Search Results (24)

Unsafe disposal of unused and expired antimicrobial drugs increases their presence in the environment, thereby contributing to the emergence and spread of antimicrobial resistance. This study addressed the lack of sufficient data on unused and expired antimicrobial disposal practices among peri-urban residents in Ghana. This knowledge–attitude–practice (KAP)-based study offers context-specific insights to inform public health education and antimicrobial disposal policy interventions. A cross-sectional study was conducted among 310 residents in the Ho municipality using a well-structured questionnaire. Data was collected on the knowledge, attitudes, and practices of households on how they dispose of unused and leftover antimicrobials. Origin Pro 2022 software was used to analyze the data. Many respondents were males (n = 175, 56.5%) and aged between 18 and 30 years (n = 196, 63.2%). About 87.1% (n = 270) of the respondents agreed that improper disposal of unused antimicrobials could negatively affect the environment. Most of the respondents (71.9%, n = 223) had not received counseling on recommended antimicrobial disposal; 75.5% (n = 234) of respondents were not aware of institutions collecting unused or expired medicines; and 73.5% (n = 228) had never participated in a medicine-return program. Discarding antimicrobials into household trash bins was the most common way of disposal among respondents. The preferred sites to return unused/leftover antimicrobials were community pharmacies and hospitals. Although respondents showed some knowledge and positive attitudes toward safe antimicrobial disposal, further education is needed. Furthermore, most respondents disposed of antimicrobials in household trash, highlighting the need for take-back programs and community pharmacy-based collection. Incorporating disposal guidance into medication counseling and patient information leaflets can enhance awareness and promote appropriate practices. 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

This discussion describes an experimental behavioral economics class implemented in a service learning format. Students implemented two nudge interventions to influence public trash behavior, which is an issue throughout the Middle East/North Africa (MENA). The aim in one project was to encourage more use of trash bins in a multi-screen theater and in the other to sort plastic, glass, and paper when throwing trash in the public bins at a university. These two organizations paid the implementation costs, which were quite low, as common for most nudges. The class was co-taught by two university faculty and several personnel from governmental and UN offices responsible for building nudge capabilities in Kuwait. In each case, results in the student projects demonstrated that nudging resulted in a substantial improvement. Students gained strong competencies in applying sustainability principles to a specific problem and in carrying out a real-world project. They learned the importance of collaborating with stakeholders and got to see that their work was actually used in policy formation by the Kuwait government and the UNDP’s Kuwait office. Full article

Purpose: This study aims to investigate the disposal practices of optical materials in Portuguese Optical Centers. Methods: This study, conducted in the Portuguese Optical Centers across 18 districts and 308 municipalities, divided the country into 4 regions for analysis. Utilizing Google Forms^®, a survey targeting Optical Center managers and related professionals was disseminated via email from February to May 2023, comprising 30 questions across 6 sections, including optical and contact lenses, maintenance solutions, eyeglass frames, and recycling participation. Data analysis employed IBM SPSS^® Statistics v.27, using non-parametric tests for variable distribution. Ethical standards and privacy policies were strictly observed throughout the research process. Results: Findings indicated that there were significant differences in the final treatment of organic and mineral lenses. Organic lenses were placed in the yellow recycling bin (22.2%), while mineral lenses were placed in the green recycling bin (37.9%). In the case of contact lenses, regardless of the type (RGP, scleral lenses, conventional hydrogels, and silicone hydrogel lenses), the majority (>50%) were disposed of in general trash. Regarding eyeglass frames, there were no significant differences between mass and metal frames, mostly being discard in general waste (~30.0%). Conclusion: Approximately half of the surveyed Portuguese Optical Centers were not actively involved in recycling. This represents a missed opportunity for optometrists to play a role in enhancing recycling rates through patient education. Full article

The preservation and reuse of historical alley spaces infuse these areas with renewed vitality, which holds significant importance for the direction of preservation and restoration efforts in historical districts. This paper focuses on Jinyu Alley in Quanzhou and identifies a study targeting tourists for the protection and reuse of historical alley spaces. Through preliminary research and interviews, a system of evaluation indicators for urban historical alley spaces post-usage was established using a factor analysis, extracting five main components: historical context, neighborhood space, commercial environment, supporting facilities, and operational management. Additionally, a modified importance–performance analysis (IPA) method was employed to conduct a quadrant analysis on tourist satisfaction evaluation indicators. Transformation quadrant distribution maps of various evaluation indicators reveal dissatisfaction among tourists with certain aspects of supporting facilities, the commercial environment, and neighborhood space. Relevant departments should prioritize improvements in dining quality, business variety, neighborhood traffic connections and transformations, neighborhood space form and scale, landscape greening, environmental elements, parking availability, and trash bin density for future enhancements. Finally, based on the results of tourist satisfaction surveys and information gathered from interviews with a minority of residents, a more inclusive and sustainable strategy for the protection and reuse of historical alley spaces is formulated. Full article

