Search Results (10,153)

This study addresses the multi-objective optimization of characterizing a flat glass processing plant. To assess the operational conditions required for a flat glass processing small and medium-sized enterprise (SME) to become a prosumer compatible with renewable energy community (REC) participation. This work is motivated by the presence of more than 300 SMEs in Italy, like this, where RECs represent one of the few viable strategies for achieving the European Union’s 2050 decarbonization targets. The research is carried out in two scenarios; Scenario-I includes Stage-i and Stage-ii with the mutual goal of forecasting and optimizing. Forecasting is used in Stage-i to optimize the factory load, and in Stage-ii to shift and curtail energy loads based on the forecast, considering the Italian national energy price and the regional price bands (“fasce orarie”) F1, F2, and F3. Forecasting and the indicators of environmental and social performance are the means to ensure the best energy utilization and management, as they prove that the reduction in CO${}_2$ emissions and benefits on the community level can be both obtainable. Subsequently, the techno-economic analysis and evaluation of prosumer-readiness conditions are carried out through the optimization of industrial energy demand: three optimization objectives are assessed in this study (i) energy cost, (ii) carbon emission, and (iii) load curtailment. Four algorithms are put into effect to solve the tri-objective optimization: multi-objective particle swarm optimization (MOPSO), multi-objective ant nesting algorithm (MOANA), non-dominated sorting genetic algorithm (NSGA-II), and multi-objective grey wolf optimization (MOGWO). The algorithms are validated in Stage-ii to find the desired optimum in the cost of energy, reduce peak formation, and carbon emissions. To achieve this goal, a stochastic approach based on Monte Carlo simulations and VIKOR is used to optimally select the results. The findings show that the NSGA-II, MOPSO, and MOANA are more effective in solving the problem, while the MOGWO algorithm more quickly finds the optimal solution. Based on the defined objectives, a new configuration for the energy community is introduced, together with a community well-being index and an evaluation of the resulting benefits for the factory. In Scenario-II, the PV plants’ installation on the factory is sized, and the excess energy shared with the grid is evaluated. The Scenario-II results show that 497.184 MWh (33.9%) of energy is shared with the grid. Both results suggest how optimized industrial demand profiles improve SME participation in future RECs. Full article

Asphalt concrete pavements in many regions suffer from premature deterioration caused by low-temperature cracking and rutting resistance under heavy traffic loads and high summer temperatures. While polymer-modified bitumen is widely used to improve pavement performance, the high cost of commercial polymers restricts its extensive application. This study evaluates the potential of polymer waste as an alternative modifier for asphalt binders to enhance mechanical performance while reducing economic and environmental costs. Experimental results demonstrate that an optimal plastic waste content of 1.0–1.5% significantly improves rutting resistance and increases binder rigidity. The incorporation of 1.5% low-density polyethylene (LDPE) and high-density polyethylene (HDPE) enhances deformation resistance, elastic modulus, and temperature stability. LDPE exhibits better compatibility with bitumen and dissolves more readily, contributing to improved binder homogeneity, whereas HDPE provides higher stiffness and thermal stability. The combined use of polymer waste with styrene–butadiene–styrene (SBS) produces a pronounced synergistic effect, leading to improvements in physical and mechanical properties exceeding 25% compared to Kazakhstan regulatory standards. Increasing polymer waste content further enhances the rigidity of both the binder and asphalt concrete, thereby improving rutting resistance and plastic deformation at elevated temperatures. The proposed approach offers a cost-effective and sustainable solution for road construction, promoting plastic waste recycling, reducing reliance on virgin polymers, and improving pavement durability, particularly under the climatic and traffic conditions of Kazakhstan. Full article

