Search Results (400)

The growing need to improve the management of end-of-life vehicle (ELV) waste and mitigate its environmental impact is a global concern. One promising approach to enhancing the recyclability of these vehicles is leveraging synergies between the automotive and construction industries as part of a circular economy strategy. In this context, ELV waste emerges as a valuable source of secondary raw materials, enabling the development of sustainable innovations that capitalize on its physical and mechanical properties. This paper aims to develop and evaluate construction industry composites incorporating waste from ELV carpets, with a focus on maintaining or enhancing performance compared to conventional materials. To achieve this, an experimental program was designed to assess cementitious composites, specifically subfloor mortars, incorporating automotive carpet waste (ACW). The results demonstrate that, beyond the physical and mechanical properties of the developed composites, the dynamic stiffness significantly improved across all tested waste incorporation levels. This finding highlights the potential of these composites as an alternative material for impact noise insulation in flooring systems. From an academic perspective, this research advances knowledge on the application of ACW in cement-based composites for construction. In terms of managerial contributions, two key market opportunities emerge: (1) the commercial exploitation of composites produced with ELV carpet waste and (2) the development of a network of environmental service providers to ensure a stable waste supply chain for innovative and sustainable products. Both strategies contribute to reducing landfill disposal and mitigating the environmental impact of ELV waste, reinforcing the principles of the circular economy. Full article

As an industrial waste, slag powder can be processed and incorporated into cement-based materials as an additive, significantly improving the engineering properties of cement–soil. The strength of slag–cement-stabilized soil is subject to nonlinear interactions among multiple factors, including cement content, slag powder dosage, curing age, and moisture content, forming a complex influence mechanism. To achieve accurate strength prediction and mix proportion optimization for slag–cement-stabilized soil, this study prepared cement-stabilized soil specimens with different slag powder contents using typical sandy soil and clay from the Nantong region, and obtained sample data through unconfined compressive strength tests. A Back Propagation (BP) neural network prediction model was also established. Addressing the limitations of traditional BP neural networks in prediction accuracy caused by random initial weight thresholds and susceptibility to local optima, the sparrow search algorithm (SSA) was introduced to optimize initial network parameters, constructing an SSA-BP model that effectively enhances convergence speed and generalization capability. Research results demonstrated that the SSA-BP model reduced prediction error by 53.4% compared with the traditional BP model, showing superior prediction accuracy and effective characterization of multifactor nonlinear relationships. This study provides theoretical support and an efficient prediction tool for industrial waste recycling and environmentally friendly solidified soil engineering design. Full article

With increasing network security threats, Internet-wide scanning has become a key technique for identifying network vulnerabilities. However, traditional scanning methods tend to ignore the impact of geographic factors on scanning efficiency. In this study, we experimentally find that the geographic location of the scanner has a significant impact on scanning efficiency. Based on this finding, we propose a large-scale network scanning method based on geographic location awareness. The method divides scanners into multiple scanner clusters based on their geographic locations and designs a similarity matrix based on the average scanning time to quantify the scanning efficiency between two geographic locations. To avoid wasting scanning resources, we implement a load-balancing mechanism between scanner clusters and between nodes within each cluster. Experimental validation in a real network environment shows that the proposed method can effectively improve the scanning efficiency while ensuring the coverage. Full article

Tiny Machine Learning (TinyML) extends edge AI capabilities to resource-constrained devices, offering a promising solution for real-time, low-power intelligence in smart cities. This review systematically analyzes 66 peer-reviewed studies from 2019 to 2024, covering applications across urban mobility, environmental monitoring, public safety, waste management, and infrastructure health. We examine hardware platforms and machine learning models, with particular attention to power-efficient deployment and data privacy. We review the approaches employed in published studies for deploying machine learning models on resource-constrained hardware, emphasizing the most commonly used communication technologies—while noting the limited uptake of low-power options such as Low Power Wide Area Networks (LPWANs). We also discuss hardware–software co-design strategies that enable sustainable operation. Furthermore, we evaluate the alignment of these deployments with the United Nations Sustainable Development Goals (SDGs), highlighting both their contributions and existing gaps in current practices. This review identifies recurring technical patterns, methodological challenges, and underexplored opportunities, particularly in the areas of hardware provisioning, usage of inherent privacy benefits in relevant applications, communication technologies, and dataset practices, offering a roadmap for future TinyML research and deployment in smart urban systems. Among the 66 studies examined, 29 focused on mobility and transportation, 17 on public safety, 10 on environmental sensing, 6 on waste management, and 4 on infrastructure monitoring. TinyML was deployed on constrained microcontrollers in 32 studies, while 36 used optimized models for resource-limited environments. Energy harvesting, primarily solar, was featured in 6 studies, and low-power communication networks were used in 5. Public datasets were used in 27 studies, custom datasets in 24, and the remainder relied on hybrid or simulated data. Only one study explicitly referenced SDGs, and 13 studies considered privacy in their system design. Full article

