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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. -
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. -
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. -
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. -
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. -
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). -
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. -
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/) -
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. -
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. -
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. -
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. -
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. -
Which is the green generation? Amultigroup analysis of millennials and Generation Zs green consumerism
Purpose This study aimed to investigate how components of green marketing mix (GMM), green product (GPD), green price (GPC), green place (GPL) and green promotion (GPM) influence consumer attitudes (ATT), subjective norms (SNM), perceived behavioural control (PBC) and purchase intention (PI) and finally green consumerism (GCM). Design/methodology/approach Using Smart PLS 4 software and PLS-SEM approach, data were analysed for structural relationships among the components of GMM, ATT, SNM, PBC, PI and GCM. The model evaluates hypotheses linking GPD, GPC, GPL and GPM to ATT, SNM and PBC and examines how ATT, SNM and PBC affect PI and GCM. Findings The study revealed that GMM, as a higher-order construct, positively impacts ATT, SNM and PBC, while ATT, SNM and PBC partially mediate the relation between GMM and PI. PI then ultimately results in GCM. The multigroup analysis indicated there is no significant difference between the age groups examined. Research limitations/implications The study may not generalize to all industries or regions. Future research could explore additional factors like cultural or technological influences, and longitudinal studies may be conducted. Practical implications As environmental concerns grow, marketers should focus on consumer attitudes towards green products. Aligning green attributes with consumer values, transparent pricing and multi-channel communication can enhance ATT, SNM and PBC over green purchases, fostering acceptance and intention. Social implications While the findings promote GCM, their broader impact is contingent on genuine environmental practices. Without systemic changes in production and policy, GCM risks perpetuating superficial sustainability narratives. Originality/value This study advances the field by investigating how GMM influences purchase intentions (PI) among Indias urban Millennials and Generation Z, two generations pivotal to shaping sustainable consumption trends in a high-pollution economy. 2025 Emerald Publishing Limited -
TRANSFORMING GREEN TRANSPARENCY INTO GREEN BRAND LOYALTY AND REPURCHASE INTENTIONS: THE ROLE OF BRAND IMAGE AND CREDIBILITY AMONG ELECTRIC VEHICLE USERS
The present study leverages the Stimulus-Organism-Behavior-Consequence (SOBC) framework to investigate how green transparency influences green brand loyalty and repurchase intention among electric vehicle consumers. Specifically, it examines the mediating roles of brand image and brand credibility in the relationships between green transparency, green brand loyalty, andrepurchase intention. Data collected from 386 electric vehicle users were analyzed using Partial Least Squares-Structural Equation Modeling (PLS-SEM). Results reveal that green transparency positively impacts brand image and brand credibility, which subsequently enhances green brand loyalty and repurchase intention. Mediation analysis further highlights brand image and brand credibility as critical mechanisms linking green transparency to green brand loyalty. This study extends the SOBC framework to green marketing, offering theoretical and practical insights into fostering sustainable consumer behavior. By emphasizing the role of green transparency in building credible and compelling brand narratives, the findings guide marketers in cultivating consumer trust and loyalty while supporting policymakers in formulating transparency regulations for a sustainable marketplace. 2025 Journal of Applied Structural Equation Modeling. -
Trust green, pay more: Decoding green brand loyalty and willingness to pay more for electric vehicles through green transparency and green perceived value
The StimulusOrganismResponse framework is applied in this study to explore the impact of Green Transparency (stimuli) and Green Perceived Value (stimuli) on Green Brand Trust (organism) and, subsequently, on Green Brand Loyalty (response) and Willingness to Pay More (response). Self-Brand Connection is examined as a moderator. An online survey was distributed to 557 EV consumers. We employed both PLS-SEM (SmartPLS 4) and CB-SEM (AMOS 29) to test the direct, mediating, and moderating effects, with CB-SEM used as a robustness check for model stability. The results show that both Green Transparency and Green Perceived Value are positive antecedents of Green Brand Trust. Green Brand Trust, in turn, positively influences Green Brand Loyalty and Willingness to Pay More and mediates the effects of the two stimuli. The results also confirm that Self-Brand Connection significantly and positively strengthens the Green Brand Trust?Green Brand Loyalty and Green Brand Trust?Willingness to Pay More relationships. This study establishes Green Brand Trust as a core green consumer behavior mechanism and identity alignment as a catalyst for Green Brand Loyalty and Willingness to Pay More, offering actionable guidance to EV brands for credibility building, customer retention, and sustainable consumption. 2026 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license. http://creativecommons.org/licenses/by-nc-nd/4.0/ -
Disentangling the association of PAH molecules with star formation
