Search Results (259)

Automated waste classification is an essential step toward efficient recycling and waste management. Traditional deep learning models, such as convolutional neural networks, rely on extensive labeled datasets to achieve high accuracy. However, the annotation process is labor-intensive and time-consuming, limiting the scalability of these approaches in real-world applications. Zero-shot learning is a machine learning paradigm that enables a model to recognize and classify objects it has never seen during training by leveraging semantic relationships and external knowledge sources. In this study, we investigate the potential of zero-shot learning for waste classification using two vision-language models: OWL-ViT and OpenCLIP. These models can classify waste without direct exposure to labeled examples by leveraging textual prompts. We apply this approach to the TrashNet dataset, which consists of images of municipal solid waste organized into six distinct categories: cardboard, glass, metal, paper, plastic, and trash. Our experimental results yield an average classification accuracy of 76.30% with Open Clip ViT-L/14-336 model, demonstrating the feasibility of zero-shot learning for waste classification while highlighting challenges in prompt sensitivity and class imbalance. Despite lower accuracy than CNN- and ViT-based classification models, zero-shot learning offers scalability and adaptability by enabling the classification of novel waste categories without retraining. This study underscores the potential of zero-shot learning in automated recycling systems, paving the way for more efficient, scalable, and annotation-free waste classification methodologies. Full article

Amid the accelerating pace of global urbanization, the volume of municipal solid garbage has surged dramatically, thereby demanding more efficient and precise garbage management technologies. In this paper, we introduce a novel garbage classification approach that leverages a dynamic graph neural network based on multimodal feature fusion. Specifically, the proposed method employs an enhanced Residual Network Attention Module (RNAM) network to capture deep semantic features and utilizes CIELAB color (LAB) histograms to extract color distribution characteristics, achieving a complementary integration of multimodal information. An adaptive K-nearest neighbor algorithm is utilized to construct the dynamic graph structure, while the incorporation of a multi-head attention layer within the graph neural network facilitates the efficient aggregation of both local and global features. This design significantly enhances the model’s ability to discriminate among various garbage categories. Experimental evaluations reveal that on our self-curated KRHO dataset, all performance metrics approach 1.00, and the overall classification accuracy reaches an impressive 99.33%, surpassing existing mainstream models. Moreover, on the public TrashNet dataset, the proposed method demonstrates equally outstanding classification performance and robustness, achieving an overall accuracy of 99.49%. Additionally, hyperparameter studies indicate that the model attains optimal performance with a learning rate of 2 × 10⁻⁴, a dropout rate of 0.3, an initial neighbor count of 20, and 8 attention heads. Full article

With rapid urbanization, effective waste classification is a critical challenge. Traditional manual methods are time-consuming, labor-intensive, costly, and error-prone, resulting in reduced accuracy. Deep learning has revolutionized this field. Convolutional neural networks such as VGG and ResNet have dramatically improved automated sorting efficiency, and Transformer architectures like the Swin Transformer have further enhanced performance and adaptability in complex sorting scenarios. However, these approaches still struggle in complex environments and with diverse waste types, often suffering from limited recognition accuracy, poor generalization, or prohibitive computational demands. To overcome these challenges, we propose an efficient hybrid-modal fusion method, the Hybrid-modal Fusion Waste Classification Network (HFWC-Net), for precise waste image classification. HFWC-Net leverages a Transformer-based hierarchical architecture that integrates CNNs and Transformers, enhancing feature capture and fusion across varied image types for superior scalability and flexibility. By incorporating advanced techniques such as the Agent Attention mechanism and the LionBatch optimization strategy, HFWC-Net not only improves classification accuracy but also significantly reduces classification time. Comparative experimental results on the public datasets Garbage Classification, TrashNet, and our self-built MixTrash dataset demonstrate that HFWC-Net achieves Top-1 accuracy rates of 98.89%, 96.88%, and 94.35%, respectively. These findings indicate that HFWC-Net attains the highest accuracy among current methods, offering significant advantages in accelerating classification efficiency and supporting automated waste management applications. Full article