Acid mine drainage (AMD) pollution from closed coal mines in karst regions represents a major environmental challenge in the global mining industry. The complexity of hydrogeological conditions in such regions leads to significant challenges in both predictability and controllability of pollution. Taking the Yudong River Basin in Guizhou Province, Southwest China, as the study area, and based on six years (2017–2023) of systematic remediation practices and monitoring data, this study systematically evaluates the effectiveness and applicable conditions of three types of treatment technologies: centralized treatment stations, source control combined with end-of-pipe treatment, and water-sealing ecological plugging. On this basis, governance models applicable to karst regions are distilled. The results show that after six years of remediation, the number of pollution points in the Yudong River Basin decreased from 27 to 12. At the outflow section, the total Fe reduction rate reached 88.3%, the total Mn reduction rate reached 62.3%, and the proportion of contaminated river length was reduced by 78.5%. Each of the three technologies has its own applicable conditions. Centralized treatment stations, characterized by mature technology but high operational costs, are suitable for emergency transition periods. Source control combined with end-of-pipe treatment addresses both symptoms and root causes, making it applicable to complex pollution points. Water-sealing ecological plugging, although cost-controllable, carries a risk of secondary pollution in karst-developed areas. The failure of water-sealing ecological plugging technology is mainly attributed to two mechanisms: bypass flow through karst conduits and overflow induced by water level rise. Based on the six-year remediation practice, this study proposes a source control model for karst conduits centered on the core concepts of “filling, isolating, plugging, intercepting, draining, and controlling”. The implementation process consists of four stages: detailed investigation, graded optimization, stepwise implementation, and long-term monitoring. The core innovation lies in the cross-disciplinary application of coal mine water control techniques to environmental remediation, achieving a shift from passive end-of-pipe treatment to active source control. This model can provide theoretical reference and practical guidance for karst mining areas in Southwest China and other regions with similar geological conditions. Full article

Remote sensing change detection (RSCD) aims to produce pixel-accurate change maps from bi-temporal images yet is fundamentally challenged by radiometric pseudo-changes (season, illumination, and atmosphere) that cause structure–environment entanglement in deep features. We propose CSD-Net, a framework centered on content–style decoupling (CSD): a physics-inspired feature decomposition mechanism that encourages separation between intrinsic geometric content and extrinsic environmental style. In the CSD module, learnable pseudo-change tokens estimate a spatially invariant global style proxy through cross-attention and broadcast, and subtraction performs feature-level radiometric-bias compensation, yielding pseudo-change-robust content features for change prediction. CSD-Net (Base) alone achieves state-of-the-art performance across four benchmarks (LEVIR-CD, LEVIR-CD+, CDD, and WHU) with favorable accuracy–efficiency trade-off (14.49M parameters and 15.26G FLOPs). We further explore an optional extension, CSD-Net⁺, that employs an MLLM (Qwen2.5-3B, LoRA-tuned) as a semantic refiner and SAM for instance mask refinement, coupled with uncertainty-aware three-way softmax fusion. This exploratory Stage 2 brings modest but consistent IoU improvements of 0.45–2.20% at the cost of significant computational overhead and is designed for offline, quality-critical scenarios. We provide a comprehensive account of both the effectiveness and the limitations of the proposed approach, including the marginal benefit–cost ratio of foundation model integration. Full article

Recognizing the limitations of traditional vehicle routing models in urban environments, this work presents the Time-Dependent Truck-Drone Green Vehicle Routing Problem with Pickup and Delivery (TDTDGVRPPD) to simultaneously optimize environmental impact and operational efficiency. We first develop a truck fuel consumption and carbon emission model that accounts for the effects of time-varying speeds and real-time loads during delivery. A nonlinear energy consumption model is then proposed for drones, considering payload weight. Based on these models, a mathematical formulation is developed to minimize the total operational cost, including truck and drone usage costs, truck fuel and carbon emission costs, drone energy consumption costs, truck–drone coordination time costs, and time-window violation penalties. The model also incorporates truck no-entry zones, time-varying speeds, and customers’ simultaneous pickup and delivery demands. An Improved Whale Optimization Algorithm (IWOA) hybridized with Variable Neighborhood Search (VNS) is developed to solve the problem. Simulation results show that the proposed model and algorithm effectively optimize truck departure times to avoid traffic congestion, reduce truck–drone coordination time, and lower total logistics costs and energy consumption, thereby contributing to energy conservation and emission reduction in logistics operations. Full article

