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Application of Corn Oil Derived Carbon Nano-onions Using Flame Pyrolysis as Durable Catalyst Support for Polymer Electrolyte Membrane Fuel Cells
The reliance of carbon black as catalyst support for Pt in PEM fuel cell has posed a major challenge in its durability as carbon blacks are highly prone to corrosion. As an alternative, CNTs, CNFs, and graphene are explored as catalyst support, however at the expense of tedious synthesis procedure and production cost. So to combat this issue, a facile flame pyrolysis route was adopted to produce carbon nano-onions using eco-friendly corn oil. Further modification in the carbon nano-onions exhibited better corrosion resistance in comparison to carbon black (Vulcan XC-72R). Also, a systematic approach was adopted towards developing a durable electrocatalyst which was designed to withstand harsh fuel cell operating conditions. The electrocatalyst was successfully analyzed using stringent standard testing protocols (< 40% ECSA loss). Among all the electrocatalyst studied, Pt/fOC exhibited only 37.1% loss in electrochemical active surface area (ECSA) after 5000 cycles, thus indicating its excellent durability. A full cell was also constructed with Pt/fOC as cathode electrocatalyst which showed a maximum power density of 365 mW cm?2comparable to commercial Pt/C (367 mW cm?2). To the best of our knowledge, this is the first study on the application of corn oil derived carbon nano-onions as catalyst support for PEM fuel cells. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
Phytochemical characterization by GC-MS and in vitro evaluation of antioxidant potential of Walsura piscidia Roxb. leaves extract
Walsura piscidia Roxb. (Family: Meliaceae) is currently known for rich sources of bioactive compounds with growing multiple therapeutic and medicinal importance. The main objectives of this study were to characterize the phytochemical profile of the leaves of W. piscidia by Gas Chromatography-Mass Spectrometry (GC-MS), followed by the evaluation of its antioxidant potential by quantifying the amounts of phenols and flavonoids present within the extracts, through the existing methods of detection. The extractive yield calculated after Soxhlet extraction was seen to be higher for the ethanolic extract with a value of 21.9 %, followed by the methanolic extracts (21.06 %) and the qualitative phytochemical tests gave similar classes of phytochemicals like triterpenoids, phenolic compounds and tannins in the methanolic and ethanolic extracts. The total phenolic content was seen to be higher in the ethanolic extract with a value of 26.192 0.401 mg GAE/g and the total flavonoid content was seen to be higher in the methanolic extract with a value of 42.972 0.214 mg QE/g. The methanolic extract showed promising results in the antioxidant assays with a significantly low IC50 value in DPPH assay and high ferric reducing power in ferric reducing antioxidant power (FRAP) assay. The GC-MS chromatograms showed almost similar compounds for both the methanolic and ethanolic leaf extracts, some important ones being n-Hexadecanoic acid, stigmasterol, campesterol, 5-hydroxymethyl furfural, etc, displaying properties of interest like antioxidant, anti-inflammatory, anti-microbial, etc. This work contributes to our better understanding of the medicinal properties of the leaves of W. piscidia and has also provided a strong scientific basis to the traditional usage claims of this tree. The Author(s). -
THE INFLUENCE OF STEM ATTITUDE, 21ST CENTURY SKILLS, AND TECHNOLOGY USE ON STUDENT OUTCOMES: A MEDIATION MODEL ANALYSIS
Aim/Purpose This study aims to investigate the relationships between students attitudes toward STEM subjects, 21st-century skills, and technology use, and how these factors influence STEM career interest and subjective well-being among adolescents in the 21st-century classroom. Background While positive attitudes, skill acquisition, and digital learning have been individually studied, their combined effect on students career aspirations and subjective well-being remains underexplored. Addressing this gap can show how these factors work together to support adolescent development. Methodology This study was grounded in established theories of learning and motivation. It employed a statistical method, Structural Equation Modeling (SEM), to test a conceptual model. Data were collected from 1,302 students (grade levels VIII-XII) across 30 schools in Kerala, India. Also analysed were the mediating effects of mathematics and science engagement, teacher efficacy, and teacher leadership. Contribution This research offers a combined model that reveals how students attitudes, skills, technology use, and classroom teaching practices are connected. These factors are shown to influence both career motivation and well-being. Together, the findings provide a broader and clearer picture of modern education. Findings Students with positive attitudes toward STEM showed more engagement in learning. Effective and supportive teachers also influenced them. This influence shaped their career interest. Their well-being was improved indirectly through this engagement and career motivation, but their direct impact on well-being was limited. The model