Daily Archives: January 7, 2026

Authors: M. O. O. Ifesemen, Dr Dulari A Rajput

Abstract: This study investigates the intricate link between workplace operational incidents and administrative errors, emphasizing the critical role of organizational and leadership accountability in mitigating error-enforcing conditions that precipitate incidents and degrade performance. Employing a robust qualitative approach, the research integrates a mixed- methods design encompassing naturalistic observation—both participant and non- participant—and unstructured interviews conducted with over 300 personnel within a Nigerian-based transnational organization. Data were meticulously analyzed using descriptive and deductive reasoning frameworks to elucidate the impact of leadership decisions and organizational practices on the prevalence of workplace errors and related incidents. The findings reveal a compelling pattern: more than 80% of workplace incidents, encompassing both physical injuries and psychological harm, originate from administrative errors linked to leadership styles and organizational culture. Key error-enforcing conditions identified include pervasive blame culture, inadequate fatigue management, favoritism, bullying, flawed performance appraisal systems, and a pronounced lack of employee empowerment. Notably, psychological injuries arising from these administrative errors—such as diminished self-esteem, depression, and chronic stress—were found to be more detrimental than physical injuries, exerting profound negative effects on employee motivation, productivity, and overall organisational performance. The study further underscores the frequent misinterpretation of incident causality and highlights the paramount importance of objective evaluation and leadership accountability as mechanisms to reduce incident recurrence effectively. In conclusion, the research advocates cultivating accountability at all organisational levels, enhancing leadership competencies, and promoting a culture grounded in empathy and objectivity within performance appraisal and incident management processes. Implementation of these measures is projected to foster safer, more productive work environments, thereby driving improved organisational outcomes. The study also calls for integrating accountability principles into corporate governance frameworks. It emphasises the need for transformational learning through causal reasoning to address the root causes of workplace errors and incidents, ultimately contributing to sustainable organisational excellence.

DOI: https://doi.org/10.5281/zenodo.18172551

 

Published by: vikaspatanker

Authors: Rithica B, Sai Srija R

Abstract: Personalized learning has emerged as a vital component of modern educational systems due to the diversity in students’ learning abilities, preferences, and academic backgrounds. Traditional e-learning platforms often provide uniform learning content to all learners, which fails to address individual needs and learning gaps. This limitation results in reduced student engagement and suboptimal learning outcomes. To overcome these challenges, this paper proposes an AI-driven personalized learning recommendation system that dynamically suggests learning materials, assessments, and learning paths tailored to individual students. The proposed system utilizes machine learning techniques to analyze learner profiles, historical academic performance, learning behavior, and preferences. Based on these parameters, intelligent recommendations are generated to support adaptive and learner-centric education. Experimental evaluation demonstrates that the proposed system significantly improves student engagement, learning efficiency, and academic performance when compared with conventional learning management systems. The findings highlight the effectiveness of artificial intelligence in transforming digital education into a personalized and adaptive learning environment. Personalized learning has emerged as a vital component of modern educational systems due to the diversity in students’ learning abilities, preferences, and academic backgrounds. Traditional e-learning platforms often provide uniform learning content, resulting in reduced engagement and limited academic effectiveness. This paper proposes an AI-driven personalized learning recommendation system that utilizes learning analytics and machine learning techniques to analyze learner profiles, academic performance, and behavioral patterns.

