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EXPLORING THE POTENTIAL OF VELVET BEAN, MUCUNA PRURIENS (L) SEED ON GROWTH AND GONADAL DEVELOPMENT OF MONO-SEX COMMON MOLY POECILIA SPHENOPS (VALENCIENNES, 1846)
Mucuna pruriens, a rich source of L-dihydroxyphenylalanine, commonly known as L-DOPA and a precursor to dopamine, holds potential as a natural nutritional supplement. This study aimed to delve into the impact of incorporating M. pruriens seed powder (MpSP) into the feed on growth parameters and gonadal development of mono-sex common molly (Poecilia sphenops). The fish population was divided into three experimental groups, such as G1, G2, and G3, and a control group (C), each comprising 20 individuals. Over 45 days, the experimental groups were nourished with a commercial diet bolstered by MpSP in different concentrations (5, 7 and 10g/kg of feed, respectively). In contrast, the control group was provided with a regular diet devoid of the supplement. At the end of the experiment, MpSP demonstrated significant modulation (p<0.05) of growth performance metrics, including specific growth rate (SGR), length gain rate (LGR), body mass gain (BMG), and feed conversion ratio (FCR). Impressively, even the lower concentration of MpSP (5g/kg diet) yielded substantial increments in sperm count (p<0.05) and gonadosomatic index (GSI). These findings were corroborated by histological changes that reflected enhanced testicular development, consistently outperforming the control group. These outcomes collectively suggest the potential of velvet bean seed powder as a feasible, natural, and costeffective dietary supplement for enhancing growth and testicular development in mono-sex P. sphenops. (2024) West Bengal Veterinary Alumni Association. All rights reserved. -
Deployable Solution for Real-Time Children Face Emotion Prediction System
Nowadays, many parents struggle to comprehend their children's emotions, which can hinder the creation of a nurturing environment. While numerous models focus on predicting adult emotions, there is a lack of standardised datasets for studying children's emotions. To address this gap, our work attempts to establish a comprehensive children's emotion dataset that can facilitate the study of emotions across various pose orientations. Furthermore, we propose an efficient and deployable system for real-time children's emotion prediction. An effective face detector with deep architecture is designed to handle all pose orientations from key image frames. Optimal features are then selected by re-ranking the features using a hybrid feature selection mechanism. The emotion category is declared by carefully analysing sequences of emotion identification from these features. This system holds promise for educational institutions and healthcare facilities, offering insights into children's behaviour through emotional analysis. Through experimental comparisons with three state-of-the-art emotion prediction models, we observed that our proposed system consistently outperforms existing models. Hence, we strongly recommend the adoption of our proposed system. With its achievement of state-of-the-art results in children's facial emotion recognition, it offers a practical solution for real-time deployment across diverse settings. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
A Novel Approach towards Key-point based Real-time Children Emotion Prediction
Emotion prediction is crucial in mental healthcare. It is vital in children as it aids in managing behavioral issues and early identification of emotional distress that can lead to helpful mental health support. The research uniquely centres on children's emotional expressions, addressing a gap in existing emotion detection studies, which often focus on adults. By specifically tailoring the model to recognize subtle expressions unique to children, the study contributes valuable insights into child psychology and emotion recognition. To address this gap, this work attempts to establish a comprehensive children's emotion dataset that can facilitate the study of emotions across various pose orientation. The approach introduces advanced key-point detection techniques that capture a higher density of facial landmarks, allowing for more nuanced analysis of emotional expressions. This fine-grained detection enables the identification of subtle changes that are critical in interpreting children's emotions. An effective face detector with deep architecture is designed to handle all pose orientations from key image frames. Optimal features are then chosen by re-ranking the features using a hybrid feature selection mechanism. The emotion category is revealed by careful analysis of sequences of emotion identification from these features and is not based on a single frame. This framework holds promise for educational institutions and healthcare facilities, offering insights into children's behavior through emotion analysis. Through experimental analysis and comparisons with three existing SOTA emotion prediction models, it is observed that the proposed system consistently outperforms existing models by exhibiting an accuracy of 77.7 on average. Overall, this study recommended that the proposed model is suitable for children's emotion prediction. 2025 The Author(s). -