The amount and composition of litter was evaluated during May and June 2021 at two urban beaches, i.e., La Victoria and La Cortadura, in Cádiz, SW Spain. Surveys were carried out daily in the morning and in the evening during the weekends to quantify the daily accumulation of beach litter and relate it to the number of beach users, which was assessed at around 1:00 p.m. Litter amount was also related to cleanup operations that were very mechanically and manually carried out each day very early in the morning. A total of 8108 items were collected at the two investigated sectors during the study period and beach visitors were quantified in 22 surveys. Plastic was the most common material, representing 82% in La Victoria and 68% in La Cortadura. The most common items were cigarette butts and small, hard plastic fragments. Some litter items that were hazardous to beach visitors were identified, such as broken glass. The number of visitors was positively related to the amount of litter. Significant differences were seen in the litter abundance between the morning and evening assessments since the beaches were cleaned daily and bins were available to facilitate trash disposal. Cleaning operations remove many of the litter items but always leave small quantities of small items uncollected. Efforts to prevent litter on these beaches should focus on informing visitors properly in order to avoid littering and on improving cleanup operations. Full article

Effective waste management is of paramount importance as it contributes significantly to environmental preservation, mitigates health hazards, and aids in the preservation of precious resources. Conversely, mishandling waste not only presents severe environmental risks but can also disrupt the balance of ecosystems and pose threats to biodiversity. The emission of carbon dioxide, methane, and greenhouse gases (GHGs) can constitute a significant factor in the progression of global warming and climate change, consequently giving rise to atmospheric pollution. This pollution, in turn, has the potential to exacerbate respiratory ailments, elevate the likelihood of cardiovascular disorders, and negatively impact overall public health. Hence, efficient management of trash is extremely crucial in any society. It requires integrating technology and innovative solutions, which can help eradicate this global issue. The internet of things (IoT) is a revolutionary communication paradigm with significant contributions to remote monitoring and control. IoT-based trash management aids remote garbage level monitoring but entails drawbacks like high installation and maintenance costs, increased electronic waste production (53 million metric tons in 2013), and substantial energy consumption for always-vigilant IoT devices. Our research endeavors to formulate a comprehensive model for an efficient and cost-effective waste collection system. It emphasizes the need for global commitment by policymakers, stakeholders, and civil society, working together to achieve a common goal. In order to mitigate the depletion of manpower, fuel resources, and time, our proposed method leverages quick response (QR) codes to enable the remote monitoring of waste bin capacity across diverse city locations. We propose to minimize the deployment of IoT devices, utilizing them only when absolutely necessary and thereby allocating their use exclusively to central garbage collection facilities. Our solution places the onus of monitoring garbage levels at the community level firmly on the shoulders of civilians, demonstrating that a critical aspect of any technology is its ability to interact and collaborate with humans. Within our framework, citizens will employ our proposed mobile application to scan QR codes affixed to waste bins, select the relevant garbage level, and transmit this data to the waste collection teams’ database. Subsequently, these teams will plan for optimized garbage collection procedures, considering parameters such as garbage volume and the most efficient collection routes aimed at minimizing both time and fuel consumption. Full article