Essential oils are extracted from various parts of plants and have many beneficial properties and applications. These include aromatherapy, healthcare, cosmetics, fragrances, agriculture, household cleaning products, and the food industry. Due to their antimicrobial and antioxidant properties, essential oils are suitable for use as natural flavorings and preservatives, ensuring food quality maintenance and facilitating clean-label product production. In this context, assessing the quality of essential oils is of paramount importance. Among the various analytical methods, electrochemical methods stand out for their simplicity, cost-effectiveness, and environmental friendliness. Consequently, this review examines the applications, advantages, disadvantages, and limitations of electroanalytical methods proposed to quantify major volatile, electroactive components and determine their antioxidant properties. The objective of this evaluation is to establish a framework for future research that will address existing gaps and shortcomings in electroanalytical methodologies. Full article

Visually impaired individuals face significant challenges in autonomous mobility and situational awareness. Most existing assistive technologies address isolated tasks, such as object recognition or text reading, while failing to capture broader environmental context. This work addresses this limitation by proposing a scene-sensitive, low-cost assistive system that delivers holistic situational information. We present Vocal-Eyes, an intelligent smart glasses platform that provides periodic audio descriptions of the surrounding environment. The system employs a cloud-based neural processing pipeline in which visual features are extracted using a Transformer-based architecture. Relational context is modeled through scene graph generation, and scene graphs are translated into natural language via a graph-to-text module. A lightweight hardware prototype captures visual data locally, while computationally intensive processing is offloaded to the cloud to reduce power consumption. The experimental results show that relational, scene-based narration produces more coherent and informative descriptions than object-centric approaches while maintaining acceptable periodic latency. Cost analysis further indicates that Vocal-Eyes is significantly more affordable than comparable commercial smart glasses solutions. These results demonstrate that Transformer-based scene understanding with cloud-assisted processing is an effective and practical approach for developing accessible, context-aware assistive technologies for visually impaired users. Full article

The present work focuses on the investigation of the turbulent Reynolds number effect on the characteristics of an open-channel flow with sediment transport, by employing the micropolar model. The micropolar model is essentially a Eulerian non-Newtonian model that has already been proven to correctly describe the secondary phase of turbulent wall-bounded flows. The current under investigation geometry, open channel, comprises an ideal candidate to further test the characteristics of the micropolar model as many environmental flows contain a secondary phase. Such flows are of great engineering and physics interest for applications such as sedimentation transport and debris flow. Direct Numerical Simulations (DNSs) have been carried out on an open channel for three different turbulent Reynolds numbers. The simulated results are compared against previous DNS data of similar flows. The micropolar model is capable of describing the same problem but in a Eulerian frame, thus significantly simplifying the computational cost and complexity. Full article

As transportation-related environmental pressures intensify, understanding the psychological mechanisms underlying battery electric vehicle (BEV) adoption has become increasingly important. Drawing on the Value–Attitude–Behavior (VAB) framework, this study investigates how perceived green value, hedonic value, and utilitarian value shape Chinese consumers’ attitudes and purchase intentions toward BEVs, while examining the moderating roles of technophilia, range anxiety, and battery cost. A cross-sectional online survey was conducted in China, yielding 596 valid responses. Partial Least Squares Structural Equation Modeling (PLS-SEM) and Necessary Condition Analysis (NCA) were employed for data analysis. The results show that perceived hedonic value exerts the strongest positive effect on Attitude Toward Using BEVs (β = 0.591, p < 0.001), followed by perceived utilitarian value (β = 0.135, p < 0.001) and perceived green value (β = 0.074, p = 0.026). Attitude Toward Using significantly predicts BEV purchase intention (β = 0.151, p = 0.002). Technophilia significantly moderates the relationship between attitude and purchase intention (β = −0.096, p = 0.002), whereas the moderating effects of range anxiety and battery cost are not significant. The structural model explains 40.9% of the variance in attitude and 24.2% of the variance in purchase intention. NCA results further reveal that hedonic value constitutes the most critical necessary condition for forming favorable attitudes toward BEVs (d = 0.079, p < 0.001). This study contributes to the sustainable mobility literature by extending the VAB framework through the integration of multidimensional perceived value and necessary condition logic within the Chinese BEV context. The findings highlight that experiential and technological enjoyment, rather than environmental concern alone, has become a central driver of BEV adoption in emerging electric mobility markets. Full article

