Volume 13 | Issue 2

 Abstract

In today's increasingly interconnected global marketplace, effective data management stands as a critical enabler for competitive supply chains. This study explores the transformative role of Master Data Management (MDM) in aligning and optimizing data quality and governance across international operations. By integrating Generative AI (Gen AI) into MDM frameworks, organizations can revolutionize their approach to data curation, error detection, and consistency assurance. The innovative application of Gen AI enhances data enrichment processes and automates validation routines, resulting in more robust, accurate, and real-time data flows that underpin strategic decision-making. In addition, the convergence of MDM and Gen AI contributes to improved transparency and traceability within supply chain networks. This synthesis not only streamlines operations but also minimizes risks related to data inconsistencies and regulatory non-compliance. Furthermore, the implementation of Gen AI fosters adaptive learning, enabling continuous improvement in data governance practices. The study underscores the potential benefits of this technology integration, including accelerated operational efficiency, better demand forecasting, and enhanced supplier collaboration. As businesses expand their global footprints, embracing advanced MDM solutions with Gen AI integration becomes imperative for maintaining a competitive edge and ensuring resilient supply chain performance. Through empirical analysis and case studies, this research offers insights into best practices and strategic considerations that drive successful digital transformation in supply chain management.

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 Keywords

Master Data Management, Global Supply Chains, Data Quality, Data Governance, Generative AI, Digital Transformation, Supply Chain Optimization

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In today's data-intensive landscape, efficient management of vast and heterogeneous datasets has become paramount. Hybrid cloud storage architectures offer scalable, flexible, and cost-effective solutions by combining on-premises resources with public cloud services. This paper explores the integration of machine learning techniques to drive advanced data management strategies within hybrid cloud environments. By leveraging machine learning algorithms, organizations can automate the classification, indexing, and retrieval of data, thereby improving system performance and reducing latency. The approach focuses on predictive analytics to forecast data access patterns and resource requirements, ensuring optimal allocation and minimizing bottlenecks. Additionally, machine learning models can enhance security protocols by detecting anomalies and potential threats in real time. This fusion of intelligent automation with hybrid cloud infrastructure not only streamlines data operations but also paves the way for proactive system maintenance and cost optimization. Experimental results indicate significant improvements in data throughput, energy efficiency, and overall user satisfaction. The study highlights the potential challenges, including model training complexities, data privacy concerns, and the need for robust integration frameworks that can adapt to rapidly evolving technologies. Future research directions include refining algorithm accuracy, expanding the range of predictive insights, and developing hybrid solutions that balance performance with regulatory compliance. Overall, this work demonstrates that machine learning-driven data management represents a transformative strategy for modern hybrid cloud storage systems, offering sustainable benefits for enterprise data governance.

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 Keywords

Machine Learning; Data Management; Hybrid Cloud Storage; Predictive Analytics; Intelligent Automation

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Disaster Recovery (DR) and High Availability (HA) are core features of modern database management systems, particularly within the SQL Server environment, because they are the backbone of business continuity in instances of system crashes, data corruption, or any other disaster. While numerous options exist, for example, geo-replication, Failover Clustering, and Always On Availability Groups, significant challenges exist to optimize these systems in a myriad of environments like hybrid cloud, big data, healthcare, and small and medium-sized enterprises (SMEs). The gap in existing literature is on integration of these technologies with new-age cloud-based technologies, the utilization of automated recovery processes, and the determination of cost-effective approaches for small-sized organizations that have limited IT infrastructures. Current research highlights the increasing demand for multi-cloud and hybrid disaster recovery solutions that reduce downtime and data loss. Although such solutions are scalable and cost-effective, they also pose challenges of performance, latency, and coordination of distributed systems. Additionally, most studies concentrate on large enterprises and do not consider the unique requirements of SMEs that need more cost-effective yet reliable disaster recovery solutions. Additionally, there is little research on fully automating disaster recovery processes, which can significantly impact reducing recovery time and human error. Moreover, the healthcare industry needs special consideration in regulatory compliance, data protection, and confidentiality in disaster recovery processes. This paper aims at filling these gaps through an analysis of the latest HA and DR technologies, such as their adaptability across industries, the integration of automated recovery solutions, and cost-effective approaches for small, medium, and large enterprises. Important findings will guide improved disaster recovery functions for SQL Server in a way that permits minimal downtime and data coherence across various operating environments.

