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Evaluating the impact of a multidimensional Well-being intervention for micro, small-, and medium-sized enterprise workers in Assam, India: A single-group feasibility study
Contemporary health research underscores the importance of comprehensive well-being, particularly within occupational environments that profoundly shape individual health experiences. The Micro, Small, and Medium Enterprises (MSME) sector represents a complex workplace ecosystem characterized by multifaceted challenges that potentially compromise worker welfare. In the northeastern Indian State of Assam, MSME sector workers represent a vulnerable population that experiences distinctive professional and personal stressors. This research aims to investigate an intervention that integrates holistic well-being strategies, specifically combining traditional Indian yoga practices with Western relaxation methodologies. The proposed approach seeks to address workers physical, psychological, and social dimensions of health within the challenging MSME sector context of Assam, India. The study is a single-group feasibility trial with pre, post- and follow-up assessments to assess the impact of the well-being intervention. A sample of 35 MSME workers from Assam, India (n=35), participated in a 3-week intervention program. Baseline, postintervention, and follow-up assessments after 3 weeks of physical, psychological, and social well-being were obtained via standardized scales. To analyze the quantitative data, repeated-measures ANOVA (RMANOVA) was performed within the intervention completing participants to evaluate the interventions effect. Two-tailed tests of significance were performed. The findings of the study indicated that the intervention led to significant improvements in physical, psychological, and social well-being, with effects being sustained and further enhanced at follow-up. Repeated-measures ANOVA with GreenhouseGeisser corrections revealed significant time effects for general health (r)' = .567), musculoskeletal pain (r)' = .825), perceived social support (r)' = .678), and mental well-being (r)' = .726), all p <. 001. The participants reported reduced general health concerns, decreased musculoskeletal pain, and increased perceived social support and mental well-being from preintervention to postintervention and follow-up. The study offers empirically grounded insights into the potential transformative effect of integrated multidimensional well-being intervention. Copyright 2023 DOI Foundation. The content of this site is licensed under a Creative Commons Attribution 4.0 International License. -
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EVALUATING THE ELEMENTS IN THE RECREATIONAL SPACE OF AN INSTITUTION
The concept of 'Recreation' justifies the human need for satisfaction, leisure, and a state of pleasure. The elements involved in a recreational space impact the activities of the user in that space. Recreational spaces act as the in-between sojourns for formal pedagogy or andragogy. Spaces of recreation are essential, especially in educational institutions, where students spend most of their time. Public, semi-public, and private spaces are all included in the institutional design, with a large percentage used by students. Open public spaces, including recreational places, are measured in terms of their physical characteristics and connections to nature. The components of a recreational area influence the activities that users engage in there. This paper seeks to list and assess the many components that are present in a recreational space. This study will evaluate those elements and their types. Informal outdoor areas or other breakout areas promote interaction and provide the students with refreshments and leisure. The focus of this paper is to draw out the quality of leisure space synonymous with a productive environment for the student, where they feel rejuvenated. Five recreational spaces of CHRIST University were studied, and the elements that combine to form this place were also observed. A survey among the students who are frequent users of these spaces was conducted, and their responses were evaluated. The elements that majorly help students go to a place were assessed, and the element's significant role was concluded. The result of this study to design professionals is to understand the need to incorporate recreational spaces while designing an educational institution and design a student-oriented space. ZEMCH Network. -
Evaluating the electrochemical performance of single and multiple heteroatom doped carbon black from waste tires for supercapacitor application
With the growing emphasis laid on the research related to energy storage systems, the need for cost-effective and efficient materials is quintessential. The present work reports a comprehensive study and a promising strategy to enhance the electrochemical behaviour of Carbon Black derived from waste tires by the incorporation of heteroatoms such as Nitrogen and Sulfur into the system. The study investigates the electrochemical performance of Carbon Black with single doping, and further examines the enhanced performance with co-doping. While the Nitrogen-doped Carbon Black exhibits a specific capacitance of 97.63F/g, the Sulfur doped Carbon Black exhibits 141.8F/g and the co-doped Carbon Black exhibits an enhanced specific capacitance of 233F/g at a current density of 1 A/g in the two-electrode system. A significant improvement in the specific surface area is achieved in the materials with post-doping techniques. Furthermore, the co-doped Carbon Black provides superior electrochemical behaviour with sustained energy density of 30Wh/kg even at a higher power density of 5.6kW/kg with an improved cyclic stability of 91% over 5000 cycles. Thus, effective valorization of Carbon Black recovered from waste tires enables the development of efficient and affordable electrode material for the fabrication of supercapacitors. 2025 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies. -
