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Validation of localised coastal wind forecasts for artisan fishers of southwestern India
[No abstract available] -
Building Global Teaching Capacity Among Pre-Service Teachers: Epistemological and Positional Framing in an Internationally Paired, Authentic Practicum
Building the capacity of pre-service teachers to work in globalized cross-cultural environments is essential to cope with the challenges of the 21st century. This study establishes the value of internationally paired, authentically collaborative practicums with strong epistemological and positional framing in pursuing such capacity development. It was conducted among 90 pre-service teachers from three different universities in Australia and India who participated in a three-week paired practicum in three schools in India. The practicum included the collaborative production of an integrated Australian and Indian combined theme presented in a whole school forum. Mixed methods and a design-based research approach yielded data affirming that such a model did indeed provide pre-service teachers with the confidence to teach in increasingly diverse classrooms and contexts, while also identifying which aspects of this practicum model were most influential in this regard. 2021 European Association for International Education. -
A Comparative Study of Machine Learning Algorithms for Recommendation Systems
This research explores recommendation algorithms for e-commerce efficacy. From e-commerce giants like Amazon to streaming services like Netflix, recommendation algorithms are integral in giving personalized experiences to attract and retain customers. It tests KNN, K-Means, Decision Tree (Gini, Entropy), and Naive Bayes on the Amazon review dataset 2018Electronics category. Decision Trees emerged as the most accurate predictor of user preferences, suggesting the trees ability to capture complex data relationships is key for relevant product recommendations. To get a better understanding, this research also examines each algorithms power and weakness in the context of recommendation systems. It offers valuable information on how to approach the optimization of their recommendation strategies in e-commerce businesses, highlighting not only the most effective approach (Decision Trees) but also the considerations for choosing an algorithm based on its strengths and weaknesses (e.g., interpretability vs. accuracy). Ultimately, this research contributes to informing data-driven decision-making for personalized recommendations in e-commerce, paving the way for a more user-centric shopping experience. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Design and Implementation of a Hybrid Solar-Grid Charging Infrastructure with IoT-Based Control
The rapid growth of e-bikes and e-scooters is straining conventional, fossil-fuel-intensive power grids and exposing a critical gap in urban charging infrastructure. In this work, a fully modular hybrid station that synergistically couples three energy vectorsphotovoltaics, second-life Li-ion battery packs, and the utility gridvia an intelligent, sub-50 ms source-arbitration network. The power-conditioning front end employs a flyback-derived SMPS delivering five tightly regulated outputs (5 V, 12 V, 37 V, 48 V DC; 230 V AC) at 9295% efficiency, while a bidirectional synchronous boost stage attains 9497% efficiency and future-proofs the system for vehicle-to-grid operation. End-to-end power quality is preserved with < 5% total harmonic distortion under dynamic loads. An ESP32-centric IoT stack with LoRaWAN back-haul furnishes kilometre-scale telemetry, secure billing, and over-the-air firmware updates, whereas a BiLSTM-assisted battery-management layer enables real-time state-of-charge and state-of-health tracking of repurposed EV modulesextending their usable life and anchoring circular-economy objectives. By fusing high-efficiency power conversion, adaptive energy-source orchestration, and cloud-native intelligence in a compact footprint, the proposed platform sets a scalable blueprint for low-carbon, resilient charging ecosystems that can keep pace with the next wave of urban micro-mobility. 2025 IEEE. -
Temperature and Performance Variations of a Li-ion Battery Pack Under Dynamic Testing Conditions
This study investigates the impact of dynamic mechanical vibrations on the temperature and performance of a 10 Ah, 37 V battery pack. The battery pack was subjected to various mechanical loads using an electrodynamic shaker, while its performance was monitored under different load conditions imposed on a 37 V, 250 W brushless direct current (BLDC) motor. The mechanical load was adjusted using a belt and spring balance arrangement. The batterys parameters, including temperature and performance metrics, were remotely monitored using a combination of sensors, an ESP32 microcontroller, and the ThingSpeak IoT platform. The results indicate that the temperature of the battery pack increased during the shaking process, with corresponding changes in performance. Future research may focus on optimizing battery pack designs to mitigate the effects of mechanical vibrations and improve overall performance under dynamic conditions. 2025 IEEE. -
