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Nitrogen-Oxygen Co-Functionalized Waste Cassava Peel-Derived Carbon Dots for White Led
White light emitting diodes (WLEDs)are most sought after, with the broad spectra ranging from cool to warm white light being skillfully utilized to create various modes of lighting effects. The fabrication of WLEDs is generally sophisticated, involving either multiple components emitting in different regions or single-component phosphors with complex elemental compositions. In the present work, WLEDs utilizing solvent-tuned carbon dots derived from waste cassava peel are reported through a facile one-step microwave-assisted solvothermal method. The carbon dots show evident UV absorption and correspondingly emit broad visible light spectra when dispersed in a dimethylformamide (DMF)-polyvinyl alcohol (PVA)blend, making themselves suitable white lightemitting down conversion materials. The successful transformation of a 400nm UV LED into a WLED with a general colour rendering index (CRI) of 83 and colour correlation temperature (CCT) of 4426 K gives a promising future outlook toward developing eco and economic-friendly WLEDs. 2025 Wiley-VCH GmbH. -
Modulating Electrochemical Energy Storage Properties of Cassava Peel-Derived Carbon Dots Via Solvent Engineering
Despite their small size, constrained within a few nanometers, Carbon dots, the tiny 0D materials, have the potential to revolutionize the realm of materials, design, and technology. The elemental composition of carbon dots, more specifically, the non-carbon elements forming functional groups, has crucial roles in determining the structure and properties, opening a wide scope for tailoring through compositional engineering. In this work, tuned carbon dots derived from waste Cassava peel, synthesized by applying solvent engineering strategy in facile single-step microwave-assisted solvothermal treatment, are reported. The proportion of charge accumulation mechanisms is found to be highly dependent on the elemental composition of oxygen and nitrogen in the carbon dots, evidencing the pivotal role of functional groups. Among the carbon dots synthesized using three different solvents. Ethylenediamine based ones show highest energy storage capability (114.57Fg?1 at 0.1Ag?1) owing to nitrogen-oxygen co-functionalization. Though these carbon dots have low storage capabilities as such, they have potential prospects to be incorporated into electrode materials to tune charge storage mechanisms desirably, often with the added advantage of enhanced stability and performance. Additionally, the redox properties exhibited by the tuned samples give promising prospects toward applications like electrochemical sensing and electrolyte engineering for energy storage. 2025 Wiley-VCH GmbH. -
Oxygen surface-functionalized carbon dots derived from waste cassava peel for UV shielding applications
UV radiation, falling in the wavelength range between 290nm and 400nm, which reaches the Earth's surface, is capable of causing potential damage to human cells, especially the skin. Sun protection products, which were earlier treated as skincare utilities, have now become indispensable and fall under the category of healthcare commodities. The requirement for skin- and environment-friendly UV absorbers that are reliable enough to substitute synthetic ones is spiking day by day. In this work, we report the conversion of waste cassava peels into UV-absorbing carbon dots through a facile one-step microwave-assisted solvothermal route. The as-synthesized carbon dots, when dispersed in NMP, show intense absorption in the UVA and UVB region, which can be effectively used for UV shielding applications. In-vitro studies based on transmittance data show that dispersion is capable of blocking 90% of the UV rays at a concentration of 0.2mg/mL, and at 0.5mg/mL, an SPF of 35+ was obtained, corresponding to a shielding capability of more than 97%. The conversion of cassava peel waste into UV-absorbing carbon dots adds to the value of this agricultural waste and, on crossing the compatibility standards, would provide a suitable alternative for existing synthetic UV shielding materials. 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies. -
Digital Pathways to Inclusive Health Reform: Addressing Mental Health Inequities Among Informal Workers in India
This chapter examines how digital transformation can address mental health inequities among informal workers in India, focusing on discrimination, labor precarity, and unequal access to care. Using empirical insights from Kerala within broader national and global contexts, it analyzes how psychological distress among native and migrant workers emerges from intersecting socioeconomic, institutional, and digital factors. Drawing on psychology, public health, economics, and digital governance, the chapter proposes a phygital mental health framework integrating physical services with digitally enabled care pathways. Emphasis is placed on ethical leadership, data governance, and economic sustainability, highlighting both opportunities and risks of digital mental health expansion. The chapter demonstrates how compassionate, inclusive, and accountable digital reforms can translate mental health policy into effective instruments for equity, social justice, and sustainable health system transformation. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Leadership and Equity in Health Reform: Overcoming Discrimination and Promoting Mental Well-Being Among Migrant Workers