Effective waste sorting is central to circular economy goals and sustainable waste management: it maximizes recycling yields, diverts waste from landfills, and reduces the environmental burden of solid waste disposal. Accurate automated sorting using deep learning can achieve this at scale, yet high-performing classifiers are too computationally demanding for the low-cost embedded hardware used in sorting facilities. We propose the KD-Garbage Framework, which applies knowledge distillation to transfer predictive knowledge from a high-capacity teacher model to a lightweight student model, enabling deployment-ready classifiers that approach or exceed teacher-level accuracy without any added inference cost. We also introduce a 15,681-image garbage dataset organized into 13 classes defined by recycling and disposal pathway, assembled from 12 public sources and original photography, with all labels manually verified. Two teacher models were paired with 16 lightweight convolutional neural network (CNN) student architectures and benchmarked on a Raspberry Pi 5 at a minimum throughput of five frames per second. Knowledge distillation reduced misclassification rates by 10–25% across all student architectures. The best-performing student, RegNetY-1.6GF, achieved a balanced accuracy of 0.9129, surpassing both teacher models while sustaining real-time throughput on the target hardware, demonstrating a practical pathway toward scalable, AI-enabled sustainable waste management. Full article

Rapid detection of heavy metals is vital for monitoring surface water contamination and preventing environmental and health risks. Traditional detection methods for metals such as lead and copper often require sophisticated, costly instrumentation, limiting their use in routine analyses. To address this challenge, we developed a cost-effective fluorescence-based approach using semiconductor quantum dots (QDs) as nanosensors for metal ion detection. The QDs were synthesized directly in aqueous medium through a reflux-assisted process employing cadmium precursors, selenium, thioglycolic acid (TGA), and branched polyethyleneimine (PEI, Mw ~25,000) as stabilizing agents. Structural analysis revealed nanoparticles with diameters below 5 nm, spherical morphology, and a zinc blende (face-centered cubic) crystalline structure. Optical characterization by UV–Vis, photoluminescence (PL), and FTIR spectroscopy confirmed effective surface functionalization and strong quantum confinement. PEI-capped QDs exhibited enhanced colloidal stability and showed pronounced fluorescence quenching in the presence of Pb²⁺ ions, indicating high sensitivity and selectivity toward lead. Both TGA- and PEI-capped QDs also demonstrated moderate responses to Co²⁺ but negligible interaction with Sn²⁺, confirming ion-specific detection. Overall, this study demonstrates that surface-engineered QDs constitute a simple, accessible platform for selective detection of toxic metals, with promising applications in environmental monitoring and water quality assessment. Full article

Decarbonising the built environment has increased the importance of prefabricated construction, yet its cost and resource efficiency are still constrained by fragmented supply chain collaboration. This study examines how lifecycle supply chain collaboration affects cost control performance in prefabricated construction. Based on supply chain management theory and expert consultation, a conceptual model was developed and tested through structural equation modelling using 517 valid responses from stakeholders in China’s prefabricated construction supply chain. The results show that management factors across all four project phases (decision and design, component production, transportation, and construction and installation) significantly improve cost control performance, with design standardisation, production scheduling, transport logistics, quality assurance, and workforce proficiency as key drivers. Process coordination exerts a significant mediating effect, while environmental factors significantly moderate the relationships. In practical terms, the findings indicate that stakeholders should prioritise design standardisation at the early stage, strengthen coordination across production, transport, and installation activities, and enhance quality control and workforce training to reduce avoidable cost overruns and resource waste. Beyond their theoretical contribution to research on supply chain collaboration in prefabricated construction, these results offer concrete direction for practitioners seeking to improve cost efficiency and make better use of resources within industrialised building systems. Full article