Influence of Pandemic-Induced Risk Awareness on Life Insurance Preferences
The COVID-19 Pandemic has created significant challenges and adjustments in several areas, including life and health insurance policies. By reviewing investors' views on life insurance as a possible investment route and studying the development of health and life insurance policies after COVID-19, this study aims to investigate the complicated elements of these changes. By means of a thorough examination of current patterns, beliefs, and obstacles within the life insurance domain, this study aims to explain the intricate relationship among outside factors, industry modifications, and personal perspectives. The study starts with a thorough analysis of the literature, which offers a theoretical framework for comprehending the ideas of life insurance, and how the COVID-19 pandemic has affected the insurance market. The latest developments in the life insurance industry since the pandemic's start are then examined using empirical research techniques, such as surveys and data analysis. This section tries to clarify the major changes in policies and practices through an examination of industry reports, changes in regulations, and market dynamics. The research looks at perceptions and trends as well as the difficulties investors have when choosing life insurance policies. It looks for typical obstacles, worries, and myths that prevent people from using life insurance products through a mix of qualitative and quantitative analysis. By comprehending these difficulties, the study hopes to shed light on possible approaches for getting over obstacles and boosting investor confidence in life insurance as a sound financial choice. Overall, by providing a thorough examination of the development of health and life insurance policies following COVID-19 and the perspectives on life insurance as an investment source, this study adds to the body of knowledge subsequently in existence. It offers insightful information to policymakers, industry players, and individual investors alike by addressing the goals of examining current trends, looking into investor views, and comprehending the difficulties experienced by investors. 2026 selection and editorial matter, Dr. Harold Andrew Patrick and Dr. Ravichandran Krishnamoorthy; individual chapters, the contributors. -
Effects of Variable Viscosity and Internal Heat Generation on RayleighBard Convection in Newtonian Dielectric Liquid
The onset of RayleighBard convection of variable-viscosity Newtonian dielectric liquid confined between two parallel plates is subject to free-free isothermal boundary condition. The combined and individual effects of temperature-dependent and electric-field-dependent variable-viscosity along with the internal heat generation are studied using the higher order Galerkin technique. This theoretical study shows that even a mild temperature-dependent variable-viscosity destabilizes the system and the electric-field-dependent variable-viscosity stabilizes the system both in the absence/presence of heat source/sink. 2021, The Author(s), under exclusive licence to Springer Nature India Private Limited. -
Building an International Entrepreneurship Index using the PSR framework
This paper builds an International Index for Entrepreneurship (IIE) for the year 2018, by using a conceptual framework named PSR (Pressures-State-Response) to encapsulate the contextual aspect of entrepreneurship globally. In the past, the indices have used a methodological framework of composite indices. This paper uses the PSR framework to show how these indicators fall into the categories of pressure, state, and response, and concentrates on how these subsystems are interrelated. The study considers 41 countries for the construction of the index. We also check the correlation between the IIE and other growth indicators such as the corruption perception index, the economic freedom summary index, GDP per capita, and trade openness using suitable statistical tools.The correlation analysis demonstrates that the IIE and the Economic Freedom Summary Index have a positive association. 2022 IEEE. -
Solid state, rapid mechanochemical route for TiO2 coated Schiff-base polymer as adsorbent for the exclusion of hexavalent Cr from water
The removal of hexavalent Cr from water is vital considering its harmful and carcinogenic effects on human health as well as the environment. Effective exclusion of Cr(VI) provides reliable water to consume, impedes bioaccumulation, and mitigates environmental pollution. The present work details the rapid, ecofriendly, solvent-free mechanochemical route for the development of a polymeric Schiff-Base-wrapped TiO2 (SBP@TiO2) nano-adsorbent for the effective removal of Cr(VI) from water. The comprehensive understanding of the structural and chemical characteristics of the newly synthesized materials were examined through Fourier transform infrared (FTIR) spectroscopy, X-Ray Diffraction (XRD), and Scanning electron microscopy (SEM) with energy dispersive X-ray (EDX) spectroscopy. To assess the adsorption capacity, kinetics, and equilibrium of