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Actualizing The Inner Self : Impact of An Online Signature Strengths Intervention On Well-Being
The PERMA Theory of Well-being states that exercising signature strengths one s most newlineprominent character strengths enhances five distinct dimensions of well-being, namely, newlinepositive emotions, engagement, relationships, meaning, and accomplishment. The present study tests this theory by examining the impact of an online signature strengths intervention on each of the aforementioned dimensions of well-being and overall well-being using an explanatory sequential mixed method experimental research design. The quantitative phase of the study implemented a randomized controlled trial (RCT) of the intervention with a wait-list control newlinegroup. A total of 82 participants recorded their levels of well-being and its dimensions at pretest and post-test using a standardized tool. Out of the 82 participants, 42 participants were in the experimental group and 40 participants in the wait-list control group. A one-month followup measure of well-being was also taken among participants in the experimental group to determine the long-term effectiveness of the intervention. Focus Group Discussions (FGDs) were conducted in the qualitative phase of the study among participants in the experimental group to explore the subjective experiences and mental processes underlying the identification and utilization of signature strengths. Results demonstrated medium to large increases in all the dimensions of well-being except for the dimension of engagement which did not show a newlinesignificant increase at either time points. Qualitative findings validated the quantitative findings and revealed important mental and emotional mechanisms underlying the experience of utilizing signature strengths, thereby providing a deeper insight into the nature and working of the intervention. Findings of the study carry far-reaching implications for organizations as well as educational and healthcare institutions to empower individuals to function optimally by utilizing their inner potential and experience the peak of well-being in all domains of life. -
Comparative Analysis of Disaster Recovery, Encryption, and Database Migration Methods in Cloud Environments
This research conducts comparative analysis and performance evaluation on disaster recovery approaches, encryption strategies, and database migration methods in cloud environments. The study highlights deeper technical insights encryption techniques and demonstrates superior performance compared to the other encryption methods in securing non-data files. This approach enhances protection against insider threats while avoiding reliance on existing Oracle wallet features, ultimately leading to a reduction in licensing expenses. This study also evaluates various database migration solutions, specifically AWS DMS, Google DMS, Azure DMS, and IBMS. Notably, IBMS stands out for its proficiency in producing cross-region data copies while achieving a 75% reduction in infrastructure costs. A comparative analysis was conducted on various disaster recovery strategies, including Standard DR, Pilot Light, Warm Standby, Hot Standby, Semi Replication, and DDI. Among these, the DDI is being observed as noteworthy since it excels in decision making capabilities and auto replication role switching advantages of standby databases. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Exchange rate, stock price and trade volume in US-China trade war during COVID-19: An empirical study
This article aims to examine the influence of international trade wars on the majority of stock market operations, both directly and indirectly affected. The impact of the trade war on the exchange rates of the participating countries was similarly negative. This article seeks to trace the conversion standards' footprints in the United States, China, and India using several indexes such as the Shanghai Composite Index, Dow Jones index, and Nifty 50. The cost of closing down various indices on a daily basis, as well as the conversion standard upsides of the participating currencies, are all examined in this study. Furthermore, utilizing the OLS and GARCH models, this work provides insights into measuring the uncertainties about the impact of exchanging scale on financial exchange. According to the findings of OLS, changes in the swapping scale have had a minor impact on the daily closing costs of stock records in the individual countries. The conversion standard, on the other hand, has a major impact on trade volumes in all three stock markets. When compared to the SSE and DJI equities, the GARCH model predicts that the contingent shift will be less shocking, resulting in a smaller impact on Nifty trade volume. To replicate the impact of trade wars during the Covid-19 crisis, the final results imply that data from domestic and international financial transactions must include securities market transactions. Author This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0). -
Effects of Peer Monitoring on Student Stress Level of College Students Based on Multi-Layer Perceptron Approach
The classroom is just one of many places where the proposed approach encounter stress. Previous studies have shown that college students experience high rates of stress. It is not known if the Student Stress Inventory-Stress Manifestations (SSI-SM) is useful in identifying stressors and evaluating stress manifestations among college students. To this end, it was created a college-specific version of the Student Stress Inventory-Stress Manifestations (SSI-SM) and administered it to students to determine its validity and reliability. These procedures comprise the proposed technique and include preprocessing, feature selection, and model training. It uses Normalization as a preprocessing approach. The term' normalization' refers to the procedure of rescaling or modifying data so that all categories have the same variance. The proposed approach employed linear discriminant analysis as a means of selecting features. The models are then trained using MLP after information gain has been used to choose relevant features. The proposed approach achieves better results than the two leading alternatives, CNN and RNN. 2024 IEEE. -