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High availability, disaster recovery, SQL Server, Always On Availability Groups, failover clustering, hybrid cloud environments, multi-cloud approaches, automated recovery processes, big data analytics, healthcare systems, small and medium-sized businesses, data replication methodologies, business continuity planning, backup strategies, Disaster Recovery as a Service (DRaaS)

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The integration of Large Language Models (LLMs) with SQL-oriented data pipelines is an emerging area that seeks to make databases more functional and usable based on the paradigm of natural language processing (NLP) methods. Although the impressive capabilities demonstrated by LLMs in the domain of text-to-SQL translation are well documented, the wider potential of LLMs for the domain of database systems is relatively unexplored. Existing academic contributions have been mostly focused on niche applications, such as query construction; however, concerns related to database schema understanding, query optimization, and scalability in dynamic environments are still open. There is a pressing need for well-tuned models with the capability to handle diverse domain-specific data, as well as the incorporation of LLMs in data preprocessing and real-time querying, which is an open research gap. Furthermore, existing solutions are not robust enough for large-scale, real-time applications and are usually beset with challenges of ensuring data privacy and security when handling sensitive data. This research effort seeks to address these gaps by suggesting an end-to-end system for the incorporation of LLMs in SQL-oriented data pipelines, with a focus on important considerations such as query construction efficiency, query optimization, and dynamism in heterogeneous domains. Through the exploration of pre-trained and well-tuned LLM approaches, this research seeks to close the gap between state-of-the-art NLP methods and real-world database management, thus improving the effectiveness and scalability of SQL-based systems for a range of real-world applications. The expected outcomes are expected to provide insights into the construction of more intelligent, autonomous database systems with reduced human query construction and enabling more natural interaction with data.

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Large Language Models, SQL data pipelines, text-to-SQL translation, database integration, query optimization, schema comprehension, domain-specific models, NLP methods, query generation, real-time query, data privacy, database automation.

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The objective of this paper is to explore the critical elements of change management in Oracle Cloud implementations, focusing specifically on user training and adoption. With organizations increasingly relying on cloud-based systems to streamline operations and drive innovation, the successful integration of Oracle Cloud solutions requires a well-structured change management framework. The study examines the strategic planning and execution of user training programs designed to facilitate smooth transitions and optimize system utilization. It further investigates the challenges encountered during adoption phases, including resistance to change, varying levels of digital literacy among users, and the cultural adjustments necessary for embracing new technologies. This research employs a qualitative approach, combining case studies and expert interviews to derive practical insights into effective change management practices. Key factors, such as communication strategies, stakeholder engagement, and the customization of training modules, are highlighted as essential components to achieving high user acceptance and productivity gains. The findings suggest that when organizations invest in comprehensive training initiatives and proactive support mechanisms, the barriers to successful cloud integration can be significantly mitigated. In conclusion, the paper emphasizes the importance of a holistic approach to change management that not only addresses technical upgrades but also prioritizes user readiness and continuous learning. The insights gathered are intended to serve as a guide for practitioners and decision-makers aiming to leverage Oracle Cloud's capabilities while minimizing disruption and fostering a resilient, agile workforce. Ultimately, effective change management and robust training are crucial for enabling organizations to thrive in a competitive digital landscape.