Nitrogen-rich dual linker MOF catalyst for room temperature fixation of CO2 via cyclic carbonate synthesis: DFT assisted mechanistic study
The benign synthesis of a novel Zn based Lewis acid-base bifunctional metal-organic framework (ITH-1) and its room temperature catalytic ability for the chemical fixation of carbon dioxide via cyclic carbonate synthesis is reported herein. ITH-1 is characterized by the presence of mono coordinated pendant imidazole groups throughout the framework inducing Lewis basicity. The synthesized material is crystallized in the monoclinic space group as revealed by the Single Crystal X-ray Diffraction Analysis and possesses a 2 D non-planar interdigitated network wherein the neighbouring sheets are connected via strong hydrogen bonding (1.947 . ITH-1 was characterized thoroughly via various physicochemical analyses such as XRD, FT-IR, Raman, FE-SEM, CHN, ICP, TGA and was found thermally stable up to 300 ?C. The co-existence of accessible and active Lewis acid (Zn) Lewis base (imidazole) moieties rendered ITH-1 the potential to catalyse the cycloaddition of CO2 with propylene oxide under solvent and co-catalyst free conditions (~95% conversion) at moderate temperatures with remarkable reusable performance (over 5 times). ITH-1 manifested excellent CO2 conversion even under room temperature and 1 bar pressure in the presence of a co-catalyst. Density Functional Theory (DFT) calculations utilizing M06 functional were exercised to envisage the mechanism behind the successful CO2 conversion by ITH-1 at room temperature and were found to be in clear agreement with the experimental results. 2022 Elsevier Ltd -
Development and psychometric validation of the three dimensional grit scale
This manuscript reports the development and validation of the three-dimensional Grit Scale (3-D Grit Scale). The psychometric measure developed has three factors, Perseverance-Commitment (PC), Interest-Passion (IP), and Goal-directed Resilience (GR) through a series of five studies; study 1 (n = 409) for item analysis, study 2 (n = 334) for exploratory factor analysis, study 3 (n = 514) for confirmatory factor analysis and study 4 (n = 214) and 5 (n = 107) to assess the validity. The sample included students and working professionals aged between 18 and 25, residing in different parts of India. Exploratory and confirmatory factor analyses indicated that the scale excellently fits in the three-correlated factor model and the two-level hierarchical model indexing grit as a total of three first-order factors. The final 17-item 3-D Grit Scale showed adequate internal consistency (Cronbachs ? = 0.86), and split-half reliability (Spearman-brown = 0.80, Guttman = 0.80). Validation studies showed that the scores of the 3-D Grit Scale were moderately correlated with (a) 12 item grit scale (Duckworth et al., 2007) and the brief resilience scale (smith et al., 2008), indicating good concurrent and convergent validities (b) conscientiousness revealing that both the constructs are mutually exclusive. 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
Curcumin Analogues as Organic Fluorophores for Latent Fingerprint Imaging
Synthesis and characterization of two curcumin analogues BAA and Br-AA via a condensation reaction was reported. Both the synthesized organic luminophores exhibited aggregation-induced emission (AIE) with bright yellow and green emission respectively. Increase in the water% enhanced the emission by both the compounds confirmed the AIE property. A detailed study of latent fingerprints visualization was also carried out for both the analogues. Both the compounds showed good to normal capability to develop latent fingerprints (LFPs). Compound BAA performed better as a fluorescent material to develop LFPs compared to Br-AA. The LFPs developed were analyzed to obtain 13 level of fingerprint patterns under UV 365 nm illumination. The LFPs developed using BAA exhibited excellent efficiency, sensitivity, high contrast with low background interreference. All three levels of fingerprint patterns were identified by BAA. However, Br-AA showed inability to develop high clarity images of latent fingerprints. The solid-state emission nature of the analogues was also evaluated from their emission spectra and CIE coordinates were found to be were (0,187, 0.518) and (0.265, 0.484) for BAA and Br-AA respectively. 2024 Wiley-VCH GmbH. -
Advancements in EEG and EMG Signals for Motor Imagery Classification and Artifact Removal: A Comprehensive Review and Analysis