Feminism in Practice: Learning from the Barefoot Solar Mamas
The Barefoot College (India) is an NGO working in the fields of education, skills development, health, drinking water, and solar power mainly to train older, rural women who are determined to challenge restrictive gender roles in their respective communities. Since its inception, the NGO has trained over 2,000 rural women as solar engineers across 93 countries worldwide and has brought electricity to over 18,000 homes. Barefoot trainers employ non-normative methods of sharing knowledge such as color coding, sign language, and practical experience. This paper conducts a critical assessment of the Barefoot College Solar Electrification Programme to explore how it empowers illiterate and semi-literate women from remote rural areas around the world to become solar engineers (or Solar Mamas). It utilizes qualitative research methods to analyze this women's empowerment project as a landmark practical application of decolonial feminist theory. The paper contends that the Barefoot approach both challenges and conforms to the Women in Development and Gender and Development approaches of the past. The research is grounded methodologically in feminist praxis and also borrows from the conceptual frameworks of Feminist Political Ecology and Women and the Politics of Place. Stories and personal experiences from Solar Mamas have been highlighted to understand the real world impact of the program. The main findings indicate that the Barefoot College's innovative approach to empower marginalized communities and educate older women is achieved through decentralizing control and demystifying technology. (2024), (Bridgewater State College). All Rights Reserved. -
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DOES COVID-19 AFFECT SHARIAH COMPLIANT STOCK? EVIDENCE FROM SELECTED OIC COUNTRIES
This study aims to examine the movements of Islamic stock markets in ten selected OIC (Organization of Islamic Cooperation) countries in relation to Covid-19 cases, providing a comprehensive analysis of market behavior during the pandemic. The countries-Saudi Arabia, Pakistan, Bangladesh, Turkey, Indonesia, Oman, Qatar, UAE, Kuwait, and Bahrain-were chosen based on their large Muslim populations. Data was collected over a one-year period from January 1, 2020, to January 31, 2021, analyzing the relationship between Covid-19 cases and Islamic stock market indices. The study employed co-integration tests to identify long-term relationships and the Vector Error Correction Model (VECM) to explore short-run dynamics. The co-integration test results show a significant long-run relationship between Covid-19 cases and Islamic stock markets in most of the selected OIC countries. Specifically, the Shariah indices in Pakistan, Bangladesh, Turkey, Qatar, UAE, Kuwait, and Bahrain have a positive and significant relationship with Covid-19 cases. Conversely, Saudi Arabia, Indonesia, and Oman exhibit a negative long-term relationship with Covid-19 cases, suggesting a different market response. These results suggest that countries with diversified economies, particularly those relying on natural resources such as oil and agriculture, were more resilient during the pandemic. This study provides novel insights into the unique responses of Islamic stock markets in OIC countries during the pandemic, highlighting regional differences in market behavior and recovery. It suggests that despite the global economic downturn, OIC countries present attractive investment opportunities, particularly due to their swift recovery and resource-based economies, offering a robust portfolio for investors during crises. 2024 by the author(s). -
Unlocking the potential of AI for efficient governance: Innovative approaches of Bahrain
The rapid development and implementation of artificial intelligence (AI) technologies will have significant economic, social, and ethical impacts. Efficient governance is essential to maximize AI's benefits while minimizing its risks. Bahrain is positioning itself as a fintech hub, with AI playing a central role in this transformation. Bahrain's smart governance efforts will be strengthened by integrating AI into public services. E-government efforts will use AI to streamline processes, improve citizen experience, and build a more responsive and efficient public administration. The study provides an overview of how artificial intelligence (AI) is transforming various sectors in Bahrain with innovative approaches to boost productivity, better decision-making, and improve the general quality of services that may also impact the Bahraini economy. Bahrain continues to drive digital innovation, paving the way for a better and more prosperous future and sustainable development. Bahrain's digital transformation has been largely successful thanks to strong government measures. 2024, IGI Global. All rights reserved. -
Pangenomics for developing salinity stress-tolerant plants
