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The role of technology in advancing psychoneuroimmunology research
This chapter explores the transformative impact of technology on Psychoneuroimmunology (PNI), emphasizing advancements in neuroimaging, genomics, and proteomics. Techniques like functional MRI (fMRI) and Positron Emission Tomography (PET) have revolutionized our understanding of brain activity and neuroinflammation. Next-generation sequencing (NGS) and proteomic profiling have unveiled genetic and protein biomarkers linked to stress and immune responses. Wearable technology and mobile health apps now enable continuous monitoring and personalized stress management. Big data analytics and machine learning enhance pattern identification and outcome prediction. Ethical considerations, including data privacy and equity, are discussed alongside emerging technologies like AI and nanotechnology. Overall, the chapter highlights how these innovations are reshaping PNI research and improving treatment for stress-related disorders. 2025 by IGI Global Scientific Publishing. All rights reserved. -
Overcoming barriers: Challenges and opportunitiesin multi-stakeholder collaboration for sustainable supply chain
Multi- stakeholder collaboration is increasingly recognized as a critical component of sustainable development, particularly in complex supply chains. A supply chain is a simple tool that sustainably excels in every TBL component. This Chapter emphasizes the adoption of sustainability in supply chains and its results which are influenced by two key elements. The first crucial component is the managerial orientation toward sustainability which is how managers and decision- makers see sustainability and how it relates to their incentives to carry out sustainability activities. National and international development organizations have started integrated multi- stakeholder projects supporting creative solutions to poverty and environmental degradation. Among the noteworthy projects are those that promote sustainable livelihoods market- driven human development and the use of ecosystem services to reduce poverty. Difficulties in communicating because of language, priority, expertise gaps, power imbalances, and divergent interests. 2025, IGI Global Scientific Publishing. All rights reserved. -
An Innovative Method for Fuel Consumption and Maintenance Cost of Heavy-Duty Vehicles based on SR-GRU-CNN Algorithm
A heavy-duty vehicle's fuel usage, and thus its carbon dioxide emissions, are significantly impacted by the driver's behavior. The average fuel economy of a car varies by about 28% between drivers. Fuel efficiency can be improved by driver education, monitoring, and feedback. Fuel efficiency-based incentives are one form of feedback that can be provided. The largest challenge for transportation companies implementing such incentive programs is how to accurately evaluate drivers' fuel consumption. The processes of preprocessing, feature extraction, and model training are all utilized in the suggested method. Principal component analysis (PCA) is widely utilized in data science's preprocessing stage. GMM is used for feature extraction. Afterwards, SR-GRU-CNN is used to train the models based on the selected features. When compared to the two most popular alternatives, CNN and SR-GRU, the proposed methodexcels. 2023 IEEE. -
Cluster analysis for european neonatal jaundice
The objective of this paper is to propose and analyze clustering techniques for neonatal jaundice which will help in grouping the babies of similar symptoms. A variety of methods have been introduced in the literature for neonatal jaundice classification and feature selection. As far as we know, clustering techniques are not used for neonatal jaundice data set. This paper studies and proposes clustering techniques such as K-Means, Genetic K-Means and Bat K-Means for jaundice disease. To find the number of clusters elbow method is used. The clusters are validated using RMSE, SI and HI. The experimental results carried out in this paper shows bat k-means clustering performs better than K-means and genetic K-means. 2018, Springer International Publishing AG. -
Diabetic Retinopathy Detection Using Various Machine Learning Algorithms
The advances in technologies have paved the way to generate huge amounts of data in a variety of forms. Machine learning techniques, accompanied by Artificial Intelligence with its challenging nature help in extracting meaningful information from such data. This will have a great impact on many sectors, such as social media analytics, construction and healthcare, etc. Computer-aided clinical decision-making plays a vital role in todays medical field. Hence, a high degree of accuracy with which machine learning algorithms can detect diabetic retinopathy is really in demand. Convolutional neural networks, a deep learning technique, have been used to recognize pathological lesions from images. Image processing and analytics methods are used and have been trained to recognize the significant complications of diabetes, which cause damage to the retina, diabetic retinopathy (DR). Though this condition does not show any symptoms in its early stages, it has to be screened, diagnosed and treated at the earliest or it may lead to blindness. Deep neural networks have proved successful in screening DR from retinal images and handling the risks that may arise due to the disease. This chapter focuses on detecting diabetic retinopathy in retinal images by using efficient image processing and deep learning techniques. It also attempts to investigate the requirements of image pre-processing techniques for diabetic retinopathy. Experiments are carried out by taking a set of retinal images and predicting the level of diabetic retinopathy on a scale of 0 to 4. Deep learning techniques like CNN and DenseNet are applied and tested. 2024 Taylor & Francis Group, LLC. -
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Censored Regressive Canonical Optimized Convolutional Deep Belief Classifier For Hate Speech Detection in Online Social Network