An essential noninvasive method for assessing brain electrical activity and gaining important knowledge about how the brain functions is electroencephalography (EEG). Understanding the brain's reactions to particular sensory, cognitive, or motor events requires understanding event-related potentials (ERPs), which are derived from EEG. By displaying variations in frequency content across time, time- frequency analysis improves ERP interpretation. Each of the five EEG frequency bands - delta, theta, alpha, beta, and gamma - has a unique clinical significance and is linked to different physiological and cognitive processes. In order to improve motor control and rehabilitation, this work focuses on the development of NeuroMotor Fusion approaches, which integrate EEG and Electromyography (EMG) signals for motor imagery classification. It looks at new developments in the classification of motor imagery and investigates cutting edge methods such as VR motor priming and brain-computer interfaces (BCIs). The study also discusses the difficulties in removing artifacts from EEG and EMG signals, using hybrid techniques to reduce ocular and muscular artifacts. The study produced a 96.2% accuracy rate in motor function enhancement using the ShallowFBCSPNet model architecture and the MOABBDataset "BNCI2014-001". These findings show that NeuroMotor Fusion has a great deal of promise for use in neurological disease support, individualized motor skill training, and rehabilitation. 2025 IEEE. -
Gene Expression Data-Based Interpretable Machine Learning Framework for Classifying Brain Cancer Subtypes
Early detection, therapeutic stratification, and precision medicine all rely on the precise classification of brain cancer subtypes. To categorize brain tumor subtypes, we examine the application of ensemble machine learning modelsRandom Forest, XGBoost, and LightGBMusing high-dimensional gene expression data from the GSE50161 dataset (CuMiDa). The top 1000 genes were selected using variance thresholding, and models were then trained and evaluated on a stratified split of the dataset. Despite the availability of models achieving similar accuracies (~9596%) in existing works, our framework integrates SHAP-based interpretability to identify biologically significant genes, such as CDK4, EGFR, and TP53, offering dual benefits of high predictive power and explainability. The use of SHAP (SHapley Additive exPlanations) values to assess model predictions and identify physiologically important gene features revealed that key gene probes, including as CDK4, EGFR, and TP53, were significant across different tumor subtypes. This study demonstrates how SHAP and interpretable ensemble learning may be used to diagnose brain tumors with excellent classification accuracy and physiologically meaningful gene identification. Published by Oriental Scientific Publishing Company 2025. -
Advances in Type II Diabetes Prediction: A Comprehensive Review of Machine Learning Techniques
Type II diabetes mellitus, on the other hand has been regarded as one of the growing concerns globally and thus clearly raises the need for making accurate forecasts of diabetes. The risk for Type II diabetes can be predicted using Ma-chine Learning as well as any other form to make the predictions much more enhanced than the traditional methods. This paper aims to give a broad overview of literature that has so far been available on the ML algorithms used in the management of Type II diabetes including such supervised algorithms as logistic regression, alphabet regression, random forest, support vector regression along with other methods such as, ensemble learning, deep learning, and hybrid. Analysis of the main aspects for the performance model such as parameter selection, the way to face and cope with imbalance parameters, interpretability and generalizability across different populations, another aspect that was regarded is the possibility of using real-time data collected with wearable devices and applying tissue and other biomarkers for better prediction. Finally, the key obstacles and future directions towards developing ML algorithms and models explainable and clinically relevant have been introduced to help researchers and practitioners toward effective, personalized, and scalable interventions. 2025 IEEE. -
Harnessing the Power of Cloud Computing for Advanced Business and Economic Research
Cloud computing has surfaced as a significant influence in the domain of business and economic research. Its ability to deliver vast computational resources, scalable storage, and unparalleled accessibility has revolutionized the way researchers analyze complex datasets, conduct simulations, and collaborate on ground-breaking projects. This paper delves into the myriad ways cloud computing is empowering researchers to unlock unprecedented economic insights. This research article delves into the key dimensions of leveraging cloud computing for advanced business and economic research. It investigates the scalability and flexibility of cloud-based infrastructure, enabling researchers to process and analyze extensive datasets, conduct complex simulations, and implement machine learning algorithms for predictive modeling. Moreover, the cloud facilitates real-time collaboration and data sharing, fostering a global research community that transcends geographical boundaries. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Building a sustainable relationship between customers and marketers
