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DECODING INTENTIONS TO PURCHASE ORGANIC FOOD PRODUCTS IN AN EMERGING ECONOMY VIA ARTIFICIAL NEURAL NETWORKS
Purpose. This study investigates the factors influencing consumers intentions to purchase organic food products in an emerging economy. It addresses the knowledge gap regarding the slower growth of the organic food market in these regions despite the global trend toward environmental sustainability. Methodology / approach. A survey approach involving 350 participants was used. Structural equation modeling (SEM) with SmartPLS 4 and Artificial Neural Network (ANN) with IBM SPSS 28 were used to analyse the impact of awareness of need, personal norms, environmental concern, and health consciousness on the intention to purchase organic food products. Results. The study found significant positive influences of awareness of need, personal norms, environmental concern, and health consciousness on the intention to purchase organic food products, explaining 63.1 % of the variance. Both the analysis approaches (PLS-SEM & ANN) revealed that, health consciousness, followed by awareness of need, emerged as the most important factor related to the intention to purchase organic food products. The results highlight the importance of awareness and personal values in driving pro-environmental behaviour. Originality / scientific novelty. This research offers essential insights into the determinants of organic food purchase intentions in an emerging economy. It emphasises the significance of awareness and personal values in fostering sustainable consumption behaviour, addressing a less explored area in existing literature. Practical value / implications. The findings have important implications for policymakers and marketers. Strategies focused on consumer education about the benefits of organic food can enhance awareness and appeal. Understanding core psychological needs and beliefs that shape consumer motivations can guide the development of effective marketing strategies. The study highlights the strong environmental consciousness among consumers and their desire to protect the environment. 2024, Institute of Eastern European Research and Consulting. All rights reserved. -
Evolution of sustainable business models: A study of past, present, and future
This chapter explores the transformative impact of sustainable business models, tracing their evolution across past, present, and future. It highlights early efforts to balance profitability with environmental and social responsibility and examines how digital technologies-such as AI, blockchain, and IoT-have accelerated this shift. These technologies enhance resource efficiency, reduce waste, and improve supply chain transparency, demonstrated through case studies of leading firms. Emerging trends like circular economies, stakeholder capitalism, and regulatory innovation are discussed alongside challenges like digital equity, data privacy, and infrastructure impacts. The chapter emphasizes collaboration among stakeholders to align digitalization with sustainability, fostering resilient, equitable, and environmentally responsible practices. 2025 by IGI Global Scientific Publishing. All rights reserved. -
Machine Learning Algorithms for Stroke Risk Prediction Leveraging on Explainable Artificial Intelligence Techniques (XAI)
Stroke poses a significant global health challenge, contributing to widespread mortality and disability. Identifying predictors of stroke risk is crucial for enabling timely interventions, thereby reducing the increasing impact of strokes. This research addresses this imperative by employing Explainable Artificial Intelligence (XAI) techniques to pinpoint stroke risk predictors. To bridge existing gaps, six machine learning models were assessed using key performance metrics. Utilising the Synthetic Minority Over-sampling Technique (SMOTE) to minimize the impact of the imbalanced nature of the dataset used in this research, the Random Forest algorithm emerged as the most effective among the algorithms with an accuracy of 94.5%, AUC-ROC of 0.95, recall of 0.96, precision of 0.93, and an F1 score of 0.95. This study explored the interpretation of these algorithms and results using Local Interpretable Model-agnostic Explanations (LIME) and Explain Like I'm Five (ELI5). With the interpretations, healthcare providers can gain insight into patients' stroke risk predictions. Key stroke risk factors highlighted by the study include Age, Marital Status, Glucose Level, Body Mass Index, Work Type, Heart Disease, and Gender. This research significantly contributes to healthcare and healthcare informatics by providing insights that can enhance strategies for stroke prevention and management, ultimately leading to improved patient care. The identified predictors offer valuable information for healthcare professionals to develop targeted interventions, fostering a proactive approach to mitigating the impact of strokes on individuals and the healthcare system. 2024 IEEE. -
Perceived organizational support and its influence on employee engagement in informatiom technology organizations
