Search Results (86)

The increasing volume of global waste presents a critical environmental and societal challenge, demanding innovative solutions to support sustainable practices such as recycling. Advances in Computer Vision (CV) have enabled automated waste recognition systems that guide users in correctly sorting their waste, with state-of-the-art architectures achieving high accuracy. More recently, attention has shifted toward lightweight and efficient models suitable for mobile and edge deployment. These systems process data from integrated camera sensors in Internet of Things (IoT) devices, operating in real time to classify waste at the point of disposal, whether embedded in smart bins, mobile applications, or assistive tools for household use. In this work, we extend our previous research by improving both dataset diversity and model efficiency. We introduce an expanded dataset that includes an organic waste class and more heterogeneous images, and evaluate a range of quantized CNN models to reduce inference time and resource usage. Additionally, we explore ensemble strategies using aggregation functions to boost classification performance, and validate selected models on real embedded hardware and under simulated lighting variations. Our results support the development of robust, real-time recycling assistants for resource-constrained devices. We also propose architectural deployment scenarios for smart containers, and cloud-assisted solutions. By improving waste sorting accuracy, these systems can help reduce landfill use, support citizen engagement through real-time feedback, increase material recovery, support data-informed environmental decision making, and ease operational challenges for recycling facilities caused by misclassified materials. Ultimately, this contributes to circular economy objectives and advances the broader field of environmental intelligence. Full article

The voluntary basis on which recycling and energy saving are performed at households brings forward the need to better understand the profile of recyclers and non-recyclers and to make meaningful comparisons between them. Hence, the aim of this study is to compare recyclers’ and non-recyclers’ profiles and practices in order to detect areas that require policy and educational interventions. To achieve this aim, this study collected a representative sample of 384 citizens in a fast-growing urban center and compared recyclers and non-recyclers in terms of their environmental practices. The results showed that both groups identified environmental protection as their leading motive to recycle, while plastic and paper were the most recycled materials. An interesting difference between the two groups was that recyclers were more engaged in energy-saving, suggesting that recycling engagement may be associated with the adoption of energy-saving practices. The Internet was the leading information source across both groups, emphasizing the role it can play in spreading accurate and motivating messages about recycling and energy-saving. This study provides a useful and nuanced picture of recyclers’ and non-recyclers’ profiles and their differences, and as such, it can introduce new angles for the design of strategies for encouraging pro-environmental behavior. Full article

Low-cost and sustainable technological systems are required to improve communication between deaf–mute and non-deaf–mute people. Herein, we report a novel low-cost and eco-friendly triboelectric nanogenerator (TENG) composed of recycled and waste components. This TENG can be connected to a smartphone using the internet of things (IoT), which allows the transmission of information from deaf–mute to non-deaf–mute people. The proposed TENG can harness kinetic energy to convert it into electrical energy with advantages such as a compact portable design, a light weight, cost-effective fabrication, good voltage stability, and easy signal processing. In addition, this nanogenerator uses recycled and waste materials composed of radish leaf, polyimide tape, and a polyethylene terephthalate (PET) sheet. This TENG reaches an output power density of 340.3 µWm⁻² using a load resistance of 20.5 MΩ at 23 Hz, respectively. This nanogenerator achieves a stable performance even after 41,400 working cycles. Also, this device can power a digital calculator and chronometer, as well as light 116 ultra-bright blue commercial LEDs. This TENG can convert the movements of the fingers of a deaf–mute person into electrical signals that are transmitted as text messages to a smartphone. Thus, the proposed TENG can be used as a low-cost wireless communication device for deaf–mute people, contributing to an inclusive society. Full article

Background: The global climate crisis has intensified the demand for sustainable solutions, positioning Reverse Logistics (RL) as a critical strategy for minimizing environmental impacts. Simultaneously, Industry 4.0 technologies are transforming RL operations by enhancing their collection, transportation, storage, sorting, remanufacturing, recycling, and disposal processes. Understanding the roles of these technologies is essential for improving efficiency and sustainability. Methods: This study employs a systematic literature review, following the PRISMA methodology, to identify key Industry 4.0 technologies applicable to RL. Publications from Scopus and Web of Science were analyzed, leading to the development of a theoretical framework linking these technologies to RL activities. Results: The findings highlight the fact that technologies like the Internet of Things (IoT), Artificial Intelligence (AI), Big Data Analytics, Cloud Computing, and Blockchain enhance RL by improving traceability, automation, and sustainability. Their application optimizes execution time, reduces operational costs, and mitigates environmental impacts. Conclusions: For the transportation and manufacturing sectors, integrating Industry 4.0 technologies into RL can streamline supply chains, enhance decision-making, and improve resource utilization. Smart tracking, predictive maintenance, and automated sorting systems reduce waste and improve operational resilience, reinforcing the transition toward a circular economy. By adopting these innovations, stakeholders can achieve economic and environmental benefits while ensuring regulatory compliance and long-term competitiveness. Full article