Food waste is a significant global issue with substantial environmental, economic, and social implications. This exploratory study aimed to evaluate the impact of an educational composting program on reducing food waste generation and promoting proper waste sorting practices within multi-family housing units in San Marcos, Texas. A comprehensive methodology was employed, encompassing pre- and post-intervention waste audits, educational interventions, weekly organic waste collection, and quantitative data analyses. Nine multi-family complexes, spanning student housing, conventional family units, low-income residences, and senior living facilities, were targeted through strategic recruitment efforts and incentivization. The treatment group, consisting of 43 participants, received ongoing education throughout the eight-week implementation period, facilitated through informational resources, feedback mechanisms, and door-to-door organic waste collection. Conversely, the control group did not partake in the educational component. Statistical analyses, including descriptive statistics and paired t-tests, facilitated comparisons across various dimensions, such as housing types, treatment versus control groups, and pre- versus post-intervention periods. The findings revealed significant reductions in organic waste and compostable materials within the treatment group’s weekly landfill trash, underscoring the effectiveness of the educational program. Furthermore, insights into contamination patterns and housing-specific waste characteristics were garnered, informing targeted intervention strategies and policy recommendations for optimizing multi-family composting initiatives. Full article

The continuous provision of nitrogen (N) to the crop is critical for optimal cotton production; however, the constant and excessive application of synthetic fertilizers causes adverse impacts on soil, plants, animals, and human health. The current study focused on the short-term effects (one-year study) of adding different rates of clinoptilolite zeolite, as part of an integrated nutrient management plan, and different rates of inorganic N fertilizer to improve soil and crop performance of cotton in three locations (ATH, MES, and KAR) in Greece. Each experiment was set up according to a split-plot design with three replications, three main plots (zeolite application at rates of 0, 5, and 7.5 t ha⁻¹), and four sub-plots (N fertilization regimes at rates of 0, 100, 150, and 200 kg N ha⁻¹). The results of this study indicated that increasing rates of the examined factors increased cotton yields (seed cotton yield, lint yield, and lint percentage), with the greatest lint yield recorded under the highest rates of zeolite (7.5 t ha⁻¹: 1808, 1723, and 1847 kg ha⁻¹ in ATH, MES, and KAR, respectively) and N fertilization (200 kg N ha⁻¹: 1804, 1768, and 1911 kg ha⁻¹ in ATH, MES, and KAR, respectively). From the evaluated parameters, most soil parameters (soil organic matter, soil total nitrogen, and total porosity), root and shoot development (root length density, plant height, leaf area index, and dry weight), fiber maturity traits (micronaire, maturity, fiber strength, and elongation), fiber length traits (upper half mean length, uniformity index, and short fiber index), as well as color (reflectance and spinning consistency index) and trash traits (trash area and trash grade), were positively impacted by the increasing rates of the evaluated factors. In conclusion, the results of the present research suggest that increasing zeolite and N fertilization rates to 7.5 t ha⁻¹ and 200 kg N ha⁻¹, respectively, improved soil properties (except mean weight diameter), stimulated crop development, and enhanced cotton and lint yield, as well as improved the fiber maturity, length, and color parameters of cotton grown in clay-loam soils in the Mediterranean region. Full article

This study examined the historical compatibility of urban furniture in Harput Sarahatun Mosque Square, Elazığ, Türkiye. It evaluated AI-generated modern and classical-style alternatives using the Semantic Differentiation Method. The research aimed to compare existing furniture with AI-assisted designs and identify key attributes influencing historical and spatial integration. The methodology consists of four stages: (1) defining adjective pairs to assess historical compatibility through expert opinions and literature review; (2) photographing existing urban furniture and generating AI-assisted modern and classical-style urban furniture (benches, trash cans, and lighting elements); (3) determination expert opinion using the survey; (4) statistical analysis of results through descriptive statistics and explanatory factor analysis (EFA). The study, which was conducted online in February 2025, involved 31 experts from the architecture and landscape architecture disciplines. The findings show that existing furniture is mainly judged by practicality and usability, with limited attention to historical integration. Modern AI-generated designs emphasize innovation, minimalism, and contemporary aesthetics. In contrast, classical-style AI-generated furniture is appreciated for its historical compatibility, cultural resonance, and aesthetic harmony. Experts favored classical alternatives for their alignment with traditional urban character. The results highlight the need for future designs to balance functionality, sustainability, and historical continuity, ensuring urban furniture contributes to cultural preservation and modern urban needs. Full article

