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Conclusion
We all can agree at one point: the COVID-19 pandemic has had a massive and unanticipated impact on all the lives of all tourists. The global tourism and hospitalityindustry has been heavily damaged, but the societal impact cannot be overlooked. Consumer behavior, and ultimately consumer spending, has been and will continue to change, and company planning must adapt to these new realities. The major findings of this edited book in the contexts of tourism, destination recovery and crisis management thus have value for the industry and for researchers seeking to understand these changes. Chapter 1 analyses evolution of tourism and hospitality during times of crisis and how these businesses might rebound. Academics in the field of tourism and hospitality can use this collection to understand the most recent studies on crises and recovery. The impact of the COVID-19 crisis on tourism and hospitality was examined in several published pieces. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022. -
Tourism and Hospitality in Asia: Crisis, Resilience and Recovery
This book analyses the ways in which crises, including COVID-19, can be managed within the tourism and hospitality industries in Asia, in ways that support the future of these industries and help to make them more resilient. This book supports efforts to develop a new direction for the tourism and hospitality industry by considering their development holistically in the context of sustainable development. Going further, this book highlights actions to make the tourism system more resilient to external shocks and crises. Readers of this book will get insights into the economic, social, technological, and environmental implications of crises on the tourism and hospitality industry in Asia, including issues within the food and beverage industry in the Asian post-COVID-19 period. This book has three major objectives: to explore the crisis context of Asian tourism and hospitality, to present multiple cases from countries in Asia, and finally to envisage the paths to make the Asian tourism system more resilient, through the discussion of new trends and issues emerging following the pandemic. This book examines the economic, social, environmental, and technological implications of crises on the Asian tourism and hospitality industry and discusses the various ways of managing these crises more efficiently, contributing new knowledge to the industry. In its wider context, this book covers tourism management, crisis management, and destination management. At the more micro level, themes explored include tourism economics, marketing management, hospitality management, food and beverage management and tourism technology. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022. -
Optimization of multiple responses using overlaid contour plot and steepest methods analysis on hydroxyapatite coated magnesium via cold spray deposition
In this work, sequential optimization strategy based statistical design was employed to enhance the mechanical properties of hydroxyapatite coatings onto a pure magnesium substrate using a cold spray technique. A fractional factorial design (24-1) was applied to elucidate the process parameters that significantly affected the mechanical properties of the coating samples. Standoff distance, surface roughness, and substrate heating temperature were identified as important process parameters affecting thickness, nanohardness, and the elastic modulus of the coating sample. The overlaid method analysis was employed to determine tradeoff optimal values from multiple regressive equations. Then, finally, steepest method analysis was used to reconfirm and relocate the optimal domain from which the factor levels for maximum mechanical properties of the coating were determined at 49.77mm standoff distance, 926.4grit surface roughness, and 456C substrate heating temperature, which can accommodate the optimum requirements for the cold spray process with a coating of 49.77?m thickness, 462.61MPa nanohardness, and 45.69GPa elastic modulus. Scanning electron microscopy revealed that a short standoff distance, high surface roughness, and high substrate temperatures improved the bond between the coated layers and substrates. 2015 Elsevier B.V. -
Cold spray deposition of hydroxyapatite powder onto magnesium substrates for biomaterial applications
A simple, modified, cold spray process was developed in which hydroxyapatite powder was coated onto pure magnesium substrates preheated to 350 or 550C and ground to either 240 or 2000 grit surface roughness, with stand-off distances of 20 or 40 mm. The procedure was repeated five and 10 times. The hydroxyapatite coatings did not show any phase changes. Atomic force microscopy revealed a uniform coating topography, and scanning electron microscopy revealed good bonding between the coated layers and the substrates. As the p values were < 0.05, all factors except the number of sprays were considered to be significant. The response optimiser indicated that a 22.7 mm stand-off distance, a 649.2 grit surface roughness and a 496C substrate heating temperature produced good hydroxyapatite coatings of 46.3 ?m thickness, 436.5 MPa nanohardness and 43.9 GPa elastic modulus. The modified cold spray technique with substrate heating showed promising results in terms of product coating thickness and mechanical properties. 2015 Institute of Materials, Minerals and Mining. -
