In today's rapidly evolving business landscape, the convergence of sustainable supply chain management (SSCM) and machine intelligence, encompassing artificial intelligence (AI) and machine learning (ML), represents a dynamic and transformative nexus. This comprehensive survey paper navigates the intricate terrain of sustainable supply chain practices, delving into its principles, challenges, and the pressing need for organizations to embrace environmental responsibility, ethical sourcing, and social equity. Simultaneously, it explores the disruptive potential of machine intelligence, offering insights into its underlying principles, vast applications, and its pivotal role in optimizing supply chain operations. Through a systematic analysis, this paper uncovers the complex interplay between SSCM and machine intelligence, starting with the foundational principles of each discipline. It then scrutinizes the challenges encountered in integrating machine intelligence with sustainability, including data complexities, ethical dilemmas, and the need for skilled personnel. Conversely, the paper illuminates the myriad opportunities that arise from this synergy, from enhancing demand forecasting and inventory management to fostering sustainable sourcing practices and reducing waste. In closing, the paper anticipates the future landscape of sustainable supply chains in the age of machine intelligence, highlighting emerging trends, technological innovations, and the ethical considerations that will shape the trajectory of this evolving field. It is our hope that this survey serves as a valuable resource for businesses, policymakers, and researchers alike, inspiring the pursuit of environmentally responsible, economically viable, and ethically sound supply chains in an increasingly interconnected world.

The deaf community faces communication barriers that hinder their interaction with the hearing world. This work aims to bridge the gap by enabling accurate recognition of Arabic sign language gestures. The proposed Convolutional Neural Networks (CNNs) architecture is designed to effectively capture the spatial features inherent in sign language gestures, thereby enhancing recognition accuracy. A distinctive aspect of our work involves the integration of a CNN architecture with a residual design, which effectively captures intricate spatial features inherent in sign language gestures, thereby enhancing recognition precision. The study leverages the ArSL2018 dataset, a comprehensive collection of grayscale sign language images with diverse lighting conditions and backgrounds. Our custom-built CNN model is trained on this dataset, utilizing a specialized learning rate scheduler for improved convergence. The experimental results showcase promising performance, demonstrating the potential of CNNs in sign language recognition. Furthermore, we present visualizations of the model's predictions using t-SNE, revealing the clustering patterns of different sign language gestures.

This study investigates the feasibility of using wearable technologies in education to improve safety. This article explores how wearables may be used to improve school safety and wellness, as well as their advantages, disadvantages, and future potential. The article covers a wide range of wearable gadgets and their respective safety-related features, from smartwatches to location trackers to panic buttons and biometric sensors. Privacy issues, data security, user acceptability, and ethical considerations are only some of the problems and hazards discussed in this research on wearables in education. This study the neutrosophic set to deal with uncertain data. The neutrosophic set is integrated with the multi-criteria decision-making (MCDM) CRITIC method. The CRITIC method is used to compute the weights of factors and rank it. There are 15 factors used in this study. The case study is applied in the education field. Educators, technologists, and legislators all need to work together to guarantee the safe and effective use of wearable devices in schools, as shown by the study's findings. The article reiterates the importance of wearables and their potential to enhance safety measures in education before making the case for more studies, pilot programmers, and policy development to fully realize their promise.

To make the change to a cleaner, more sustainable energy future, it is essential that power production systems be evaluated sustainably. To estimate the overall sustainability performance of various power production systems, this evaluation takes into account a wide range of parameters. Impact on the environment, availability of renewable resources, resource efficiency, social and economic implications, economic feasibility, grid integration and dependability, technical maturity, and scalability are all taken into account. Stakeholders may make better judgments and give higher priority to power production systems that take into account environmental impacts, resource efficiency, social impacts, and economic viability by evaluating these factors. So, we used the multi-criteria decision-making (MCDM) approach VIKOR to assess the power production systems in the sustainability criteria. The VIKOR method is used to rank the several alternatives. We integrated the neutrosophic set with the VIKOR method to deal with inconsistent information. We used seven criteria and ten alternatives in this study. The development of a sustainable and resilient energy sector is aided by the sustainable evaluation of power production systems, which in turn helps to reduce the effects of climate change and safeguard the environment.

Studying the impact of beneficiaries on yoga is the aim of this article. Due to its significance, yoga is extremely important for both mental and physical wellness. Numerous, complicated, and incompatible supporting criteria exist for the impact of yoga. The multi-criteria decision making (MCDM) strategy is an option for resolving this problem. A potent instrument for communicating the significance and influencing factors on others is the Decision-making Trial and Evaluation Laboratory (DEMATEL) technique. As a result, DEMATEL included neutrosophic sets for system analysis and manipulation. To demonstrate how to defeat this strategy, we also provide several instances.