Exploring the Shadows: Dark Tourism in India

Dark tourism, an emerging niche in India’s travel landscape, offers a unique exploration of sites marked by history’s tragedies and mysteries. This form of tourism invites travelers to engage with the stories of resilience, loss, and humanity’s complex relationship with mortality. From the haunting silence of Jallianwala Bagh to the ghostly allure of Bhangarh Fort, these destinations challenge visitors to reflect on the past and its enduring impact on identity and culture. India’s vast and diverse history makes it a fertile ground for this growing segment, providing not only emotional and educational experiences but also opportunities to preserve heritage and foster deeper connections with history. Despite its potential, dark tourism raises critical ethical, sustainability, and social considerations. Questions about respecting the memories of those affected, balancing commercialization with authenticity, and the equitable distribution of economic benefits remain significant challenges. By addressing these concerns, dark tourism can evolve into a meaningful form of travel that honors the lessons of the past while enriching the travel experience. As India’s tourism industry continues to expand, integrating dark tourism thoughtfully can diversify offerings and contribute to a more holistic understanding of the nation’s multifaceted history

Revisiting PPP Models for Climate Resilience and Disaster Risk Reduction in Indian Local Bodies: Challenges, Opportunities, and Financial Perspectives

Natural hazards and climate change are frequently disrupting the communities. These effects are further amplified due to anthropogenic causes and unsustainable development. More often than not, due to limited capabilities, local government is the first victim of such disruptions. In this context, Public-private partnerships (PPP) models present a unique opportunity and overcome limited government funding limitations. This study attempts to explore the potential of PPP models in supporting and financing Climate Change Adaptation (CCA) and Disaster Risk Reduction (DRR) initiatives at the local-body levels. This study reviews both the international case studies from Jamaica, Japan and Latin America and national case studies from Delhi, India’s Smart City Mission. The study identifies the gaps in existing PPP frameworks and emphasises the need for innovative funding methods.  The paper also discusses the importance of community engagement, multi-stakeholder engagement and traditional knowledge. Recommendations include enhancing financial resilience through blended finance, resilience bonds, and risk-sharing mechanisms. The paper proposes locally tailored PPP frameworks that prioritise long-term sustainability, capacity building, and responsive financing to foster climate-adaptive infrastructure

A Systematic Review of Literature on the Strategies of Talented Employee’s Attraction, Retention and Management

Today, organizations face numerous challenges in the discipline of talent management. Human resource management is influenced by a variety of factors, among which generational typologies within organizations are particularly significant. Since employees from different generations are born during distinct time periods, they possess unique personalities, perspectives, and values, which can create fundamental challenges to the organizations. Talent management’s main function is to ensure the talents and special skills of the employees have been identified and encouraged. Employees have to be deputed in the right place with the right responsibilities as per their individual talents’ suitability. Bringing the right people at the right time and developing their skills and capabilities to achieve the organizational goals is the strategy behind the concept of talent management. Most of the multi-national companies across the globe are giving top priority to the discipline of talent management.  Bringing in and developing skilled workers is one of the most crucial parts of talent management. (Pruis, 2011). Talent management (TM) is a strategic procedure pointed to attract, identify, develop, motivate and retain the most qualified and talented employees of the organization. This method surpasses conventional HR functions by centering on recognizing, nurturing, and promoting outstanding talent within the business. (McDonnel et al., 2011). During the current scenario, hiring and retaining the talented personnel is becoming more challenging due to the worldwide job market opportunities. Employees of the organizations have to be considered as the most valuable resources and capital. Their contribution is really worthwhile to retain the market share among the competitors (Héder et al., 2018). An employee\u27s everlasting commitment towards their employer could be raised through the precautions implemented by the employer towards talent retention. While previously overlooked, retention has become a priority in the post-pandemic job market (Gallup’ 2022)