demonstrated good fit indices, supporting its structural validity. By addressing the gap in integrative models that link learner attitudes, competencies, technology use, and instructional mediators to both career and well-being outcomes, this study contributes a holistic framework for understanding adolescent development in modern classrooms. Recommendations Teachers should focus on fostering STEM-positive attitudes. They can enhance for Practitioners engagement through inquiry-based methods. They should also integrate 21st-century skills into everyday classroom activities. Teacher professional development should include leadership training and technology-enhanced pedagogy. Purposeful and regulated technology integration should be prioritized to avoid negative impacts on student focus and well-being. Recommendations Future research should explore the longitudinal effects of these variables across for Researchers diverse educational contexts and age groups. Alternative indicators of academic success beyond grades should be considered. These indicators could examine how digital and thinking skills apply across different subjects. Impact on Society By identifying pathways to both academic and emotional development, the study supports policy and curriculum reforms. These reforms aimed at preparing future-ready learners capable of contributing to innovation-driven economies while maintaining adaptability. Future Research Investigations should extend to virtual or immersive environments, examine differential impacts across demographic groups, and develop standardised tools for measuring digital literacy and well-being in diverse settings. (2025), (Informing Science Institute). All rights reserved. -
"Sparks" off a narrative as starting "Dots" for qualitative research in business education
The authors undertake to prepare a chapter on quality research in business education. The authors compose and choose two narratives one on the required initial drive for research and the way initiatives and ideas get generated in a continuum. On the strength and support of the prepared narratives, the authors elucidate how the sparks as starting points are generated and how through experience generated research gets proceeded and performed. All these experiences produce theories and these theories get transformed as models for study and research in the days to come. The second narrative exemplifies the externalization of sparks into experiences in growth and development continuum. The narrative one is extracted from an Indian puranic narrative and the other from a business man's biographical narrative. Both these chosen narratives enhance the qualitative research in business. 2025, IGI Global Scientific Publishing. All rights reserved. -
Indian folk art form grooms and improves education with personalized learning methods
The chapter investigates the intricacies and complexities involved in the making of an art form kathakali from India known for its unique formation process and exposition procedure. It unravels its mysteries involved in the exceptional personalized learning methods born out of them. The chapter discusses the various nuances in merging personalized learning methods with education, each in a selected singular appropriate method. It refers to a much refined and reformed process. It focuses on whetting of learner's comprehension skills to evolve befitting solutions to education problems producing effective redress for every serious global issue. The chapter proposes convincing concepts exemplified to apply in individual instances. The researcher suggests that these personalized methods are exemplary and worth emulating for the future generations. 2025, IGI Global Scientific Publishing. All rights reserved. -
The impact of the COVID-19 pandemic on e-learning adoption in an emerging market: a longitudinal study using the UTAUT model
Purpose: The COVID-19 pandemic provided unprecedented impetus to the evolution of the e-learning learning ecosystem by compelling students to adopt e-learning systems. This paper aims to use the UTAUT model to provide insight into the differences in factors influencing the adoption of e-learning systems before and after the pandemic. Design/methodology/approach: This longitudinal study uses two surveys conducted among graduate students in the city of Bengaluru in India. One prior to the start of the COVID-19 pandemic and a second in its aftermath. PLS-SEM is used to analyze both data sets to draw insights into the constructs that influence Behavioral intention to adopt e-learning systems. The moderating effect of gender is also analyzed. Findings: Pre COVID-19, Facilitating Conditions, Performance Expectancy and Effort Expectancy (quadratic behavior) were dominant factors influencing the adoption of e-learning technologies. Post pandemic, Performance Expectancy and Social Influence are drivers of e-learning adoption. Effort Expectancy and Facilitating Conditions grouped as Ease of Use is a significant driver of e-learning adoption post pandemic. Gender is found to not have a moderating influence. Originality/value: The unique longitudinal study of the differences in factors influencing students intention to adopt e-learning pre- and post-COVID-19 can prove useful to policy makers in the higher education sector. Academics can use the post-pandemic e-learning models findings in multiple contexts such as generational cohorts, educational contexts and social contexts. 2024, Emerald Publishing Limited. -
