Volume 13 | Issue 3 |

 Abstract

Smokeless tobacco product (SLT) and Smoking products are available in different brands like Rajnignadha, Vimal, Hira, and RMD (Rasikala Manikchand Dhariwal) cigar manufactured by various smokeless tobacco and smoking tobacco industries. The impact of the use of SLT users and Cigar users on plasma biochemical profile especially the levels of glucose, lipids and lipoprotein patterns, proteins, free amino acids and iron. SLT/smoking tobacco (cigar)-exposure caused permissive but significant effect role by elevating the plasma glucose levels by 12% and 15% in group II and III human exposed subjects respectively when compared to control group I subjects who do not use any SLT/smoking tobacco (cigar) products, there is a net drop in plasma iron, glycolipids with a significant increase in free amino acids. There was no change in HDL-C levels among the three groups. Triglycerides and VLDL-C were significantly elevated in SLT/smoking tobacco (cigar) exposed subjects in group II and III indicating coronary risk.

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 Keywords

Smokeless tobacco, smoking tobacco, Oxidative stress

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Launched in 2023, the Mukhyamantri Ladli Behna Yojana (LBY) in Madhya Pradesh provides direct monthly financial assistance to women with the aim of fostering their economic independence and improving overall socio-economic conditions. This research explores the long-term viability of the scheme's secondary effects, such as increased local market activity, enhanced participation in entrepreneurship, and expanded financial inclusion, in the context of fiscal constraints and growing state-level debt. Employing a mixed-methods approach, the study examines whether these outcomes contribute to sustained economic development or create risks of financial dependency. Findings indicate that LBY has a positive impact on grassroots economic stimulation and women's empowerment; however, its future success hinges on complementary measures like skill development, expanded access to microfinance, and diversified state revenue streams. The study recommends structural reforms to ensure the program's long-term fiscal and economic sustainability.

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Long-Term Economic Sustainability

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Indian social justice scenario in Premchand's fiction: a study

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Indian social justice scenario in Premchand's fiction: a study

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Human sensibilities in the fiction of storyteller Shivani - a study

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Human sensibilities in the fiction of storyteller Shivani - a study

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In today's digital era, the convergence of robust security measures with personalized user experiences is essential for creating trusted digital identity platforms. This study introduces a comprehensive framework that addresses the dual imperatives of safeguarding sensitive information while offering tailored functionalities to meet individual user needs. The framework leverages advanced encryption techniques, biometric verification, and adaptive authentication protocols to mitigate risks without compromising usability. It further integrates machine learning algorithms that analyze user behavior to dynamically adjust security settings, ensuring that the protection level is both context-aware and responsive to emerging threats. By adopting a user-centric approach, the framework empowers individuals to have greater control over their personal data and the manner in which it is shared, thereby fostering a sense of ownership and trust. Additionally, the study discusses the challenges related to interoperability among different digital systems and offers potential solutions that align with global standards. Through iterative testing and real-world applications, the framework demonstrates its capability to reduce instances of unauthorized access while maintaining a high degree of personalization. Overall, the proposed approach not only elevates the security posture of digital identity systems but also enhances the user experience by ensuring that security measures adapt to diverse behavioral patterns and risk profiles. This balance between security and personalization paves the way for more resilient, user-friendly platforms in an increasingly interconnected digital world.

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 Keywords

Digital identity, security, personalization, user-centric framework, adaptive authentication, biometric verification, machine learning.

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Data Governance and Automation Strategies for AI and Cloud Supply Chain Operations represents an emerging paradigm that integrates data integrity with advanced automation to transform supply chain management. This approach emphasizes establishing robust data governance frameworks that ensure the quality, security, and accessibility of data across complex cloud infrastructures. By leveraging artificial intelligence, organizations can enhance decision-making processes, streamline operations, and predict potential disruptions in real time. The synthesis of AI with cloud-based operations facilitates the automation of routine tasks, thereby increasing operational efficiency and reducing human error. Additionally, automated processes support proactive risk management and compliance adherence, which are critical in today's data-driven business environments. This strategy also involves the implementation of dynamic monitoring tools that continuously assess data flows and trigger corrective actions when deviations occur. As supply chains become increasingly global and interconnected, the integration of data governance with automation offers a competitive edge by improving responsiveness and resilience. The seamless coordination between various components of the supply chain is achieved through scalable cloud solutions that support rapid data analysis and integration. Overall, this strategy is not only pivotal in enhancing operational performance but also in fostering innovation within the supply chain ecosystem. Through rigorous governance and state-of-the-art automation techniques, organizations are better equipped to navigate the challenges of digital transformation while maintaining a secure and efficient operational framework.