Evaluating the effectiveness of virtual reality-based rehabilitation programs for post-injury recovery in adolescent athletes: a mixed-methods study; [Evaluaci de la eficacia de los programas de rehabilitaci basados en realidad virtual para la recuperaci de deportistas adolescentes tras lesiones: un estudio de modos mixtos]
Introduction: the importance of post-injury rehabilitation for teenage athletes demands innovative methods because traditional practices fail to sustain student athlete participation. VR-based rehabilitation creates interactive recovery programs which might advance physical healing together with mental drive. Objective: the research investigates how well VR-based rehabilitation works against traditional approaches for both physical healing and psychological involvement in adolescent athletes. Methodology: sixty adolescent athletes (aged 1318) received their rehabilitation through random assignment into two groups: one involved traditional approaches while the other received VR-based rehabilitation. The research measured recovery outcomes at three time points: baseline, 4 weeks and 8 weeks. The measured outcomes included range of motion (ROM), muscle strength, return to sport (RTS) time and pain perception. The VR group members shared their experiences through semi-structured interview methods. Results: the subjects in the VR group achieved greater improvements in ROM (p = 0.02) and muscle strength (p = 0.03) and RTS time (p = 0.01). People who used VR reported stronger motivation and engagement although these benefits brought increased worry about re-injuring their knee. Subject participants achieved better results in their rehabilitation by using immersive VR interventions. Conclusions: virtual reality-based rehabilitation enables adolescent athletes to restore physical well-being as well as emotional well-being. The interactive features of this approach improve patient commitment which accelerates their recovery time. Future investigations need to analyze extended advantages and expanded medical applications within sports medicine. 2025 Federacion Espanola de Docentes de Educacion Fisica. All rights reserved. -
Evaluating the Effectiveness of GraphSAGE with Reinforcement Learning in Suicide Risk Prediction
Suicide is considered to be a major mental health issue that has affected most individuals worldwide. According to World Health Organization, it shows the rise of suicidal rates among students has increased drastically. This vulnerability shows the rising need to encounter this issue with immediate effect. Therefore, proper detection methods have to be incorporated so that we can reduce the number of suicidal rates. Many computational models were implemented to address this issue. This study was conducted to compare various algorithms such as traditional machine learning models random forest and also various deep learning models like GraphSAGE, Graph Convolutional Network, Convolutional Neural Network, and Convolutional Neural Network with Long Short Term Memory with the proposed GraphSAGE Reinforcement Learning. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Evaluating the Effectiveness of a Facial Recognition-Based Attendance Management System in a Real-World Setting
Face recognition technology has been extensively used in multiple verticals of security, surveillance, and human-computer interaction. Conventional techniques including manual sign-ins, identity cards, or biometric verification have been used by traditional attendance systems. Face recognition systems have, however, become a popular way to track attendance, thanks to developments in computer vision and machine learning. The construction of an attendance registration application is the main topic of this research study, which also offers a thorough overview of facial recognition attendance systems. This study seeks to provide light on the benefits, drawbacks, and potential applications of these fast-developing technologies. Face recognition technology may be integrated into attendance systems to increase productivity, accuracy, and user comfort. However, issues like privacy worries and technological constraints must be resolved. With predicted future improvements in machine learning algorithms and hardware capabilities, face recognition attendance systems look to have a bright future. This research article adds to a deeper understanding and successful application of facial recognition technology in attendance systems by examining these features. 2023 IEEE. -
Evaluating the effect of different ligands on the supercapacitance and hydrogen evolution reaction studies of Zn-Co MOF