Localised actor roles in post-disaster housing recovery: A case study from Kerala
The effectiveness of post-disaster housing reconstruction (PDHR) is increasingly being challenged by the frequency and complexity of climate-induced disasters. In the Indian state of Kerala-particularly the highland regions of Kottayam and Idukki-landslides and floods have caused significant housing losses in recent years. While the government initiated housing recovery interventions after the 2021 landslide event, multiple civil society actors, including faith-based organisations, political parties, and professional groups, also participated in reconstruction efforts. This study examines the actor-specific approaches to community consultation in PDHR and their impact on beneficiary satisfaction. Using a qualitative case study design, the analysis identifies variations in participation across planning, design, and construction stages, and maps these to outcomes such as reconstruction speed, satisfaction levels, and community cohesion. While some actors offered comprehensive engagement strategies, others limited their consultation, resulting in mismatches between needs and outcomes. Findings suggest that community consultation remains uneven and often symbolic, with beneficiaries perceiving external aid as benevolence rather than entitlement. The study underscores the importance of meaningful participation in PDHR, especially in the context of localized climate events. These insights offer practical implications for designing inclusive recovery frameworks and enhancing community resilience in hazard-prone regions. The Authors, published by EDP Sciences, 2025. -
Housing sector recovery trajectories after the 2021 landslides in Kerala, India
Tackling homelessness is a critical priority after disasters especially in the Western Ghats of India, where landslides are becoming increasingly frequent and severe. This situation has rendered recovery in the housing sector more essential than ever. The present study specifically aims at investigating the recovery in this sector among households impacted by the 2021 landslides in Kerala, India, using Recovery Trajectory Index (RTI). It shows how the dynamic interplay between initial severity of impacts and subsequent delays in reconstruction together can shape the changing recovery status of households. Focusing on four critically impacted wards under the jurisdiction of Koottickal, a village level local government body, the research aims at understanding the current trajectory of recovery efforts. The highest RTI value refers to a slower recovery pace, and the lowest value signifies a faster pace. The index calculation is intended to contribute to prevailing quantitative recovery assessment frameworks for the identification of spatial and household-level disparities in recovery performance in similar hazard-prone contexts. The results highlight the need to prioritize equity in resource allocation, promote inter-agency collaboration to ease financial strain, and maintain a detailed household recovery database. Incorporating these measures would enhance preparedness and support more efficient, inclusive recovery planning. 2025 Informa UK Limited, trading as Taylor & Francis Group. -
Navigating Brand Hate Research: Deciphering Enablers and Emanating Potential Implications
In todays world, consumers possess a unique power, i.e., the power to praise, to critique, or even to reject a brand. At the zenith of challenges stands an issue known as Brand Hate which is a negative set of perceptions consumers develop towards a brand or product etc. Unlike simple dissatisfaction, brand hate stems from frustration, ethical objections and betrayal, with reactions from boycotting to even running anti-brand campaigns online. Social media amplified these feelings of frustration and can spread to more people, greatly affecting the reputation of the brand. The roots of brand hate vary, for some, it begins with a disappointing product experience; while for others, it is the result of unethical steps takenby the company. Whether it's an issue with the quality or a failure of the brand to meet the expectations of the consumers, regardless of the case the consequences are severe. Our study aims to explore the complexity in these variables leading to brand addiction and provide dependence and driving power of these factors and provide recommendations for the same. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Mind and Nature: Study on Mental Health, Nature Connectedness, Pro-Nature Conservation Behaviors and Geographical Green Cover among Indian Adults