Smart cities aim to enhance the quality of life by advancing infrastructure, leveraging technology, and promoting sustainability, balancing environmental, societal, and economic needs for long-term efficiency. Given resource scarcity and environmental regulations, advanced supply chains play a crucial role in developing smart cities by adopting the circular economy concept, which emphasizes maximizing resource efficiency through recycling and remanufacturing. This study delves into the competition between two types of supply chains in the context of reverse logistics: the hybrid supply chain, which utilizes a dual channel including traditional and e-channels for collecting used products, and the traditional supply chain, which relies solely on a traditional channel. Both supply chain models are actively involved in remanufacturing and recycling used products, and each considers varied policies, including incentive-based policies, advertising investments, the acceptance return quality level, the return processing time, and transportation investments, to enhance their performance. Specifically, this research has two primary objectives: (1) evaluating the economic and environmental outcomes across three competitive scenarios, and (2) analyzing the impact of varied policy settings on these outcomes. These objectives frame the analysis of optimal incentive values, return rates, and profitability across the Nash equilibrium and Nash–Stackelberg structures, providing insights into both the strategic and policy dimensions of supply chain operations. The findings indicate that a hybrid supply chain in this case achieves higher return rates and profitability, highlighting the success of its dual-channel strategy and associated policies. Regarding economic goals, both supply chains achieve the highest profits under the Nash–Stackelberg traditional supply chain leadership structure. However, for environmental goals, the traditional supply chain favors Nash equilibrium for higher return rates, while the hybrid supply chain prefers Nash–Stackelberg with traditional leadership. These scenario-specific results emphasize the importance of aligning economic and environmental goals through tailored strategies. A sensitivity analysis, supported by Pareto prioritization, identifies the return quality level and processing time as critical for the hybrid supply chain, and advertisement investments and the return processing time as key for the traditional supply chain. These insights suggest that H-SCs should prioritize stricter quality standards, efficient inspection protocols, and automation (e.g., AI or optical scanning) to improve the quality and processing time efficiency. Meanwhile, T-SCs should focus on advertising traditional channels by emphasizing faster processing time and less restrictive quality standards, while adopting automated time management strategies similar to H-SCs to enhance engagement and profitability. These findings show that by integrating smart city internet-based initiatives and managing related policies, supply chains can enhance circular economy objectives by optimizing both the economic and environmental performance, ultimately fostering more resilient and sustainable supply chains. Full article

The ever-growing volume of global electronic waste (e-waste) poses significant environmental and health challenges. Printed circuit boards (PCBs), which form the core of most electronic devices, contain valuable metals as well as hazardous materials. The efficient disassembly and recycling of e-waste is critical for both economic and environmental sustainability. The traditional manual disassembly methods are time-consuming, labor-intensive, and often hazardous. The integration of edge computing and the Internet of Things (IoT) provides a novel approach to automating the disassembly process, potentially transforming the way e-waste is managed. Automated disassembly of WPCBs involves the use of advanced technologies, specifically edge computing and the IoT, to streamline the recycling process. This strategy aims to improve the efficiency and sustainability of e-waste management by leveraging real-time data analytics and intelligent decision-making at the edge of the network. This paper explores the application of edge computing and the IoT in the automated disassembly of WPCBs, discussing the technological framework, benefits, challenges, and future prospects. The experimental results show that the YOLOv10 model achieves 99.9% average precision (AP), enabling accurate real-time detection of electronic components, which greatly facilitates the automated disassembly process. Full article

This article reviews the literature surrounding innovative computational tools for waste management within smart cities. With the rise of urbanization and the increasing challenges of waste management, innovative technologies play a pivotal role in optimizing waste collection, sorting, recycling, and disposal processes. Leveraging computational tools such as artificial intelligence, Internet of Things, and big data analytics, smart waste management systems enable real-time monitoring, predictive modeling, and optimization of waste-related operations. These tools empower authorities to enhance resource efficiency, minimize environmental impact, and improve the overall quality of urban living. Through a comprehensive review of recent research and practical implementations, this article highlights the key features, benefits, and challenges associated with the development of cutting-edge computational tools for waste management. Emerging trends and opportunities for research and development in this rapidly evolving field are identified, emphasizing the importance of integrating technological innovations for building sustainable and resilient waste management in smart cities. Full article