Soil salinity is a critical agricultural challenge that significantly reduces crop productivity and threatens global food security. With approximately 20% of irrigated land affected by salinity, innovative strategies are essential to develop salinity stress-tolerant crops. The field of pangenomics, a comprehensive approach to studying the genetic diversity within species, has immense potential to address this issue. Pangenomics includes core genomes, spanning the entire genus, and accessory genomes, which are species-specific, thus capturing the full spectrum of genetic variation. This approach enables the identification of novel genes and alleles associated with salinity tolerance, providing a robust foundation for genetic improvement programs. Salinity stress has a profound molecular and physiological impact on plants with multiple phenotypic manifestations, such as stunted growth, lesser crop yield, and reduced reproductive success. To solve these issues, advanced sequencing technologies and bioinformatics tools used in constructing and analyzing pangenomes play a crucial role. This chapter goes into detail about techniques such as comparative genomics and genome-wide association studies (GWAS), which are important for their effectiveness in identifying salinity tolerance genes. Functional validation methods, including CRISPR/Cas9 and RNA interference (RNAi), have also been discussed. This chapter highlights case studies on crops like rice and wheat to demonstrate the practical applications of pangenomics in developing salinity-tolerant varieties. Furthermore, by addressing the challenges and future directions in the field, one can emphasize the need for integrating multiomics data and refining analytical methods. Such an approach can help guide future research and breeding efforts toward sustainable agricultural practices and enhanced global food security. 2025 Elsevier Inc. All rights reserved. -
Integrating Advanced Metabolomics with Plant Functional Genomics
Metabolomics encompasses the entire suite of small-molecule compounds or metabolites synthesized by an or ganism, whereas functional genomics refers to the gene-level functioning of an organism. The genome of a plant will dictate its metabolome, but the link between the two omics data may not always be clearly visible or properly explored. This chapter delves into the integration of advanced metabolomics with plant functional genomics, highlighting its pivotal role in advancing our understanding of plant biology and its applications in agriculture. Metabolomics provides a comprehensive analysis of small molecules, bridging the gap between genotype and phenotype by elucidating the dynamic interactions within plant systems. Key techniques such as mass spectrom etry and nuclear magnetic resonance are explored, emphasizing their importance in high-throughput and high-resolution metabolite profiling. The chapter further discusses the synergy between metabolomics and other omics technologies, including genomics, transcriptomics, and proteomics, underscoring its significance in iden tifying gene functions and metabolic pathways linked to complex traits such as stress tolerance. Applications in plant breeding are also highlighted, showcasing how metabolomics can drive the development of crops with en hanced stress resilience, yield, and nutritional quality. The chapter concludes by emphasizing the transformative potential of this integrated approach in shaping future agricultural practices and improving food security. CAB International 2025. All rights reserved. -
ENHANCING HEALTHCARE SECURITY WITH BLOCKCHAIN-POWERED SMART CONTRACTS
The rationale behind this research stems from the increasing frequency of data breaches in healthcare and the inadequacy of centralized systems to ensure privacy, interoperability, and regulatory compliance. The Present study emphasizes the importance of applying security in healthcare. This model was prepared by utilizing Smart Contracts. It has been noted that there are some emerging concerns about data security and privacy as well as interoperability within healthcare organizations. The focus of a research paper is on the deployment of Smart Contracts along with blockchain technologies. The fundamental vision is to improve healthcare infrastructures security. Blockchain is transforming healthcare systems for the better by eliminating inefficiencies caused by fraud and outdated technologies, allowing for the efficient, transparent, and secure issuance of Smart Contracts. The challenges of confidentiality, data security, and access to relevant patient information for medical professionals have been a problem in the healthcare sector. Most of the existing EHR systems do not have adequate mechanisms for enforcing security access controls, which hampers cooperation between healthcare institutions. These security concerns pose risks for patients privacy and cripple the adoption of modern information technology within the health sector. Simulation works shows that Transaction processing time in case of proposed model is below 1.5 second where as it is 2.5 in case of conventional model. Security breach probability of proposed model has been reduced to 0.05 that was 0.35 in case of conventional model. Data integrity verification time in case of proposed model is below 1.0 that is above 1.75 in case of conventional model. While with the existing Electronic Health Record (EHR) systems face limitations in security, privacy enforcement, and interoperability, this study addresses the lack of automated, decentralized access control mechanisms. It proposes a blockchain-powered Smart Contract model to fill these gaps and enhance healthcare data governance and trust. Little Lion Scientific -
'Angry Young Women': Evolving Forms of Female Resistance in Contemporary Bollywood Cinema