Pruning waste (PW) and agricultural timber residue are rarely treated, creating environmental pollution issues. The lack of regulations and environmental control criteria has led to poor ecosystems. In this study, it is proposed to transform PW and turn it from a nuisance into a valuable energy source and other alternative resources under environmental constraints. Current reuse and recycling options include turning the waste into a food source or using it to generate energy, compost, soil fertilizer, and other products. A linear programming model with Boolean variables and a management model are defined and run for the reuse of PW. The management model defines the diverse options for PW reuse in terms of resource recovery. These options depend, to a considerable extent, on the country’s production capacity and the preferred applied alternatives. The country of Israel is split into separate regions, which are classified according to the preferred alternatives for PW treatment and reuse. These alternatives include factors such as the annual amounts of trash generated, transportation expenses, energy demands, and requirements based on annual and daily needs. An optimization model (based on operations research methods) is defined, solved, and subjected to a series of constraints. The goal of the study is to find out the best location for PW treatment facilities and optimal recycling product technology using linear programming software with Boolean variables. The results show that a net profit of approximately 3.5 million USD/year for a total community of close to 10 × 10⁶ residents could be derived from the amounts of waste, including improved environmental control, in addition to the additional energy source. This work raises an urgent need to control and regulate recycling policies for PW in various environmental regions worldwide. Full article

To reduce solid waste production and enable the synergistic conversion of solid waste into high-value-added products, we introduce a novel, sustainable, and ecofriendly method. We fabricate nanofiber and nanosheet silicon carbides (SiC) through a carbothermal reduction process. Here, the calcined coal gangue, converted from coal gangue, serves as the silicon source. The carbon sources are the carbonized waste tire residue from waste tires and the pre-treated kerosene co-refining residue. The difference in carbon source results in the alteration of the morphology of the SiC obtained. By optimizing the reaction temperature, time, and mass ratio, the purity of the as-made SiC products with nanofiber-like and nanosheet-like shapes can reach 98%. Based on the influence of synthetic conditions and the results calculated from the change in the Gibbs free energy of the reactions, two mechanisms for SiC formation are proposed, namely the reaction of intermediate SiO with CO to form SiC-nuclei-driven nanofibrous SiC and the SiO-deposited carbon surface to fabricate nuclei-induced polymorphic SiC (dominant nanosheets). This work provides a constructive strategy for preparing nanostructured SiC, thereby achieving “turning trash into treasure” and broadening the sustainable utilization and development of solid wastes. Full article

This manuscript presents a real-time monitoring system for urban garbage levels using Time-of-Flight (ToF) sensing technology. The experiment employs the VL53L8CX sensor, which accurately measures distances, along with an ESP32-S3 microcontroller that enables IoT connectivity. The ToF-Node IoT system, consisting of the VL53L8CX sensor connected to the ESP32-S3, communicates with an IoT gateway (Raspberry Pi 3) via Wi-Fi, which then connects to an IoT cloud. The ToF-Node communicates with the IoT gateway using Wi-Fi, and after with the IoT cloud, also using Wi-Fi. This setup provides real-time data on waste container capacities, facilitating efficient waste collection management. By integrating sensor data and network communication, the system supports informed decision-making for optimizing collection logistics, contributing to cleaner and more sustainable cities. The ToF-Node was tested in four scenarios, with a PCB measuring 40 × 18 × 4 mm and an enclosure of 65 × 40 × 30 mm. We used an office trash box with a height of 250 mm (25 cm), and the ToF-Node was located on the top. Results demonstrate that the effectiveness of ToF technology in environmental monitoring and the potential of IoT to enhance urban services. For detailed monitoring, additional ToF sensors may be required. Data collected are displayed in the IoT cloud for better monitoring and can be viewed by level and volume. The ToF-Node and the IoT gateway have a combined power consumption of 153.8 mAh Full article