Human-Computer Interaction (HCI) research has illuminated how technology can influence users’ awareness of their environmental impact and the potential for mitigating these impacts. From hot water saving to food waste reduction, researchers have systematically and widely tried to find pathways to speed up achieving sustainable development goals through persuasive technology interventions. However, motivating users to adopt sustainable behaviors through interactive technologies presents significant psychological, cultural, and technical challenges in creating engaging and long-lasting experiences. Aligned with this perspective, there is a dearth of research and design solutions addressing the use of persuasive technology to promote sustainable recycling behavior. Guided by a participatory design approach, this investigation focuses on the design opportunities for leveraging persuasive and human-centered Internet of Things (IoT) applications to enhance user engagement in recycling activities. The assumption is that one pathway to achieve this goal is to adopt persuasive strategies that may be incorporated into the design of sustainable applications. The insights gained from this process can then be applied to various sustainable HCI scenarios and therefore contribute to HCI’s limited understanding in this area by providing a series of design-oriented research recommendations for informing the development of persuasive and socially engaged recycling platforms. In particular, we advocate for the inclusion of educational content, real-time interactive feedback, and intuitive interfaces to actively engage users in recycling activities. Moreover, recognizing the cultural context in which the technology is socially situated becomes imperative for the effective implementation of smart devices to foster sustainable recycling practices. To this end, we present a case study that seeks to involve children and adolescents in pro-recycling activities within the school environment. Full article

Electronic and electrical products have deeply permeated all aspects of life, bringing a lot of convenience to individuals. However, the generation of e-waste after their end-of-life has resulted in serious risks both to the ecological environment and human health due to a lack of scientific and effective recycling and treatments. As two major types of components in e-waste, heavy metals and plastics can not only directly enter the human body via inhalation, ingestion, and skin absorption, but also accumulate in the human body indirectly through the food chain. E-waste is full of resources such as valuable metals like gold, silver, and copper that are often discarded incorrectly. Environmental and health risks derived from unregulated e-waste dismantling activities may be finally addressed through the application of advanced e-waste recycling technology, policy support of governments, legislation on recycling laws and regulations, and the improvement of public environmental protection awareness. This review gives a brief overview of the history, current situation, and future development of e-waste in China, which may provide novel thinking and approaches to e-waste management in the world. Full article

This study investigates the perceived ease of recycling in Glendale, Salt Lake City, Utah, USA, by household size, age, income, and gender. While existing research has broadly explored how sociodemographic factors impact recycling, there is a lack of comprehensive studies analyzing these factors within specific local contexts. This study aims to identify specific barriers and motivators across different demographics to enhance local recycling efforts using Glendale as a case study. Data were collected through an online survey of 111 respondents and analyzed using both quantitative and qualitative methods. The survey included questions about the demographic information, perceptions of recycling ease, and barriers to recycling. The analysis revealed that one-person households and young adults (18–35) face constraints such as limited space for recyclables, a lack of access to recycling bins in rental units, or high costs. Older adults (56 years or older) are highly committed but may face physical challenges. Higher-income households report higher participation due to better access and awareness, whereas lower-income households encounter significant barriers such as limited facility access and insufficient information. Gender differences indicate that women are slightly more proactive and committed to recycling compared to men. Recommendations include expanding recycling facilities, targeted educational campaigns, and economic incentives to encourage lower-income households, males, younger, and older adults. Addressing these demographic-specific barriers can improve recycling rates and contribute to more sustainable communities. Future studies should include in-person surveys as one of the limitations of this study is that an online survey format may introduce biases and the exclusion of residents without internet access. Full article

The increasing demand for reliable and sustainable electricity has driven the development of microgrids (MGs) as a solution for decentralized energy distribution. This study reviews advancements in MG planning and optimization for renewable energy integration, using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses methodology to analyze peer-reviewed articles from 2013 to 2024. The key findings highlight the integration of emerging technologies, like artificial intelligence, the Internet of Things, and advanced energy storage systems, which enhance MG efficiency, reliability, and resilience. Advanced modeling and simulation techniques, such as stochastic optimization and genetic algorithms, are crucial for managing renewable energy variability. Lithium-ion and redox flow battery innovations improve energy density, safety, and recyclability. Real-time simulations, hardware-in-the-loop testing, and dynamic power electronic converters boost operational efficiency and stability. AI and machine learning optimize real-time MG operations, enhancing predictive analysis and fault tolerance. Despite these advancements, challenges remain, including integrating new technologies, improving simulation accuracy, enhancing energy storage sustainability, ensuring system resilience, and conducting comprehensive economic assessments. Further research and innovation are needed to realize MGs’ potential in global energy sustainability fully. Full article