Cutting-edge Tech Integration in Education and Teaching Practices

This paper provides a comprehensive analysis of the intersection between emerging technologies and pedagogical approaches in modern education. It explores how innovations such as Artificial Intelligence (AI), Virtual Reality (VR), gamification, and Learning Management Systems (LMS) are transforming traditional teaching and learning models. Despite the growing adoption of these technologies, there remains a gap in research regarding their long-term effectiveness, the adaptability of educators, and the disparities in access across different educational settings. This study aims to address this gap by identifying the most influential technologies in education, assessing their impact on pedagogical strategies, and examining the challenges educators face in integrating these tools into their practice. Using a secondary research methodology, this study synthesizes existing literature, reports, and case studies to evaluate trends and challenges in educational technology adoption. Key findings indicate that while these technologies offer significant benefits, including personalized learning, increased student engagement, and improved accessibility, their implementation is often hindered by barriers such as resistance to change, infrastructure limitations, and digital equity concerns. The paper highlights the importance of aligning teaching methodologies with technological advancements to foster dynamic, student-centered learning environments. Understanding the intersection of these technologies with contemporary pedagogies is essential for educators, policymakers, and institutions to effectively navigate and adapt to the evolving educational landscape

The Role of Machine Learning in Predicting Patient Outcomes and Hospital Readmissions

With an aging population, ascendent prevalence of chronic disease and rising therapy costs, the demands on global health care systems have reached new levels, calling for new solutions to improve patients’ care and health care delivery efficiency. Thus, in a clinical context, Machine Learning (ML) is a rapidly evolving subbranch of Artificial Intelligence (AI) which can provide a transformational potential to automate the data-intensive decision making. Vast and complicated datasets spawned from electronic health records (EHRs), laboratory results, diagnostic imaging, patient histories and other sources can be analysed by ML algorithms to find patterns that humans cannot. Moreover, these predictive capabilities come into play when it comes to predicting patient outcome or patients at high risk of readmission so that suitable interventions can be taken place and healthcare costs can be claimed. This paper systematically studies the application of ML in predicting clinical outcomes and readmissions through a comparative analysis of different ML model: such as logistic regression, decision trees, ensemble, and different deep learning architectures. We evaluate the performance, accuracy, and practical utility of these models in hospital settings by leveraging real world datasets. We also discuss broader ML adoption related to healthcare, including model interpretability and integration issues and ethics. We show that ML has the unique potential to drive precision medicine and improve the entire healthcare delivery

A Study of 10-Minute Delivery Apps in the e-Business Ecosystem

Time never waits for anyone. In today’s fast-paced world, everyone wants everything to happen quickly. Whether it’s food delivery, grocery shopping, or medical needs, people want instant services. This demand has given rise to a new trend in business — 10-minute delivery. In this competitive era, everyone wants to stay ahead. That’s why e-business is evolving every day. Modern businesses are adopting advanced techniques and technologies to satisfy customers and provide faster services. With this background, this research paper tries to explore how these quick delivery apps have become a part of the e-business ecosystem. We will understand the significance, objectives, and impact of these apps. In this research, the focus will be on understanding how 10-minute delivery apps affect consumers, what their business model looks like, and what the future of these apps in India may hold. Major apps like Instamart, Zepto, and Blinkit have introduced this concept of 10-minute delivery and are continuously trying to improve their services. Using such apps, goods reach the customer quickly. Due to this, customers are becoming dependent on these apps. This research will also study both the advantages and disadvantages of this rapid delivery model

A Literature Review on Integrating Enterprise Resource Planning and Supply Chain Management

The Integration of Enterprise Resource Planning (ERP) with Supply Chain Management (SCM) is the key to enhancing the effectiveness and efficiency of supply chains in today\u27s business environment. This paper explores the large body of research into the implementation of ERP systems within SCM and their related functions. It emphasizes the role that ERP plays in achieving operational efficiency and performance enhancement across supply chains. It indicates that the adoption of ERP solutions enhances business performance significantly, which includes better management of production processes, optimum management of inventories, and proper decision-making by enhancing data sharing. This research intends to conduct a systematic review of existing literature on ERP technologies in SCM, answering two primary sub-research questions: the implementation of ERP systems in SCM and the integration between these systems within supply chain processes. The research will be able to analyze numerous scholarly articles in order to identify trends, benefits, and challenges associated with ERP implementation in SCM. This will be invaluable insight for organizations contemplating the adoption of ERP, providing them with knowledge that will enhance their supply chain performance. Finally, this paper contributes to a deeper understanding of how ERP transforms SCM practices and drives competitiveness in an increasingly digitalized industrial landscape