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 Keywords

Oracle Cloud, Change Management, User Training, Adoption, Cloud Integration, Digital Transformation, Training Programs, Organizational Change

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This abstract addresses the sophisticated strategies that are involved in effective management and leadership of offshore and onshore Business Intelligence (BI) support teams. The paper emphasizes the paramount importance of reconciling varying work cultures, time zones, and communication channels to create an integrated team atmosphere. The paper touches on the unique challenges and opportunities that come with managing globally distributed teams, highlighting the value of vision, communication, and culturally sensitive leadership styles. From a review of best practices in project management, performance measurement, and stakeholder management, the research identifies essential strategies that enable leaders to develop operational effectiveness and drive innovation. The analysis is centered on how tailored leadership strategies can effectively overcome challenges like knowledge transfer, technology integration, and collaborative problem-solving, ensuring onshore and offshore teams work as a unified unit. Finally, the abstract promotes a light-footed and adaptive management style that leverages the strength of a global workforce to deliver positive BI outcomes, building a sustainable competitive advantage in an ever-changing business environment.

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Effective Leadership, Offshore BI Management, Onshore BI Support, Global Team Integration, Cross-Cultural Communication, Strategic Decision-Making, Operational Efficiency, Business Intelligence Solutions

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The rapid evolution of generative artificial intelligence (GenAI) has witnessed extensive application across industry sectors, bringing in new technologies and changes in business model deployment. Though its widespread applicability, more development of efficient pipelines to track the usage and performance of these tools is needed to enhance the integration of GenAI technology in organizational systems. This paper seeks to balance the need in current methodology and frameworks dedicated to tracking the performance metrics for GenAI systems with specific reference to real-time integration of data, accuracy, and scalability. Most current practices of measuring the performance of AI overlook the nature of GenAI application, i.e., its requirement to learn and adapt continuously, in addition to requiring multiple data inputs. Most importantly, the absence of measurable benchmarks for measuring GenAI output only makes measurement more challenging. This study proposes the development and deployment of data pipelines that enable the gathering, processing, and analysis of GenAI usage and performance metrics. The proposed pipelines are designed to provide comprehensive insights into system efficiency, output quality, user engagement, and computational resource usage. Through the application of cutting-edge data engineering techniques, such as automated data gathering and real-time performance monitoring, this study offers a framework for increasing the transparency and accountability of GenAI applications. The findings of this study will guide the development of resilient monitoring systems that can be integrated into various GenAI-powered platforms, guaranteeing optimal performance and well-informed decision-making. The findings of this study have the potential to guide future advancements in GenAI deployment and management, paving the way for more reliable and effective AI-powered solutions.

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 Keywords

Generative AI, data pipelines, performance monitoring, real-time data integration, AI performance metrics, system efficiency, computational resource usage, data engineering, AI monitoring, performance optimization, scalability, real-time analysis, user interaction.

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Due to its widespread use, English is commonly used by non-native speakers of other languages to communicate. Therefore, in order to be able to successfully convey themselves in a range of contexts, learners must possess essential abilities in it. To be proficient in the English language, one must possess much more than a knowledge of linguistic structures. One among the components is grammar which supports the learning of language with structural understanding. And, preposition usage is common among ESL students. This research focuses on exploring the efficacy of ESL students and to understand the preposition and its involvement in speaking English.

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ESL, linguistic structure.

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Use of artificial intelligence (AI) in enhancing web accessibility for the disabled has generated enormous interest over the past few years. Even with enhanced assistive technologies, numerous problems still continue in offering a simple and effective online experience for users with vision, hearing, motor, and cognitive disabilities. AI-based systems such as predictive text, image recognition, and natural language processing have demonstrated extensive potential in offering customized, adaptive solutions to specific needs. However, existing literature has focused on isolated methodologies such as text-to-speech or speech recognition without exploring the entire, multi-modal web interaction in users with different disabilities. This research demonstrates profound shortcomings in existing research, specifically the need for more integrated artificial intelligence systems that incorporate multiple forms of assistance, including gesture recognition, speech recognition, and predictive action, thus offering a comprehensive solution for people with complex or co-occurring disabilities. Existing models are also plagued by a lack of capacity for real-time adaptation to the cognitive state of the user, limiting the potential for enhancing user participation and reducing cognitive load. Additionally, while many AI models are extremely proficient at single tasks, integrating these systems within existing web frameworks is a significant problem. Future work must concentrate on creating end-to-end AI-driven systems that are capable of perceiving user needs across various contexts and hence making interactions more natural and intuitive. In addition, it is critical to incorporate continuous learning processes that are capable of adjusting to changing user preferences and behaviors. By addressing these limitations, a line will be drawn to more accessible, inclusive, and personalized web experiences so that AI technologies can fulfill their potential of crossing accessibility divides for individuals with disabilities.