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Data Governance, Automation Strategies, AI, Cloud Supply Chain, Operational Efficiency, Risk Management, Digital Transformation

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This paper presents an in-depth exploration into the implementation of quality assurance frameworks tailored for AI-driven fraud detection within vehicles registration and titling services. As automotive fraud continues to evolve in complexity, traditional detection methods struggle to keep pace with sophisticated schemes. In response, emerging AI technologies have been deployed to analyze vast datasets and identify anomalies indicative of fraudulent behavior. However, the rapid adoption of these advanced systems has underscored the necessity for robust quality assurance protocols to guarantee accuracy, reliability, and fairness. Our research outlines a comprehensive framework that integrates systematic validation processes, performance metrics evaluation, and continuous improvement cycles. Key elements include real-time data monitoring, cross-verification techniques, and adaptive learning mechanisms that respond to emerging fraud patterns. Through rigorous testing and simulation exercises, the framework has demonstrated its capacity to significantly reduce false positives and negatives, ensuring that only legitimate cases trigger alerts. Additionally, the framework supports compliance with regulatory standards and provides transparency for stakeholders. The study also discusses the challenges of integrating legacy systems with modern AI platforms, addressing potential issues such as data quality, bias in algorithmic decisions, and cybersecurity vulnerabilities. In conclusion, the proposed quality assurance framework not only enhances the overall effectiveness of fraud detection systems in vehicle registration and titling but also contributes to the broader field of AI ethics and governance by promoting accountability and trust in automated decision-making processes.

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 Keywords

AI-driven fraud detection, quality assurance, vehicles registration, titling services, data validation, continuous improvement, regulatory compliance

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Robotic Process Automation (RPA) and Intelligent Automation (IA) are transforming business processes by enabling the automation of repetitive, rule-based tasks, and enhancing the efficiency of complex workflows with artificial intelligence. In the context of business reporting, RPA and IA technologies offer significant improvements by automating data extraction, transformation, and reporting tasks. These technologies have become instrumental in streamlining business reporting processes, reducing manual errors, improving data accuracy, and accelerating decision-making. RPA automates routine, time-consuming tasks by mimicking human actions within digital systems. In business reporting, RPA can automate the collection and consolidation of data from multiple sources, including spreadsheets, databases, and enterprise systems. This leads to faster generation of reports and frees up employees from repetitive tasks, allowing them to focus on higher-value activities. IA, which includes machine learning, natural language processing, and advanced analytics, enhances RPA by enabling the automation of more complex decision-making processes that were previously reliant on human judgment. In business reporting, IA can process unstructured data and identify trends or anomalies that would be difficult for traditional reporting methods to uncover. For instance, IA can automatically detect patterns in financial reports, flag discrepancies, and provide real-time insights, all without human intervention. The integration of AI in reporting workflows not only boosts productivity but also helps organizations ensure compliance with regulations and maintain high data integrity. Additionally, IA systems can continually learn from historical data and improve their decision-making capabilities over time. The combination of RPA and IA enables organizations to achieve a more agile, responsive business reporting environment. This synergy accelerates the report generation process, allows for real-time data analysis, and enhances the accuracy and relevance of business intelligence. Furthermore, RPA and IA can contribute to better scalability in reporting systems, as they can handle increasing data volumes and more complex reporting requirements without compromising performance or quality. Despite these benefits, organizations must address challenges such as integrating RPA and IA technologies with existing legacy systems, ensuring data security, and managing change within teams. Successful implementation requires strong collaboration between IT, business, and data teams to design and deploy automation solutions that align with organizational goals. In the future, advancements in AI and machine learning will likely expand the scope of intelligent automation in business reporting, enabling even greater efficiencies and insights.

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Robotic Process Automation, Intelligent Automation, Business Reporting, Automation, AI, Machine Learning, Data Integration, Business Intelligence, Decision-Making, Process Optimization.