As the main drivers of the market economy, enterprises must fully grasp the importance and urgency of building an ecological civilization and hasten the transition to green practices. Due to the fundamental goal of enterprises being to maximize profits, the cost-effectiveness of enterprises is directly related to their initiative and implementation effectiveness in carrying out green transformation. This article uses panel data from heavily polluting companies listed on the Shanghai and Shenzhen stock exchanges in China from 2011 to 2020 to empirically test the cost-economic effects of corporate green transformation (CGT). Results reveal: (1) CGT has a positive effect on firm performance, and managerial incentives and capital intensity can strengthen the positive relationship between CGT and firm performance. In addition, in economically developed regions with high levels of environmental regulation, the green transformation of heavily polluting enterprises with lower management agency costs has a more significant positive impact on corporate performance. (2) Environmental costs mediate the link between CGT and firm performance, with the mediating effect of corporate environmental costs playing a role only in the non-three major economic circles. Full article

The intensification and continuous evolution of dairy sheep and goat farming have played an essential role in the development and implementation of milking equipment. The increasing demand for time-efficient milking procedures, reduced labour costs, sustained milk production, and optimal mammary health have driven the widespread adoption and optimisation of machine milking technologies. The objectives of this article are (i) the review of milking systems and relevant technological developments in milking equipment and (ii) the evaluation and description of their impact on udder health, as applied on dairy small ruminant farms. Milking systems used on farms depend on the available space and number of animals on the farms. Appropriate settings in milking systems are important for ensuring good milk quality; among them, vacuum level, pulsation rate and ratio are important characteristics that must be monitored regularly. Further, use of appropriate teatcups specific to the animal species to be milked is significant. An important aspect of proper maintenance of the milking system is the cleaning procedure after completion of milking. Points for consideration are quality and temperature of the water used for cleaning, use of detergents and disinfectants, and maintenance schedule and teatcup replacement. Some technological features that are part of milking systems include automatic vacuum shut off, electronic milk recording, electronic identification of animals, automatic flushing of milking clusters and automatic pre-stimulators. Farms will benefit from applying precision technologies, which will use data from tools related to animal genetic background, animal behavioural indicators, environmental conditions and disease-related functions for more holistic and cost-effective farm management. In this context, integration of sensor-based technologies in milking systems will be able to provide real-time information regarding quality of milk produced at individual and farm levels. Moreover, the introduction of automatic system flushing in-between animals during the milking procedure can contribute to breaking chains of potential bacterial transfer and reducing animal infections during milking. Overall, although machine milking has certainly contributed to improved efficiency, milk quality and labour conditions, flaws in system function may adversely affect mammary health. Full article

Optoelectronic devices are emerging as a cornerstone of advanced renewable energy technologies, offering innovative routes for energy harvesting, conversion, and management with high efficiency and versatility. This review summarizes recent advances in the semiconductor materials engineering field, device configurations, and light–matter interaction mechanisms that underpin advanced optoelectronic systems for solar energy harvesting, solar-driven chemical conversion, and smart grid integration, among others. Emphasis is placed on the breakthroughs achieved in the perovskite and hybrid photovoltaics, photoelectrochemical energy conversion, and nanostructured optoelectronic platforms that enable much-increased light absorption, reduced recombination losses, and scalable large-scale fabrications. Moreover, the challenges closely linked with long-term stability, environmental durability and benevolence, and worldwide deployment are critically addressed, together with the emerging opportunities in AI design, tandem device technological solutions, integrated energy systems, and machine learning approaches for optimizing device performance, thermal management, and energy storage capabilities. Finally, the present review concludes by outlining the future research directions that could accelerate the transition toward high-performance, cost-effective, and sustainable optoelectronic solutions responsive to global renewable energy requirements. Full article