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Web accessibility, image recognition, Predictive, disabled, vision, hearing, Artificial Intelligence

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This paper examines performance tuning strategies for ATG-based e-commerce platforms, focusing on techniques and tools that enhance system efficiency and customer experience. ATG e-commerce systems often face challenges related to high traffic loads, dynamic content management, and complex transaction processing. The study provides a comprehensive overview of performance bottlenecks, ranging from server response delays to database query inefficiencies. By integrating advanced monitoring tools and tuning methodologies, organizations can identify resource-intensive operations and optimize code execution. The analysis highlights the significance of caching mechanisms, load balancing, and session management in mitigating performance degradation. Additionally, the research underscores the importance of proactive maintenance, such as regular system updates and parameter adjustments, to preemptively address potential issues. Real-world case studies are used to illustrate how targeted tuning interventions can lead to reduced latency, increased throughput, and improved overall system stability. The findings suggest that a systematic approach--incorporating both hardware enhancements and software optimizations--can significantly elevate the performance of ATG platforms. Moreover, the paper explores emerging technologies that facilitate real-time performance analytics, providing e-commerce enterprises with actionable insights. Ultimately, the research advocates for a balanced tuning strategy that aligns technological capabilities with business requirements, ensuring that customer satisfaction and operational efficiency are sustained. This performance tuning framework is expected to serve as a valuable resource for IT professionals aiming to enhance the scalability and reliability of ATG e-commerce solutions.

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ATG performance tuning, e-commerce optimization, caching, load balancing, session management, real-time analytics

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The rapid evolution of medical technologies has led to increasingly complex regulatory landscapes that govern the development and commercialization of medical devices. This paper examines the multifaceted challenges faced by manufacturers in ensuring regulatory compliance during the product development cycle. Regulatory bodies worldwide impose stringent standards to ensure device safety, efficacy, and quality, necessitating a comprehensive understanding of various international, national, and local regulations. The study outlines key challenges, including navigating diverse regulatory requirements, aligning product design with evolving standards, and integrating risk management into clinical evaluations. Moreover, the research highlights the strategic importance of early regulatory engagement, iterative testing, and cross-functional collaboration among engineering, quality assurance, and legal teams. The analysis also explores the potential benefits of digital tools and automated compliance systems in streamlining documentation and monitoring post-market performance. By investigating both the challenges and the strategic responses, the paper provides insights into best practices that can lead to more robust and agile development processes. These practices not only facilitate smoother regulatory approvals but also foster innovation in device design and patient care. The findings underscore the importance of an integrated compliance strategy that addresses the dynamic regulatory environment and anticipates future changes, thereby ensuring that medical device development remains both safe and forward-looking in a competitive market. Overall, the paper contributes to the broader discourse on enhancing regulatory frameworks to support innovation while maintaining public safety.

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Regulatory compliance, medical device development, challenges, strategies, innovation, safety, quality assurance, risk management, international standards.

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This study explores the evolution and optimization of Conquesting strategies within retail media networks and examines their profound impact on contemporary marketing practices. It delves into how targeted digital advertising, competitive bidding, and dynamic consumer engagement models are reshaping the retail media landscape. By leveraging data-driven approaches, the research provides insights into how brands can effectively reposition their advertising efforts against competitors. Key performance indicators such as audience reach, conversion metrics, and return on investment are analyzed to understand the effectiveness of these strategies. The findings underscore the importance of predictive analytics and artificial intelligence in refining bidding tactics and personalizing marketing content in real time. Furthermore, the study highlights the influence of mobile and online platforms in modifying consumer behavior and enhancing the overall impact of Conquesting campaigns. It also addresses challenges like market saturation and regulatory constraints while proposing innovative solutions to mitigate these issues. The results indicate that optimized Conquesting strategies not only boost brand visibility but also cultivate enhanced customer loyalty and engagement, thereby driving sustainable growth. The interplay between technological advancements and creative advertising emerges as a critical success factor within retail media networks. This research contributes to the academic discourse by synthesizing contemporary methodologies and offering practical recommendations for marketers seeking to secure a competitive edge in a dynamic market environment.