Social networking uses internet-based platforms to facilitate users to make connections with others and share various forms of content, including text, images, videos, and links. Social networking services are mainly used for non-social interpersonal communication. Many approaches have been developed for hate speech detection, but they still face significant challenges, particularly in classifying text into multiple labels accurately and in a timely manner. For accurate hate speech detection in social networks, a Censored Regressive Canonical Optimized Convolutional Deep Belief Classifier (CRCOCDBC) model is developed. The objective of the developed CRCOCDBC is to detect multi-class hate speech with minimal time and error rate. Comparative analysis shows improved performance in terms of minimum error and higher authentication accuracy and precision than other well-known methods. 2026 Seventh Sense Research Group. -
Unveiling the root causes of diabetes using explainable AI
Diabetes is a non-communicable wide spread disease across the world. To investigate the risky factors that are associated with diabetes, and to start early and customized treatment, researchers are fascinated to explore existing machine learning or deep learning models and to develop more reliable algorithms. The advancement in technology and the increase in world population is an enriching source to prompt and explore the factors that decide a person to be diabetic. Several algorithms and approaches are in place to address these factors but are lacking in emphasizing with more interpretable features which convinces patent to trust the medicine, treatment, and have meaningful conversation with the physicians and artificially intelligent systems. To encourage the participation of people with diabetes for customized treatment and considering societal needs, this chapter explores the possibility of Explainable Artificial Intelligence (XAI) in diabetes detection and figuring out the significant features that dominate diabetes. 2025 Elsevier Inc. All rights reserved. -
Unveiling mental health nuances of male Indian classical dancers.
This study explores the lives of male Indian classical dancers, highlighting the duality of dance as a sanctuary and a stressor. As male Indian classical dancers negotiate and redefine norms of masculinity, the study calls for recognition of diverse masculine identities within traditionally feminized spaces. (PsycInfo Database Record (c) 2026 APA, all rights reserved) 2025 American Psychological Association All rights, including for text and data mining, AI training, and similar technologies, are reserved.; This research explores the mental health nuances of male Indian classical dancers (MICDs), through a lens of redefining masculinity, focusing on their perceived quality of life, psychosocial challenges, and coping strategies. This study follows an interpretive phenomenological approach to follow the lived experiences of MICDs. The participants are male, fluent in English, and pursuing Indian classical dance styles professionally, like Kathak, Bharatanatyam, Chhau, etc. Six participants were recruited for personal, semistructured, in-depth interviews, whereas, a focus group discussion with four participants was conducted to explore the stigma. The data were analyzed using interpretive phenomenological analysis, revealing themes of (a) identity fragmentation and negotiation in gendered social contexts, (b) gendered experiences, (c) emotional distress and psychological challenges, (d) coping mechanisms and resilience, and (e) stigmatization and social integration dynamics. MICDs grapple with identity formation, navigating a paradox of self-perception, artistic identity, and societal expectation. They reported experiencing emasculation, compromising artistic expression, and struggling with gender norms and gendered training constraints. They have faced name-calling, bullying, taunting, slandering, and discrimination leading to psychological challenges and distress. However, the paradox continues as male dancers use adaptive coping strategies despite the adversities that intertwine self-perception, societal pressures, and their passion for dance. These findings provide a strong foundation for making changes in the dance community for acceptance of male dancers, policy making for better job opportunities for male dancers, and mental health services to be provided to help them deal with distress. (PsycInfo Database Record (c) 2026 APA, all rights reserved) 2025 American Psychological Association All rights, including for text and data mining, AI training, and similar technologies, are reserved. -
Exploring the nexus of climate change and vector-borne disease transmission
Climate change is a critical global challenge that significantly impacts the redistribution of malaria endemicity worldwide. While efforts have been made to model malaria transmission using climatic factors, relying solely on these factors can lead to discrepancies and ineffective decision-making. To address this, we used the VECTRI modela dynamic framework developed by the International Center for Theoretical Physics (ICTP) that integrates both climatic and entomological factorsto map malaria risk for India and project its potential future under the SSP370 warming scenario. Our findings indicate that the length of malaria transmission is expected to increase across India by the end of this century. The shift of malaria endemicity to further north and also into highland areas could increase the at-risk population due to lower immunity in these regions. Therefore, integrated climate and entomological modeling is essential for effectively anticipating malaria transmission risks and enhancing public health responses. 2025 -
A stakeholder theory approach to analysing strategies for improving pandemic vaccine supply chain performance