Published by: vikaspatanker

Authors: M. O. O. Ifesemen, Dr Dulari Rajput

Abstract: This research critically examines the pervasive effects of blame culture on organisational productivity, using Mcpee Limited—a production-oriented company based in Southern Nigeria—as a case study. The study explores how blame culture is embedded within the operational and social fabric of the company and investigates its impact on employee behaviour, work procedures, and overall organisational performance. This research investigates the pervasive effects of blame culture on organisational productivity, using Mcpee Limited, a production-oriented firm in Southern Nigeria, as a case study. The study aims to explore how blame culture is embedded within the company’s operational and social environment and its influence on employee behaviour, work procedures, and overall productivity. An inductive research approach with a descriptive design was adopted, employing a mixed- methods data collection strategy. Quantitative data were gathered through questionnaires administered to 314 employees across varied departments, while qualitative insights were obtained from 80 department heads and supervisors via in-depth interviews. This triangulation enabled a comprehensive understanding of how blame culture permeates the organization and affects its functioning. The findings reveal that blame culture cultivates a tense and insecure workplace, where employees avoid assuming responsibility for mistakes due to fear of punitive consequences. This environment suppresses risk-taking and innovation, thereby constraining the organization’s ability to adapt and improve continuously. Several factors perpetuate this culture, including rigid procedural frameworks that restrict employee discretion, entrenched favoritism and nepotism, and ineffective recognition and reward systems that fail to engage or motivate staff adequately. Moreover, blame culture fosters demotivation, learned helplessness, micromanagement, and erodes employee empowerment, trust, and cooperation. Managers, concerned about protecting their reputations, frequently shift blame downward instead of promoting accountability, resulting in excessive bureaucracy and decreased employee engagement. To counteract these detrimental effects, the study recommends shifting organizational culture from blame-oriented to accountability-focused. This transformation calls for promoting fairness and meritocracy by eliminating favouritism, encouraging teamwork and collaboration aligned with shared goals, and streamlining work processes to reduce unnecessary rigidity. Empowering employees to exercise discretion, creativity, and problem- solving initiative without fear of unjust repercussions is emphasized as critical for fostering innovation and boosting productivity. The study concludes that blame culture significantly undermines organizational productivity by creating a fearful and rigid work environment. It recommends transforming the culture from blame-oriented to accountability-focused by promoting fairness, teamwork, flexible work practices, and problem-solving approaches. Empowering employees to take initiative without fear of unjust punishment and recognizing their contributions can foster innovation and enhance productivity. These findings offer valuable insights for organizations seeking to cultivate a positive, supportive, and accountable workplace culture conducive to sustained performance improvement.

DOI: https://doi.org/10.5281/zenodo.18171125

Published by: vikaspatanker

Authors: Samarth Upadhyay

Abstract: The rapid growth of Internet of Things (IoT) deployments has intensified the demand for architectures capable of processing high-velocity data streams and enabling real-time decision making. Traditional centralized cloud models are often inadequate for meeting the strict latency, scalability, and reliability requirements of modern IoT applications such as smart cities, industrial automation, healthcare monitoring, and autonomous systems. Cloud-native architectures, built on microservices, containerization, orchestration, and serverless computing, have emerged as a foundational paradigm for addressing these challenges. This review paper presents a comprehensive analysis of cloud-native architectures that support real-time IoT data processing and decision making. It systematically examines IoT system fundamentals, cloud-native design principles, streaming data pipelines, edge–cloud collaboration models, and decision-making mechanisms ranging from rule-based engines to machine learning–driven intelligence and digital twins. The paper further reviews data management strategies, performance evaluation metrics, and critical security and privacy considerations in distributed IoT environments. By synthesizing existing architectural approaches and comparative studies, this review identifies key design trade-offs, limitations, and research gaps, including challenges related to latency management, interoperability, system complexity, and trust. Finally, the paper outlines future research directions such as AI-driven self-adaptive architectures, edge intelligence, federated learning, and integration with next-generation networks. The findings provide valuable insights for researchers and practitioners seeking to design scalable, resilient, and intelligent cloud-native IoT systems capable of supporting real-time decision making.

DOI: http://doi.org/10.5281/zenodo.18170303

Published by: vikaspatanker

Authors: Nandini Bhalla

Abstract: Machine learning–driven risk management has gained significant attention as organizations increasingly rely on SAP-based financial and enterprise information systems to support critical business operations. Traditional risk management approaches in SAP environments are predominantly rule-based and reactive, limiting their effectiveness in detecting complex, evolving, and previously unknown risks. With the growing volume, velocity, and complexity of enterprise data, machine learning techniques offer advanced capabilities for predictive risk assessment, anomaly detection, and continuous monitoring. This review paper presents a comprehensive analysis of machine learning–driven risk management models applied to SAP-based financial and enterprise information systems. It systematically examines SAP system architectures, risk management frameworks, and the integration of supervised, unsupervised, and hybrid machine learning techniques for managing financial, operational, compliance, and access control risks. The paper also reviews SAP-specific data sources, data preprocessing requirements, and evaluation metrics used to assess model performance, with particular attention to challenges such as data quality, model interpretability, regulatory compliance, and system integration. Furthermore, the review identifies key research gaps and emerging trends, including explainable artificial intelligence, federated learning, and real-time continuous auditing within SAP environments. By synthesizing existing literature and highlighting practical and research implications, this paper provides valuable insights for researchers, practitioners, and organizations seeking to design and implement intelligent, scalable, and compliant risk management solutions in SAP-based enterprise systems.

