This study aims to investigate the effectiveness of community involvement in waste management through participatory research. Its objective is to bridge the theoretical underpinnings of participatory research with its practical implementation, particularly within the realm of waste management. The review systematically analyzes global instances where community engagement has been incorporated into waste management initiatives. Its principal aim is to evaluate the efficacy of participatory strategies by scrutinizing methodologies and assessing outcomes. To achieve this, the study identified 74 studies that met rigorous criteria through meticulous search efforts, encompassing various geographical locations, cultural contexts, and waste management challenges. In examining the outcomes of participatory research in waste management, the study explores successful practices, shortcomings, and potential opportunities. Moving beyond theoretical discourse, it provides a detailed analysis of real-world applications across various settings. The evaluation not only highlights successful engagement strategies and indicators but also critically assesses challenges and opportunities. By conducting a comprehensive review of existing research, this study establishes a foundation for future studies, policy development, and the implementation of sustainable waste management practices through community engagement. The overarching goal is to derive meaningful insights that contribute to a more inclusive, effective, and globally sustainable approach to waste management. This study seeks to inform policymaking and guide future research initiatives, emphasizing the importance of community involvement in addressing the complexities of waste management on a global scale.

Enhancing the emphasis on incorporating sustainable practices reinforces a linear transition towards a circular economy by organizations. Nevertheless, although studies on circular economy demonstrate an increasing trend, the drivers that support circular economy practices towards sustainable business performance in the Small and Medium-Sized Enterprise (SME) sector, especially in developing nations, demand exploration. Accordingly, the study examines circular economy drivers, i.e., green human resource management, in establishing sustainability performance and environmental dynamism as moderating variables. The study engaged 207 SMEs and 621 respondents who were analyzed utilizing structural equation modeling. The analysis indicated that sustainable business performance was affected by green human resource management and a circular economy. Subsequently, the circular economy mediated the linkage between green human resources management and sustainable business performance. The environmental dynamism moderated the linkage between green human resources management and the circular economy.

This study simultaneously examined the linkages among environmental dynamism, three dynamic capabilities, and the competitive advantages of retail businesses, which have not been identified before. Furthermore, this study fills the significant gaps in the literature and practical guidelines for retail development through improving retailer’s dynamic capabilities in response to environmental dynamism. The study used a quantitative approach by partial least squares SEM (PLS-SEM) to examine the hypotheses. Data were collected from 304 Vietnamese retail business managers. The results show that environmental dynamism plays a significant role in fostering the improvement of retailers’ dynamic capabilities. The findings also reveal positive linkages among the three dynamic capabilities before they significantly improve retailers’ competitive advantage. These are the valuable guidelines for retailers to nurture their dynamic capabilities, including service innovation capabilities, multi-channel integration, and brand orientation for sustaining their competitive advantages.

The cost of diagnostic errors has been high in the developed world economics according to a number of recent studies and continues to rise. Up till now, a common process of performing image diagnostics for a growing number of conditions has been examination by a single human specialist (i.e., single-channel recognition and classification decision system). Such a system has natural limitations of unmitigated error that can be detected only much later in the treatment cycle, as well as resource intensity and poor ability to scale to the rising demand. At the same time Machine Intelligence (ML, AI) systems, specifically those including deep neural network and large visual domain models have made significant progress in the field of general image recognition, in many instances achieving the level of an average human and in a growing number of cases, a human specialist in the effectiveness of image recognition tasks. The objectives of the AI in Medicine (AIM) program were set to leverage the opportunities and advantages of the rapidly evolving Artificial Intelligence technology to achieve real and measurable gains in public healthcare, in quality, access, public confidence and cost efficiency. The proposal for a collaborative AI-human image diagnostics system falls directly into the scope of this program.