This chapter examines leadership and governance responses to workplace discrimination and mental- health inequities among migrant workers within Indias healthreform landscape, using Kerala as an empirical case. Drawing on quantitative evidence linking discrimination with psychological distress, diminished self- esteem, and reduced well- being, the chapter situates these outcomes within established frameworks such as health equity, social determinants of mental health, and traumainformed leadership. It advances a compassion- driven leadership model that integrates behavioral insights, inclusive policy design, public- health marketing, and participatory governance to address structural and systemic inequities. By moving beyond descriptive narratives, the chapter demonstrates how ethical and inclusive leadership can translate evidence into equitable reform. It argues that sustainable health reform must embed human dignity, social justice, and mental well- being at the core of governance, rather than prioritizing efficiency alone. 2026 by IGI Global Scientific Publishing. All rights reserved. -
A study on the relationship between personality traits and job satisfaction of nurses
Human resource development in nursing involves training, continuing education, and mentorship to enhance nurses' skills and knowledge. By investing in the professional development of nurses, human resource development contributes to improved patient care, and employee satisfaction. The present study was carried out to examine the relationship between personality traits and job satisfaction of nurses and to find out whether there are any differences based on marital status. The data was collected from 92 nurses from five hospitals in Kerala and Karnataka states. Tools used for data collection were the Job satisfaction scale and the Big Five Personality Inventory. The data was analyzed using a t- test and correlation. Results of the study indicated that job satisfaction shows no significant differences based on marital status. The personality trait conscientiousness positively correlated with job satisfaction. Implementing Human resources development (HRD) practices in nursing significantly impacts nurses' job satisfaction, contributing to hospitals' performance excellence. 2025, IGI Global Scientific Publishing. All rights reserved. -
Computational identification of microbial metabolites as potential inhibitors of mosquito juvenile hormone binding protein for vector control
A group of acyclic sesquiterpenoids, that form the Juvenile hormone is crucial in the developmental physiology of insects. Aedes aegypti is crucial in spreading fatal diseases such as dengue, and dengue hemorrhagic fever. The mosquito undergoes several stages of development, from the egg to the adult stage, utilizing its innate immunity system and juvenile hormone proteins. Thus, targeting the juvenile hormone-binding proteins can potentially inhibit the developmental stages of the mosquito. The mosquito juvenile hormone binding protein (mJHBP) of Aedes aegypti was obtained from the RCSB (PDB). The study identified that Talaromyces islandicus and Bacillus velezensis produced secondary metabolites that act as efficient ligand complexes. The secondary metabolites were procured from PubChem and docked to the binding sites of mJHBP. Among the 26 listed ligand compounds, oxalic acid, decyl 3,5-difluorophenyl ester, oxalic acid 3,5-difluorophenyl undecyl ester, and poctylacetophenone were found to have higher binding affinity, marking their efficiency in inhibiting the protein. Normal mode analysis studies were performed using iMODs to analyze the B-factor, variance, covariance, and Eigenvalues of the docked protein-ligand complexes. The Absorption, Distribution, Metabolism and Excretion (ADME) properties of the efficient ligand molecules were analyzed using the Swiss ADME tool to segregate potential drug candidates. Targeting the mJHBP complex using the microbial metabolite ligand molecules can inhibit the development of the mosquitoes. The work enlightens the futuristic development of potential candidates in the production of insecticides. The literature confirms it is the first of its type to utilize microbial bio compounds as ligands targeting the mJHB protein complex. : Author (s). Publishing rights @ ANSF. -
Multimodal sentiment analysis: integrating text, image, and audio