Educational Leadership Theories and Their Role in School Improvement: A Study of the Haitian Secondary School System

This study investigates the application of educational leadership theories within the Haitian secondary school system, aiming to identify the dominant leadership styles and their impact on school improvement. Drawing from a wide array of theoretical frameworks—including transformational, instructional, transactional, and distributed leadership—the research uses a mixed-methods approach combining qualitative interviews with quantitative data analysis. Findings indicate that transformational and instructional leadership styles are prevalent, but often blended with administrative and transactional elements. The study emphasizes the importance of contextual adaptation and recommends strategic leadership development tailored to the Haitian educational landscape

Quantum-Inspired Resource Allocation in Cloud-IoT Networks Using Hybrid Classical-Quantum Algorithms

The rapid expansion of Cloud-IoT networks has created significant challenges in resource allocation, requiring advanced optimization techniques to efficiently manage computational power, storage, and bandwidth. The increasing demand for low-latency, high-efficiency allocation mechanisms necessitates adaptive and scalable solutions. Traditional resource management techniques, including heuristic-based algorithms and machine learning approaches, often struggle to handle dynamic workloads, heterogeneous IoT devices, and unpredictable traffic fluctuations. These conventional models suffer from limited adaptability, slower convergence rates, and suboptimal resource utilization, leading to higher operational costs and resource wastage. To address these limitations, this research introduces a hybrid classical-quantum model integrating the Quantum Approximate Optimization Algorithm (QAOA) to enhance real-time resource allocation. The proposed model combines classical computing for handling routine data processing with quantum-inspired optimization to solve complex allocation problems more efficiently. This approach ensures dynamic adaptability, minimizing latency and maximizing energy efficiency. The experimental evaluation was conducted using dynamic IoT workload scenarios, where key performance metrics such as accuracy, convergence speed, adaptation latency, energy efficiency, and operational cost reduction were analyzed. The results show that QAOA achieves 97.8% accuracy, significantly outperforming WOA (87.5%), HHO (85.2%), MPA (83.1%), and AHA (82.4%). Additionally, it reduces latency from 105 ms to 85 ms, increases energy efficiency from 1.82 to 2.48, and lowers resource wastage from 6.5% to 3.8%, demonstrating superior optimization capabilities. These findings confirm that the proposed hybrid model is highly effective in addressing resource allocation complexities, significantly improving cost efficiency, scalability, and computational performance in Cloud-IoT networks

GIS-Based Landslide Mapping and Analysis using QGIS: A Study in Palakkad, Kerala

Landslides are a major natural hazard in hilly regions, posturing critical dangers to life, infrastructure, and the environment. This study centers on landslide vulnerability mapping in Veezhumala, Palakkad, utilizing QGIS as the essential apparatus for geospatial investigation. Different conditioning components such as slope, elevation, aspect, soil type, land use, and rainfall patterns have been considered to evaluate landslide-prone regions.Information collection included getting Digital Elevation Models (DEMs), meteorological rainfall data, and chronicled landslide events. Thematic maps for each calculate were generated in QGIS to establish their spatial dispersion and impact on landslide vulnerability. The another stage of the ponder will include applying the Evidence-Based Frequency (EBF) Method, which can assign probability weights to each calculate based on its relationship with past landslides. This will empower the creation of a landslide vulnerability index, categorizing the think about region into distinctive chance zones.The discoveries of this study will contribute to disaster readiness, urban planning, and natural administration by recognizing high-risk zones and suggesting moderation measures. The ultimate vulnerability outline will serve as a important instrument for policymakers, engineers, and nearby specialists in landslide risk evaluation and administration methodologies