DOI: http://doi.org/10.5281/zenodo.18170299

Published by: vikaspatanker

Authors: Atharv Joshi

Abstract: The convergence of Artificial Intelligence (AI), Cloud Computing, and the Internet of Things (IoT) has enabled intelligent, data-driven transformation across healthcare and financial systems. However, the integration of these technologies presents significant challenges related to security, scalability, interoperability, and real-time decision-making. Healthcare and financial domains demand highly reliable and secure architectures due to the sensitive nature of their data and strict regulatory requirements. Existing solutions often address these technologies in isolation, resulting in fragmented architectures and increased exposure to operational and security risks. This paper proposes a unified artificial intelligence framework that securely integrates cloud and IoT infrastructures to support intelligent healthcare and financial applications. The framework adopts a layered architecture encompassing IoT data acquisition, cloud-based storage and processing, AI-driven analytics, and embedded security mechanisms. Machine learning and deep learning models are employed to enable predictive analytics, anomaly detection, and decision support while ensuring data confidentiality, integrity, and availability. The framework supports both real-time and batch data processing, enabling scalable and low-latency operations. The proposed framework is validated through healthcare and financial use case scenarios, including remote patient monitoring and real-time financial transaction analysis. Performance evaluation demonstrates improved system efficiency, enhanced decision-making accuracy, and robust security compared to traditional siloed systems. The results confirm that the unified framework effectively addresses integration challenges while maintaining compliance and adaptability. This research contributes a comprehensive and scalable solution for next-generation intelligent healthcare and financial ecosystems, offering a foundation for future advancements in AI-enabled cloud and IoT integration.

DOI: http://doi.org/10.5281/zenodo.18169572

Published by: vikaspatanker

Authors: Parthiv Yodhan

Abstract: The rapid expansion of clinical data and the growing demand for personalized, efficient healthcare necessitate innovative approaches to data management and analytics. Traditional clinical data management (CDM) processes often struggle with data fragmentation, manual processing, and delayed insights, which can negatively impact patient outcomes and operational efficiency. This article explores the integration of Artificial Intelligence (AI) with SAP Digital Health platforms as a transformative solution for optimizing clinical data management and analytics. AI technologies, including machine learning and natural language processing, enhance data cleaning, validation, predictive modeling, and decision support, while SAP platforms provide a secure, interoperable, and scalable infrastructure for data integration and real-time analytics. By leveraging this synergy, healthcare organizations can improve diagnostic accuracy, enable personalized care, optimize operational workflows, and accelerate clinical research. The article also examines implementation challenges such as data privacy, interoperability, adoption barriers, and ethical considerations, and highlights emerging trends including real-time patient monitoring, genomics integration, and telemedicine analytics. Ultimately, AI-powered SAP Digital Health platforms offer a pathway toward a data-driven, patient-centric healthcare ecosystem, where predictive insights and proactive interventions significantly enhance clinical outcomes, operational efficiency, and population health management.