Agricultural production waste (APW) is characterized by pollution, increasing volume, spatial dispersion, and temporal and spatial variability in its generation. The improper handling of APW poses a growing risk to the environment and public health. This paper focuses on the planning of APW recycling networks, primarily analyzing the selection of temporary storage sites and treatment facilities, as well as vehicle scheduling and route optimization. First, to minimize the required number of temporary storage sites, a set coverage model was established, and an immune algorithm was used to derive preliminary site selection results. Subsequently, the analytic hierarchy process and fuzzy comprehensive evaluation method were employed to refine and determine the optimal site selection results for recycling treatment facilities. Second, based on the characteristics of APW, with the minimization of recycling transportation costs as the optimization objective, an ant colony algorithm was used to establish a corresponding vehicle scheduling route optimization model, yielding the optimal solution for recycling vehicle scheduling and transportation route optimization. This study not only improved the recycling efficiency of APW but also effectively reduced the recycling costs of APW. Full article

The technology of road extraction serves as a crucial foundation for urban intelligent renewal and green sustainable development. Its outcomes can optimize transportation network planning, reduce resource waste, and enhance urban resilience. Deep learning-based approaches have demonstrated outstanding performance in road extraction, particularly excelling in complex scenarios. However, extracting roads from remote sensing data remains challenging due to several factors that limit accuracy: (1) Roads often share similar visual features with the background, such as rooftops and parking lots, leading to ambiguous inter-class distinctions; (2) Roads in complex environments, such as those occluded by shadows or trees, are difficult to detect. To address these issues, this paper proposes an improved model based on Graph Convolutional Networks (GCNs), named FR-SGCN (Hierarchical Depth-wise Separable Graph Convolutional Network Incorporating Graph Reasoning and Attention Mechanisms). The model is designed to enhance the precision and robustness of road extraction through intelligent techniques, thereby supporting precise planning of green infrastructure. First, high-dimensional features are extracted using ResNeXt, whose grouped convolution structure balances parameter efficiency and feature representation capability, significantly enhancing the expressiveness of the data. These high-dimensional features are then segmented, and enhanced channel and spatial features are obtained via attention mechanisms, effectively mitigating background interference and intra-class ambiguity. Subsequently, a hybrid adjacency matrix construction method is proposed, based on gradient operators and graph reasoning. This method integrates similarity and gradient information and employs graph convolution to capture the global contextual relationships among features. To validate the effectiveness of FR-SGCN, we conducted comparative experiments using 12 different methods on both a self-built dataset and a public dataset. The proposed model achieved the highest F1 score on both datasets. Visualization results from the experiments demonstrate that the model effectively extracts occluded roads and reduces the risk of redundant construction caused by data errors during urban renewal. This provides reliable technical support for smart cities and sustainable development. Full article

