Search Results (146)

An unsustainable rate of resource production and consumption is evident in urban environments. The absence of innovative approaches in conjunction with the exponential urbanization and expansion of the global population will inevitably result in substantial environmental consequences. There are two emerging alternatives: circular economy (CE) and urban metabolism (UM). The integration of these principles into novel methodology casts doubt on the linear model of contemporary economic and urban systems, which includes extraction, production, utilization, and disposal. In the development of a distinctive urban framework known as circular urban metabolism, the current study has illustrated the application of these principles. We design this study to motivate urban planners and decision-makers to investigate, develop, and supervise ecologically sustainable cities. Scholars from a variety of academic disciplines, intrigued by the intricacies of urban planning, design, and administration, can foster interdisciplinary collaboration in the circular urban metabolism (CUM) region. To address the research question, we implemented a bibliometric analysis, which involved the examination of 627 pertinent research papers, utilizing the R (R 3.6.0+) statistical programming language. The results emphasize the fundamental characteristics and significance of CUM in the management of refuse. In addition, the findings underscore the importance of creating a novel framework that incorporates the principles of urban political ecology, CUM, sustainability, and the novel dimension of waste metabolism. It is the goal of this framework to emphasize the significance of recycling in the informal sector as a waste management strategy in low- and medium-income countries (LMICs). Full article

The increasing complexity of managing modern database systems, particularly in terms of optimizing query performance for large datasets, presents significant challenges that traditional methods often fail to address. This paper proposes a comprehensive framework for integrating advanced machine learning (ML) models within the architecture of a database management system (DBMS), with a specific focus on PostgreSQL. Our approach leverages a combination of supervised and unsupervised learning techniques to predict query execution times, optimize performance, and dynamically manage workloads. Unlike existing solutions that address specific optimization tasks in isolation, our framework provides a unified platform that supports real-time model inference and automatic database configuration adjustments based on workload patterns. A key contribution of our work is the integration of ML capabilities directly into the DBMS engine, enabling seamless interaction between the ML models and the query optimization process. This integration allows for the automatic retraining of models and dynamic workload management, resulting in substantial improvements in both query response times and overall system throughput. Our evaluations using the Transaction Processing Performance Council Decision Support (TPC-DS) benchmark dataset at scale factors of 100 GB, 1 TB, and 10 TB demonstrate a reduction of up to 42% in query execution times and a 74% improvement in throughput compared with traditional approaches. Additionally, we address challenges such as potential conflicts in tuning recommendations and the performance overhead associated with ML integration, providing insights for future research directions. This study is motivated by the need for autonomous tuning mechanisms to manage large-scale, heterogeneous workloads while answering key research questions, such as the following: (1) How can machine learning models be integrated into a DBMS to improve query optimization and workload management? (2) What performance improvements can be achieved through dynamic configuration tuning based on real-time workload patterns? Our results suggest that the proposed framework significantly reduces the need for manual database administration while effectively adapting to evolving workloads, offering a robust solution for modern large-scale data environments. Full article

The rapid growth in the use of AI techniques, mainly machine learning (ML), is revolutionizing different industries by significantly enhancing decision-making processes through data-driven insights. This study investigates the influence of using ML, particularly supervised and unsupervised learning, on rational decision-making (RDM) within Jordanian e-government, focusing on the mediating role of trust. By analyzing the experiences of middle-level management within e-government in Jordan, the findings underscore that ML positively impacts the rational decision-making process in e-government. It enables more efficient and effective data gathering, improves the accuracy of data analysis, enhances the speed and accuracy of evaluating decision alternatives, and improves the assessment of potential risks. Additionally, this study reveals that trust plays a critical role in determining the effectiveness of ML adoption for decision-making, acting as a pivotal mediator that can either facilitate or impede the integration of these technologies. This study provides empirical evidence of how trust not only enhances the utilization of ML but also amplifies its positive impact on governance. The findings highlight the necessity of cultivating trust to ensure the successful deployment of ML in public administration, thereby enabling a more effective and sustainable digital transformation. Despite certain limitations, the outcomes of this study offer substantial insights for researchers and government policymakers alike, contributing to the advancement of sustainable practices in the e-government domain. Full article