For centuries the relation between mind and nature has been represented through literature, songs and cultural traditions. However with increasing urgency of the climate crisis and the corresponding growing distance between humans and nature, we find very limited scientific work exploring their relationship, which could perhaps help re-bridge the connection between the two. A significant, yet not directly observable, and often overlooked impact of the climate crisis is its impact on mental health. This study looks at this relationship in the Indian context, through a relatively unexplored perspective, by investigating the effects of nature connectedness (NC), pro-nature conservation behaviours (ProCoB) and geographical green cover (GGC) on mental health (MH) among middle-aged adults residing in India, and the existing inter-relationships. 180 middle-aged Indian adults, selected through purposive and snowball sampling, from across 21 states and 2 Union Territories (UTs), were administered questionnaires through a Google form. Their data was collected and scored, and the GGC was calculated for each state/ UT from the India State of Forest Report 2021. Correlation and Regression analysis were conducted on the scores using SPSS. A positive and statistically significant correlation exists between the variables NC, ProCoB and MH; NC, MH and GGC; and NC and ProCoB. NC and ProCoB predict MH. Gardening also predicts MH. The findings are new and contribute to the field of Environmental Psychology. It provides a scientific basis for the often romanticized relationship between man and nature as found in literature. It has great implications for the future, such as increasing awareness and understanding, and planning interventions to improve both environment and wellbeing. 2024 selection and editorial matter, Dr. Sundeep Katevarapu, Dr. Anand Pratap Singh, Dr. Priyanka Tiwari, Ms. Akriti Varshney, Ms. Priya Lanka, Ms. Aankur Pradhan, Dr. Neeraj Panwar, Dr. Kumud Sapru Wangnue; individual chapters, the contributors. -
ANALYZING THE BEHAVIORAL CORRELATES OF GUILT-FREE FOOD CONSUMPTION: STUDY OF GEN Z PERSPECTIVE
This study investigated the factors influencing Gen Z's purchase intentions for guilt-free food products in India. The aim of the study is to examine the relationship of food habits among the current generation using the Theory of Planned behaviour. Using purposive sampling, the data was collected from 318 Gen Z students in major Indian cities. SPSS and AMOS were utilised to conduct the analysis of the sample. The analysis revealed that attitude, perceived behaviour control and subjective norms significantly influenced the purchase behaviour. The study provides valuable insights for marketers, policymakers, and food producers seeking to promote guilt-free food products among this influential demographic. 2025 Amity University. All rights reserved. -
Employee experience, well-being and turnover intentions in the workplace
Purpose This study aims to examine the role of employee experience in influencing employee well-being and turnover intentions within organizations. The mediating role of well-being will also be investigated, along with an exploration of whether these relationships differ across genders, specifically in the Indian corporate context. Design/methodology/approach A descriptive, quantitative study was conducted using structured questionnaires to gather data from 111 employees in the Indian corporate sector. The study used a non-probability judgment sampling method. Data was analyzed through SPSS for descriptive and inferential statistics, and partial least squares was used to explore mediation and model fit. Findings The study found a significant impact of employee experience on well-being, as well as a negative correlation between both employee experience and turnover intention and well-being and turnover intention. Well-being was found to partially mediate the relationship between employee experience and turnover intention. Gender-based analysis revealed no significant differences in the relationships between these variables for men and women. Originality/value This research highlights the universal applicability of employee experience as a predictor of well-being and turnover intention, irrespective of gender. By establishing that gender does not moderate these relationships, this study provides new insights challenging traditional assumptions about gender disparities in workplace outcomes. 2024 Emerald Publishing Limited -
Workplace aesthetics and employee behaviour: introducing the Office Peacocking Scale (OPS)
Purpose This study introduces the concept of Office Peacocking, defined as the deliberate enhancement of office aesthetics and amenities to attract attention and influence employee behaviour. The purpose of this paper is to develop and validate the Office Peacocking Scale (OPS), a psychometric tool that measures how workplace aesthetics impact employee engagement, time spent in the office and social dynamics. Design/methodology/approach Using the DeVellis scale development method, the study followed a multi-stage process involving item generation, expert validation, pilot testing and exploratory factor analysis. A survey was administered to 375 employees across corporate sectors such as IT, finance, marketing and operations. Reliability was assessed using Cronbachs alpha, and factor analysis was conducted to determine underlying dimensions. Findings The OPS scale demonstrated acceptable reliability and revealed two dimensions: Aesthetic-Experiential Display and Symbolic-Social Signalling. The results suggest that enhanced office aesthetics significantly influence employee motivation, visibility-seeking behaviours and emotional connection to the