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Data Modeling and Database Design for High-Performance Applications is a critical field that addresses the challenges of managing large-scale, complex data environments while ensuring rapid query response times and robust scalability. This study explores innovative techniques in data modeling that serve as the backbone for designing databases capable of handling high volumes of transactions and analytical queries simultaneously. It discusses the transition from traditional relational database structures to hybrid and NoSQL architectures, highlighting the trade-offs between consistency, scalability, and performance. The research outlines key strategies for optimizing database schemas, including normalization, indexing, partitioning, and denormalization, tailored to the unique needs of high-performance systems. Emphasis is placed on real-world applications such as e-commerce, financial services, and IoT platforms, where data integrity and low latency are paramount. Furthermore, the paper reviews emerging trends like in-memory databases, distributed computing frameworks, and cloud-based data management, offering insights into future directions for database technology. The integration of machine learning techniques for predictive analytics and automated tuning is also examined as a means to further enhance performance. By synthesizing theoretical insights with practical case studies, this work provides a comprehensive framework for practitioners and researchers aiming to design and implement databases that not only meet current performance demands but also adapt to evolving technological landscapes. The study concludes that a well-architected data model is essential for unlocking the full potential of high-performance applications, ensuring both efficiency and scalability in today's data-driven world.

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Data Modeling, Database Design, High-Performance, Scalability, NoSQL, Optimization, In-Memory, Distributed Systems, Cloud Computing

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Mobile-first development has emerged as a pivotal strategy in the modern digital era, driving a paradigm shift in how web and application interfaces are designed, developed, and deployed. This research paper explores the impact of mobile-first approaches on both user experience and development efficiency, highlighting the evolving role of mobile devices in shaping digital interactions. With the increasing prevalence of smartphones and tablets, developers and designers are compelled to prioritize mobile usability, which in turn influences broader design and development practices. The study systematically reviews existing literature and case studies to identify key factors that contribute to improved user experience through mobile-first design. By prioritizing essential content and streamlined functionality, mobile-first development ensures that users engage with the most critical elements of an application, thereby reducing cognitive overload and enhancing navigational clarity. The research further investigates how responsive design, adaptive interfaces, and performance optimization are achieved when mobile constraints are placed at the forefront of the development process. Furthermore, the paper examines the effect of mobile-first strategies on development efficiency. Traditional development approaches often involve designing for desktop environments first, followed by subsequent adaptation for mobile platforms. This sequential method can lead to inefficiencies, as it may necessitate significant redesign and reengineering to accommodate mobile constraints. In contrast, a mobile-first approach encourages the development of leaner code, early testing on mobile platforms, and a more integrated design process that minimizes redundancies. The empirical findings from multiple case studies demonstrate that organizations adopting mobile-first practices experience faster time-to-market, improved code maintainability, and reduced development costs. In addition to technical benefits, the research underscores the importance of mobile-first development in enhancing user engagement and satisfaction. By focusing on a streamlined, intuitive user interface, mobile-first strategies help create more accessible and user-friendly digital environments. This focus not only caters to users with limited bandwidth or older devices but also aligns with current trends in user behavior where immediate, efficient access to information is paramount. The paper also highlights challenges associated with mobile-first development, such as the need for robust testing frameworks and the potential for oversimplification of complex functionalities. However, the overall evidence suggests that the advantages outweigh the challenges, particularly in an era where mobile connectivity continues to dominate. Finally, the research concludes with a set of best practices and recommendations for developers and organizations aiming to optimize both user experience and development workflows. It advocates for a collaborative, iterative design process that embraces mobile constraints as opportunities for innovation. Future research directions are proposed to further explore the intersection of mobile-first development and emerging technologies such as artificial intelligence, augmented reality, and IoT integration. This comprehensive analysis provides valuable insights into how mobile-first development reshapes digital experiences and development methodologies, ultimately contributing to more efficient and effective digital solutions. In summary, mobile-first development represents a transformative approach that not only elevates user satisfaction through optimized design but also streamlines development processes, thereby fostering innovation and long-term sustainability in an increasingly mobile-centric digital landscape. These findings encourage future innovation in mobile strategies.

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 Keywords

mobile-first, user experience, development efficiency, responsive design, agile development, digital innovation, UI/UX, performance optimization

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