Metal-Organic Frameworks (MOFs) have recently attracted a lot of interest because of their potential uses in energy storage and catalysis. In this study, we investigate the impact of various ligands on the electrochemical performance of Zn-Co MOFs for both energy storage and hydrogen evolution reaction (HER) studies. Specifically, Zn-Co MOFs are synthesized using different ligands, and their structural and electrochemical properties are characterized by a range of analytical techniques. 2,5-dihydroxybenzoic acid (DBA) and benzene-1,2,4,5-tetracarboxylic acid (BTC) are employed due to their distinct structural features and potential effects on MOF performance. Subsequently, electrochemical studies are conducted to assess the supercapattery performance and HER activity of these MOFs. The specific capacitance and overpotential value at 10 mA/cm2 of Zn-Co/DBA MOF is observed to be 1775.3 F/g and 186 mV, whereas that of Zn-Co/BTC MOF is found to be 136.6 F/g and 279 mV. The MOF synthesis using DBA as a ligand is more effective for energy-related applications. This study aims to report a multifunctional MOF composite for energy and environmental applications with better efficiency than other reported systems. Our findings provide insights into how the choice of ligand influences the structural properties and electrochemical behavior of Zn-Co MOFs, shedding light on the potential of these MOFs as versatile materials for energy storage and HER applications. 2024 Elsevier B.V. -
Evaluating the Categorical Exclusion of Khasi Women from Inheritance and Property Rights : A Case of East Khasi Hills
Customary laws govern inheritance among many tribal communities that fall within the ambit of the fifth and sixth schedules of the Indian Constitution. Under this papers scope, we shall look at the Khasi community hailing from the state of Meghalaya which is a matrilineal community. Where the Khasis draw their lineage from their mothers, there is a misnomer that women inherit and own the entire property. In light of the abovementioned background, the paper makes an analytical study of the customary inheritance rights of Khasi women, the nature of resource ownership and attempts to understand the grounds behind the claims of gender preference in the existing matrilineal system practised by the Khasis of Meghalaya. We also look at the intersection of gender and matrilineal system of inheritance in the Khasi community, the dispute between customs and legislations and examine whether there exists a need for codification. The paper also discusses the findings of the survey and focus group discussions including 90 Khasi women from East Khasi Hills and their growing consensus on equal inheritance rights but resistance towards statutory laws to govern their lives. JYOTI SINGH AND KAJORI BHATNAGAR, 2024. -
Evaluating Technostress: Work-Life Balance and Well-Being in Varied Work Contexts
In the contemporary digital landscape, the phenomenon of technostress, defined as stress induced by technology usage, has emerged as a crucial factor influencing work-life balance and employee well-being. This study will explore the impact of technostress in varied work modes such as traditional office-based, remote, and hybrid models. Employing a quantitative approach, the researcher conducted surveys on a representative sample of employees across multiple industries. The results indicate that technostress adversely influences work-life balance and well-being, and the difference in various work modes is observable. Further, it has also been observed that there are significant differences in technostress and well-being concerning the various work modes; working from home comes out to be a positive option, which is related to lesser levels of technostress and higher outcomes of well-being. The present study shows how organisational interventions may be implemented in mitigating technostress-inducing effects: induction of digital literacy, instillation of appropriate communication policies, and embedding of supportive work culture. Essentially, an intervention could help organisations improve well-being 0f employees and achieve better work-life balance in a digital environment. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Evaluating Social Priorities in Environmental Social Governance for the BFSI Sector: A Fuzzy Analytic Hierarchy Process Perspective
As global financial systems evolve, the Banking, Financial Services, and Insurance (BFSI) sector faces increasing pressure to balance financial performance with Environmental, Social, and Governance (ESG) obligations. However, integrating social factors such as employee welfare, community engagement, customer satisfaction, and diversity and inclusion remains challenging due to their subjective and often intangible nature. This study addresses this issue by applying the Fuzzy Analytic Hierarchy Process (Fuzzy AHP) to evaluate and prioritize social factors within the ESG framework. The Fuzzy AHP method, which combines traditional AHP with fuzzy logic to manage uncertainty in expert judgments, was used to gather and analyze input from BFSI sector experts. The study assessed the relative importance of social factors through structured pairwise comparisons, providing a clear hierarchy of priorities for BFSI institutions. The results reveal that employee welfare and customer satisfaction emerged as the most critical social aspects, reflecting stakeholder expectations and regulatory pressures. By focusing on these key areas, BFSI institutions can enhance their ESG performance and meet sustainability goals. These findings offer actionable insights for decision-makers in the BFSI sector, allowing them to better allocate resources to social initiatives that not only satisfy regulatory requirements but also contribute to long-term business value and societal impact. This study underscores the importance of prioritizing social factors in sustainable strategies and provides a robust framework for navigating the complexities of ESG integration. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Evaluating social media content's effect on consumer engagement in the context of digital marketing