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Retail Media, Conquesting Strategies, Optimization, Digital Advertising, Data-Driven Marketing, Consumer Engagement, Competitive Strategy, Artificial Intelligence, Predictive Analytics, Market Innovation

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The rapid evolution of digital technology has ushered in an era defined by the proliferation of big data and the adoption of cloud computing solutions, fundamentally transforming the way organizations manage and leverage information. This paper explores the intricate interplay between data governance and security in this dynamic landscape, addressing the dual imperative of harnessing vast data resources while safeguarding them against emerging threats. Effective data governance provides a structured framework for managing data quality, integrity, and compliance, ensuring that information remains accurate, accessible, and consistent across diverse environments. Simultaneously, robust security measures are essential to counteract vulnerabilities inherent in cloud infrastructures and large-scale data ecosystems, including unauthorized access, data breaches, and cyberattacks. By integrating advanced analytics, machine learning, and real-time monitoring, organizations can develop adaptive strategies that not only protect sensitive information but also drive innovation and operational efficiency. This study reviews current practices, highlights challenges such as regulatory compliance and risk management, and proposes comprehensive approaches that synergize governance policies with state-of-the-art security protocols. Through detailed analysis, the paper underscores the necessity for organizations to continually evolve their data management frameworks in response to technological advancements and shifting threat landscapes. Ultimately, the research aims to provide a strategic roadmap for integrating data governance and security measures, ensuring that enterprises can confidently navigate the complexities of big data and cloud computing while maintaining trust, resilience, and competitive advantage in the digital age. This comprehensive review contributes to the growing literature by providing actionable insights and strategic recommendations for organizations navigating this critical intersection.

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Big Data, Cloud Computing, Data Governance, Data Security, Data Privacy, Regulatory Compliance, Risk Management, Cybersecurity, Digital Transformation

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Modern software architectures are increasingly embracing microservices due to their inherent scalability, flexibility, and resilience. However, the distributed nature of microservices poses unique challenges for quality assurance and reliability. This abstract presents an in-depth analysis of testing strategies specifically tailored for microservices, focusing on methods that ensure robust performance and fault tolerance in complex systems. The discussion begins by exploring traditional testing approaches, such as unit and integration testing, and extends to more advanced techniques like contract testing and chaos engineering. By isolating individual services, developers can more effectively identify and rectify issues before they propagate through the system. Furthermore, the abstract examines the critical role of automated testing frameworks and continuous integration pipelines in detecting regressions and streamlining deployment processes. Emphasis is placed on the importance of end-to-end testing and monitoring to validate inter-service communications and simulate real-world operational scenarios. The paper also addresses challenges such as dependency management, asynchronous operations, and dynamic service orchestration, proposing solutions that leverage containerization and virtualization to recreate production-like environments. Overall, this analysis provides a comprehensive framework for testing microservices that balances rapid development cycles with the need for rigorous quality control. It highlights how integrating innovative testing methodologies within agile and DevOps practices can significantly enhance system reliability and customer satisfaction in ever-evolving digital ecosystems. These additional strategies are vital in today's competitive landscape, where minor service disruptions can cause significant setbacks; a systematic, proactive testing approach not only reduces downtime but also instills confidence in deploying resilient microservices architectures for success.