Most people in the world now live in urban areas and their shared quest for better cities is embodied in several Sustainable Development Goals of the United Nations. These indicate that successful cities need jobs, adequate housing stock, effective governance, and other support systems. At the most basic level, they need a basket of core public works services like clean water and efficient transit, among others. These must be provided to improve public trust in government by addressing equity and affordability while also improving operational and cost efficiency. These targets are moving as transitions are occurring from stove-piped to integrated services, even while social contracts between government and the private sector are also shifting. Essential tools to improve cities include urban planning and infrastructure development, but applying them effectively faces challenges like climate change, inequality, social disorder, and even armed conflicts. This paper focuses on seven core public works services for drinking water, wastewater, stormwater, trash collection, mass transit, streets and traffic control, and disaster management. It reviews how these have evolved in the US, how they are organized under the federalism system, and how the goal of integrated management is being pursued. Challenges to integrated approaches include increasing responsibilities but lack of funding, political stress, and rule-driven and internally oriented management. Methods for performance assessment are explained under legacy systems based on methods like indicators and benchmarking applied to public works systems. Current methods focus on regulatory targets and the details; information has been shallow and not always timely. This paper projects how the performance assessment of core public works systems can be broadened to address goals like those of the SDGs and assesses why it is difficult to rate major systems. Examples of the activities of NGOs are given and an example of how progress toward SDG6 is included to show why performance management of integrated management applied to linked systems is needed. Performance dashboards with open government are currently the most common pathways, but emerging methods based on data analytics and visualization offer new possibilities. Reviewing the status of public works management shows that it is an important branch of the field of public administration, and it can be presented as a professional field with its own identity. The findings will support educators and researchers as well as provide policy insights into public works and stakeholder engagement. Full article

This study investigates the changes in population size, distribution, and habitat preferences of the Eurasian magpie Pica pica in Zielona Góra over 23 years, emphasising the effects of urbanisation and habitat transformation. A comprehensive survey conducted in 2022 identified 953 magpie pairs, with an average density of 8.8 pairs/km² across the current administrative boundaries of Zielona Góra (without forests), and 27.7 pairs/km² in strictly urbanised zones. The highest densities were observed in the old town (36.5 pairs/km²) and residential blocks (34.5 pairs/km²), while peripheral areas, like allotment gardens and industrial zones, showed significantly lower densities. The nests were predominantly located in coniferous trees, especially spruces, marking a shift from the previously favoured poplars. The mean nest height was 11.8 m, varying by habitat type, with the highest nests found in the old town and parks. Environmental factors, such as proximity to trash bins, water sources, and tall trees, were significant predictors of nest density and placement. These findings underscore the magpie’s adaptability to urban environments, influenced by the availability of anthropogenic resources, habitat structure, and surrounding urban features. Full article

Background: The Healthy, Hunger-Free Kids Act was passed in 2010 to update nutrition standards in the National School Lunch and Breakfast Programs to be in accordance with evidence-based guidelines. In 2017 and 2020, the United States Department of Agriculture proposed flexibilities to the nutrition standards for milk, whole grains, and sodium. Objective: This study examines the positions used by stakeholders in support for or opposition to the proposed rules. Methods: We conducted a longitudinal qualitative content analysis of public comments posted to the U.S. Federal Register in response to the USDA’s proposed rules in 2017 and 2020. All public comments submitted by individuals and organizations were analyzed (n = 7323, 2017 and n = 2513, 2020). Results: Results indicated three categories of arguments: (1) comments favoring the original law, (2) comments favoring flexibilities, and (3) other. In both comment periods, constituents opposed the implementation of flexibilities, while schools and manufacturers/industry predominately supported them. Academic and advocacy organizations opposed the original proposed change (2017) but relaxed their position in 2020. Conclusions: Any flexibility to the required nutritional standards of school meals has the potential to affect the health trajectory of youth. It is imperative to understand how stakeholders view this issue and inform policy change. Full article