The increasing disparity in global food distribution has amplified the urgency of addressing food waste and food insecurity, both of which exacerbate economic, environmental, and social inequalities. Traditional food bank models often struggle with logistical inefficiencies, limited accessibility, and a lack of transparency in food distribution, hindering their effectiveness in mitigating these challenges. This study proposes a novel Food Bank Network Redesign (FBNR) that leverages the Quito Metro system to create a decentralized food bank network, enhancing efficiency and equity in food redistribution by introducing strategically positioned donation lockers at metro stations for convenient drop-offs, with donations transported using spare metro capacity to designated stations for collection by charities, reducing reliance on dedicated transportation. To ensure transparency and operational efficiency, we integrate a blockchain-based traceability system with smart contracts, enabling secure, real-time tracking of donations to enhance stakeholder trust, prevent food loss, and ensure regulatory compliance. We develop a multi-objective optimization framework that balances food waste reduction, transportation cost minimization, and social impact maximization, supported by a mixed-integer linear programming (MIP) model to optimize donation allocation based on urban demand patterns. By combining decentralized logistics, blockchain-enhanced traceability, and advanced optimization techniques, this study offers a scalable and adaptable framework for urban food redistribution, improving food security in Quito while providing a replicable blueprint for cities worldwide seeking to implement circular and climate-resilient food supply chains. Full article

The construction industry urgently requires sustainable alternatives to conventional concrete to reduce its environmental impact. This study addresses this challenge by developing machine learning-optimized geopolymer concrete (GPC) using industrial waste fly ash as cement replacement. An integrated Taguchi–Grey relational analysis (GRA) and artificial neural network (ANN) approach was developed to simultaneously optimize mechanical properties and environmental performance. The methodology analyzes over 1000 data points from 83 studies to identify key mix parameters including fly ash content, NaOH/Na₂SiO₃ ratio, and curing conditions. Results indicate that the optimized FA-GPC formulation achieves a 78% reduction in CO₂ emissions, decreasing from 252.09 kg/m³ (GRC rank 1) to 55.0 kg/m³, while maintaining a compressive strength of 90.9 MPa. The ANN model demonstrates strong predictive capability, with R² > 0.95 for strength and environmental impact. Life cycle assessment reveals potential savings of 3941 tons of CO₂ over 20 years for projects using 1000 m³ annually. This research provides a data-driven framework for sustainable concrete design, offering practical mix design guidelines and demonstrating the viability of fly ash-based GPC as high-performance, low-carbon construction material. Full article

To investigate the feasibility of utilizing waste rock wool fiber as an additive in asphalt mixtures for resource recycling, this study evaluates and analyzes the performance of asphalt and asphalt mixtures, as well as their environmental benefits. Initially, the properties and mechanisms of modified asphalt mortar are examined under different shapes (powdery rock wool fiber (RWP) and fibrous rock wool fiber (RWF)) and varying rock wool fiber contents (0%, 1%, 2%, 3%, and 4% of matrix asphalt mass). Subsequently, the pavement performances of asphalt mixtures with different RWF contents (0%, 0.1%, 0.2%, 0.3%, and 0.4% of asphalt mixture mass) are compared. The environmental and economic impacts of RWF-modified asphalt mixtures are assessed using the life cycle assessment (LCA) method and the benefit cost analysis (BCA) method. Finally, the carbon property ratio (CPR), an innovative index, is proposed. It comprehensively evaluates the pavement performances and economic benefits of RWF modified asphalt mixtures in relation to carbon emissions (CEs). The results indicate that compared to RWP, RWF primarily functions as an inert fiber stabilizer. It provides a physical reinforcing effect through its three-dimensional network skeleton structure. Both RWP and RWF-modified asphalts exhibit improved performance compared to matrix asphalt. RWF demonstrates superior temperature susceptibility and high temperature performance. The optimal contents for achieving the best high temperature, water stability, and low-temperature crack resistance performances of RWF-modified asphalt mixtures are 0.3%, 0.2%, and 0.2%, respectively. As the RWF content increases, the energy consumption (EC) and CEs during the pavement construction stage slightly rise within an acceptable range, while positive economic benefits also increase. Additionally, the CPR index can comprehensively assess the favorable effects of pavement performances or economic benefits against the adverse effects of CEs. It offers theoretical guidance for the design of optimal rock wool fiber content. Full article

This study addresses the challenges of high-power consumption and complexity in conventional infrared (IR) gas sensors by integrating metamaterials and gold coatings into IR radiation sources to reduce radiation loss. In addition, emitter design optimization and material selection were employed to minimize conduction loss. Our metasurface exhibited superior performance, achieving a narrower full width at half maximum at 4197 and 3950 nm, resulting in more confined emission spectral ranges. This focused emission reduced energy waste at unnecessary wavelengths, improving efficiency compared to traditional blackbody emitters. At 300 °C, the device consumed only 6.8 mW, while maintaining temperature uniformity and a fast response time. This enhancement is promising for the operation of such sensors in IoT networks with ultra-low power consumption and at suitably low costs for widespread demands in high-technology farming. Full article