Cr(VI) adsorbate on adsorbent material (TiO2 and SBP@TiO2) and to understand the interplay between the critical parameters and their impact on adsorption, a series of batch adsorption studies were carried out. The adsorption equilibrium data for the Cr(VI) adsorption process fitted well with the Freundlich isotherm model of adsorption and adsorption kinetics disclosed that the data are in excellent agreement with R2 values of 0.98721 for the pseudo-second-order, indicating that the sorption process is by chemisorption. Thermodynamic measurements revealed that the adsorption of Cr(VI) on SBP@TiO2 was spontaneous and endothermic, as corroborated by the ?ve value of ?Go and the +ve value of ?Ho, respectively. It was discovered that the sorption of 10 and 50 ppm of Cr(VI) on SBP@TiO2 was 96% and 75.4% under optimal conditions, respectively. In contrast, the sorption study of Cr(VI) on TiO2 under identical conditions was found to be 49%. The study found that surface functionalization of TiO2 by SBP admirably improved the adsorption capacity, signifying SBP@TiO2 as an efficient Cr(VI) adsorbent. 2024 The Authors -
Solid state, rapid mechanochemical route for TiO2 coated Schiff-base polymer as adsorbent for the exclusion of hexavalent Cr from water
The removal of hexavalent Cr from water is vital considering its harmful and carcinogenic effects on human health as well as the environment. Effective exclusion of Cr(VI) provides reliable water to consume, impedes bioaccumulation, and mitigates environmental pollution. The present work details the rapid, ecofriendly, solvent-free mechanochemical route for the development of a polymeric Schiff-Base-wrapped TiO2 (SBP@TiO2) nano-adsorbent for the effective removal of Cr(VI) from water. The comprehensive understanding of the structural and chemical characteristics of the newly synthesized materials were examined through Fourier transform infrared (FTIR) spectroscopy, X-Ray Diffraction (XRD), and Scanning electron microscopy (SEM) with energy dispersive X-ray (EDX) spectroscopy. To assess the adsorption capacity, kinetics, and equilibrium of Cr(VI) adsorbate on adsorbent material (TiO2 and SBP@TiO2) and to understand the interplay between the critical parameters and their impact on adsorption, a series of batch adsorption studies were carried out. The adsorption equilibrium data for the Cr(VI) adsorption process fitted well with the Freundlich isotherm model of adsorption and adsorption kinetics disclosed that the data are in excellent agreement with R2 values of 0.98721 for the pseudo-second-order, indicating that the sorption process is by chemisorption. Thermodynamic measurements revealed that the adsorption of Cr(VI) on SBP@TiO2 was spontaneous and endothermic, as corroborated by the ?ve value of ?Go and the +ve value of ?Ho, respectively. It was discovered that the sorption of 10 and 50 ppm of Cr(VI) on SBP@TiO2 was 96% and 75.4% under optimal conditions, respectively. In contrast, the sorption study of Cr(VI) on TiO2 under identical conditions was found to be 49%. The study found that surface functionalization of TiO2 by SBP admirably improved the adsorption capacity, signifying SBP@TiO2 as an efficient Cr(VI) adsorbent. 2024 The Authors -
Efficacy of Psychosocial Care Training Programme for the Staff Working in Old Age Homes
Background: Training the old-age home staff is essential in raising geriatric mental health care standards in India. Inadequate knowledge on ageing and psychosocial interventions is a significant issue in old-age homes. Old-age home staff must know how to provide individualized psychosocial care and support for older adults. Hence this study aimed to test the feasibility of the psychosocial care training program for the staff working in old-age homes. Methods: A quasi-experimental research design (pre-post without a control group) was used. Forty-two staff members participated. Mary Starke Harper Aging Knowledge Exam (MSHAKE) and structured checklist to measure the staffs knowledge on ageing, psychosocial interventions, welfare legislations, schemes, and support services were administered before, immediately after, and two months after the program and the self-efficacy checklist was administered immediately and two months after the program, to examine the efficacy of the program. Results: Significant improvement was found in the ageing knowledge and the knowledge of psychosocial intervention and psychosocial care. These improvements continued for two months (p <.001). Similarly, their self-efficacy in managing such problems was also sustained across two post-measurements (p =.045). Conclusions: Face-to-face training programs would enhance the knowledge of the old age home staff. This Psychosocial Care Training module can be used for training old age home staff to address various psychosocial needs, concerns and other psychosocial problems of the residents. 2023 The Author(s). -
Increasing underutilised data in India: Opportunities and challenges