The advancement of social media platforms in promoting consumer participation in brand development and sustainable consumption has been substantial. Social media's popularity has increased significantly in the twenty-first century. To enhance sales performance, enterprises consistently seek novel strategies to integrate these platforms into their promotional initiatives. Social media functions as a platform for networking and communication; consequently, organizations must imbue their brands with personality to connect with consumers. Despite extensive academic research on corporate social media marketing techniques, the influence of these activities on consumer purchase choices remains largely unexplored. Organizations have recently embraced influencer marketing as a tactic to promote and publicize their content by leveraging the support of influential individuals. The growing frequency of product endorsements on social media highlights the importance of understanding the impact that these influencers have on customers. This research aims to analyze the influence of social media content and its characteristics on consumer engagement in the digital domain. Additionally, this study will serve as a foundation for future investigations in this area. The insights regarding the content elements of social media marketing that foster consumer engagement were contributed by seventy-five unique social media users. 2025 by the authors; licensee Learning Gate. -
Evaluating Sentiment Classification Models for Bollywood Movies
The rise in popularity of Bollywood cinema, fueled by streaming services such as Netflix and Amazon Prime, has increased the global availability of Hindi-language films. This study investigates the sentiment analysis of Bollywood movie reviews using a dataset of 1,698 movies released between 2005 and 2017. The research examines three main dimensions: confusion matrix, evaluation metric, and random forest model. The results highlight the model's ability to accurately predict emotions, especially when detecting neutral and positive emotions. Challenges in identifying negative emotions persist. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Evaluating prolonged corrosion inhibition performance of benzyltributylammonium tetrachloroaluminate ionic liquid using electrochemical analysis and Monte Carlo simulation
Corrosion inhibition performance of a newly synthesized ionic liquid Benzyltributylammonium tetrachloroaluminate [BTBA]+[AlCl4]?on carbon steel has been studied using electrochemical impedance and noise analysis in 2 N HCl medium. The synthesized product was characterized by ATR-FTIR and1H NMR spectroscopic studies. The investigation revealed that the synthesized ionic liquid, [BTBA]+[AlCl4]?showed a remarkable noise and charge transfer resistance against corrosion. The adsorption behaviour of [BTBA]+[AlCl4]- on metal surface was found to follow Langmuir adsorption isotherm. The inhibition efficiency is measured as a function of immersion time and exhibited prolonged protection against acidic corrosion. Results derived from UVVis spectra explained the complex formation between the metal surface and ionic liquid in acid medium. SEM/EDAX has been used to examine the surface protection offered by the ionic liquid. [BTBA]+[AlCl4]?ionic liquid exhibited good corrosion inhibitor property with an efficiency of 97% at the optimum concentration. Quantum chemical analysis and molecular simulation studies were performed to support the experimental data. 2019 Elsevier B.V. -
Evaluating Generalization and Robustness of U-Net Based Image Steganography
This paper investigates the effectiveness and generalization ability of U-Net based image steganography models across multiple datasets, with comparisons to the classical Least Significant Bit (LSB) substitution method. Models were trained on STL-10, CIFAR-10, and Stanford Cars datasets and evaluated both in-distribution and on out-of-distribution internet images. Results show that the STL-10 model consistently achieved the best trade-off between imperceptibility and recovery quality, while the CIFAR-10 model failed to generalize due to its low resolution and limited diversity. Baseline experiments confirmed that LSB achieves extremely high PSNR and SSIM at low payloads, but suffers sharp increases in Bit Error Rate (BER) under higher payloads or even mild distortions such as JPEG compression and Gaussian noise. By contrast, the U-Net model provided more stable recovery and greater robustness, highlighting the advantages of learned feature embeddings over handcrafted substitution. These findings underscore the importance of dataset diversity and robustness testing in developing practical steganographic systems for real-world deployment. 2025 IEEE. -
Evaluating forces associated with sentient drivers over the purchase intention of organic food products
