Lack of knowledge, attitude, and behavior in managing leftover foods in households impacts the natural ecosystem and food chain, particularly in developing countries. This research aims to analyze appropriate methods for reducing and processing food waste produced in household areas. This research method uses qualitative research with operational research methods carried out for 6 months on 25 housewives in Pondok Labu Village in South Jakarta, Indonesia. The research was carried out in 3 stages, the first stage before the intervention, the second stage providing the intervention, and the third stage after the intervention. Results showed that before the intervention, on average each respondent produced 351 g of food waste each day. This amount decreased to 8.43 g/day after respondents participated in socialization to reduce food waste and training to manage food waste. The concluded that a combination of education and training improves knowledge, attitude, and behavior in household food waste management and helps moderate food waste generation.

This research explores the advancement of Artificial Intelligence (AI) in Occupational Health and Safety (OHS) across high-risk industries, highlighting its pivotal role in mitigating the global incidence of occupational incidents and diseases, which result in approximately 2.3 million fatalities annually. Traditional OHS practices often fall short in completely preventing workplace incidents, primarily due to limitations in human-operated risk assessments and management. The integration of AI technologies has been instrumental in automating hazardous tasks, enhancing real-time monitoring, and improving decision-making through comprehensive data analysis. Specific AI applications discussed include drones and robots for risky operations, computer vision for environmental monitoring, and predictive analytics to pre-empt potential hazards. Additionally, AI-driven simulations are enhancing training protocols, significantly improving both the safety and efficiency of workers. Various studies supporting the effectiveness of these AI applications indicate marked improvements in risk management and incident prevention. By transitioning from reactive to proactive safety measures, the implementation of AI in OHS represents a transformative approach, aiming to substantially reduce the global burden of occupational injuries and fatalities in high-risk sectors.

This review provides an overview of the importance of nanoparticles in various fields of science, their classification, synthesis, reinforcements, and applications in numerous areas of interest. Normally nanoparticles are particles having a size of 100 nm or less that would be included in the larger category of nanoparticles. Generally, these materials are either 0-D, 1-D, 2-D, or 3-D. They are classified into groups based on their composition like being organic and inorganic, shapes, and sizes. These nanomaterials are synthesized with the help of top-down bottom and bottom-up methods. In case of plant-based synthesis i.e., the synthesis using plant extracts is non-toxic, making plants the best choice for producing nanoparticles. Several physicochemical characterization techniques are available such as ultraviolet spectrophotometry, Fourier transform infrared spectroscopy, the atomic force microscopy, the scanning electron microscopy, the vibrating specimen magnetometer, the superconducting complex optical device, the energy dispersive X-ray spectrometry, and X-ray photoelectron spectroscopy to investigate the nanomaterials. In the meanwhile, there are some challenges associated with the use of nanoparticles, which need to be addressed for the sustainable environment.

In this review are developed insights from the current research work to develop the concept of functional materials. This is understood as real modified substrates for varied applications. So, functional and modified substrates focused on nanoarchitectures, microcapsules, and devices for new nanotechnologies highlighting life sciences applications were revised. In this context, different types of concepts to proofs of concepts of new materials are shown to develop desired functions. Thus, it was shown that varied chemicals, emitters, pharmacophores, and controlled nano-chemistry were used for the design of nanoplatforms to further increase the sizes of materials. In this regard, the prototyping of materials was discussed, affording how to afford the challenge in the design and fabrication of new materials. Thus, the concept of optical active materials and the generation of a targeted signal through the substrate were developed. Moreover, advanced concepts were introduced, such as the multimodal energy approach by tuning optical coupling from molecules to the nanoscale within complex matter composites. These approaches were based on the confinement of specific optical matter, considering molecular spectroscopics and nano-optics, from where the new concept nominated as metamaterials was generated. In this manner, fundamental and applied research by the design of hierarchical bottom-up materials, controlling molecules towards nanoplatforms and modified substrates, was proposed. Therefore, varied accurate length scales and dimensions were controlled. Finally, it showed proofs of concepts and applications of implantable, portable, and wearable devices from cutting-edge knowledge to the next generation of devices and miniaturized instrumentation.

This study aimed to examine the impact of digital leadership among school principals and evaluate the mediating effect of Professional Learning Communities (PLCs) on enhancing teachers’ innovation skills for sustainable technology integration, both in traditional classroom settings and e-learning environments. Employing a quantitative approach with a regression design model, Structural Equation Modelling (SEM) and Partial Least Squares (PLS-SEM) were utilized in this research. A total of 257 teachers from 7 excellent senior high schools in Makassar city participated in the study, responding to the questionnaires administered. The study findings indicate that while principal digital leadership does not directly influence teachers’ innovation skills in technology integration, it directly impacts Professional Learning Communities (PLCs). Moreover, PLCs themselves have a significant influence on teachers’ innovation skills in technology integration. The structural model presented in this study illustrates a noteworthy impact of principal digital leadership on teachers’ innovation skills for technology integration through Professional Learning Communities (PLCs), with a coefficient value of 47.4%. Principal digital leadership is crucial in enhancing teachers’ innovation skills for sustainable technology integration, primarily by leveraging Professional Learning Communities (PLCs). As a result, principals must prioritize the creation of supportive learning environments and implement programs to foster teachers’ proficiency for sustainable technology integration. Additionally, teachers are encouraged to concentrate on communication, collaboration, and relationship-building with colleagues to exchange insights, address challenges, and devise solutions for integrating technology, thereby contributing to sustained school improvement efforts. Finally, this research provides insights for school leaders, policymakers, and educators, emphasizing the need to leverage PLCs to enhance teaching practices and student outcomes, particularly in sustainable technology integration.

Objective: This study synthesizes current evidence on the role of Artificial Intelligence (AI) and, where relevant, Open Science (OS) practices in enhancing Human Resource Management (HRM) performance. It focuses on recruitment processes, ethical considerations, and employee participation. Methodology: A systematic literature review was conducted in Scopus covering the period 2019–2024, following PRISMA guidelines. The initial search yielded 1486 records. After de-duplication and screening using Rayyan, 66 studies (≈ 4.4%) met the inclusion criteria, which targeted peer-reviewed works addressing AI-supported HR decision-making. A combined content and bibliometric analysis was performed in R (Bibliometrix) to identify thematic patterns and conceptual structures. Results: Analysis revealed four thematic clusters: 1) Implementation and employee participation emphasizing human-in-the-loop approaches and effective change management; 2) ethical challenges including algorithmic bias, transparency gaps, and data privacy risks; 3) data-driven decision-making delivering higher accuracy, fewer errors, and personalized recruitment and performance assessment; 4) operational efficiency enabling faster workflows and reduced administrative workloads. AI tools consistently improved selection quality, while OS practices promoted transparency and knowledge sharing. Implications: The successful adoption of AI in HRM requires employee engagement, strong ethical safeguards, and transparent data governance. Future research should address the long-term cultural, organizational, and well-being impacts of AI integration, as well as its sustainability.