This study aims to formulate strategies that impact the vaccine supply chain (VSC). This study measures the VSC performance using the proposed strategy concerning stakeholders theory. From the literature review and experts consent, the strategies are classified into six broad strategies as-VSC traceability, VSC visibility, VSC velocity, digitalising VSC, localising VSC, and vaccine inventory. A questionnaire is developed for surveying healthcare organisations and hospitals. All six proposed hypotheses got accepted. The developed model satisfies all the model fit parameters. Strategies like VSC traceability, VSC visibility, VSC velocity, digitalising VSC, localising VSC, and vaccine inventory have positively impacted vaccine supply chain performance. This research will be helpful for healthcare professionals and organisations for the faster delivery of the vaccine. This research will also help policymakers in improving the performance of VSC. This study is also the first to use the stakeholder theory approach for measuring VSC performance. Copyright 2024 Inderscience Enterprises Ltd. -
Implementation of vendor-managed inventory in hospitals: an empirical investigation
This research aims to determine critical success components for implementing the vendor-managed inventory (VMI) and test their influence on the inventory in Indian hospitals. The independent and dependent components of the research are identified from the extensive literature review. The independent variables are top management commitment, supply chain strategy, business process integration, continuous improvement, resource sharing, and information technologies adoption. The dependent variable identified is the adoption of vendor-managed inventory. The study results suggest that the proposed latent variables significantly impact the VMI and significantly contribute to VMIs implementation and sustainability. The study highlights the importance of VMI in Indian hospitals, and therefore, it will help the management focus on the VMI for enhanced operational efficiency. Previous studies have not empirically tested the impact of the suggested practices for VMI in Indian hospitals. The analysis would help evaluate VMI adoption in Indian hospitals. Copyright 2024 Inderscience Enterprises Ltd. -
Analysis of factors impacting firm performance of MSMEs: lessons learnt from COVID-19
Purpose: The micro, small and medium scale enterprises (MSMEs) faced various challenges in the ongoing COVID-19 pandemic, making it challenging to remain competitive and survive in the market. This research develops a model for MSMEs to cope with the current pandemic's operational and supply chain disruptions and similar circumstances. Design/methodology/approach: The exhaustive literature review helped in identifying the constructs, their items and five hypotheses are developed. The responses were collected from the experts working in MSMEs. Total 311 valid responses were received, and the structural equation modeling (SEM) approach was used for testing and validating the proposed model. Findings: Critical constructs identified for the study are-flexibility (FLE), collaboration (COL), risk management culture (RMC) and digitalization (DIG). The statistical analysis indicated that the four latent variables, flexibility, digitalization, risk management culture and collaboration, contribute significantly to the firm performance of MSMEs. Organizational resilience (ORS) mediates the effects of all the four latent variables on firm performance (FP) of MSMEs. Practical implications: The current study's findings will be fruitful for the manufacturing MSMEs and other firms in developing countries. It will enable them to identify the practices that significantly help in achieving the firm performance. Originality/value: The previous researches have not considered the effect of organizational resilience on the firm performance of MSMEs. This study attempts to fill this gap. 2022, Emerald Publishing Limited. -
Psychometric Properties of the Interpersonal Emotion Regulation Questionnaire Among Couples in India
The aim of the present study was to translate the Interpersonal Emotion Regulation Questionnaire (IERQ) into the Tamil language and examine its psychometric properties in the Indian cultural context. Data were collected from a dyadic sample of 340 married heterosexual couples (N = 680) currently residing in India. The mean age of husbands was 39.57 (SD = 6.10; 26 ? range ? 58), and the wives was 35.33 (SD = 5.72; 23 ? range ? 54). Descriptive results indicated that husbands and wives reported similar levels of interpersonal emotion regulation. Confirmatory factor analysis showed a 20-item model with four factorsenhancing positive affect, perspective-taking, soothing and social modeling, similar to the original version, fits the data well. Furthermore, the multiple-group analysis indicated robust measurement invariance across gender (husbands vs. wives), family type ( joint vs. nuclear) and marriage type (arranged vs. love), indicating that the Tamil version of the IERQ operates similarly across these groups. Besides, the Tamil version of the IERQ showed good convergent and discriminant validity with measures of dyadic coping and relationship satisfaction. Implications for research and couples therapy in the Indian cultural context are discussed. 2022, PsychOpen. All rights reserved. -
Investor behaviour, market efficiency, and regulatory challenges in digital currency investments: A comprehensive literature review
The rapid proliferation of digital currencies has ignited significant interest in understanding investor behaviour and the regulatory challenges within this evolving financial landscape. The study seeks to elucidate the factors that influence investor behaviour in the cryptocurrency/digital currency market and explore the regulatory implications associated with digital currency investments. A systematic approach was adopted to identify, review, and analyze relevant scholarly articles from reputable databases. The selected articles span various dimensions of the cryptocurrency/digital currency ecosystem, including investor behaviour, market efficiency, technological innovation, and regulatory measures. It reveals that investor behaviour in the digital currency market is influenced by a complex interplay of emotions, market sentiment, and information asymmetry. Additionally, market efficiency theories are being reconsidered in light of the highly volatile nature of cryptocurrencies. Regulatory challenges encompass issues related to fraud, pump-and-dump schemes, and legal ambiguity. 2024, IGI Global. All rights reserved. -