Role of Machine Learning in the Analysis of Mental Health Data: An Empirical Approach
As funding for mental health research has grown, so too has the body of knowledge about how best to address and alleviate issues related to mental health. However, there is still a lack of certainty and clarity on the precise causes of mental diseases. Discovery of new drugs, analysis of radiological data, forecasting of disease outbreaks, and the diagnosis of illnesses are just some of the medical applications of machine learning algorithms. Machine learning algorithms are commonly used to sift through the mountains of medical data. Since their performance has improved to the point where it can be relied upon, they are now used to aid in medical diagnosis. To assess and address the issues with mental health, numerous new approaches and algorithms had been devised. There are still a lot of issues that can be resolved. So the main purpose of this study is to examine the effectiveness of machine learning in mental health problems. For fulfilling this purpose, this study is descriptive in nature. Primary data is collected with the help of interview method in which 50 individuals suffering from mental illness were asked to answers some questions. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Cybersecurity vulnerabilities in federated learning
Federated Learning (FL) has been conceived as a dispersed machine learning paradigm facilitating collaborative learning at edge devices without exposing raw data. The model is amenable to privacy preservation and data protection regulation, for example, General Data Protection Regulation compliance. Yet, more widespread deployment of FL reveals a new and extreme spectrum of cybersecurity risks. These consist of data poisoning attacks that can potentially severely contaminate model integrity, model inversion attacks that can potentially recover sensitive data from exchanged gradients, adversarial manipulations where malicious agents take advantage of model weaknesses, and incidental privacy leakage. The impact and real world implication of these attacks differs, for example, a successful poisoning attack in medicine can result in misdiagnosis, model inversion in the finance sector could leak client confidential data, and adversarial attacks in Internet of Things (IoT) would control autonomous devices with safety consequences. This chapter critically reviews these threats taking into consideration attack feasibility, harm extent, and detectability, inspired by recent case studies illustrating their applicability in real world FL deployments. We also analyze the effectiveness of current state of the art countermeasures like robust aggregation methods, differential privacy, and cryptographic methods like secure multiparty computation and homomorphic encryption. By synthesizing current research on attack paradigms and counterattack architectures, the chapter offers practical knowledge towards constructing secure, robust, and trustworthy FL systems, particularly in high-risk applications like medicine, finance, and critical infrastructure. 2026 selection and editorial matter, Swati Sah, Rejwan Bin Sulaieman, and Aditya Dayal Tyagi; individual chapters, the contributors. -
Generative AI for Next-Generation Recommender Systems: Architectures, Applications, and Future Directions
The recommender systems have become a must in delivering personalized experiences across digital platforms. Still, traditional approaches, such as collaborative and content-based filtering, suffer from some inherent limitations: data sparsity, scalability, and dynamic user adaptation. In this context, generative AI emerges as a game-changing solution empowered by state-of-the-art models like variational autoencoders (VAEs), generative adversarial networks (GANs), and transformers to overcome the above-mentioned limitations. These models make possible the synthesis of user-item interaction data, uncovering latent patterns and providing context-aware recommendations, thereby redefining personalization in recommender systems. This chapter provides a detailed survey on the role of generative AI in recommender systems, their components, architectures, and applications. Case studies in e-commerce, entertainment, and education provide insights into how generative models help drive personalization, tackle the cold-start problem, and adapt dynamically to the evolution of user behaviors. Nevertheless, open issues regarding computational complexity, privacy protection, and ethical considerations remain. To address these, the chapter outlines the future enhancements in the areas of federated learning for privacy-preserving collaboration, multimodal data integration for holistic user profiling, and explainable AI frameworks to foster transparency and trust. Bridging these gaps would let generative AI-driven recommenders further revolutionize personalization, scalability, and inclusiveness, opening up a way to innovative solutions across the board in various industries. 2026 by John Wiley & Sons Inc. All rights reserved. -