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microservices, testing strategies, quality assurance, reliability, integration testing, contract testing, chaos engineering, automated testing, DevOps, agile

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The rapid expansion of cloud-based applications and microservices has intensified the demand for robust API management strategies. AI-THROTTLE introduces an intelligent throttling mechanism that harnesses predictive machine learning models to dynamically regulate API traffic and optimize resource allocation. By analyzing historical and real-time data, the system forecasts usage patterns and detects anomalies before potential overloads occur, thereby enabling proactive adjustments in rate limiting. The framework employs a combination of regression analysis and classification algorithms to fine-tune throttling parameters, ensuring that service quality is maintained even during peak demand. Experimental evaluations conducted on simulated high-traffic environments demonstrate significant reductions in latency and improved throughput compared to conventional static throttling methods. In addition, case studies on enterprise-level deployments reveal that AI-THROTTLE effectively mitigates system downtime and enhances overall reliability. The adaptive learning component continuously refines predictive accuracy by integrating feedback from ongoing operations, allowing the system to evolve in response to changing usage trends. Challenges related to computational overhead and data privacy are addressed through efficient algorithm design and robust security protocols. Overall, AI-THROTTLE represents a significant advancement in API management, offering a scalable and resilient solution for modern distributed systems. This research underscores the transformative potential of integrating artificial intelligence with traditional network management techniques to achieve smarter, more responsive API services. Extensive simulations and real-world deployments further validate the efficiency of AI-THROTTLE, demonstrating its ability to seamlessly integrate with existing infrastructures while reducing system bottlenecks and ensuring continuous, high-quality service delivery. These results highlight the promise of adaptive API control.

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API Throttling, Predictive Machine Learning, Intelligent API Management, Adaptive Learning, Cloud Services, Distributed Systems, Anomaly Detection, Rate Limiting, Resource Optimization, AI-Driven Control

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Recent advances in deep learning have revolutionized medical imaging, particularly in the domain of tumor segmentation. This study introduces an innovative framework that integrates high-resolution imaging data with detailed clinical notes using state-of-the-art deep learning models and large language models (LLMs). In our approach, convolutional neural networks (CNNs) are employed to analyze imaging modalities, while transformer-based LLMs process unstructured clinical narratives to extract valuable contextual information. By merging these complementary data sources, the model achieves enhanced delineation of tumor boundaries and improved segmentation accuracy. Extensive experiments on diverse datasets reveal that the combined analysis mitigates common challenges such as imaging noise, variable tumor morphology, and limited contrast in tumor regions. The integration of clinical notes enriches the imaging analysis by providing patient history, biomarker information, and treatment context, thereby enabling more personalized segmentation outcomes. Results indicate a significant reduction in segmentation errors and a notable increase in model robustness, highlighting the potential of multi-modal fusion in clinical applications. This research underscores the critical role of combining imaging data with textual clinical information to overcome the limitations of single-modality approaches. Future directions include refining the fusion algorithms, expanding the framework to other cancer types, and real-time implementation in clinical settings to support diagnostic decision-making and personalized treatment planning. This innovative methodology adapts robustly across varied clinical scenarios, underscoring its promise for integration in routine oncological diagnostics.

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Deep Learning, Automated Tumor Segmentation, Clinical Notes Integration, Imaging Data, Large Language Models, Multi-modal Fusion, Precision Oncology

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The inclusion of compliance at design and implementation stages of Warehouse Management System (WMS) assumes a vital importance in providing conformity with legal, environmental, and industry-related needs. Recent studies have highlighted the serious lack of proactive incorporation of compliance programs at the design stage, which tend to result in expensive rework, delays, and legal issues. The previous literature is largely confined to compliance matters at the backend, with no regard to the need to incorporate regulatory factors onboard from the design stage. Such practical restriction results in WMS systems failing to respond to changing regulations or needing extensive rework after the implementation stage, thereby adding to efficiency problems in the operations. Research has shown that the early integration of regulatory compliance in WMS development facilitates smoother implementation, reduces legal risk, and enhances operating efficiency. Early integration of compliance enables the system to handle regulatory changes more effectively, maintaining consistent compliance with changing laws and regulations without affecting operations. Furthermore, involving different stakeholders--such as legal experts and software developers--during WMS development makes technical specifications more appropriate to legal requirements. This approach not only minimizes compliance risk but also makes the WMS scalable, flexible, and future-proof. While such advantages do exist, they are not supported by complete frameworks and methodologies. Hence, filling this research gap by developing compliance-based WMS design frameworks would greatly improve the efficiency, flexibility, and legality of warehouse management systems to the advantage of both industry practitioners and regulatory authorities.

