Artificial intelligence (AI) is revolutionizing the fashion industry through increased operational efficiency and the promotion of sustainable innovation. One important topic that this research aims to answer is how fashion companies may effectively use AI technologies to optimize lean production processes while addressing environmental challenges. This study will look at how AI can be applied to lean manufacturing and sustainability to better understand how it may alter corporate management practices in the US fashion industry. This study is significant because it can provide insight into how AI may advance efficiency and sustainability, two aspects that are critical to the future competitiveness of the industry. The lack of comprehensive frameworks that systematically integrate AI into the current manufacturing processes used in the apparel sector is the main emphasis of this study. Fashion firms must adjust to technology developments to stay relevant in a market that is changing quickly due to changing consumer preferences and heightened environmental scrutiny, which is why this study is significant. The scope of this study includes an analysis of AI's contribution to sustainable innovation and lean manufacturing, offering a comprehensive perspective of the effects of the industry. The study aims to explore how artificial intelligence (AI) may improve operational efficiency and encourage sustainable practices in the fashion sector in the United States. A substantial research gap concerning the use of AI technology in lean manufacturing frameworks designed especially for fashion companies is identified by this study. The methodology used involves a qualitative study of secondary data, with an emphasis on industry reports and case studies that provide light on contemporary issues and practices. Important discoveries show that artificial intelligence (AI) technologies greatly enhance demand forecasting, streamline production processes, and enable customized consumer experiences, all of which contribute to lower waste and more operational efficiency. To ensure successful implementation, however, issues including staff displacement and change resistance must be resolved. Best practices for utilizing AI in fashion management are highlighted in this paper, which offers insightful information to practitioners and scholars alike. In summary, this study offers a solid basis for further research into the revolutionary potential of AI, even while it recognizes limitations regarding the generalizability of findings across various fashion sector areas. Longitudinal research evaluating the long-term effects of AI adoption and consumer attitudes toward AI-driven personalization are potential avenues for future investigation. Furthermore, legislative suggestions can emphasize encouraging cooperation between digital companies and fashion labels to enable successful AI integration. This abstract summarizes the main findings of the study and lays the groundwork for a more thorough investigation of how AI may influence business management in the US fashion sector going forward.

This study explores the transformative role of short video marketing in modern digital campaigns, with a focus on its ability to engage audiences, enhance brand visibility, and foster consumer trust. By systematically reviewing 78 peer-reviewed articles published between 2018 and 2024, this research synthesizes insights into the dynamics of short video platforms like TikTok, Instagram Reels, and YouTube Shorts. The study highlights the effectiveness of algorithmic personalization in delivering tailored content, driving a 60% increase in engagement rates compared to non-personalized campaigns. It also emphasizes the pivotal role of influencers, particularly micro-influencers, in connecting with niche audiences and fostering trust through relatability and authenticity. Key performance metrics such as retention rates, shares, and real-time analytics are identified as critical tools for evaluating campaign success, while emerging trends like augmented reality, live shopping, and interactive advertisements demonstrate the innovative potential of short video marketing. This review also identifies significant research gaps, including the long-term impact of short video campaigns, ethical considerations in data-driven marketing, and strategies for cross-platform content optimization. By providing a comprehensive understanding of the current landscape and future directions, this study offers valuable insights for researchers and practitioners seeking to navigate the evolving domain of short video marketing effectively and ethically.

Diabetes mellitus is a chronic metabolic disorder with significant global prevalence and associated healthcare burdens, necessitating early detection and effective management strategies. The integration of Machine Learning (ML) and Artificial Intelligence (AI) has revolutionized diabetes care, offering innovative approaches to prediction, monitoring, and personalized management. This study conducted a systematic review of 82 high-quality peer-reviewed articles, following the PRISMA guidelines, to provide a comprehensive evaluation of ML and AI applications in diabetes prediction and management. The review highlights the widespread adoption of supervised learning models, such as Random Forest and Support Vector Machines (SVM), which consistently demonstrate high accuracy and reliability in predicting diabetes risk. Ensemble learning methods, particularly Gradient Boosting, emerged as superior techniques for predictive performance, while deep learning models, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), proved effective in analyzing unstructured data such as medical images and time-series glucose data. The integration of AI into wearable devices and mobile health applications has further enhanced real-time monitoring and glycemic control, bridging the gap between technological advancements and practical healthcare solutions. Despite these advancements, challenges such as data imbalance, limited external validation, and the need for explainable AI frameworks persist, underscoring the necessity for methodological rigor and standardization. This review provides critical insights into the current state, limitations, and opportunities of ML and AI in diabetes care, emphasizing their transformative potential in addressing this global health challenge.