The effective management of Human Resources (HR) or People Resources of an organization through proactive and futuristic design of HR policies is pivotal for an organization s growth. Many of the current challenges faced by businesses globally are owing to industry slowdowns, loss of clientele, lower margins, stiff competition for skilled resources, and high attrition. A diverse set of workforce belonging to different generations having aspirations and expectations galore has entered the corporate sector. This diversity and advent of the generational workforce needs to be taken into consideration while designing HR policies, Reward systems and benefits; as also the factors such as changing family structures and the emergence of a gender equal workforce. Human Resources professionals and organizations are hence tasked with the responsibility of employing different ways and means to fine-tune existing HR strategies and develop new one s that could potentially increase Employee Engagement (EE) and reduce the Intention the Quit (ITQ) among the Information Technology (IT) workforce. The prime emphasis of the current study has been on assessing appropriate HR strategies that can increase engagement and retention at a minimal or no cost to the organization. This study leveraged upon organizational support and care variables such as Perceived Organizational Support (POS), Perceived Supervisor Support (PSS), and Flexible Work Options (FWO) in increasing Employee Engagement (EE) and reducing the Intention to Quit (ITQ). This study goes on to prove that by leveraging upon organizational support and care variables such as POS, PSS, and FWO; organizations can increase the level of engagement of employees, as well as improve employee retention. -
The relationship of workplace flexibility to employee engagement among information technology employees in India
Historically, organizations have been provisioning flexible work arrangement (FWA) options in the workplace to help employees achieve a balance between work obligations and private obligations. We explore the utilization of FWA offerings in the Indian information technology (IT) industry and its relation to employee engagement (EE). Employees working in IT organizations in Bengaluru, India, were approached and data were obtained from 504 participants. The study found that availability of FWA options coincided with their utilization. Disconcertingly, only 7 per cent of the employees were highly engaged, 51 per cent were neither engaged nor disengaged and 41 per cent of the employees were disengaged with their current organization. FWAs were positively related to EE. We find congruence that FWA options lead to better EE warranting further exploration that can guide FWA policies. 2018 SAGE Publications India Private Limited. -
UWB Radar based Respiratory Rate Detection for Driver
Continuous health monitoring and the early detection of physiological abnormalities play an important role in vehicular environments. In particular, respiration rate and heart rate estimations are crucial for preventing accidents caused by sudden health impairments to the driver. Impulse radio ultra-wideband (IR-UWB) radar provides an effective solution for long-duration and non-invasive respiration rate monitoring. UWB systems offer sub-nanosecond time resolution while operating at low transmitted power levels, making them suitable for continuous monitoring of the human body. UWB pulses possess strong penetration capability, allowing signals to pass through obstacles such as clothing and vehicle seat covers. This paper presents an IR-UWB radar-based framework for estimating respiration rate using a seat-integrated monostatic radar configuration, where UWB signals propagate through the thoracic region from the posterior side toward the lung. Respiration-induced variations in lung geometry and dielectric properties under different physiological conditions result in corresponding changes in the reflected pulses, which can be analysed for respiration monitoring. Furthermore, variations in the antenna reflection coefficient (S11) exhibit noticeable differences under different lung conditions, from which respiration waveforms can be derived. The extracted respiration-related signal is subsequently transformed into the frequency domain using the Fast Fourier transform (FFT), which enables the accurate estimation of the respiration rate. In this paper, the UWB signal for radar communication complies the Federal Communications Commission (FCC) spectral mask from 3.1 - 10.6 GHz to ensure human safety. The results presented in this paper confirm that the proposed UWB Gaussian seventh-derivative IR-UWB Radar combined with FFT-based processing enables reliable respiration rate estimation and is well-suited for continuous in-seat vital sign monitoring in driving environments. 2026 IEEE. -
UWB Monostatic RADAR-Based Heartbeat Monitoring in an Autonomous Vehicle
Monitoring a driver's physiological state in real time is vital for enhancing road safety by detecting fatigue, medical emergencies, and enabling future health-intervention systems in autonomous vehicles. Ultra-Wideband (UWB) impulse radio monostatic Radar emerges as an attractive alternative due to its ability to perform non-invasive and highly sensitive detection of vital signs, including respiration and heart rate, through obstacles such as clothing or car seats. This paper presents a radar setup located in the seat, which propagates a UWB signal through human tissues from the back side of the driver up to the heart location. The transmitted and reflected UWB signal and antenna reflection coefficient S11 parameter are analysed to detect the heart rate for a heartbeat-induced heart model. Various UWB pulse types and their spectral characteristics are analysed to ensure efficient energy transmission within the FCC mask safety constraints. Time-domain analysis of the transmitted and received pulses reveals clear heartbeat analysis with minimal distortion, achieving accurate heart detection rates. Reflected-pulse analysis shows clear differences in amplitude between systole and diastole for normal and abnormal heart-radius conditions, allowing reliable detection of heart states. Time-of-flight and range estimation help in tracking the heart-wall movement accurately. FFT-based analysis of the time-varying S11 parameter estimates the heart rate, confirming precise non-invasive heartbeat detection through the thorax. . This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) -