Finding new customers is costlier than retaining the existing customers for the business. Therefore, building a strong relationship with customers helps marketers to retain their existing customers. Incorporating ethical and moral values into marketing activities offer a way to build a strong relationship. This study identifies the factors that bind customers and marketers into a sustainable relationship in the Indian context. This study constitutes a framework to understand and apply sustainable relationship marketing in the personal care industry. This study touches certain marketing disciplines such as marketing mix policy, transparency in trades, building trust, product delivery, promises delivery, and sustainable relationship. The convenience sampling technique used for the selection of respondents from the Mohali City of Punjab, and interview them. The finding suggests that promises delivery is the most important factor for a sustainable relationship. If promises are delivered effectively then the life of the relationship will be longer. Copyright 2024 Inderscience Enterprises Ltd. -
Development and characterization of Fe2O3 nanoparticles coated with chitosan and folic acid for biomedical applications
Polymeric inorganic nanoparticles have emerged as promising nanomedicines due to their unique properties, offering enhanced antibacterial and anticancer effects. Thus, the study focus on the synthesis of Fe2O3 and Fe2O3 coated with chitosan and folic acid nanoparticles (Fe2O3-CS-FA NPs) mediated by Tagetes erecta (T. erecta) extract and assess their biological effects. The synthesized NPs are analysed by various characterisation techniques. FTIR spectroscopy of Fe2O3 and Fe2O3 -CS-FA NPs revealed characteristic peaks corresponding to Fe2O3, chitosan, and folic acid molecules. The XRD pattern confirmed the successful synthesis of Fe2O3 NPs and Fe2O3 -CS-FA NPs, indicating a rhombohedral structure. FESEM demonstrated spherical structures for both Fe2O3 and Fe2O3 -CS-FA NPs. Antimicrobial activity was assessed against various pathogens using the disk diffusion method, showing that Fe2O3-CS-FA NPs demonstrated superior antibacterial activity compared to Fe2O3 NPs. In terms of antioxidant activity, Fe2O3 -CS-FA NPs showed the highest scavenging activity against DPPH, outperforming Fe2O3 NPs. The anticancer activity of both Fe2O3 NPs and Fe2O3 -CS-FA NPs was tested against the HCT-116 human colon cancer cell line, where Fe2O3 -CS-FA NPs demonstrated greater anticancer activity with an IC50 value of 10.2 ?g/mL compared to Fe2O3 at 13.8 ?g/mL. Based on the findings of this research, there is a strong indication that Fe2O3 -CS-FA NPs hold significant potential as a nanomaterial well-suited for advanced biomedical applications in the industry. 2025 Indian Chemical Society -
Theoretical Studies ond(?,p)n atAstrophysical Energies
The photonuclear reactions using deuterium target finds application in nuclear physics, laser physics and astrophysics. The studies related to deuteron photodisintegration using polarized photons has been the focus of interest since 1998 which influenced many experimental studies which were carried out using 100% linearly polarized photons at Duke free electron Laser laboratory. Theoretical study on deuteron photodisintegration was carried out and in these studies the possibility of 3 different E1v amplitudes leading to the final n-p state in the continuum was discussed. As there is experimental evidence about the splitting of 3 E1vp- wave amplitudes at slightly higher energies, we hope that the same may be true at near threshold energies also. As the spin dependent variables are more sensitive to theoretical inputs and the data obtained on polarization observables are more sensitive to theoretical calculations, there is a considerable interest on studies related to the reaction. More recently, neutron polarization in d(?,n)p was studied at near threshold energies. In this regard the purpose of the present contribution is to extend this study to discuss proton polarization in d(?,p)n reaction using model independent irreducible tensor formalism at near threshold energies of interest to astrophysics. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Model independent approach to proton polarization in photodisintegration of deuteron
In addition to other photonuclear reactions, the study of photonuclear reactions on deuterium targets is important for laser physics, nuclear physics, astrophysics, and a number of applications, including nondestructive testing of nuclear materials. In this paper, we have carried out a model independent analysis of proton polarization in photodisintegration of deuterons with initially unpolarized beam and unpolarized target. The angular dependence of the polarization is studied by expressing it in terms of multipole amplitudes. 2023 Elsevier Ltd. All rights reserved. -
Data-driven triumph: CRM sales insights revolutionize customer retention