This paper explores the representation of select female characters from two contemporary Hindi filmsLipstick Under My Burkha (2016) and Gully Boy (2019)to trace the emergence of the angry young woman archetype within the broader framework of contemporary Indian cinema. The protagonists in these films confront their marginalisation across multiple intersecting axesclass, religion, and gender. Through nuanced roles, they navigate their personal and political struggles, breaking traditional norms by expressing anger, both violent and silent, as forms of resistance. This shift marks a departure from the stereotypical portrayals of women in earlier Bollywood films, where they were relegated to roles of victims, damsels in distress, or moral custodians of Indian values. The paper focuses on how female characters in recent Bollywood films deploy anger not only to challenge or subvert patriarchal structures but also as a tool for asserting new forms of agency and autonomy. This evolving depiction of anger signals a broader reconfiguration of female empowerment, where rage becomes a means of self-expression, identity formation, and personal liberation. 2025 Australian National University, Dept. of Gender, Media and Cultural Studies. All rights reserved. -
Valuation of the Capital Assets Pricing Model on the Islamic Retails Banks in Bahrain
The Gulf countries are rapidly changing after the coronavirus pandemic. It has many notable impacts on the banking sectors especially in the Islamic retail banks. This study has focused on the valuation of the Islamic retail banks in Bahrain. There are only six Islamic retail banks that follow Shariah rules and regulations. This paper consists of the monthly data from July 2016 to June 2021. The CAPM model has been applied for the valuation of Islamic retail banks and the multiple regression method run for the impact of Islamic retail banks on the Bahrain Bank index during the period. The outcomes of CAPM have identified overvalued BISB, Baraka, Salam, KHCB, and ITHMR except for the KFH, which is undervalued. The significant results have found the relationship between the six retail Islamic banks and the Bahrain banks index in Bahrain. The interpretation of t-statistics shows a substantial difference between the CAPM and actual returns of Islamic retail banks in Bahrain. The banking system will be a modern economic world which helps to create the nation. The nation will develop if society will be aware about financial literacy and analytics. This paper will help the industrialist, practitioner, brokers, promoters, and investors. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Impact of Learning Functions on Prediction of Stock Data in Neural Network
Digitization has made a vast impact on the modern society. Financial sector is one field where a huge revolution has been experienced because of digitization. Financial data especially time series data is being stored in the digital repositories where it can be used for prediction and analysis. One such data is a stock market data which is a time series data and is generated in a huge amount every second. The stock market data is of great importance as the proper analysis and prediction of data can transform the fate of the global market. Thus the companies and the individuals are looking forward for the development of the automated techniques that can predict stock market data accurately in a real time. In this regard, many researchers developed machine learning techniques such as use of neural network for prediction of stock data. The most common learning function used in neural network is sigmoid function. However, we found that there are many learning functions are available for building neural network. In this paper we are studying the impact of four different learning functions in estimating/predicting the stock value. From the experimental study we found that unipolar sigmoid learning function produced an accuracy of 95.65%, bipolar sigmoid produced an accuracy of 91.34%, tan hyperbolic equation produced an accuracy of 91.02%, and radial base equation produced an accuracy of 87.53%. Clearly, unipolar sigmoid function emerged as the best learning function to build stock data prediction model. The main reason behind its out-performance of unipolar sigmoid is its less complex structure and the 0 to 1 range. 2018 IEEE. -
The Taos and Trait Meta-mood on Transpersonal Gratitude: Tracing Their Influences
The mainstream empirical research has always viewed gratitude in its triadic form involving a typical human giver, gift, and receiver. But it is not the same in the case of transpersonal gratitude. Instead, it is directed towards abstract entities beyond self like God, their own state of being, or the cosmos. The previous literature had affirmed that a selfless attitude and better mood could determine overall gratitude. But this relation is not mainly known in the context of this newer form of gratitude. Indian young adults (N = 456) completed scales on transpersonal gratitude, trait meta-mood, and ego-grasping orientationa Taoist concept. The preliminary analysis revealed that the selfless nature was unrelated to transpersonal gratitude. Subsequently, the predictive effect of trait meta-mood on transpersonal gratitude is quantified. The findings explain the distinguishable features of the young adults' populace and positive transpersonal experiences. The need to identify groups, cultural differences, and the utility of interventions on transpersonal gratitude in the future gratitude research is emphasised. 2023, The Author(s) under exclusive licence to National Academy of Psychology (NAOP) India. -
A Comparative Study on Customer??s Expectations and Perceptions on Credit Card Services in Old and New Generation Banks