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Compliance, Warehouse Management System (WMS), initial design stage, legal compliance, operational efficiency, risk reduction, compliance integration, system adaptability, regulatory frameworks, stakeholder collaboration.

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Generations X, Gen Y, and Generation Z are increasingly shaping the modern workplace with their unique perspectives, work values, attitudes, beliefs, methods, experiences. The research investigates how generational differences affect workplace behavior in engineering and management colleges situated in Meerut. The research is a combination of quantitative and qualitative research and aims at studying the gaps of work ethics, the structure of communication, readiness for change and technology between three generations. The results showed that the prior generation focuses more on constancy, organization, hierarchy in the management structure, whereas the present generation is more about juggling work and personal life or about working collaboratively and attending to it or about pathways in family - work issues. Conversely, the youngest generation, referred to as Generation Z, is highly digital, entrepreneurial, and has a fondness for flexibility and frequent feedback. Disparities between teamwork, leadership, and job satisfaction can arise due to generational differences in communication preferences and workplace expectations. The study suggests that workplace policies, mentoring opportunities for diverse workforces, and HR strategies should be designed to accommodate collaboration among different generations. To address these issues, institutions can improve workplace cohesion and harness generational strengths for organizational effectiveness.

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Generational diversity, workplace behavior, Generation X, Millennials, Generation Z, intergenerational conflict.

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Creative Commons Attribution 4.0 and The Open Definition

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This study explores how literature and art function as cultural mediators in fostering interfaith dialogue and understanding in a pluralistic society like India. It examines these mediums' historical and contemporary contributions to transcending religious boundaries, promoting shared values, and addressing communal conflicts. By analysing select literary works, visual arts, and performing, the paper highlights the transformative potential of creative expressions in shaping a harmonious interfaith narrative. Introduction India's religious diversity has historically been a source of cultural richness and socio-religious tension. Literature and art have often emerged as powerful tools in bridging interfaith divides by providing shared spaces for dialogue, reflection, and mutual understanding. This paper investigates the multidimensional roles played by these culturaThis study explores how literature and art function as cultural mediators in fostering interfaith dialogue and understanding in a pluralistic society like India. It examines these mediums' historical and contemporary contributions to transcending religious boundaries, promoting shared values, and addressing communal conflicts. By analysing select literary works, visual arts, and performing, the paper highlights the transformative potential of creative expressions in shaping a harmonious interfaith narrative. Introduction India's religious diversity has historically been a source of cultural richness and socio-religious tension. Literature and art have often emerged as powerful tools in bridging interfaith divides by providing shared spaces for dialogue, reflection, and mutual understanding. This paper investigates the multidimensional roles played by these cultural forms in promoting interfaith harmony.l forms in promoting interfaith harmony.

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Interfaith Dialogue, literature, art, diversity, culture, pluralistic society

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Creative Commons Attribution 4.0 and The Open Definition

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Bankruptcy prediction is a critical task in financial risk management, aiding businesses and investors in making informed decisions. This survey explores recent advancements in bankruptcy prediction models using deep learning techniques. Traditional financial models often struggle with high-dimensional, nonlinear financial data, whereas deep learning methods, including artificial neural networks (ANNs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer-based architectures, offer improved accuracy and robustness. We analyze various datasets, feature selection methods, and performance metrics used in bankruptcy prediction studies. The survey highlights key challenges such as data imbalance, feature interpretability, and model generalization. Additionally, we discuss the effectiveness of hybrid approaches that combine deep learning with traditional statistical models for enhanced predictive capabilities. Our findings suggest that deep learning significantly improves bankruptcy prediction accuracy, making it a valuable tool for financial forecasting. Future research directions include explainable AI, transfer learning, and real-time bankruptcy risk assessment.

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Bankruptcy, Bankruptcy prediction system Classification, machine learning techniques, Credit Card Fraud Detection

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