The artificial general intelligence (AGI) industry, represented by ChatGPT, has impacted social order during its development, and also brought various risks and challenges, such as ethical concerns in science and technology, attribution of liability, intellectual property monopolies, data security, and algorithm manipulation. The development of AI is currently facing a crisis of trust. Therefore, the governance of the AGI industry must be prioritized, and the opportunity for the implementation of the Interim Administrative Measures for Generative Artificial Intelligence Services should be taken. It is necessary to enhance the norms for the supervision and management of scientific and technological ethics within the framework of the rule of law. Additionally, it is also essential to continuously improve the regulatory system for liability, balance the dual values of fair competition and innovation encouragement, and strengthen data-security protection systems in the field of AI. All of these will enable coordinated governance across multiple domains, stakeholders, systems, and tools. Full article

Partnerships among professionals within collegiate teacher preparation programs and school districts are needed to address current teacher shortages, especially critical in urban, high-needs schools. This research study showcases a collaborative model of teacher recruitment, preparation, and support that envisions and co-constructs reconceptualized roles, opportunities, and responsibilities for university faculties, supervising teachers, and teacher candidates. The concept is supported through a continuum of professional learning and reflection. The enhanced partnership model (EPM) for internship requires a partnership between faculties in teacher preparation programs and school districts to develop, engage, and evaluate an EPM for urban-school teacher preparation in multiple urban school sites in the southeastern United States. The goals of this innovative EPM revolve around recruiting diverse teacher candidates, collaboratively preparing them to focus on rigorous academic coursework as well as practical, classroom-based experiences, and retaining the new teachers. Employment data were accessed via administrative sources. A chi-square test of association was used to examine the relationship between participation in the EPM and employment (n = 158). The findings highlight that pre-service teachers participating in the EPM are 4.5 times as likely to be employed in a high-needs school, and 75% of those employed are still employed three years later. The implications of these results are shared. Full article

To protect the safety of food bought from the online catering sector, the former State Food and Drug Administration of China issued two separate regulations in 2016 and 2017. Independently, the Shanghai government formulated local regulations, including the Shanghai Online Catering Service Supervision and Management Measures, to strengthen food safety supervision in this megacity with the largest catering sector in China. This study explored factors that influence consumer perceptions of the performance of online catering food safety regulations using survey data from 1050 respondents collected in 2019. The results indicate that consumers believe that Shanghai’s online catering industry has improved by varying degrees in terms of food freshness, ingredient traceability, foreign matter control, food temperature control, internal packaging hygiene and environmental protection, and satisfaction with food safety. The factors that influenced the listed features include the number and effectiveness of government-issued regulations regarding the online catering sector, effectiveness of ordering platform food safety regulations, employee training frequency, employee food safety awareness, delivery box cleanliness and courier personal hygiene, consumer trust in ordering platform services, and consumer confidence in government supervision. These factors significantly and positively affected the consumers’ perceptions of the performance of food safety regulations in the online catering sector. Full article

(1) Background and rationale: To validate the reported therapeutic coverage, a lymphatic filariasis post-mass drug administration (MDA) campaign survey was conducted in the Lukonga health zone from 10 June to 15 July 2023. (2) Materials and methods: This was a descriptive, cross-sectional study conducted at the community level in 30 villages in the Lukonga health zone from 10 June to 15 July 2023. The study population included all individuals from the visited communities. The study variables included age, sex, drug use (ivermectin + albendazole), adverse events, and adherence to MDA guidelines for supervised drug use. Questionnaires were administered on Android phones using the SurveyCTO platform. Stata version 17 was used for data analysis. (3) Results: Of the 1092 respondents, 54.8% were female and one-third were between the ages of 5 and 14. Two-thirds of the households surveyed, or 64%, had more than six people living in them, and 1031 individuals, or 94%, reported being present during the community mass drug distribution. Notably, 678 individuals, or 66%, reported taking the drugs offered, and 66.4% of those who took the drugs reported doing so in the presence of drug distributors. Thus, the survey coverage was 65.7% [95% CI: 62.9–68.7]. The results of this study show that the survey coverage was above the 65% threshold recommended by the WHO but below the 82.3% reported by the Lukonga health zone. The main reason for non-compliance was a fear of ivermectin-related side effects (47%). Supervised or directly observed treatment was not adhered to (66.4%). (4) Discussion and conclusions: Key challenges to further increase treatment coverage include assessing data quality, building capacity, motivating drug distributors, improving data reporting tools, proper recording by drug distributors, and accurate reporting on non-residents who take the drugs during the MDA. In addition, harmonization of the numerator for calculating drug coverage in the health zone is critical. It is imperative to provide the public with explicit information regarding the objective of drug distribution and the probable adverse effects. Full article