Context: The examination of the CRM data is anchored in a comprehensive analysis of sales performance metrics, with a significant role played. It was found a gap in the literature, considering the scarcity of pertinent case studies within the academic literature. Method: The geographical factor is paramount in this analysis, as it unveils divergent results across different regions. Moreover, the venture into predictive analytics for sales forecasting, capitalizing on CRM primary data spanning from 2018 to 2023, facilitating more informed decision-making. The sample comprises around 1500 Business to Business customer clusters for in-depth analysis is considered. Findings: From the Business Intelligence analysis, it was found the presence of long-standing customers with a lower purchase rate, favouring average industrial product models preferred by the customers. Conclusion:The study also explores the link between CRM can shape business strategies, enhance customer relationships, and boost organizational performance and customer retention. 2025 by IGI Global Scientific Publishing. All rights reserved. -
Chatbots in health care: AI-based personalization and EHR integration in patientdoctor communication
The artificial intelligence (AI)-driven chatbots in healthcare integration revolutionizes patientprovider interactions for real-time support, communication streamline, and patient engagement. These chatbots connected to natural language processing (NLP) and machine learning provide medical queries resolution, chronic condition management, and scheduling appointments. Despite the advancements, there are gaps remain in the chatbot personalization interactions and Electronic Health Records (EHR) seamless integration. Personalization is crucial for satisfied patient and medical advice. EHR integration enables context-aware responses, error reduction, and better healthcare outcomes. This study effectiveness fosters the evaluation of AI-driven chatbots in healthcare communication personalization and potential benefits examination and EHR integration challenges. Using a mixed-methods approach includes sentiment analysis for patient satisfaction sentiments understanding, thematic analysis for key themes and findings from Patient Message, regression analysis for personalization, EHR integration, and patient outcomes understanding, and structural equation modeling (SEM) highlights the personalization and EHR integration impact on patient satisfaction, engagement, and trust in chatbot technology. The findings reinforcing the healthcare providers need to adopt AI-driven solutions and personalized communication priorities and seamless data integration for patient experience improvement and overall healthcare efficiency. 2026 Elsevier Inc. All rights reserved. -
Workforce Transformation and Value Creation in the Era of Industry 4.0, 5.0 and 6.0: Challenges and Enablers
Industry 4.0, Industry 5.0, and Industry 6.0 have been empowered by the acceptance of several advanced technologies, including the Internet of Things (IoT), artificial intelligence, robotics, and human-centric innovation in releasing industries. Comprehensive studies that can include barriers as well as enablers are difficult to conduct. This study employs a mixed-methods research approach, integrating of AHP (Analytic Hierarchy Process) and TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) to evaluate key challenges and enablers in workforce transformation. The Findings indicate that leadership vision, digital investment, and employee up-skilling play a crucial role in transformation navigation. Additionally, automation and AI adoption present both opportunities and challenges for workforce adaptability. The study provides strategic insights for organizations to enhance their workforce resilience, competi-tiveness in the evolving industrial landscape. The study offers actionable advice for businesses, policymakers, and educators, and suc-cessfully adaptation the paradigm and sustainable development in the modern era. Authors. -
Eco-friendly operations in Reality: Analysis of key factors of sustainability performance in manufacturing companies
Sustainable product design (SPD) focuses on eco-friendly products. The energy efficiency (EE) optimizes energy use in buildings, transportation, and industry. The policies that reduce the consumption at the macro-level rebound effects are debated. The evidence leans toward waste management (WM) needing strategies like recycling and source reduction in developing countries. This study examines the sustainable product design, energy efficiency, and waste management on overall sustainability performance in manufacturing companies. The study used correlation analysis, descriptive statistics, and multiple linear regression to quantify the significance of these factors. Results show that all three variables significantly contribute to sustainability performance and support the hypotheses. SPD, EE, and WM positively impact overall sustainability performance, and evidence leans toward strong relationships among variables, but multicollinearity could complicate findings. 2026 selection and editorial matter, Jossy George, Kamal Upreti, Ramesh Chandra Poonia, Ankit Gautam, and Danish Nadeem; individual chapters, the contributors.