AI in Financial Fraud Detection and Prevention
Fraud has always posed problems to financial institutions and with the rapid growth of digital transactions, and its complexity has increased beyond detection. Normal methods of fraud detection that depend on rules only are severely outdated and ineffective against newer types of schemes. It is now imperative to employ more sophisticated mechanisms for fraud detection considering the evolvement of financial crimes. The massive amounts of transnational data that need to be analyzed to detect fraudulent patterns can now be processed with medium to high levels of accuracy using AI with the help of machine learning, deep learning, and natural language processing (NLP), and fraud detection and prevention have been transformed for the better. Algorithms of machine learning like supervised, unsupervised, and reinforcement learning are central to the features of fraud detection. Suspicious transactions are detected during supervised learning by using already existing fraudulent data, whereas unsupervised learning detects all anomalies without any prior defined labels. Through real-time data input, reinforcement learning adjusts its detection methodologies. Deep learning models such as convolutional neural networks and recurrent neural networks identify and process fraud indicators hidden within messages or intricate datasets. Moreover, through intricate analysis of customer interactions, NLP techniques detect fraudulent activities by identifying phishing attempts and deceptive communications. The chapter touches upon the issues of implementing AI oriented fraud detection in realms like e-commerce and entertainment. Identifying fraud from e-commerce is complicated by factors like high volume of transactions, false positives, privacy issues, and the endless frameworks of fraud. Finally, the chapter provides a summary of the main insights and makes recommendations for further investigation like incorporating blockchain, federated learning, and higher explainability to bolster AI powered fraud detection systems. 2026 Scrivener Publishing LLC. -
Artificial Intelligence-Powered Stock Market Forecasting with Metaheuristic Feature Selection Techniques
This study proposed a hybrid stock market forecasting model which consists of Artificial Intelligence (AI) and metaheuristic feature selection algorithms to improve the accuracy in prediction and efficiency of the prototypical. It uses PSO (Particle Swarm Optimization) algorithm to pick the most relevant feature out of a pool of technical indicators and sentiment data and temporally learns the pattern using the LSTM (Long Short-Term Memory) network. The model yields better learning by diminishing noise and dimensionality and prevents over fitting. The efficiency of the anticipated system is seen through comparative analysis with such baseline models as SVM (Support Vector Machine), RF (Random Forest), and standard LSTM. This prototypical obtained MAE of 11.2RMSE of 18.18, and the mean absolute percentage error (MAPE) of 5.36 percent, with R2 of 0.91 and directional accuracy of 86.4 percent. The above results confirm the effectiveness of the suggested method, providing a solid and generalizable solution in terms of intelligent stock market prediction and investment decision support. 2025 IEEE. -
Frictionless shopping in the digital era: A comprehensive analysis of instant gratification, ethical considerations, and future prospects
This research chapter investigates the dynamic interplay between instant gratification and smart retailing, focussing on the transformative impact of frictionless shopping experiences. This chapter examines various channels, including omnichannel shopping, personalized recommendations, gamification, and rewards, highlighting their roles in bridging the gap between physical and digital realms. Additionally, it delves into the immersive world of virtual try-ons, analyzing their influence on immediate decision-making and customer satisfaction. This chapter also explores the intersection of instant gratification and smart retailing, emphasizing frictionless shopping's transformative impact. It examines ethical implications, envisions future frameworks, and anticipates enhanced consumer empowerment in evolving retail landscapes. This chapter employs a comprehensive literature review approach to analyze the existing body of knowledge on frictionless shopping, instant gratification, and smart retail technologies. This chapter highlights the transformative potential of smart retail, emphasizing ethical considerations. Anticipating enhanced consumer empowerment, it envisions a moral evolution in retail practices for immediate satisfaction. Informing retailers on ethical considerations, influencing policy development, and guiding the industry towards transparent, consumer-centric smart retail practices for sustainable growth and customer satisfaction. This chapter contains updated work from Frictionless shopping, Smart retailing, Instant gratification, and Ethical considerations. A comprehensive exploration of instant gratification's interplay with smart retailing, emphasizing ethical considerations and forecasting future frameworks to guide the evolving landscape towards responsible and transparent consumer-centric practices. 2025 Talasila Harshitha, Prathiba S., Narasimha Murthy H. and Joel Jebadurai Devapictahi. Published under exclusive licence by Emerald Publishing Limited. All rights reserved. -