A startling shift in waste composition has been brought on by a dramatic change in lifestyle, the quick expansion of consumerism brought on by fierce competition among producers of consumer goods, and revolutionary advances in the packaging sector. The overflow or overspill of garbage from the bins causes poison to the soil, and the total obliteration of waste generated in the area or city is unknown. It is challenging to pinpoint with accuracy the specific sort of garbage waste; predictive image classification is lagging, and the existing approach takes longer to identify the specific garbage. To overcome this problem, image classification is carried out using a modified ResNeXt model. By adding a new block known as the “horizontal and vertical block,” the proposed ResNeXt architecture expands on the ResNet architecture. Each parallel branch of the block has its own unique collection of convolutional layers. Before moving on to the next layer, these branches are concatenated together. The block’s main goal is to expand the network’s capacity without considerably raising the number of parameters. ResNeXt is able to capture a wider variety of features in the input image by using parallel branches with various filter sizes, which improves performance on image classification. Some extra dense and dropout layers have been added to the standard ResNeXt model to improve performance. In order to increase the effectiveness of the network connections and decrease the total size of the model, the model is pruned to make it smaller. The overall architecture is trained and tested using garbage images. The convolution neural Network is connected with a modified ResNeXt that is trained using images of metal, trash, and biodegradable, and ResNet 50 is trained using images of non-biodegradable, glass, and hazardous images in a parallel way. An input image is fed to the architecture, and the image classification is achieved simultaneously to identify the exact garbage within a short time with an accuracy of 98%. The achieved results of the suggested method are demonstrated to be superior to those of the deep learning models already in use when compared to a variety of existing deep learning models. The proposed model is implemented into the hardware by designing a three-component smart bin system. It has three separate bins; it collects biodegradable, non-biodegradable, and hazardous waste separately. The smart bin has an ultrasonic sensor to detect the level of the bin, a poisonous gas sensor, a stepper motor to open the lid of the bin, a solar panel for battery storage, a Raspberry Pi camera, and a Raspberry Pi board. The levels of the bin are maintained in a centralized system for future analysis processes. The architecture used in the proposed smart bin properly disposes of the mixed garbage waste in an eco-friendly manner and recovers as much wealth as possible. It also reduces manpower, saves time, ensures proper collection of garbage from the bins, and helps attain a clean environment. The model boosts performance to predict waste generation and classify it with an increased 98.9% accuracy, which is more than the existing system. Full article

Waste segregation, management, transportation, and disposal must be carefully managed to reduce the danger to patients, the public, and risks to the environment’s health and safety. The previous method of monitoring trash in strategically placed garbage bins is a time-consuming and inefficient method that wastes time, human effort, and money, and is also incompatible with smart city needs. So, the goal is to reduce individual decision-making and increase the productivity of the waste categorization process. Using a convolutional neural network (CNN), the study sought to create an image classifier that recognizes items and classifies trash material. This paper provides an overview of trash monitoring methods, garbage disposal strategies, and the technology used in establishing a waste management system. Finally, an efficient system and waste disposal approach is provided that may be employed in the future to improve performance and cost effectiveness. One of the most significant barriers to efficient waste management can now be overcome with the aid of a deep learning technique. The proposed method outperformed the alternative AlexNet, VGG16, and ResNet34 methods. Full article

The main source of urban waste is the daily life activities of residents, and the waste sorting of residents’ waste is important for promoting economic recycling, reducing labor costs, and protecting the environment. However, most residents are unable to make accurate judgments about the categories of household waste, which severely limits the efficiency of waste sorting. We have designed an intelligent waste bin that enables automatic waste sorting and recycling, avoiding the extensive knowledge required for waste sorting. To ensure that the waste-classification model is high accuracy and works in real time, GECM-EfficientNet is proposed based on EfficientNet by streamlining the mobile inverted bottleneck convolution (MBConv) module, introducing the efficient channel attention (ECA) module and coordinate attention (CA) module, and transfer learning. The accuracy of GECM-EfficientNet reaches 94.54% and 94.23% on the self-built household waste dataset and TrashNet dataset, with parameters of only 1.23 M. The time of one recognition on the intelligent waste bin is only 146 ms, which satisfies the real-time classification requirement. Our method improves the computational efficiency of the waste-classification model and simplifies the hardware requirements, which contributes to the residents’ waste classification based on intelligent devices. Full article

This study describes the development of a smart trash bin that separates and collects recyclables using a webcam and You Only Look Once (YOLO) real-time object detection in Raspberry Pi, to detect and classify these recyclables into their correct categories. The classification result rotates the trash bin lid and reveals the correct trash bin compartment for the user to throw away trash. The performance of the YOLO model was evaluated to measure its accuracy, which was 91% under an optimal computing environment and 75% when deployed in Raspberry Pi. Several Internet of Things hardware, such as ultrasonic sensors for measuring trash bin capacity and GPS for locating trash bin coordinates, are implemented to provide capacity monitoring controlled by Arduino Uno. The capacity and GPS information are uploaded to Firebase Database via theESP8266 Wi-Fi module. To deliver the capacity monitoring feature, the uploaded trash bin capacity information is displayed on the mobile application in the form of a bar level developed in the MIT App Inventor for the user to quickly take action if required. The system proposed in this study is intended to be implemented in a rural area, where it can potentially solve the recyclable waste separation problem. Full article