Two feeding strategies based on the use of trash fish (TF) and an artificial compound feed (ACF) were compared in terms of growth performance, feed efficiency, muscle quality and health status in Micropterus salmoides. For this purpose, fish (128 ± 14 g; n = 102) were divided into two groups and fed with the TF and ACF in triplicate for 90 days. Results showed that the growth performance and condition factor were not affected by the diet, whereas the viscerosomatic and hepatosomatic indexes in the ACF group were higher than in the TF group. The muscle from the TF group had higher levels of 20:5n-3, 22:6n-3, and total n-3 PUFA contents, which resulted in lower thrombogenicity index values. No differences in the amino acid profile were found. Regarding muscle texture properties, only the gumminess and chewiness were significantly lower in the ACF. The use of histological and gene expression biomarkers showed that fish fed TF had a healthier hepatic condition compared to the ACF. The only disadvantage of TF in the current study was the higher values of FCR in comparison to ACF. Full article

The ubiquitous presence of marine litter has brought huge environmental pressure. A wide range of measures have been developed to address this problem. This paper focuses on the removal measure—Fishing for Litter (FEL). It aims to identify the potential factors affecting fishers’ participation in the FFL program. A two-step approach, including interviews and questionnaire surveys, was employed. A total of 10 fishers participated in the interviews, and 8 factors were initially identified using thematic analysis and utilized in the questionnaire design. A total of 412 valid samples were collected. Descriptive statistics and binary logit regression were used for data analysis. The results showed that rewards, the participation of other friends, and inconveniences or troubles incurred from handling trash feature most in fishers’ decision-making on the participation. Furthermore, fishers’ views toward marine environments also had a behavioral impact on their participation in the program. Potential management measures were proposed, including reducing inconveniences incurred from handling trash on board as well as at ports, providing rewards, encouraging environmental education for fishers, and distributing information regarding the program. It is hoped that fishers will eventually make it a normal onboard practice to collect trash found at sea and develop a sense of marine environmental stewardship. Full article

This study aimed to compare the environmental performance of plastic bags made of three different polymers, considering two product functions: carrying goods and packing municipal solid waste. The three polymers studied were HDPE, LDPE, and thermoplastic starch (TPS). Life cycle assessment and emergy accounting were used to evaluate the environmental performance of each scenario in analysis. To develop this research, eight scenarios were created to represent the customs of use and consumption in the Brazilian population. The LCA results showed that, in general, the scenarios with HDPE plastic bags presented the best environmental performances, while those with TPS presented the worst. The processes that contributed most to these results, representing 70% or more of the environmental impact in each impact category, are related to the use of raw materials, electricity, and water for the manufacture of plastic bags and the treatment in landfills. In other words, the fact that TPS has a mass around six times greater than that of HDPE and two times greater than that of LDPE ends up leaving this type of polymer with the worst environmental performance. In the comparative analysis of scenarios for the same polymer, scenarios that involve the use and reuse of plastic bags present the lowest potential environmental impacts. In contrast, those related to the use and disposal in landfills present the highest possible environmental impacts. The results of emergy accounting showed that the HDPE scenarios had the lowest total emergy flow, ranging from 1.77 × 10¹³ seJ to 2.40 × 10¹³ seJ. In contrast, the LDPE scenarios had the highest total emergy flow, ranging from 1.15 × 10¹⁴ to 1.21 × 10¹⁴ seJ. Although LDPE had the highest total emergy flow values, these results are similar to those obtained by the fossil resource scarcity impact category, which focuses on resource consumption analysis. Thus, through a real approach to the use of plastic bags and solid waste management in the Brazilian context, this study brings essential insights to direct public policies related to the consumption of plastic bags. Full article