Context. Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous complex molecules in the interstellar medium and are used as an indirect indicator of star formation. On the other hand, the ultraviolet (UV) emission from young massive stars directly traces the star formation activity in a galaxy. The James Webb Space Telescope (JWST), along with the UltraViolet Imaging Telescope (UVIT), opened up a new window of opportunity to better understand the properties of PAH molecules that are associated with star-forming regions. Aims. We investigate how the resolved scale properties of PAH molecules in nearby galaxies are affected by star formation. Methods. We analyzed the PAH features observed at 3.3, 7.7, and 11.3 m using F335M, F770W, and F1130W images obtained from the JWST. These images helped us identify and quantify the PAH molecules. Additionally, we used UVIT images to assess the star formation associated with these PAH-emitting regions. Our study focused on three galaxies, namely NGC 628, NGC 1365, and NGC 7496, which were selected based on the availability of both JWST and UVIT images. Bright PAH emission regions were identified in the JWST images, and their corresponding UV emission was estimated using the UVIT images. We quantified the star formation properties of these PAH emitting regions using the UVIT images. Furthermore, we investigated the relation between the star formation surface density (?SFR) and the PAH ratios to better understand the impact of star formation on the properties of PAH molecules. Results. Based on the resolved scale study of the PAH-bright regions using JWST images, we found that the fraction of ionized PAH molecules is high in the star-forming regions with high ?SFR. We observed that emission from smaller PAH molecules is higher in star-forming regions with higher ?SFR. Conclusions. Our study suggests that the PAH molecules excited by the photons from star-forming regions with higher ?SFR are dominantly smaller and ionized molecules. UV photons from the star-forming regions could be the reason for the higher fraction of the ionized PAHs. We suggest that the effect of the high temperature in the star-forming regions and the formation of smaller PAH molecules in the star-forming regions might also result in the higher emission in the F335MPAH band. The Authors 2024. -
Analysis of Membership Probability in Nearby Young Moving Groups with Gaia DR2
We analyze the membership probability of young stars belonging to nearby moving groups with Gaia DR2 data. The sample of 1429 stars was identified from "The Catalog of Suspected Nearby Young Moving Group Stars." Good-quality parallax and proper motion values were retrieved for 890 stars from the Gaia DR2 database. The analysis for membership probability is performed in the framework of the LACEwING algorithm. From the analysis it is confirmed that 279 stars do not belong to any of the known moving groups. We estimated the U, V, W space velocity values for 250 moving group members, which were found to be more accurate than previous values listed in the literature. The velocity ellipses of all the moving groups are well constrained within the "good box," a widely used criterion to identify moving group members. The age of moving group members are uniformly estimated from the analysis of the Gaia color-magnitude diagram with MIST isochrones. We found a spread in the age distribution of stars belonging to some moving groups, which needs to be understood from further studies. 2020. The American Astronomical Society. All rights reserved.. -
Understanding the secular evolution of NGC 628 using UltraViolet Imaging Telescope
Secular and environmental effects play a significant role in regulating the star-formation rate and hence the evolution of the galaxies. Since ultraviolet (UV) flux is a direct tracer of the star formation in galaxies, the UltraViolet Imaging Telescope (UVIT) onboard AstroSat enables us to characterize the star-forming regions in a galaxy with its remarkable spatial resolution. In this study, we focus on the secular evolution of NGC 628, a spiral galaxy in the local Universe. We exploit the resolution of UVIT to resolve up to ?63 pc in NGC 628 for identification and characterization of the star-forming regions. We identify 300 star-forming regions in the UVIT far-UV image of NGC 628 using ProFound and the identified regions are characterized using Starburst99 models. The age and mass distribution of the star-forming regions across the galaxy supports the inside-out growth of the disc. We find that there is no significant difference in the star-formation properties between the two arms of NGC 628. We also quantify the azimuthal offset of the star-forming regions of different ages. Since we do not find an age gradient, we suggest that the spiral density waves might not be the possible formation scenario of the spiral arms of NGC 628. The headlight cloud present in the disc of the galaxy is found to be having the highest star-formation rate density (0.23 Myr-1 kpc-2) compared to other star-forming regions on spiral arms and the rest of the galaxy. 2022 The Author(s) Published by Oxford University Press on behalf of Royal Astronomical Society. -
A Review on Flood Prediction Algorithms and A Deep Neural Network Model for Estimation of Flood Occurrence
Flood occurs as often as possible happens due to many environmental changes in our planet in the present years. The occurrence and damages caused by flood is very high. Major cause of flood is due to heavy rainfall which in turn increases the water level of the rivers and other water bodies. The various factors that play a major role in the occurrence of rainfall are rise in temperature, humidity level, dew point, pressure in and around the area of concern, wind speed, etc. In order to reduce the number of victims due to flood it is necessary to have a system to predict flood occurrence. In this paper, we classify and analyzed the various prediction algorithms which show usage of Deep Neural Network produces better results. In addition, a design model has been proposed to predict the flood by training the Deep Neural Network with the above-mentioned factors. 2020, Asian Research Association. All rights reserved.