As a consequence of swiftly growing populations in the urban areas, larger quantities of solid waste also form rapidly. Since urban local bodies are found to be unable to manage this perilous situation effectively, there is a high probability of risks relative to the environment and public health. A sudden change is indispensable in the existing systems that are developed for the collection, transportation, and disposal of solid waste, which are entangled in turmoil. However, Smart sensors and wireless technology enable cyber-physical systems to automate solid waste management, which will revolutionize the industry. This work presents a comprehensive study on the evolution of automation approaches in solid waste management systems. This study is enhanced by dissecting the available literature in solid waste management with Radio Frequency Identification (RFID), Wireless Sensor Networks (WSN), and Internet of Things (IoT)-based approaches and analyzing each category with a typical architecture, respectively. In addition, various communication technologies adopted in the aforementioned categories are critically analyzed to identify the best choice for the deployment of trash bins. From the survey, it is inferred that IoT-based systems are superior to other design approaches, and LoRaWAN is identified as the preferred communication protocol for the automation of solid waste handling systems in urban areas. Furthermore, the critical open research issues on state-of-the-art solid waste handling systems are identified and future directions to address the same topic are suggested. Full article

This paper presents the development of a bin-picking solution based on low-cost vision systems for the manipulation of automotive electrical connectors using machine learning techniques. The automotive sector has always been in a state of constant growth and change, which also implies constant challenges in the wire harnesses sector, and the emerging growth of electric cars is proof of this and represents a challenge for the industry. Traditionally, this sector is based on strong human work manufacturing and the need arises to make the digital transition, supported in the context of Industry 4.0, allowing the automation of processes and freeing operators for other activities with more added value. Depending on the car model and its feature packs, a connector can interface with a different number of wires, but the connector holes are the same. Holes not connected with wires need to be sealed, mainly to guarantee the tightness of the cable. Seals are inserted manually or, more recently, through robotic stations. Due to the huge variety of references and connector configurations, layout errors sometimes occur during seal insertion due to changed references or problems with the seal insertion machine. Consequently, faulty connectors are dumped into boxes, piling up different types of references. These connectors are not trash and need to be reused. This article proposes a bin-picking solution for classification, selection and separation, using a two-finger gripper, of these connectors for reuse in a new operation of removal and insertion of seals. Connectors are identified through a 3D vision system, consisting of an Intel RealSense camera for object depth information and the YOLOv5 algorithm for object classification. The advantage of this approach over other solutions is the ability to accurately detect and grasp small objects through a low-cost 3D camera even when the image resolution is low, benefiting from the power of machine learning algorithms. Full article

Future university campuses will be characterized by a series of novel services enabled by the vision of Internet of Things, such as smart parking and smart libraries. In this paper, we propose a complete solution for a smart waste management system with the purpose of increasing the recycling rate in the campus and provide better management of the entire waste cycle. The system is based on a prototype of a smart waste bin, able to accurately classify pieces of trash typically produced in the campus premises with a hybrid sensor/image classification algorithm, as well as automatically segregate the different waste materials. We discuss the entire design of the system prototype, from the analysis of requirements to the implementation details and we evaluate its performance in different scenarios. Finally, we discuss advanced application functionalities built around the smart waste bin, such as optimized maintenance scheduling. Full article

Recent reports, initiatives, and activities around higher education institutions revealed the relevance and value of sustainability education through both formal curriculum and informal curriculum activities. While the significance of sustainability education has continuously improved by raising awareness among new generations of students, it has not adequately promoted pro-environmental behaviors or attitude changes. This research study used a linear pretest–posttest experimental approach to understand whether two codesigned interventions; a trash and recycling bin system, and a Materials Exchange program, could improve sustainability literacy and material conservation behaviors across the School of Design. Additionally, a mid-experiment focus group study was conducted to provide text-rich data for analysis of 3R behaviors. Analysis of the data collected revealed that these interventions were reasonably successful in improving responsible material management. To have a greater impact on sustainable behavior, it is suggested that a formal educational experience should supplement the informal interventions described in this paper to onboard students as they enter the design studio culture. Additionally, the expansion of the trash and recycling bin station system into the university dormitories is discussed. This work has successfully catalyzed a collaboration between all School of Design stakeholders to address studio waste in a tangible way. Full article