The increasing popularity of moped scooter-sharing as a direct and eco-friendly transportation option highlights the need to understand travel demand for effective urban planning and transportation management. This study explores the use of machine learning techniques to forecast travel demand for moped scooter-sharing services in Madrid, Spain, based on origin–destination trip data. A comprehensive dataset was utilized, encompassing sociodemographic characteristics, travel attraction centers, transportation network attributes, policy-related variables, and distance impedance. Two supervised machine learning models, linear regression and random forest, were employed to predict travel demand patterns. The results revealed the effectiveness of ensemble learning methods, particularly the random forest model, in accurately predicting travel demand and capturing complex feature relationships. The feature scores emphasize the importance of neighborhood characteristics such as tourist accommodations, public administration centers, regulated parking, and commercial centers, along with the critical role of trip distance. Users’ preference for short-distance trips within the city highlights the appeal of these services for urban mobility. The findings have implications for urban planning and transportation decision-making to better accommodate travel patterns, improve the overall transportation system, and inform policy recommendations to enhance intermodal connectivity and sustainable urban mobility. Full article

The emerging digitalization in today’s industrial environments allows manufacturers to store online knowledge about production and use it to make better informed management decisions. This paper proposes a multi-agent framework enhanced with digital twin (DT) for production scheduling and optimization. Decentralized scheduling agents interact to efficiently manage the work allocation in different segments of production. A DT is used to evaluate the performance of different scheduling decisions and to avoid potential risks and bottlenecks. Production managers can supervise the system’s decision-making processes and manually regulate them online. The multi-agent system (MAS) uses asset administration shells (AASs) for data modelling and communication, enabling interoperability and scalability. The framework was deployed and tested in an industrial pilot coming from the bicycle production industry, optimizing and controlling the short-term production schedule of the different departments. The evaluation resulted in a higher production rate, thus achieving higher production volume in a shorter time span. Managers were also able to coordinate schedules from different departments in a dynamic way and achieve early bottleneck detection. Full article

Economic risk is a probability that measures the possible alterations, as well as the uncertainty, generated by multiple internal or external factors. Sometimes it could cause the impossibility of guaranteeing the level of compliance with organizational goals and objectives, which is why for their administration they are frequently divided into multiple categories according to their consequences and impact. Global indicators are dynamic and sometimes the correlation is uncertain because they depend largely on a combination of economic, social, and environmental factors. Thus, our proposal consists of a model for prediction and classification of multivariate risk factors such as birth rate and population growth, among others, using multiple neural networks and General Type-2 fuzzy systems. The contribution is the proposal to integrate multiple variables of several time series using both supervised and unsupervised neural networks, and a generalized Type-2 fuzzy integration. Results show the advantages of utilizing the method for the fuzzy integration of multiple time series attributes, with which the user can then prevent future threats from the global environment that impact the scheduled compliance process. Full article

Show-through phenomena have always been a challenging issue in color-document image processing, which is widely used in various fields such as finance, education, and administration. Existing methods for processing color-document images face challenges, including dealing with double-sided documents with show-through effects, accurately distinguishing between foreground and show-through parts, and addressing the issue of insufficient real image data for supervised training. To overcome these challenges, this paper proposes a self-supervised-learning-based method for removing show-through effects in color-document images. The proposed method utilizes a two-stage-structured show-through-removal network that incorporates a double-cycle consistency loss and a pseudo-similarity loss to effectively constrain the process of show-through removal. Moreover, we constructed two datasets consisting of different show-through mixing ratios and conducted extensive experiments to verify the effectiveness of the proposed method. Experimental results demonstrate that the proposed method achieves competitive performance compared to state-of-the-art methods and can effectively perform show-through removal without the need for paired datasets. Specifically, the proposed method achieves an average PSNR of 33.85 dB on our datasets, outperforming comparable methods by a margin of 0.89 dB. Full article