Anticancer potential of Ipomoea alba: Induction of apoptosis and cell cycle arrest in MDA-MB-231 cells
Cancer is one of the major global health concerns, which supports the investigation of novel therapeutic agents with enhanced potency and minimal side effects. Ipomoea alba, belonging to the Convolvulaceae family, was selected for this study due to its known anti-inflammatory properties and the presence of secondary medical applicability. In this study, the cytotoxic, apoptotic, and cell cycle arrest effects of the methanol extract of Ipomoea alba were evaluated against MDA-MB-231 human breast cancer cell lines. Cytotoxicity was evaluated using the 3-(4,5-dimethylthiazol-2-yl)-2,5 diphenyltetrazolium bromide (MTT) assay across varying concentrations, revealing an IC50 value of 167.02 g/mL after 24 hours of treatment. Apoptosis and necrosis were examined through Annexin V/propidium iodide (PI) staining via flow cytometry, with treated cells showing 58.1% apoptosis (p < 0.001) and 16.71% necrosis (p < 0.01) compared to the control. Cell cycle distribution, evaluated using PI/ribonuclease (RNase) staining, revealed a significant increase in the G2 /M phase (56.31%, p < 0.001) and a mild increase in the Sub G0 /G1 phase (1.12%, p < 0.05), indicating arrest at critical regulatory checkpoints. These findings demonstrate that I. alba methanol extract possesses potent anticancer properties by inducing apoptosis and interfering with cell cycle progression in breast cancer cells. The results suggest its potential as a candidate for anticancer drug development and alternative therapeutic strategies. 2025, Indian journals. All rights reserved. -
Phytochemical profiling and evaluation of antioxidant and anti-inflammatory activities of Ipomoea alba L.
Plant-based medicine has been one of the oldest therapeutic practices in India and continues to offer valuable treatments for various ailments. Ipomoea alba, commonly known as morning glory, belongs to the family Convolvulaceae and is native to the tropical and subtropical regions of North and South America. It is renowned for its large, fragrant, nocturnal blooms, this plant holds significant potential in traditional medicine, particularly for managing gastrointestinal disorders, inflammation, and skin conditions. The nutrient content of Ipomoea alba leaves and seeds has demonstrated promising health benefits. This study investigated the phytochemical profile of Ipomoea alba leaves using three solvents: water, methanol, and chloroform. Phytochemical analysis confirmed the presence of carbohydrates, proteins, alkaloids, flavonoids, saponins, and tannins. HPLC analysis identified the presence of phenols in the aqueous extract, albeit in small quantities. Among the three extracts,the methanolic extract exhibited the highest antioxidant activity, as determined by DPPH, ABTS, and FRAP assays. Anti-inflammatory activity, assessed using a proteinase inhibitory assay, demonstrated that the methanolic extract showed the greatest inhibition at lower concentrations compared to the aqueous and chloroform extracts. The results suggest that the antioxidant and anti-inflammatory properties of Ipomoea alba may hold potential applications in cancer prevention and treatment. Future studies will aim to evaluate its cytotoxic effects, thereby exploring its potential role in cancer therapy. The Author(s). -
A Review On Geospatial Intelligence For Investigative Journalism
Throughout the ongoing Russian invasion of Ukraine, satellite images like the vast convoy of Russian military vehicles approaching the beleaguered Ukrainian city of Kyiv, Russian aircraft deployed at Zyabrovka, Belarus and many more such visuals have been in circulation and are being used as a tool to facilitate investigative journalistic studies. Such satellite-based images are one of the latest means of accessing vital data that can help in expanding the scope and impact of investigative journalism. Geospatial intelligence uses varied graphical content to convey information about the activities that occur on the surface of the earth. It includes colour and panchromatic (black and white) aerial photographs, multispectral or hyperspectral digital imagery, and products such as shaded relief maps or three-dimensional images produced from digital elevation models. The improving technology in geospatial spectra has broadened the scope of its usage to investigative journalism which lies at the core of this review paper. Some of the path-breaking journalistic stories that have come up in the past decade - imaging of the Uttarakhand landslide in 2021 using satellite images, coverage of the Fukushima nuclear plant since 2011, and 2021 tracking of Asia's border disputes emerging due to climate change and the satellite journalism built around the blockage of Suez canal in 2021 - showcase the potential that geospatial intelligence