DOI: http://doi.org/10.5281/zenodo.18169570

Published by: vikaspatanker

Authors: Kashvi Uprex

Abstract: The rapid expansion of wireless Internet of Things (IoT) devices has created unprecedented opportunities and challenges for modern IT infrastructures. Traditional systems often struggle to accommodate the massive data volumes, real-time processing demands, and heterogeneous device ecosystems that characterize IoT deployments. Cloud-driven platforms offer scalable, flexible, and centralized solutions, yet integrating them with wireless IoT networks requires careful reengineering of foundational IT infrastructure. This article explores strategies for designing scalable, secure, and efficient cloud-enabled wireless IoT platforms. Key principles such as microservices-based architectures, edge computing, dynamic resource allocation, and robust security frameworks are discussed in detail. The article also examines cloud infrastructure models, data management techniques, performance optimization, and emerging technologies that enhance IoT capabilities, including AI, 5G/6G, and blockchain. Challenges related to legacy integration, interoperability, security, and sustainability are addressed, alongside recommendations for building resilient and future-ready systems. By providing a comprehensive framework for reengineering IT infrastructure, this work aims to guide organizations in deploying efficient, secure, and scalable wireless IoT platforms that can support the next generation of intelligent, connected applications.

DOI: http://doi.org/10.5281/zenodo.18169506

Published by: vikaspatanker

Authors: M. O. O. Ifesemen, Dr Dulari A Rajput

Abstract: This thesis examines the persistent rise of land-related conflicts and associated criminal activities in Nigeria, tracing their roots to historical, cultural, administrative, and governance-related inadequacies in the management of landed property. Land, traditionally communally owned and essential for livelihood, has evolved into a highly contested asset due to population growth, modernization, and weak implementation of the Land Use Act. The study highlights how ineffective administration, corruption, poor enforcement of regulations, and conflicting customary and statutory land rights have created conditions enabling violence, territorial claims, extortion, communal clashes, and other atrocities across the country. Materials and Methods: The research adopts a qualitative approach grounded in criminological theory, supported by documentary analysis, non-participant observation, and unstructured interviews. Data were sourced through long-term observational studies of land-related activities in communities, motor parks, markets, land registries, and informal settlements across Nigeria. A combination of cross-sectional and longitudinal designs enabled the researcher to observe patterns, behaviours, and criminal tendencies linked to land ownership struggles. Content analysis was used to interpret data within the theoretical framework of causes of crime—including cultural, economic, psychological, and environmental determinants. Results and Discussion: Findings reveal that inadequacies in land administration—such as corrupt allocation practices, weak enforcement of land regulations, multiple sales of land, extortion by traditional actors (e.g., “omo-onile”), unregulated territorial control, and government-enabled demolitions—have significantly fueled criminal activities. These include communal clashes, armed conflicts, thuggery, property destruction, kidnapping, territorial cultism, and conflict between farmers and herdsmen. The study establishes that such crimes persist largely because of institutional weaknesses, inconsistent policies, and failure to implement culturally sensitive, transparent systems of land governance. Conclusion: The study concludes that strengthening policy enforcement, enhancing governance structures, and implementing culturally aligned regulatory frameworks are essential to reducing land-related atrocities. Effective land administration and accountability at all levels will help curb crime, promote peace, and support sustainable national development.

DOI: https://doi.org/10.5281/zenodo.18169532

 

Published by: vikaspatanker

Authors: Devansh Rithala

Abstract: Distributed wireless cloud–IoT architectures are increasingly critical in enabling real-time monitoring, data analytics, and intelligent decision-making across various industries, including smart cities, healthcare, industrial automation, and agriculture. However, the complexity, heterogeneity, and geographic distribution of these systems introduce significant operational risks that can compromise performance, reliability, and security. This article provides a comprehensive analysis of operational risks in distributed wireless cloud–IoT architectures, including hardware failures, network disruptions, cybersecurity threats, data integrity issues, and cloud service outages. It examines risk assessment and analysis techniques, such as fault tree analysis, failure mode effects analysis, and probabilistic modeling, to identify and prioritize vulnerabilities. The article also presents mitigation strategies, including redundancy, edge computing, network optimization, real-time monitoring, predictive maintenance, and security measures, while discussing challenges in implementation, such as scalability, interoperability, cost, and performance trade-offs. Future directions, including the integration of artificial intelligence, blockchain, next-generation wireless networks, and standardized risk management frameworks, are explored to enhance system resilience. By adopting a proactive and systematic approach to operational risk management, organizations can ensure reliability, efficiency, and sustainability in complex distributed wireless cloud–IoT ecosystems.

DOI: http://doi.org/10.5281/zenodo.18169504

Published by: vikaspatanker