Rapid urbanization and changing climatic procedures can activate the present surface urban heat island (SUHI) effect. An SUHI was considered by temperature alterations among urban and rural surroundings. The urban zones were frequently warmer than the rural regions because of population pressure, urbanization, vegetation insufficiency, industrialization, and transportation systems. This investigation analyses the Surface-UHI (SUHI) influence in Kolkata Municipal Corporation (KMC), India. Growing land surface temperature (LST) may cause an SUHI and impact ecological conditions in urban regions. The urban thermal field variation index (UTFVI) served as a qualitative and quantitative barrier to the SUHI susceptibility. The maximum likelihood approach was used in conjunction with supervised classification techniques to identify variations in land use and land cover (LULC) over a chosen year. The outcomes designated a reduction of around 1354.86 Ha, 653.31 Ha, 2286.9 Ha, and 434.16 Ha for vegetation, bare land, grassland, and water bodies, correspondingly. Temporarily, from the years 1991–2021, the built-up area increased by 4729.23 Ha. The highest LST increased by around 7.72 °C, while the lowest LST increased by around 5.81 °C from 1991 to 2021. The vegetation index and LST showed a negative link, according to the correlation analyses; however, the built-up index showed an experimentally measured positive correlation. This inquiry will compel the administration, urban planners, and stakeholders to observe humanistic activities and thus confirm sustainable urban expansion. Full article

SDN has the ability to transform network design by providing increased versatility and effective regulation. Its programmable centralized controller gives network administration employees more authority, allowing for more seamless supervision. However, centralization makes it vulnerable to a variety of attack vectors, with distributed denial of service (DDoS) attacks posing a serious concern. Feature selection-based Machine Learning (ML) techniques are more effective than traditional signature-based Intrusion Detection Systems (IDS) at identifying new threats in the context of defending against distributed denial of service (DDoS) attacks. In this study, NGBoost is compared with four additional machine learning (ML) algorithms: convolutional neural network (CNN), Stochastic Gradient Descent (SGD), Decision Tree, and Random Forest, in order to assess the effectiveness of DDoS detection on the CICDDoS2019 dataset. It focuses on important measures such as F1 score, recall, accuracy, and precision. We have examined NeTBIOS, a layer-7 attack, and SYN, a layer-4 attack, in our paper. Our investigation shows that Natural Gradient Boosting and Convolutional Neural Networks, in particular, show promise with tabular data categorization. In conclusion, we go through specific study results on protecting against attacks using DDoS. These experimental findings offer a framework for making decisions. Full article

Many patients require administering one or more blood components during hospitalisation in the Intensive Care Unit (ICU). Therefore, nurses’ knowledge of who is responsible for immediately administering blood transfusions, monitoring patients, and identifying and managing transfusion reactions is crucial. This cross-sectional descriptive-analytical study aimed to assess the knowledge of ICU nurses in tertiary healthcare institutions about blood transfusion procedures. The questionnaire about the transfusion procedure was designed and reviewed by experts. The questionnaire consisted of 29 items divided into three domains. The scores on the knowledge test ranged from 10 to 27. Generally, 57.7% of nurses had moderate, 23.4% low, and 18.9% high levels of knowledge about the transfusion procedure. Most nurses answered correctly about refreezing fresh frozen plasma, verifying the transfusion product, and identifying the patient. Of the nurses, 91.0% would recognise mild allergic reactions, and 98.2% knew about the supervision of sedated patients. Nurses showed poor knowledge of the length of usage of the same transfusion system for red blood cells, labelling, and transfusion administration in febrile patients. Nurses with higher education and longer working experience had significantly better outcomes (p = 0.000) on the knowledge test. Continuous education of ICU nurses on safe transfusion usage is recommended. Full article

Conducting quantitative research involving adolescents demands a thoughtful approach to the question of supervision, given that each option comes with its distinct set of implications. This study reviews these implications and empirically tests whether differences in data quality can be found among three modes of standardized survey research with medium-sized groups of adolescents (12–17 years). The data basis is a quasi-experimental survey study testing different forms of digital, hybrid, or in-person supervision that took place in 2021 in secondary schools in Germany (N = 923). The aim of this study is to test how aspects of data quality—item nonresponse, interview duration, drop-out rate, and response patterns—differ between these forms of supervision. Results could help researchers surveying young people to decide (1) whether they allow confidants or other adults to be present during interviews, (2) if they can rely on teachers alone when surveying classrooms, and (3) if it is cost-efficient to send out external supervisors for classroom sessions. While drop-out rates do not differ, item non-response, interview duration, and response patterns differ significantly; students supervised at home by external interviewers answered more questions, took more time to answer, and were less likely to give potentially meaningless answers in grid questions. The implications drawn from the findings question the common approach of solely relying on teachers for survey administration without the support of external supervisors or adequate training. Recruiting respondents via schools and surveying them online in their homes during school hours has been shown to be a robust method with regard to the analyzed indicators. Full article