has in the domain of journalism. All four identified stories point out how we can practice satellite-based investigative studies, especially, for scrutinizing and comparing historical records regarding cross-border issues as well as the disappearance of pastures and forests in vast open lands. However, the arena of using geospatial intelligence, enabled by satellite images, remains underutilized and limited to specific journalistic organizations, based in a few countries. This exploratory review of the four mentioned journalistic accounts identifies the contexts where such efforts are feasible, methods that are required, sources that could be tapped, associated skill sets needed for its usage, the dynamics of such investigative approaches, and their scope and limitations. This review and the conclusions drawn from the above-mentioned cases provides a direction for journalists from small organizations and low income countries to explore the potential of satellite-based images in furthering their investigative reporting with a technological edge that persists to be sovereign in the geopolitical powerplay. Copyright 2022 by the International Astronautical Federation (IAF). All rights reserved. -
Enhancements in anomaly detection in body sensor networks
Anomaly detection in Body Sensor Networks (BSNs), have recently received much attention from the healthcare community. This is partly due to the development of sensor based real-time tracking and monitoring networks. These networks have been responsible not only for ensuring critical medical treatment at times of emergency, but have also made it easier for health-care personnel to administer critical treatment. In this paper we consider improvements to existing machine learning methods that detect anomalous sensor measurements. The improved methods are a step in the right direction in ensuring unduly overheads due to faulty sensors don't interfere while administering life-critical treatment in a limited resources scenario. 2019 IEEE. -
The Role of Humility in School Counselling Relationships: A cross-cultural Comparison
In this multi-continent, cross-cultural study, we investigated the role of humility in counselling relationships between school counsellors and students across Southeast Asia, South Asia, and Central Europe. A culturally diverse research group interviewed 45 school counsellors and analysed the data thematically. The findings suggest that humility is a relational and context-dependent trait, highlighting how cultural understandings and enactments of humility shape counsellor-student relationships. The study has implications for developing culturally relevant counselling practices in schools, where cultural characteristics, organisational factors, and counsellors professional practices interact. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025. -
Organizational justice in higher educational institutions /
NHRD Network Journal, Vol.7, Issue 4, pp.254-270, ISSN No: 0974-1739. -
Managing workplace diversity: Issues and challenges
Sage Open pp.1-5 DoI No. 10.1177/2158244012444615 -
Moderating influence of critical psychological states on work engagement and personal outcomes in the telecom sector /
Sage Journals, Vol.4, Issue 2, pp.584-592. -
Factors effecting employees willingness to stay in information technology industry /
The International Journal of Nepalese Academy of Management, Volume 1, Issue 1, pp.284-303, 2091-2471 (Print), 2091-248X (Online) -
An Effective Strategy and Mathematical Model to Predict the Sustainable Evolution of the Impact of the Pandemic Lockdown
There have been considerable losses in terms of human and economic resources due to the current coronavirus pandemic. This work, which contributes to the prevention and control of COVID-19, proposes a novel modified epidemiological model that predicts the epidemics evolution over time in India. A mathematical model was proposed to analyze the spread of COVID-19 in India during the lockdowns implemented by the government of India during the first and second waves. What makes this study unique, however, is that it develops a conceptual model with time-dependent characteristics, which is peculiar to Indias diverse and homogeneous societies. The results demonstrate that governmental control policies and suitable public perception of risk in terms of social distancing and public health safety measures are required to control the spread of COVID-19 in India. The results also show that Indias two strict consecutive lockdowns (21 days and 19 days, respectively) successfully helped delay the spread of the disease, buying time to pump up healthcare capacities and management skills during the first wave of COVID-19 in India. In addition, the second waves severe lockdown put a lot of pressure on the sustainability of many Indian cities. Therefore, the data show that timely implementation of government control laws combined with a high risk perception among the Indian population will help to ensure sustainability. The proposed model is an effective strategy for constructing healthy cities and sustainable societies in India, which will help prevent such a crisis in the future. 2022 by the authors. Licensee MDPI, Basel, Switzerland.