Optimizing Phishing Email Classification Through Scalable Feature Extraction Using MapReduce
A bag of features (BOF) may be made using either map reduction techniques or a combination of a thesaurus and domain knowledge. This research presents the BOFMR (Bag of Features using MapReduce) and BOFWT (Bag of Features with Weighted Terms) algorithms, a scalable and efficient technique for processing large email datasets and generating feature vectors based on pre-defined characteristics. The outcomes from using both BOFs on identical datasets are compared. The algorithm leverages the parallel processing capabilities of the MapReduce framework to handle the extensive data, ensuring performance and scalability. When creating a bag of words from a training dataset, the BOFMR technique is useful. The map-reduce technique will help to create a bag of features faster even in case of a larger chunk of data. In this experiment, as data size was limited, the performance of map reduce was not measured. In another BOFWT approach, the building of BOF with domain knowledge by using the word thesaurus was a challenge. The experimental result shows that the results of BOFWT are nearer to the output of BOFMR, and both algorithms show the highest accuracy among other methods. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Indian Parents Vocational Inferences of Play and Communal Strategies to Regulate the Adverse Effects of Childrens Excessive Screen Time
Background: Since time immemorial, childrens intrinsic desire to immerse themselves in amusing, autonomous activities in vast, open spaces has engendered their holistic development through play. However, the steady decline in playgrounds has compelled children to seek solace in a digital landscape, which offers them a plethora of innovative play opportunities that pushes them away from the outdoors and pull them indoors. This infiltration of technology in urban households enhance childrens propensity to engage in media-centric activities due to their proclivity toward electronic devices. Consequently, the passive entertainment afforded by the dominance of digital culture predisposes children to inertia, insomnia, sedentary lifestyle diseases and public health issues like screen dependency disorders (SDD) and gaming disorders, which are officially recognized by organizations like the World Health Organization (WHO). This necessitates a conscious, synergetic effort by health practitioners, researchers and policy makers to ensure that children have access to safe and affordable spaces for active, outdoor play to counter the adverse effects of excessive screen time (EST). Methods: Therefore, this qualitative research aims at understanding how urban parents perspectives of play are governed by their sociocultural milieu and their respective professions by expounding their outlook on the relevance of play in a digital era. It also delineates the pivotal role of parents in curtailing the mediating role of discretionary screen time (DST) over childrens physical inactivity through semi-structured interviews of 13 mother-father dyads who reside in the metropolitan city of Bengaluru in Karnataka, India. Results and Conclusions: Findings from the directed content analysis revealed parents initiation of healthy movement behaviors during childrens formative years by employing mediation tactics and role modeling healthy screen habits like digital detox to regulate the aftermath of prolonged screen time on childrens psychosocial development 2025 The Author(s). This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage). -
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Deconstructing Feminist Perspectives on Work: Insights from Uchoi Indigenous Tribal Women's Worldview in Northeast India
Womens work is commonly conceptualized through an economic lens that prioritizes the valuation of paid and unpaid labor and assesses womens status and empowerment primarily in monetary terms. Such approaches are limiting as a means of understanding the lived realities of Indigenous Tribal women. In contrast, this article adopts a sociological and anthropological perspective to examine the multifaceted meanings of work among Uchoi Indigenous Tribal women in Tripura, Northeast India. The Uchoi community is a recognized Scheduled Tribe (ST) under Article 342 of the Constitution of India. By situating work within its sociocultural, ecological, and communal contexts, this article demonstrates how work shapes identity, respect, and social standing within the Uchoi community beyond mere economic valuation. Drawing on nine months of ethnographic fieldwork, this study incorporates womens memories, lived experiences, and narratives to challenge dominant feminist interpretations of work that remain largely universalist and focused on monetary value. Methodologically, the research employs elderly visitation, conversational methods, and engaged observation. The findings reveal a contextual worldview regarding work among Uchoi women, expressed through practices such as marriage by trial and service, weaving as both work and cultural expression, relational and reciprocal forms of work, collective labour in jhum cultivation, and the preservation of Indigenous knowledge systems. These practices are rooted in Indigenous epistemologies encompassing arts and crafts, weaving traditions, ecological knowledge, and communal labor arrangements. The article emphasizes the necessity of recognizing these distinct contextual experiences, cosmologies, and epistemologies of Indigenous Tribal women in theorizing work and empowerment. It contributes to feminist scholarship by reconceptualizing work through the framework of Indigenous feminism, which foregrounds Indigenous worldviews as embedded in context-specific practices. The paper argues for moving beyond universalized frameworks of work towards an approach that acknowledges the socially embedded, relational, and culturally situated dimensions shaping Indigenous womens work and lived experiences. 2026 Bridgewater State College. All rights reserved. -