workplace. Research limitations/implications The findings are based on a cross-sectional survey within a limited geographic and sectoral scope, which may affect generalizability. Future studies could explore longitudinal validation and cross-cultural applicability of the scale. Practical implications The OPS scale offers HR professionals and workspace designers a practical tool to evaluate how employees perceive and respond to office enhancements. It supports strategic decisions in workplace design aimed at boosting engagement, retention, and organizational identity. By understanding the psychological and social effects of office aesthetics, organizations can foster inclusive and meaningful work environments that go beyond superficial design trends. Originality/value This study pioneers the empirical measurement of Office Peacocking, contributing a validated scale and offering fresh insights into the symbolic and behavioural implications of workplace aesthetics. 2026 Emerald Publishing Limited -
Relationship Between Job Stress, Employee Engagement and Job Satisfaction: A Study Based on Women Managers in 4 and 5 star Hotels in India
Women account for a very small percentage of the employee population in Indian luxury hotels. While they have proved themselves as valuable assets, the average tenure of a woman in a managerial role in the sector is still around 2 to 4 years. The Government of India in its India Skills report has identified the sector as a focus area, in the drive to achieve better gender ratios. This study takes a small step towards understanding the factors that could influence the tenure of women in the hotel sector. The study examines the role of job stress in determining the levels of job satisfaction of women in the Indian hotel industry. The study also examines the mediating effect that employee engagement may have on the relation. The researchers have studied women in managerial roles in 4 and 5-star hotels, across India. The findings suggest that there is a strong negative correlation between job stress and job satisfaction and that this relationship is partially mediated by the presence of employee engagement. The findings are particularly important for the hospitality sector in India, as it struggles to retain its talented female employees. 2022 K. J. Somaiya Institute of Management -
NEUROPLASTICITY UNLEASHED: Receiving the Brain for Recovery
Neuroplasticity, the brains dynamic ability to reorganize and adapt across the lifespan, underpins contemporary approaches to neurorehabilitation. This chapter critically examines the clinical, neuroimaging, and neurophysiological evidence for plasticity-driven recovery. Drawing on longitudinal studies and case-based analyses, we illuminate how recovery can occur even in late stages, challenging the traditional notion of static chronic phases. The chapter highlights the role of task-specific practice, intensity, and timing in shaping neural reorganization, emphasizing that plasticity is not merely a spontaneous biological process but one that can be modulated through structured intervention. We further explore how electroencephalography (EEG)-based markers offer temporally precise insights into reorganization across cognitive, sensory, and affective domains. Neuroimaging findings reveal compensatory activation, network shifts, and bilateral engagement as hallmarks of adaptive plasticity. Affect, motivation, and goal-directed behavior are positioned as central to driving experience-dependent changes, especially when integrated into patient-centered therapy. In addition, we examine the intersection of individual difference factorsincluding personality and cognitive reservewith neuroplastic potential and propose frameworks for personalized rehabilitation. Finally, the chapter outlines emerging directions in tech-enabled plasticity interventions and translational models of care. Together, the evidence underscores neuroplasticity not only as a recovery mechanism but also as a target for strategic, evidence-based rehabilitation. The interdisciplinary approach adopted here aims to bridge neuroscience, clinical practice, and lived patient experiences to inform future research and therapeutic innovation. 2026 selection and editorial matter, K. Jayasankara Reddy; individual chapters, the contributors. All rights reserved. -
Tri-projection gated cross-modal fusion for robust multilingual emotion recognition
Existing multimodal approaches in emotion recognition (ER) rely on static or pairwise fusion strategies. These systems do not adequately address the challenges in real-world conversational systems, which require resilience to both multilingual code-switching and variable reliability of multiple modalities. We propose a transformer-based tri-modal emotion identification framework with a novel Tri-Projection Gated Cross-Modal Fusion (T-GCMF) module the first multimodal emotion recognition architecture explicitly designed for code-switched conversational input. T-GCMF simulates tri-modal interactions by explicitly calculating modality-specific confidence and cross-modal consistency, allowing for dynamic suppression of unreliable modalities during inference. Acoustic and visual cues are retrieved using CNNLSTM and deep CNN encoders, respectively. Textual representations are generated using XLM-RoBERTa to handle code-switched language reliably. We introduce Hinglish-MELD, the first multimodal emotion recognition dataset with aligned text, audio, and visual streams containing code-switched conversational content, filling a critical gap in the literature. With an accuracy of 88.3% and an F1-score of 87.0, the suggested confidence-aware fusion technique greatly surpasses unimodal, monolingual, and non-gated multimodal baselines. These findings demonstrate T-GCMF as a successful approach for emotion recognition in linguistically heterogeneous, real-world interactive systems and emphasize the significance of confidence-driven tri-modal integration. 2026 -