Multimodal sentiment analysis aims to integrate text, images, and audio information to provide a more comprehensive understanding of human emotions and opinions. This chapter reviews key aspects of multimodal sentiment analysis, including feature extraction techniques, fusion methods, modeling approaches, and applications. For feature extraction the chapter discusses lexical, syntactic, and semantic features for text; visual attributes and facial expressions for images; and acoustic properties for audio. Three primary fusion techniques are examined: early fusion, which combines features before classification; late fusion, which integrates outputs from unimodal models; and model-based fusion, which learns joint representations across modalities. The chapter explores traditional machine learning and deep learning modeling approaches, highlighting the effectiveness of neural architectures like CNNs and RNNs. Key application areas discussed include social media analysis, emotion recognition, intelligent transportation, and education. The chapter also outlines future research directions, such as crossmodal learning, multimodal pretraining, and explainable AI. As multimodal data increases, sentiment analysis techniques that can effectively integrate information across modalities will become increasingly crucial for understanding human emotions and opinions in diverse contexts. This review provides a comprehensive overview of current approaches and emerging trends in this rapidly evolving field. 2026 Elsevier Inc. All rights reserved. -
AI and Big Data: Harnessing Data Science for Enhanced Consumer Insights
Data science is revolutionizing modern marketing strategies. These strategies enhance consumer intelligence and streamline decision-making processes. Data science techniques are helping businesses to get deep insights into consumer behavior, preferences, and emerging trends. In this paper, we focus on how the trident of artificial intelligence, machine learning, and quantum computing is reshaping marketing practices. Additionally, we focus on how artificial intelligence, machine learning, and quantum computing will impact data processing capabilities in future. The paper emphasizes the need for responsible data practices and discusses ethical issues such as data privacy and algorithmic bias. Several case studies focused on personalized marketing to improve customer satisfaction for companies like Netflix, Amazon, and Spotify are used. The findings suggest that businesses aiming to stay competitive will need to integrate data science in the complex data-driven world. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Credibility, Engagement, and Purchase: How Virtual Influencers Drive Gen Zs Consumer Behavior
This study focuses on how virtual influencers (VIs) impact the buying behavior of Indian Generation Zs (Gen Z). Using a structured questionnaire, data were collected from 519 Gen Z consumers across major metropolitan cities. The result of study revealed that the VIs credibility impacts how well they are able to engage with their followers. The results indicated that online engagement influenced brand credibility and brand familiarity. The results also showed that online engagement did not affect brand trust and purchase intent. Artificial intelligence (AI) is enabling VIs to become a more cost-effective and sustainable tool for brands. 2026 Aarti Saini and Vikas Garg. -
Neuroplasticity, Stress, and Resilience in the Mordern Workplace
Workplace stress profoundly impacts well- being, leading to burnout and low productivity despite traditional interventions. This chapter explores how neuroplasticity offers a solution for enhanced resilience. Chronic stress alters brain regions like the amygdala and prefrontal cortex, impairing decision- making and memory. Fortunately, processes like synaptic reorganization and neurogenesis enable brain recovery. Evidence- based interventions mindfulness, exercise, social support, and sleep hygienestrengthen stress tolerance. Workplace- level changes, including resilient leadership and ergonomics, also foster adaptability. Integrating neuroscience, psychology, and organizational behavior, this framework highlights neuroplasticity's role in building individual and organizational resilience, creating more adaptive and humane workplaces. 2026 by IGI Global Scientific Publishing. -
Building resilient and sustainable operations through cloud security
With the evolution of the digital era, organizations are increasingly employing cloud computing for enhanced scalability, operational effectiveness, and innovation. With this rapid evolution in cloud technology comes vulnerability to advanced and dynamic cybersecurity threats for enterprises. This chapter discusses the idea of cloud security intelligence (CSI) as a strategic method for developing sustainable and resilient cloud operations. CSI employs state-of-the-art technologies such as artificial intelligence (AI), machine learning (ML), real-time monitoring, and automation for threat detection, investigation, and threat management proactively. The chapter discusses the very essence of CSI, i.e., collecting data, threat detection, incident response, and ubiquitous monitoring, and reflects how CSI contributes to regulatory compliance, business resilience, cost reduction, and sustainability of the environment. Through real-life instances of CSI deployment in healthcare, finance, and e-commerce spaces, the chapter illustrates the paradigm-breaking function of CSI in the organizational security stance. Furthermore, it brushes upon current issues of CSI deployment and maps future directions involving AI, blockchain, Internet of Things (IoT), and quantum-safe encryption. Last but not least, CSI also offers itself not merely as a technical solution but as a strategic enabler of secure, compliant, and sustainable digital realms. 2026 selection and editorial matter, Jossy George, Kamal Upreti, Ramesh Chandra Poonia, Ankit Gautam, and Danish Nadeem; individual chapters, the contributors. -
Building resilient and sustainable operations through cloud security