High trait emotional intelligence lessens the impact of the dark triad on trolling propensity
Trolling is a pervasive form of online aggression, often rooted in adverse personality traits and amplified by the disinhibiting affordances of social media. The current study applies the I3 aggression model to examine the role of Dark Triad (DT) traits as impelling factors that increase trolling propensity, and Trait Emotional Intelligence (TEI) as an inhibiting factor that could constrain such behavior. The study also investigates whether TEI buffers the impact of DT traits on trolling and whether age further moderates this moderating effect. A total of 427 adult social media users (Mage = 22.71 years, SD = 3.71) participated in the study. Correlation analysis indicated that all three DT traits were positively correlated with trolling propensity, whereas TEI showed a negative association. Hierarchical regressions demonstrated that all three DT traits uniquely and positively predicted trolling. Machiavellianism and narcissism emerged as robust predictors even after accounting for shared variance with more callous traits such as psychopathy. TEI remained a significant negative predictor, and higher TEI levels attenuated the influence of each DT trait on trolling. Three-way interactions further suggested that the protective role of TEI in the relationship between psychopathy and trolling became stronger with age. Still, this pattern did not generalize to Machiavellianism or narcissism. Although three-way interactions were modest and inconsistent across traits, they underscore a concerning developmental trend as trolling appears to be most pronounced when dark traits surface during the emotionally formative period of emerging adulthood. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2026. -
Elemental abundances in the interstellar medium
One method to investigate the chemical composition of the interstellar medium (ISM) and interstellar dust grains is to conduct interstellar elemental depletion studies, especially of highly abundant species. The role refractory element, silicon (Si) in extinction is not clearly understood and the distribution and evolution of moderately volatile sulfur (S) in the ISM is still an open problem. The key motivation of the work is to investigatethe chemical composition of ISM of our Galaxy, and the formation, processing and distribution of interstellar dust in its different environments, mainly focusing on silicon and sulfur abundances, both in gas and dust.In the work outlined in this thesis, I will be describing the gas and dust phase abundances of Si and S in the interstellar medium using archival observations, and their probable role in the observed extinction. In this work, we also have measured the column density of S II along 9 Galactic sight lines using archival high-resolution observations from the Space Telescope Imaging Spectrograph and determined the abundances of S in both gas and dust phases. Using Archival spectral data towards 131 target stars in the Galaxy, interstellar Si abundances and depletion along those lines of sight has been surveyed. Oscillator strength correction has been performed to account for its improvements, using most recent values. This is an extensive survey done using a much larger data sample compared to previous investigations, but it substantiate the majority of the findings, which show that Si depletion is linked to both the average hydrogen density (n (H)) and the fraction of molecular hydrogen (f(H2)) along the lines of sight. -
Elemental Abundances in the Interstellar Medium
One method to investigate the chemical composition of the interstellar medium (ISM) and interstellar dust grains is to conduct interstellar elemental depletion studies, especially of highly abundant species. The role refractory element, silicon (Si) in extinction is not clearly understood and the distribution and evolution of moderately volatile sulfur (S) in the ISM is still an open problem. The key motivation of the work is to investigate the chemical composition of ISM of our Galaxy, and the formation, processing and distribution of interstellar dust in its different environments, mainly focusing on silicon and sulfur abundances, both in gas and dust. In the work outlined in this thesis, I will be describing the gas and dust phase abundances of Si and S in the interstellar medium using archival observations, and their probable role in the observed extinction. In this work, we also have measured the column density of S II along 9 Galactic sight lines using archival high-resolution observations from the Space Telescope Imaging Spectrograph and determined the abundances of S in both gas and dust phases. Using Archival spectral data towards 131 target stars in the Galaxy, interstellar Si abundances and depletion along those lines of sight has been surveyed. Oscillator strength correction has been performed to account for its improvements, using most recent values. This is an extensive survey done using a much larger data sample compared to previous investigations, but it substantiate the majority of the findings, which show that Si depletion is linked to both the average hydrogen density (n (H)) and the fraction of molecular hydrogen (f(H2)) along the lines of sight. Using this data, the distribution of Si and the variation of dust attributes with Si abundances also has been investigated and found that the linear component of the extinction curve is unrelated to depletion of silicon. -