The enormous increase in the volume of waste caused by the population boom in cities is placing a considerable burden on waste processing in cities. The inefficiency and high costs of conventional approaches exacerbate the risks to the environment and human health. This article proposes a thorough approach that combines deep learning models, IoT technologies, and easily accessible resources to overcome these challenges. Our main goal is to advance a framework for intelligent waste processing that utilizes Internet of Things sensors and deep learning algorithms. The proposed framework is based on Raspberry Pi 4 with a camera module and TensorFlow Lite version 2.13. and enables the classification and categorization of trash in real time. When trash objects are identified, a servo motor mounted on a plastic plate ensures that the trash is sorted into appropriate compartments based on the model’s classification. This strategy aims to reduce overall health risks in urban areas by improving waste sorting techniques, monitoring the condition of garbage cans, and promoting sanitation through efficient waste separation. By streamlining waste handling processes and enabling the creation of recyclable materials, this framework contributes to a more sustainable waste management system. Full article

The rapid growth of urbanization and industrialization has brought the issue of waste management to the forefront. Industrial, household, and medical waste management and disposal are major issues affecting the whole world. The adoption of digital technologies across society is largely a result of the increasing processing power of waste and decreasing costs. Waste management and recycling is also benefiting from emerging digital technologies. The Internet of Things, cloud computing, artificial intelligence, robotics, and data analytics are a few examples of specific digital technologies that are currently in use and are predicted to have a significant impact on the efficiency of the waste recycling industry in the future. The objective of this review, which was conducted using the bibliometric method and visualized with scientific mapping, is to demonstrate how the digital transformation of waste recycling has evolved over the last decade and to identify which issues have been overlooked or have become more prominent. The scope of the research is based on studies carried out all over the world and on digital applications and works in the field of waste recycling. In this review, bibliometric analysis was used to scan the entire field and the results were classified and interpreted according to the PRISMA (preferred reporting of systematic reviews and meta-analyses) methodology. Full article

As a highly energy-intensive and carbon-emitting industry with significant emissions of volatile organic compounds (VOCs), the petroleum and chemical industry is a major contributor to the global greenhouse effect and ozone layer destruction. Improper treatment of petrochemical waste gas (PWG) seriously harms human health and the natural environment. This study uses CiteSpace and VOSviewer to conduct a scientometric analysis of 1384 scholarly works on PWG and carbon sequestration published between 1981 and 2022, revealing the basic characteristics, knowledge base, research topic evolution, and research hotspots of the field. The results show the following: (1) In the early stages of the petrochemical industry, it was processed tail gas, plant leakage waste gas, and combustion flue gas that were investigated in PWG research. (2) Later, green environmental protection technology was widely studied in the field of PWG treatment, such as biotechnology, catalytic oxidation technology, membrane separation technology, etc., in order to achieve efficient, low energy consumption and low emissions of waste gas treatment, and the number of publications related to this topic has increased rapidly. In addition, researchers studied the internet of things and technology integration, such as the introduction of artificial intelligence, big data analysis, and other technologies, to improve the accuracy and efficiency of exhaust gas monitoring, control, and management. (3) The department has focused on how to reduce emissions by optimizing petrochemical process lines or improving energy efficiency. Emission reduction and low-carbon transition in the petrochemical industry will become the main trend in the future. Switching from renewable carbon to feedstock carbon derived from captured carbon dioxide, biomass, or recycled chemicals has become an attractive strategy to help curb emissions from the chemical industry. The results of our analysis can provide funding agencies and research groups with information to better understand the global trends and directions that have emerged in this field from 1981 to 2022 and serve as a reference for future research. Full article

Developers of electronics for the Internet of Things are considering nonstandard substrate materials like recyclable, low-cost, and eco-friendly cardboard paper. From this perspective, this article reviews the design and experimental results of a 2D-rectenna for scavenging radio-frequency energy at 2.45 GHz on various cardboard paper substrates for both the antenna and rectifier. Four types of recycled cardboard material, each with different thicknesses, air gaps, and surface roughness, are selected for characterization. A linearly polarized rectangular microstrip patch antenna with microstrip transmission feeding is adopted for ease of fabrication. At 2.45 GHz, the antenna has a simulated and measured global gain of 2.98 dB and 2.53 dB, respectively, on a 2.2 mm thick cardboard material. The rectifying element consists of a voltage-doubler configuration connected through a T-matching network to the antenna. At low RF input power (−10 dBm), the maximum available DC output power is experimentally evaluated at 1.73 μW, 7.5 μW, and 8.5 μW for HSMS-2860, HSMS-2850, and SMS7306-079L diodes, respectively. The respective rectifiers with diodes SMS7306-079L, HSMS-2850, and HSMS-2860 exhibit optimal load values of 2 kΩ, 2.6 kΩ, and 8 kΩ. The rectifier designed using the SMS7306-079L diode experimentally reaches a maximum power conversion efficiency (PCE) of 14.2% at −5 dBm input power when the optimal load value is 1.5 kΩ. Full article