Card usages have been drastically increasing in India due to the convenience and safety provided by the issuers. At the same time, these issuers are finding it very hard to maintain and gain the market share for this particular service product. In the present scenario credit cards are playing a vital role in every one??s life. Thus, the study has attempted to find out the customer??s expectations and perceptions on credit card services in old and new generation banks. A sample of 225 respondents, who were the users of credit cards in the Bangalore city, was concentrated upon for the study. This was analyzed and tested using Factor analysis, ANOVA and Multiple Linear Regressions. The paper has found the factor the people are expecting more on card services and as well the factor where the users have perceived more. The gap was also analyzed between the expectations and perceptions of the users. The results from the study pointed out the factors the banks have to concentrate upon in order to delight its users and maintain its market share. Hence, businesses should focus on the factors analyzed so as to improve the quality of services provided on cards and to retain the customer base. Keywords: Credit Cards, Customer Expectations, Customer Perceptions,New Generation Banks and Old Generation Bank. -
Children's Well-Being in Traditional Vs. Montessori Schools. A test of Self-Determination Theory
The present study is a test of Self -Determination theory, which is well established in the field of education with a huge body of empirical evidence to support its assumptions that when the three universal psychological needs (Autonomy, Competence & Relatedness) of a child are met they will grow and function optimally leading to enhanced well-being. It is evident that Montessori philosophy is overlapping with the components of SDT. This study was conducted to examine the extent to which the three psychological needs are satisfied in Montessori schools in comparison to the Traditional schools. A purposive sample size of 80 children in elementary grades was selected from both Montessori and Traditional schools. Perceived support experienced by the children and their Well-Being was determined to establish the assumption of the SDT. The results showed that children in Montessori schools experienced greater satisfaction of needs when compared to traditional school children. However, the well-being of children from both school types didn???t vary much and the causes can be attributed to factors outside classroom. These findings have some strong implications for policy makers, educators and parents. -
Fraud Detection in Credit Card Transaction Using ANN and SVM
Digital Payment fraudulent cases have increased with the rapid growth of e-commerce. Masses use credit card payments for both online and day-to-day purchasing. Hence, payment fraud utilizes a billion-dollar business, and it is growing fast. The frauds use different patterns to make the transactions from the cardholders account, making it difficult for the organization or the users to detect fraudulent transactions. The studys principal purpose is to develop an efficient supervised learning technique to detect credit card fraudulent transactions to minimize the customers and organizations losses. The respective classification accuracy compares supervised learning techniques such as deep learning-based ANN and machine learning-based SVM models. This studys significant outcome is to find an efficient supervised learning technique with minimum computational time and maximum accuracy to identify the fraudulent act in credit card transactions to minimize the losses incurred by the consumers and banks. 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. -
Enhancing Customer Satisfaction and Sales in Retail Environments: A Personalized Augmented Reality Approach for Dynamic Product Recommendations
This article explores the potential transformative impact of integrating augmented reality (AR) technology with personalized product recommendations in the retail industry. By leveraging ARs ability to overlay digital information onto the physical world, retailers can offer tailored suggestions based on individual preferences, past purchases, and real-time contextual cues, thereby enhancing customer satisfaction and driving sales. Through a comprehensive literature review and empirical analysis, the study investigates user experience, adoption factors, and the long-term effectiveness of AR-deep learning integration in retail settings. Findings reveal significant improvements in customer satisfaction, sales performance, inventory management, and employee productivity with the implementation of AR-Deep Learning technology. Additionally, the article presents an innovative framework that seamlessly integrates AR and deep learning models, demonstrating high accuracy in object recognition, real-time interaction, and enhanced user experience across various industries. While highlighting the studys limitations and areas for further research, this article underscores the importance of customer-centric strategies and technological innovation in optimizing the retail experience and driving business growth. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Behavioral Bias as an Instrumental Factor in Investment Decision-An Empirical Analysis
Investment decisions are always complex in nature. Investment assets are volatile in nature there are less volatile, medium volatile and high volatile investment assets in the financial market. In the current study how, the behavioral biases of the investors affecting their investment decisions in the less volatile asset classes is examined using an extensive survey method among the IT professionals in the Bangalore city. The relationship between the demographic variables and behavioral biases is tested. Also, a detailed study is conducted to examine the risk-taking behavior of the investors in the less volatile assets. There are basically three type of investors on the basis of their risk-taking behavior i.e. Risk seeking, Risk Neutral and Risk averse investors. Current study reveals that investors in the less volatile asset classes are very much cautious about the risk factor and therefore they are risk averse in nature. The Author(s), under exclusive license to Springer Nature Switzerland AG. 2024.