Initial coin offerings as a form of alternative financing for start-ups: A systematic literature review and bibliometric analysis
This chapter aims to provide a comprehensive overview of the field of initial coin offerings ICOs as a form of alternative financing for start-ups. Through a systematic literature review and bibliometric analysis, the study explores the current understanding of ICOs, the potential uses of fractional ownership of digital assets, and the opportunities and challenges associated with this form of fundraising. The study also highlights the importance of study, which provided the first comprehensive explanation of ICOs and their characteristics. The findings of the research suggest that ICOs have the potential to revolutionize the way start-ups raise funds, but they also present significant challenges and risks. The study encourages further research to continue exploring the opportunities and challenges presented by ICOs, as well as the potential impact of regulatory and legal frameworks on the ICO market. Overall, this chapter provides valuable insights into the complex and evolving topic of ICOs and their implications for start-ups, investors, and the broader economy. 2024, IGI Global. All rights reserved. -
A study of mediating factors influencing student motivation and behaviour: A literature review
This study aims to examine the mediating factors of learning environment, technology, good behaviour, and effective communication, sports, media, problem-based case study, and psychological influence on student motivation and behaviour. The study employs secondary data based on peer-reviewed articles, conference papers, and books collected from a variety of sources such as Scopus, WoS, J-Store, EBSCO, ProQuest, etc. between 1982 and 2022. The 103 articles have been selected based on the keywords which related to study. The results evidenced that the mediating factors have a significant positive impact on student motivation and behaviour. This study contributes to the existing literature by providing a com- prehensive understanding of the relationship between the mediating factors and student motivation and behaviour. The findings of this study have implications for educational institutions, policy makers, and educators in creating effective learning environments that can enhance student motivation and behaviour. 2023, IGI Global. All rights reserved. -
Driving sustainable development through climate finance in India: A case study of the National Clean Energy Fund (NCEF)
This case study examines the national clean energy fund (NCEF) as a climate finance policy in India. The NCEF was established with the objective of promoting renewable energy projects and sustainable development in the country. The study explores the background and context of climate finance, providing an overview of the NCEF's goals and implementation. The case study analyzes the impact of the NCEF by examining its funding allocations and utilization over the years. It highlights the challenges faced in effectively utilizing the funds, such as administrative hurdles, limited capacity, policy uncertainties, project development barriers, financial constraints, and governance issues. Furthermore, the case study discusses the socio-economic impacts of the NCEF, including job creation, clean energy adoption, and environmental benefits. It also explores the lessons learned from the NCEF implementation, identifying areas for improvement and providing recommendations for enhancing climate finance mechanisms in India. This chapter creates a contribution to renewable energy development in India. 2023, IGI Global. All rights reserved. -
Accounting fraud and bankruptcy: The case of wirecard AG
This chapter examines the scandal at Wirecard AG, a German payment processing and financial services company, that became one of the most valuable companies on the German stock exchange in the 2010s. From 2010 to 2018, Wirecard reported consistent revenue growth and profitability. In 2019, the company reported revenues of 2.8 billion ($3.2 billion). As of September 2018, its market capitalization was over 24 billion ($27 billion). In late 2019, the Wirecard scandal was discovered through investigative reports by the Financial Times, which raised questions about Wirecard's accounting practices. The company faced a major scandal in 2020 when it was revealed that 1.9 billion ($2.1 billion) was missing from its balance sheet. Subsequent investigations revealed a massive accounting fraud that had been going on for years. Subsequently, the company filed for bankruptcy. Multiple Wirecard executives, including its CEO, were charged with fraud and market manipulation. German regulators and auditors were criticized for failing to detect and prevent the fraud. 2023, IGI Global. -
Exploring the influence of emotional intelligence on decision-making across diverse domains: A systematic literature review
Emotional intelligence (EI) is increasingly recognized as a crucial factor in decisionmaking across various professional domains. This systematic literature review, covering the period from 2017 to 2023, explores the intricate relationship between EI and decision-making in healthcare, business, education, ethics, and the realms of neuroscience and artificial intelligence (AI). Employing a qualitative research design, the study systematically analyzes articles retrieved from renowned databases including Scopus, ProQuest, EBSCO, J Gate, and Google Scholar. The findings reveal that elevated EI levels among healthcare professionals lead to improved clinical decision-making, characterized by enhanced patient-centered care and ethical considerations. In business and legal contexts, EI competencies are associated with ethical decision-making, effective leadership, and strong client relationships. In education, emotionally intelligent educators create supportive learning environments conducive to better pedagogical decisions and student emotional well-being. 2024, IGI Global. All rights reserved.