Impact of Multi-domain Features for EEG Based Epileptic Seizures Classification
Accurate detection and classification of epileptic seizures play a pivotal role in clinical diagnosis and treatment. This study introduces an innovative approach that leverages multi-domain features extracted from Electroencephalogram (EEG) data in conjunction with Supervised learning classification techniques. Initially, EEG data undergoes preprocessing through data standardization, followed by the extraction of essential features per instance, encompassing combination of Time domain, Frequency domain, and Time-Frequency domain features. These extracted feature combinations are subsequently fed into the machine learning-based boosting classifier Adaptive Boosting (ADABOOST) for an accurate and precise classification of epileptic signals. Validation of the proposed method is conducted using EEG data from the BEED (Bangalore EEG Epilepsy Dataset) and BONN (University of BONN, Germany) database to detect epileptic seizures. The experimental results show remarkably high levels of classification accuracy for various conditions: 99% accuracy for BEED data, 98% accuracy for BONN data for classifying seizures from healthy states, and 91% accuracy for classifying seizure onset from seizure events. Furthermore, the study applies the Gaussian Nae Bayes (GNB) classifier to differentiate various types of epileptic seizures, employing evaluation metrics such as the confusion matrix, ROC curve, and diverse performance measures. This method demonstrates significant potential in supporting experienced neurophysiologists decision in the clinical classification of epileptic seizure types. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Automated epileptic seizure classification using adaptive fast Fourier transform with non-uniform sampling and improved deep belief network
In automated brain-computer interaction (BCI), EEG signals are essential. This research uses AI to detect epileptic seizures, employing data from the BONN dataset (UCI), CHB-MIT dataset (physionet server), and Bangalore EEG Epilepsy Dataset (BEED). The goal is to develop an automated system for accurate seizure detection using adaptive fast Fourier transform with non-uniform sampling (AIFFT-NS) and an improved deep belief network (IDBN) model to enhance classification accuracy. The AIFFT-NS model serves as a channel for transforming spectro-temporal data. Using various EEG datasets, a number of extensive experiments are carried out, resulting in the validation of the efficacy of the proposed approach. High accuracy metrics, with 96.16% for the BEED dataset, 99.41% for the BONN dataset, and 96.31% for the CHB-MIT dataset, represent the evidentiary outcomes. This study emphasises the critical function of AI-facilitated spectro-temporal EEG analysis within the domain of medical diagnostics, going beyond the realm of automated seizure onset classification. Copyright 2024 Inderscience Enterprises Ltd. -
Feature Engineering for Epileptic Seizure Classification Using SeqBoostNet
Epileptic seizure, a severe neurological condition, profoundly impacts patients social lives, necessitating precise diagnosis for classification and prediction. This study addresses the need for reliable automated seizure detection in epilepsy by employing Artificial Intelligence (AI) driven analysis of Electroencephalography (EEG) signals. Key innovations include combining spectral and temporal features using Uniform Manifold Approximation and Projection (UMAP) with Fast Fourier Transformation (FFT), and the introduction of the Sequential Boosting Network (SeqBoostNet), a robust stacking model integrating machine learning and deep learning for effective seizure classification. Validated on benchmark datasets such as the BONN dataset from the UCI repository and the BEED from the Bangalore EEG Epilepsy Dataset, this approach achieved high accuracy, distinguishing Focal and Generalized seizure onsets with 95.91% accuracy and overall average accuracies of 96.71% on BEED and 97.11% on BONN. Existing models frequently struggle with the variability of seizure events. However, these findings underscore the models strength in distinguishing between seizure onset types, even with the inherent fluctuations in seizure patterns. This research not only advances automated seizure detection but also underscores the value of integrating AI with EEG analysis to improve neurological diagnostics, offering the potential for significant enhancements in diagnostic accuracy and patient outcomes. 2025 University of Bahrain. All rights reserved.