Efficient cooling and heat recovery systems are becoming increasingly critical in large-scale commercial and industrial facilities, especially with the rising demand for sustainable energy solutions. Traditional air-conditioning and refrigeration systems often dissipate significant amounts of waste heat, which remains underutilized. This study addresses the challenge of harnessing low-potential waste heat from such systems to support fifth-generation district heating and cooling (5GDHC) networks, particularly in moderate-temperate regions like Flanders, Belgium. To evaluate the technical and economic feasibility of waste heat recovery, a methodology is developed that integrates established performance metrics—such as the energy efficiency ratio (EER), power usage effectiveness (PUE), and specific cooling demand (kW/t)—with capital (CapEx) and operational expenditure (OpEx) assessments. Empirical correlations, including regression analysis based on manufacturer data and operational case studies, are used to estimate equipment sizing and system performance across three operational modes. The study includes detailed modeling of data centers, cold storage facilities, and large supermarkets, taking into account climatic conditions, load factors, and thermal capacities. Results indicate that average cooling loads typically reach 58% of peak demand, with seasonal coefficient of performance (SCOP) values ranging from 6.1 to a maximum of 10.3. Waste heat recovery potential varies significantly across building types, with conversion rates from 33% to 68%, averaging at 59%. In data centers using water-to-water heat pumps, energy production reaches 10.1 GWh/year in heat pump mode and 8.6 GWh/year in heat exchanger mode. Despite variations in system complexity and building characteristics, OpEx and CapEx values converge closely (within 2.5%), demonstrating a well-balanced configuration. Simulations also confirm that large buildings operating above a 55% capacity factor provide the most favorable conditions for integrating waste heat into 5GDHC systems. In conclusion, the proposed approach enables the scalable and efficient integration of low-grade waste heat into district energy networks. While climatic and technical constraints exist, especially concerning temperature thresholds and equipment design, the results show strong potential for energy savings up to 40% in well-optimized systems. This highlights the viability of retrofitting large-scale cooling systems for dual-purpose operation, offering both environmental and economic benefits. Full article

Bullfrog skin, as a by-product of bullfrog processing, is an ideal source of high-quality collagen due to its high protein content and low-fat characteristics. However, its comprehensive utilization is relatively low, and the discarded skins cause resource waste and environmental pollution. In this study, a citric acid extraction process for frog skin collagen was established through single-factor optimization. A multifunctional double-network hydrogel was developed by combining the prepared high-purity type I collagen with oxidized hyaluronic acid (OHA). Due to the network structure design of Schiff base bonds and Zn²⁺ coordination bonds, the mechanical strength of the hydrogel based on collagen and OHA compositing Zn²⁺ (Gel–CO@Zn) enhanced significantly. It was found that the Gel–CO@Zn hydrogel had strong tissue adhesion (16.58 kPa shear strength), rapid self-healing (<6 h), and low hemolysis (<5%). Furthermore, the Gel–CO@Zn hydrogel could reduce the survival rate of Staphylococcus aureus and Escherichia coli to 1.06% and 6.73%, respectively, showing good antibacterial properties. Through the treatment of Gel–CO@Zn, the clotting time was shortened from 433 s to 160 s and greatly reduced the blood loss (>60%) in the liver injury model of male Kunming mice. This research not only presents a novel approach for the high-value utilization of aquaculture by-products but also establishes a new paradigm for developing cost-effective, multifunctional biomedical materials, demonstrating the transformation of waste into high-value resources. Full article