With the evolution of the digital era, organizations are increasingly employing cloud computing for enhanced scalability, operational effectiveness, and innovation. With this rapid evolution in cloud technology comes vulnerability to advanced and dynamic cybersecurity threats for enterprises. This chapter discusses the idea of cloud security intelligence (CSI) as a strategic method for developing sustainable and resilient cloud operations. CSI employs state-of-the-art technologies such as artificial intelligence (AI), machine learning (ML), real-time monitoring, and automation for threat detection, investigation, and threat management proactively. The chapter discusses the very essence of CSI, i.e., collecting data, threat detection, incident response, and ubiquitous monitoring, and reflects how CSI contributes to regulatory compliance, business resilience, cost reduction, and sustainability of the environment. Through real-life instances of CSI deployment in healthcare, finance, and e-commerce spaces, the chapter illustrates the paradigm-breaking function of CSI in the organizational security stance. Furthermore, it brushes upon current issues of CSI deployment and maps future directions involving AI, blockchain, Internet of Things (IoT), and quantum-safe encryption. Last but not least, CSI also offers itself not merely as a technical solution but as a strategic enabler of secure, compliant, and sustainable digital realms. 2026 selection and editorial matter, Jossy George, Kamal Upreti, Ramesh Chandra Poonia, Ankit Gautam, and Danish Nadeem; individual chapters, the contributors. -
Deploying Deep Learning in Real-Time for Lung Cancer Diagnosis via Medical Imaging
In this research, deep learning models were used to diagnose lung cancer automatically using hospital image data. A dataset with 3,400 lung cancer images from online repositories and hospital archives was used for model training and evaluation. After preprocessing and feature extraction, various deep learning architectures such as VGG-16, CNN, ResNet and RNN were adopted in this study. The VGG-16 model had the highest accuracy rate of 96.86%, showing strong performance. This rate of accuracy is actually higher than their accuracy of 91%. These results serve to highlight the impressive accuracy achieved by our study relative to prior research. By accurately and effectively altering lung cancer diagnosis into a process entirely reliant on algorithms, deep learning models show promise for their potential. Diagnostic tools should be able to catch cancer early and accurately, identify the present type and classification for tumors. For all its promise, limitations such as dataset size and generalizability mean that clinical trials will be needed for further validation. Focus should turn toward this as the direction of future research in order to enhance model robustness and applicability against challenges. This research allows us to better the well-being of patients and reduce the burden of lung cancer through timely intervention and personalized treatment strategies by making use of advanced techniques in medical diagnostics. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Cognitive Cloud Computing: Building Intelligent Systems for Tomorrow
This book serves as a comprehensive guide, covering the fundamentals of cloud computing, advanced concepts, and practical applications. Discusses cyber-physical systems, cloud integration for environmental management, and cloud security intelligence systems for sustainable operations. Presents case studies based on computational intelligence-based optimization for sustainable operations and cloud integration. Emphasizes how cloud computing revolutionizes traditional processes, enabling direct input of assembly details into a computation model, streamlining development cycles, and reducing costs. Bridges the gap between theory and practice by offering guidelines on problem encoding and implementation strategies, empowering readers to apply their acquired knowledge to solve complex industrial problems. Covers wireless security in the cloud era, mechatronics and cloud integration, and cloud-enabled manufacturing for eco-friendly operations. The text is primarily written for senior undergraduates, graduate students, and academic researchers in electrical engineering, electronics and communications engineering, computer engineering, and information technology. 2026 selection and editorial matter, Jossy George, Kamal Upreti, Ramesh Chandra Poonia, Ankit Gautam, and Danish Nadeem; individual chapters, the contributors. -
Integrating LightGBM and XGBoost for Robust Plant Disease Classification: A Homogenous Stacking Approach
In addressing the critical challenge of early and accurate plant disease diagnosis, this study explores the application of a novel homogeneous multi-layered stacking model utilising Light Gradient Boosting Model (LGBM) and Extreme Gradient Boost (XGB) for the detection of plant diseases. Traditional approaches often rely on basic stacking methods; however, this research seeks to explore the intricacies of altering model architecture, combining the strengths of LGBM and XGB classifiers to build a highly accurate and efficient disease detection system. Comprehensive evaluations were conducted using metrics such as AUCROC curves, Confusion matrix and F1 scores. The ROC curve for the stacked model demonstrated superior performance with a score of 85.12%, compared to 83.09% for the single LightGBM model used for comparative analysis. The future scope of ML in agriculture includes integrating such models with real-time monitoring systems and expanding its applications to diverse crops and environments. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Benchmarking Ensemble Methods: Stacking, Hard Voting, and Soft Voting
