways, and addresses challenges associated with recalcitrant biomass. Additionally, currently employed and emerging pre-treatment methods are briefly discussed, highlighting how they affect digestibility and methane production. The study also assesses the potential of various nanocatalysts to enhance anaerobic biodegradation and identifies research gaps that limit the transition from laboratory research to industrial-scale applications. Further investigation is necessary to ensure consistent performance and economic feasibility before widespread adoption can be achieved. Full article

Wastewater treatment plants (WWTPs) are critical infrastructure that lessen the environmental impacts of human activity by stabilizing wastewaters laden with organics, chemicals, and nutrients. WWTPs face an increasing global population, greater wastewater volumes, stricter environmental regulations, and additional societal pressures to implement more sustainable and energy-efficient waste management strategies. WWTPs are energy-intensive facilities that generate significant GHG emissions and involve high operational costs. Therefore, improving the process efficiency can lead to widespread environmental and economic benefits. One promising approach is to integrate anaerobic digestion (AD) with hydrothermal carbonization (HTC) to enhance sludge treatment, optimize energy recovery, create valuable bio-based materials, and minimize sludge disposal. This study employs an LCA to evaluate the environmental impact of coupling HTC with AD compared to conventional AD treatment. HTC degrades wastewater sludge in an aqueous medium, producing carbon-dense hydrochar while reducing sludge volumes. HTC also generates an aqueous byproduct containing >30% of the original carbon as simple organics. In this system model, the aqueous byproduct is returned to AD to generate additional biogas, which then provides heat and power for the WWTP and HTC process. The results indicate that the integrated AD + HTC system significantly reduces environmental emissions and sludge volumes, increases net energy recovery, and improves wastewater sludge valorization compared to conventional AD. This research highlights the potential of AD + HTC as a key circular bioeconomy strategy, offering an innovative and efficient solution for advancing the sustainability of WWTPs. Full article