With the global emphasis on sustainable growth and development, the depletion of natural energy reserves due to reliance on fossil fuels and non-renewable sources remains a critical concern. Despite strides in transitioning to electrical mobility, rural and agricultural communities depend heavily on liquefied petroleum gas and firewood for cooking, lacking viable, sustainable alternatives. This study focuses on community-led efforts to advance biogas adoption, providing an eco-friendly and reliable energy alternative for rural and farming households. By designing and developing balloon-type anaerobic biodigesters, this initiative provides a robust, cost-effective, and scalable method to convert farm waste into biogas for household cooking. This approach reduces reliance on traditional fuels, mitigating deforestation and improving air quality, and generates organic biofertilizer as a byproduct, enhancing agricultural productivity through organic farming. The study focuses on optimizing critical parameters, including the input feed rate, gas production patterns, holding time, biodigester health, gas quality, and liquid manure yield. Statistical tools, such as descriptive analysis, regression analysis, and ANOVA, were employed to validate and predict biogas output data based on experimental and industrial-scale data. Artificial neural networks (ANNs) were also utilized to model and predict outputs, inspired by the information processing mechanisms of biological neural systems. A comprehensive database was developed from experimental and literary data to enhance model accuracy. The results demonstrate significant improvements in cooking practices, health outcomes, economic stability, and solid waste management among beneficiaries. The integration of statistical analysis and ANN modeling validated the biodigester system’s effectiveness and scalability. This research highlights the potential to harness renewable energy to address socio-economic challenges in rural areas, paving the way for a sustainable, equitable future by fostering environmentally conscious practices, clean energy access, and enhanced agricultural productivity. Full article

With the rapid development of smart grid technologies, communication networks have become the core infrastructure supporting control and energy optimization in microgrids. However, the excessive reliance of microgrid control on communication networks faces dual challenges: On one hand, the high-frequency information exchange under traditional periodic communication patterns causes severe waste of network resources; on the other hand, cyberattacks may cause information loss, abnormal delays, or data tampering, which can ultimately lead to system instability. To address these challenges, this paper investigates the secure dynamic integral event-triggered stabilization of photovoltaic microgrids under multimodal deception attacks. To address the communication resource constraints in photovoltaic (PV) microgrid systems, a dynamic integral-event-triggered scheme (DIETS) is proposed. This scheme employs average processing of historical state data to filter out redundant triggering events caused by noise or disturbances. Simultaneously, a time-varying triggering threshold function is designed by integrating real-time system states and historical information trends, enabling adaptive adjustment of dynamic triggering thresholds. In terms of cybersecurity, a secure control strategy against multi-modal deception attacks is incorporated to enhance system resilience. Subsequently, through the Lyapunov–Krasovskii functional and Bessel–Legendre inequality, collaborative design conditions for the controller gain and triggering matrix are formed as symmetric linear matrix inequalities to ensure system stability. The simulation results demonstrate that DIETS recorded only 99 triggering events, achieving a 55.2% reduction compared to the normal event-triggered scheme (ETS) and a 52.6% decrease relative to dynamic ETS, verifying the outstanding communication effectiveness of DIETS. Full article

This study introduces a novel framework for the inverse design of sustainable food packaging materials using generative adversarial networks (GANs) and the recently released OMat24 dataset containing 110 million DFT-calculated inorganic material structures. Our approach transforms traditional material discovery paradigms by enabling end-to-end design from desired performance metrics to material composition. We developed a GAN-driven inverse design architecture specifically optimized for food packaging applications, integrating sensor-derived data on critical constraints such as biodegradability and barrier properties directly into the generative process. This integration occurs at three levels: (1) sensor-measured properties define conditioning targets for the GAN, (2) sensor data train the property prediction network, and (3) sensor-based characterization validates generated materials. An enhanced EquiformerV2 graph neural network was employed to accurately predict the formation energy, stability, and sensor-measurable properties of candidate materials. The model achieved a mean absolute error of 12 meV/atom for formation energy on the OMat24 test set (25% improvement over baseline models), while predictions of sensor-measured functional properties reached $R^2$ values of 0.84–0.89 through the integration of experimental measurements and physics-based proxy models. The framework successfully generated over 100 theoretically viable candidate materials, with 20% exhibiting superior barrier properties and controlled degradation characteristics. Our computational approach demonstrated a 20–100× acceleration in screening efficiency compared to traditional DFT calculations while maintaining high accuracy. This work presents a significant advancement in computational materials discovery for sustainable packaging applications, offering a promising pathway to address the urgent global challenges of food waste and plastic pollution. Full article