This study evaluates three ensemble techniquesbasic stacking, hard voting, and soft votingfor predicting diabetes onset using the Pima Indians Diabetes dataset. While traditional methods often focus on single models, this research emphasizes the benefits of combining models like Cat Boost, random forest, logistic regression, linear discriminant analysis, and gradient boosting classifier (LightGBM) within ensemble frameworks. The models were rigorously assessed using metrics for evaluation such as AUC-ROC curves, confusion matrices, F1 scores, etc. The advanced calibrated model achieved the highest performance, with an accuracy of 90.10%, precision of 90.32%, recall of 81.16%, and an F1 score of 85.50%. The soft voting model also delivered strong results, with an accuracy of 89.06%, precision of 87.50%, recall of 81.16%, and F1 score of 84.21%. In comparison, the hard voting model recorded an accuracy of 88.02%, precision of 85.94%, recall of 79.71%, and F1 score of 82.71%. These results highlight the potential of advanced ensemble methods to enhance predictive accuracy. Future work could involve integrating these models with real-time monitoring systems for improved healthcare diagnostics and applying them to diverse datasets and medical conditions. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Benchmarking Ensemble Methods: Stacking, Hard Voting, and Soft Voting
This study evaluates three ensemble techniquesbasic stacking, hard voting, and soft votingfor predicting diabetes onset using the Pima Indians Diabetes dataset. While traditional methods often focus on single models, this research emphasizes the benefits of combining models like Cat Boost, random forest, logistic regression, linear discriminant analysis, and gradient boosting classifier (LightGBM) within ensemble frameworks. The models were rigorously assessed using metrics for evaluation such as AUC-ROC curves, confusion matrices, F1 scores, etc. The advanced calibrated model achieved the highest performance, with an accuracy of 90.10%, precision of 90.32%, recall of 81.16%, and an F1 score of 85.50%. The soft voting model also delivered strong results, with an accuracy of 89.06%, precision of 87.50%, recall of 81.16%, and F1 score of 84.21%. In comparison, the hard voting model recorded an accuracy of 88.02%, precision of 85.94%, recall of 79.71%, and F1 score of 82.71%. These results highlight the potential of advanced ensemble methods to enhance predictive accuracy. Future work could involve integrating these models with real-time monitoring systems for improved healthcare diagnostics and applying them to diverse datasets and medical conditions. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Euclid: Early Release Observations of ram-pressure stripping in the Perseus cluster: Detection of parsec-scale star formation within the low surface brightness stripped tails of UGC 2665 and MCG +07-07-070
Euclid is delivering optical and near-infrared imaging data over 14 000 deg2 on the sky at spatial resolution and surface brightness levels that can be used to understand the morphological transformation of galaxies within groups and clusters. Using the Early Release Observations (ERO) of the Perseus cluster, we demonstrate the capability offered by Euclid in studying the nature of perturbations for galaxies in clusters. Filamentary structures are observed along the discs of two spiral galaxies, UGC 2665 and MCG +07-07-070, with no extended diffuse emission expected from tidal interactions at surface brightness levels of a30 mag arcseca 2. The detected features exhibit a good correspondence in morphology between optical and near-infrared wavelengths, with a surface brightness of a25 mag arcseca 2, and the knots within the features have sizes of a 100 pc, as observed through IE imaging. Using the Euclid, CFHT, UVIT, and LOFAR 144 MHz radio continuum observations, we conducted a detailed analysis to understand the origin of the detected features. We constructed the Euclid IEaYE, YEaHE, and CFHT u ar, g ai colour-colour plane and show that these features contain recent star formation events, which are also indicated by their H? and NUV emissions. Euclid colours alone are insufficient for studying stellar population ages in unresolved star-forming regions, which require multi-wavelength optical imaging data. There are features with red colours that can be explained by dust being stripped along with the gas in these regions. The morphological shape, orientation, and mean age of the stellar population, combined with the presence of extended radio continuum cometary tails can be consistently explained if these features formed during a recent ram-pressure stripping event. This result further confirms the exceptional qualities of Euclid in the study of galaxy evolution in dense environments. 2025 EDP Sciences. All rights reserved.