Evaluation of national rural health mission in Bangalore rural district /
Indian Journal Of Applied Research, Vol.5, Issue 6, pp.836-838, ISSN No: 2249-555X. -
Value added tax and its impact on revenue generation in India /
Scholedge International Journal Of Multidisciplinary And Allied Studies, Vol.2, Issue 8, pp.43-50, ISSN No: 2394-336X. -
Hard Money vs. Soft Money and the Battle for Higher Education's Future: How Universities Can Leverage Both to Survive and Thrive
In the ever-evolving landscape of higher education, the debate between hard money and soft money is more than just a financial discussion it's a survival strategy. Hard money, with its stability, keeps universities running, while soft money fuels innovation and growth. But how can institutions balance the two to thrive in an era of shrinking budgets and rising demands? This chapter explores the critical differences between hard and soft money, their roles in shaping higher education, and actionable strategies for achieving financial resilience. Through real-world case studies and global insights, we uncover how universities can diversify funding, manage risks, and secure their future. Whether you're a policymaker, administrator, or educator, this chapter offers a roadmap to navigating the complex financial challenges of higher education. Don't miss this essential guide to financial survival in a changing world!. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Machine Learning Approaches for Predicting Player Position in Football
This paper presents a comparative study of different machine learning algorithms, including K- Nearest Neighbor (KNN), Random Forest, Gradient Boosting, XG Boost, Support Vector Machine, Voting Classifier and Logistic regression to develop a Player Position Prediction System in football. Initially, the study utilized a modified dataset containing 18434 records, focusing on simplicity for ease of analysis. Through experimentation, it was found that Logistic regression provided a strong balance between efficiency and scalability, making them ideal for rapid decision-making in environments with limited features. In contrast, Support Vector Machine, XGboost and voting classifier excelled in offering more detailed, feature-rich analyses, which are particularly beneficial when handling complex data. Building on these findings, the plan is to apply the same algorithms to improve the system's overall accuracy and efficiency. By leveraging the strengths of each approach, the aim is to create a scalable, effective recommendation system tailored for real-world applications in the car industry. This study highlights the importance of choosing the right algorithm based on the tradeoffs between computational efficiency and the depth of analysis required in recommendation systems. 2025 IEEE. -
The Future of Insurance: How AI is Shaping the Sector
The combination of AI and insurance was once an idyllic idea, but it has now become an imperative, transforming how insurers analyze, manage, and mitigate risks. AI benefits the insurance sector in a variety of ways. It aids in accelerated underwriting, hazard assessment, more equitable pricing, and tailored policies. AI helps to speed up claim processing and detect fraudulent activity. Furthermore, AI enables the performance of operations by means of lowering administrative overhead via automation. The purpose of the study is to examine the current and potential AI applications in the insurance sector and also to analyse the impact of AI in the insurance sector. The study employed a descriptive research design and the data has been collected through secondary sources like journals, books, reports, websites, etc. The study found that AI has enabled insurers to streamline processes, enhance efficiency, and offer extra customized danger evaluation for its clients. This has led to a seamless and handy revel in for policyholders, in addition to extra correct underwriting and pricing for insurers. Additionally, AI has facilitated the automation of claims processing, taking into account quicker and extra correct claims settlements. However, the implementation of AI within the coverage enterprise also presents demanding issues that need to be solved. 2026 Alka Agnihotri, Anuja Pandey and Balamurugan Balusamy. -
A study on comparisons of additive regression frailty models to counter heterogeneity: Bayesian strategies and case study
Historically, the primary goal of conventional survival study methods has been to reduce the frequency of failures over time. If the associated observed and unobserved variables are not known when studying such events, this can have detrimental effects. Frailty models offer a tempting solution for investigating the impact of unknown variables in such a case. In this article, we assume that frailty affects the hazard rate. We find that the weighted Lindley frailty models, which use general versions of the Weibull and log-logistic type II distributions as the baseline distributions, are a reliable method for ensuring the influence of endogenous variability. The parameters involved are estimated according to different loss functions using the Bayesian structure as the basis of Markov Chain Monte Carlo. Bayesian evaluation strategies are then implemented to evaluate the models. The results are demonstrated on known data of kidney infections. It is shown that the novel models outperform those based on the inverse Gaussian and gamma frailty distributions. 2024 Taylor & Francis Group, LLC.