The National Helpline 333 is Bangladesh’s nationwide non‑emergency, citizen‑service contact center. As the platform’s scope grows from information and grievance services to proactive referrals and social protection, its human‑agent–only operating model faces constraints in scale, cost, and 24/7 availability. This paper examines whether and how Artificial Intelligence (AI) Voice Bots can augment 333. Using a qualitative, mixed‑sources approach (policy/operational documents, international case studies, and expert inputs), we (i) map opportunities across access, efficiency, inclusivity, and analytics; (ii) identify technical, ethical, and socio‑organizational risks; (iii) propose an architecture for Bangla/English voice automation integrated with CRM/KMS and human escalation; and (iv) outline policy and governance guardrails aligned with international AI principles. Findings suggest that a tiered ‘AI‑first, human‑assured’ model can reduce average handling time, increase first‑contact resolution, and extend after‑hours coverage—provided investments are made in local‑language ASR/NLU, quality knowledge bases, human‑in‑the‑loop review, and robust data governance (UNDP, 2023; OECD, 2019).

Environmental Supply Chain Management (ESCM) has emerged as a critical component of sustainable business strategies, addressing the environmental, social, and economic impacts of supply chain activities. This study explores the current trends, strategies, and challenges of implementing ESCM practices, with a focus on developing countries. By employing a mixed-method approach, the research identifies key drivers such as regulatory compliance, green product design, and reverse logistics as pivotal in achieving sustainable outcomes. Findings from 57 reviewed articles reveal significant progress in integrating green practices, though developing countries face barriers including inadequate infrastructure, limited access to technology, and insufficient stakeholder awareness. The study emphasizes the importance of tailored strategies, cross-sectoral collaboration, and targeted investments to overcome these challenges. By bridging existing gaps, this research contributes to the growing body of knowledge on ESCM and provides actionable recommendations for policymakers and industry practitioners to foster sustainable supply chain operations globally.

This study provides a comprehensive meta-analysis of 100 high-quality research articles to evaluate the advancements, challenges, and future directions in geotechnical engineering practices within urban environments. The review focuses on four key themes: remote sensing and monitoring technologies, artificial intelligence and machine learning applications, Building Information Modeling (BIM), and sustainable geotechnical engineering practices. Remote sensing tools such as LiDAR, ground-penetrating radar, and satellite imagery have revolutionized geotechnical investigations, enabling accurate subsurface mapping and real-time risk monitoring. Artificial intelligence and machine learning have demonstrated significant potential in predictive modeling, soil stabilization optimization, and groundwater management, offering cost-effective and efficient solutions to complex geotechnical challenges. BIM has emerged as a powerful tool for improving collaboration, data integration, and decision-making in large-scale geotechnical projects, particularly in urban megacities. Furthermore, sustainable practices such as the use of recycled materials, bio-cementation, and green infrastructure are becoming increasingly integral to aligning geotechnical engineering with global sustainability goals. This meta-analysis synthesizes findings from the reviewed articles, identifying gaps in research and highlighting emerging trends that underscore the importance of interdisciplinary approaches. By integrating innovative technologies and sustainable practices, this study emphasizes the need for continuous advancements to address the complexities of urban geotechnical projects and contribute to the development of resilient and environmentally sustainable infrastructure.

The construction industry, with its complex supply chains and critical reliance on rebar for reinforced concrete structures, faces significant challenges in managing the diverse risks associated with rebar procurement. This systematic review, drawing from 52 articles published between 2010 and 2024, identifies key risk areas impacting rebar procurement, encompassing financial, operational, and environmental dimensions. Financial risks, including price volatility and currency fluctuations, are exacerbated by global economic conditions and evolving trade policies. Although less addressed in the literature, environmental risks highlight the increasing need for sustainable procurement practices, considering the carbon footprint and resource intensity tied to rebar production and transport. Despite these advancements, a notable gap exists regarding the integration of environmental considerations within existing procurement risk frameworks, with limited research on green procurement practices for rebar. As environmental sustainability becomes increasingly prioritized, incorporating eco-friendly supplier selection and reducing emissions in logistics are expected to redefine risk management in the industry. This review not only underscores the multi-layered risks in rebar procurement but also identifies future avenues for innovation, particularly in sustainability-focused strategies, digital transformation, and predictive analytics, which can bolster resilience in the rebar supply chain and drive forward the construction industry’s risk management practices.