The study proposes to find out the factors which influence awareness among the consumers towards purchasing organic food product. The study is based on primary data by using tools Chi-square test, Cronbach alpha, KMO, and Bartlett's test, ANOVA, regression, correlation, and cross-tabulation. The study found that awareness driver's nutritional information, price, certification, brand name, and logos have an essential influence on the purchase intention of the product of organic food. However, labeling and food standards do not show a noteworthy rapport between labeling and organic food products' purchase plans. The core commitment and flow to explore are to analyze purchasers with respect to organic guarantee systems (accreditation, guidelines, logo, imprints, and confirmation) so we can distinguish the genuine organic products. The independent factors of awareness like organic buying preference and buying frequency, have a significant influence on the purchase intention of organic food. The research provided evidence of consumer awareness and purchase intention of organic food that would help the organic food industry to promote their products according to the attribute of customers. 2020 Asian Economic and Social Society. All rights reserved. -
Evaluating Energy Consumption Patterns in a Smart Grid with Data Analytics Models
With the rapid pace of technological advancement, it is a well established fact that in todays era, economical and industrial development go hand in hand with the growth in technology. Today, massive amounts of data are generated everyday and are only growing exponentially. The collected data, whether structured or unstructured, could prove to be very beneficial in terms of improving operational efficiency by analyzing and extracting valuable information to find patterns to optimize asset utilization and improve asset intelligence. Big data analytics can very well contribute to the evolution of the digital electrical power industry. The objective of this paper is to explore how smart grid technology can be enhanced by leveraging big data analytics. Different predictive models are used for the purpose. Among them, decision tree model outperformed others recording a training and tetsing accuracy of 94.4% and 92.7% respectively while noting a least execution latency of only 4.3 seconds. 2023 IEEE. -
Evaluating Building Damage Classification Accuracy: A Benchmarking Study of UNet
Building damage classification must be done accurately and quickly in order to support disaster response and recovery activities. Deep learning models, particularly U-Net, have demonstrated strong potential in automating damage assessment from satellite and aerial imagery. This study benchmarks the accuracy of U-Net in classifying building damage across multiple datasets, evaluating its performance against ground truth labels. Key factors such as data preprocessing, augmentation techniques, and model variations are analyzed to determine their impact on classification accuracy. The results provide insights into the strengths and limitations of variations in U-Net for damage assessment, highlighting areas for improvement and future research directions 2025 IEEE. -
Evaluating Allocations of Opportunities
This paper provides a robust criterion for comparing lists of probability distributionsinterpreted as allocations of opportunitiesfaced by different social groups. We axiomatically argue in favor of comparing those lists of probability distributions on the basis of a uniformamong groupsvaluation of their expected utility. We identify an empirically implementable criterion for comparing allocations of opportunities that coincides with the unanimity of all such uniform valuations of expected utility that exhibit aversion to inequality of opportunity. We illustrate our criterion by evaluating allocations of educational opportunities among castes and genders in 14 Indian states. 2025 The Author(s). International Economic Review published by Wiley Periodicals LLC on behalf of The Economics Department of the University of Pennsylvania and the University of Osaka Institute of Social and Economic Research Association. -
Evaluate Machine Learning Techniques for Early Disease of Cardiovascular Disease
Cardiovascular diseases are one of the major causes of death around the world, and their early detection is critical for effective intervention. The paper presents a systematic review of machine learning techniques used for the early prediction of cardiovascular diseases, focusing on studies carried out between 2019 and 2024. Widely used models considered in the review include Logistic Regression, Support Vector Machines, Random Forest, K-Nearest Neighbors, Gradient Boosting, and hybrid ensemble methods with the aim of ascertaining predictive accuracy, interpretability, and clinical relevance. In most of the reviewed studies, ensemble and Random Forest models attained the highest accuracies of 90% - 98%, while Gradient Boosting and SVMs were mostly above 90% in balanced datasets. Logistic Regression had a moderate accuracy of 85%-91% but remained the most interpretable, while KNN established the lowest performance of 80%-86%. Despite the promising strides, there are a number of limitations, such as imbalance in datasets, limited external validation, and small benchmark datasets, that are limiting general application in health. This systematic review highlights strengths and weaknesses of the contemporary machine learning approaches and makes it evident that clinically validated, interpretable, and generalizable models should be developed in order to assist real-world medical decision-making. 2025 IEEE.