Volume 6 Issue 11 2025

Aipan painting, a traditional painting form from kumaon, India, is characterized by intricate geometric patterns created with red ochre and white rice paste. Practiced predominantly by women, these ritualistic paintings adorn floors and walls during religious and auspicious occasions, depicting deities, symbols and elements of nature to invoke good fortune and protection. This project explores translating the unique aesthetic of Apian paintings on to T Shrits, merging traditional art with contemporary fashion to create visually appealing and culturally rich clothing. Motifs are digitally reproduced, thoughtfully adapted for T Shirt while preserving essence of the original artwork. By interplay of composition, color and CAD techniques, the project aims to create wearable art seamlessly blends the beauty of Aipan painting with modern design. The T Shirts serves as canvas for celebrating the artistic legacy, offering a unique way individual to connect and carry this rich cultural heritage forward in their everyday lives.

The network of research collaborations within a certain academic topic is mapped using bibliometric analysis in this study, which identifies important contributors and the structure of their links. Through data analysis from reputable academic databases, the study visualizes author clusters and evaluates their impact using metrics related to publications and citations. The results highlight key authors and possible areas of cooperation while providing strategic insights into the primary research communities. There is discussion of the practical consequences for academic highlighting how these organizations may use the analysis to improve research output and innovation through financing methods, research networks, and institutions. The study's concentration on quantitative metrics, which might not adequately reflect the dynamic and qualitative components of human contributions, and possible database biases are among its limitations. Not withstanding these difficulties, bibliometric analysis offers insightful advice for academic and research communities' strategic decision-making.

The field of artificial intelligence (AI) has advanced significantly over the last ten years. The extent to which management research employs AI algorithms is not well-documented, despite the abundance of AI research. Despite being essential to coordinating, expanding, and fortifying the use of AI, the context, goal, and kind of AI employed in earlier projects are still unknown. By doing a systematic literature review (SLR) on 12 top information systems (IS) journals and employing a specially designed generative pre-trained transformer (GPT) in our analysis, we fill this knowledge gap. To address the lack of a succinct AI taxonomy, we offer a conceptual framework with eight dimensions to group our findings according to applied AI application domains, methodologies, and algorithms. As a result, we highlight underrepresented algorithms with significant promise, uncover and debate trends, and determine research directions. We provide a conceptual review of the literature that could be used as a foundation for future study into the developing field of artificial intelligence.

Software reliability is a crucial component of overall software quality, particularly in mission-critical and commercial software systems. Classical software reliability models often fail to capture the multifaceted dynamics of real-world failure patterns, particularly in the presence of fluctuating testing efforts and environmental shifts. In practice, software projects experience fluctuating testing efforts, process improvements, shifts in operational environments, and evolving fault profiles over time. Such variations introduce complex, multi-phase failure behaviors that classical models struggle to capture, motivating the need for more flexible modeling frameworks capable of integrating diverse growth patterns and accounting for dynamic change points in the software testing lifecycle. This paper proposes a hybrid software reliability growth model (SRGM) that integrates the characteristics of delayed S-shaped and logarithmic growth models while incorporating dynamically identified change points. The suggested model aims to enhance the precision and adaptability of reliability estimates. Through mathematical formulation, parameter estimation techniques, and empirical validation, this study demonstrates the advantage of the hybrid model over conventional SRGMs.

In an increasingly interconnected global economy, migration and skills portability have emerged as critical determinants of national and corporate competitiveness. The movement of skilled professionals across borders not only addresses labor market imbalances but also facilitates knowledge transfer, innovation, and productivity growth. However, disparities in qualification recognition, policy fragmentation, and limited alignment between domestic skilling initiatives and international labor market demands pose significant challenges. This paper explores the role of migration and skills portability policies in enhancing global competitiveness, with a focus on frameworks such as mutual recognition agreements, international qualification standards, and cross-border training programs. Drawing on comparative case studies and global policy experiences, the study highlights how governments, industries, and educational institutions can collaborate to create enabling ecosystems that support workforce mobility. The findings emphasize that strategic policies fostering skills portability not only strengthen the employability of migrants but also enhance the resilience of economies in adapting to technological, demographic, and geopolitical shifts. Ultimately, fostering inclusive, portable, and globally recognized skill systems is essential to harness the full potential of human capital for sustainable growth and competitiveness.

In this study, rate = k[oxidant]o [CPL]o [NaOH]x is the rate law applied, At 298 K, BAB kinetically oxidized the chloroprocaine hydrochloride. An appropriate rate law and mechanism were discovered using experimental data. The products of oxidation were found. Decomposition constants and composition activation parameters were computed. Kinetic modeling and mechanical routes have been computed for this redox system. This approach has a lot of industrial promise because of its mild reaction conditions, excellent product yields, and ease of use and experimentation.

The notion of connecting objects to the Internet isn't novel. In the early 1990s, the first instances of controlling everyday items over the Internet emerged, laying the groundwork for today's IoT. Interactions with the Internet, whether personal, social, or economic, are undergoing a transformation. Currently, most users actively download and create content via computers and smartphones, but this trend is expected to change. Many IoT devices operate autonomously in the background, transmitting and receiving data with minimal human involvement. Some are designed to manage physical assets like vehicles and buildings, or to monitor human activities. As this shift occurs, it's essential to consider the potential impact of a world where passive interactions with connected devices become more common than active engagement with online content. Governments may need to adjust policies to address this changing dynamic. While the concept of IoT has existed for some time, its rapid growth presents both opportunities and challenges, requiring policy revisions. Privacy and data security policies will need to evolve to address the technological advancements and their effects on users. To support the development of IoT, efforts should focus on strengthening internet infrastructure, optimizing the use of wireless spectrum, expanding data centers, and empowering users. This will likely influence policies related to privacy, data security, healthcare, transportation, and technological innovation.