India, a country with a population of 1.33 billion has observed a substantial increase in the number of smart phones and internet users. This increase in internet users has not only changed the way Indian spend their time but has also led to generation of huge chunks of structured and unstructured data in India. In todays world where data of consumers is significant, this rise of internet users in India can change the game. This study focuses on how this increase in underutilised data in India, especially unstructured data can provide various opportunities and challenges to businesses, government and healthcare sectors in India. 2019 SERSC. -
The Jigsaw of Capital Structure
European Journal of Business and Management Vol. 5, No.13, pp. 192-197, ISSN No. 2222-288X -
Determinant of Capital Structure in Indian Manufacturing Sector
Asia-Pacific Journal of Management Research and Innovation Vol. 8, No. 3. pp 265-269, ISSN No. 2319-510X -
Generalized viscoelastic flow with thermal radiations and chemical reactions
Background: A generalized model of mathematical nature is considered to address the viscoelastic flow problem using fractional derivatives. Control/freedom of the flow mechanism is achieved with these derivatives. In simulations of industrial interest, more variations are available with fractional derivatives when compared with ordinary derivatives. Relaxation times are incorporated to handle the abrupt changes in the flow domain. Fluid flow is carried out under the influence of thermal radiations and when a heat source or sink is present. Chemical reactions of the first order are observed in the mathematical modeling of the flow. Methods: Flow is induced with the movement of the lower surface while applying force on the x-plane. Simulations of the governing mathematical problem are computed with the combination of finite element and finite difference algorithms. Significant Findings: It is noted that velocity, temperature, and concentration change with the variation of fractional order derivatives which was not possible with the classical derivatives. Moreover, with greater relaxation times, velocity, temperature, and concentration remained at a lower level. The modeled mechanism can be considered to avoid costly trials in chemical and polymer casting industries. 2023 Elsevier B.V. -
Integrating Explainable Machine Learning (XAI) in Stroke Medicine: Opportunities and Challenges for Early Diagnosis and Prevention
Stroke is a leading cause of mortality and disability worldwide, emphasizing the critical need for early diagnosis and prevention. Machine learning (ML) has demonstrated significant potential in improving stroke prediction and management by analysing complex datasets for risk stratification, diagnosis, and treatment planning. However, the adoption of ML in stroke medicine is limited by the opacity of these models, which can hinder clinical trust and decision-making. Explainable Artificial Intelligence (XAI) addresses this challenge by making ML models more interpretable and transparent, enabling healthcare professionals to understand, validate, and trust their outputs. This research work explores the integration of XAI in stroke medicine, highlighting its potential to enhance early diagnosis, personalized prevention strategies, and treatment planning. We discuss the opportunities XAI provides in identifying high-risk patients, uncovering critical predictors, and enabling informed clinical decisions. Furthermore, we examine challenges such as ensuring model reliability, addressing biases in stroke datasets, and navigating ethical considerations related to patient data privacy and algorithmic accountability. 2025 IEEE. -
Enhancing Software Cost Estimation using COCOMO Cost Driver Features with Battle Royale Optimization and Quantum Ensemble Meta-Regression Technique
This research suggests a unique method for improving software cost estimates by combining Battle Royale Optimisation (BRO) and Quantum Ensemble Meta-Regression Technique (QEMRT) with COCOMO cost driver characteristics. The strengths of these three strategies are combined in the suggested strategy to increase the accuracy of software cost estimation. The COCOMO model is a popular software cost-estimating methodology that considers several cost factors. BRO is a metaheuristic algorithm that mimics the process of the fittest people being selected naturally and was inspired by the Battle Royale video game. The benefits of quantum computing and ensemble learning are combined in the machine learning approach known as QEMRT. Using a correlation-based feature selection technique, we first identified the most important COCOMO cost drivers in our study. To get the best-fit model, we then used BRO to optimize the weights of these cost drivers. To further increase the estimation's accuracy, QEMRT was utilized to meta-regress the optimized model. The suggested method was tested on two datasets for software cost estimating that are available to the public, and the outcomes were compared with other cutting-edge approaches. The experimental findings demonstrated that our suggested strategy beat the other approaches in terms of accuracy, robustness, and stability. In conclusion, the suggested method offers a viable strategy for improving the accuracy of software cost estimation, which might help software development organizations by improving project planning and resource allocation. 2023 IEEE. -
A Comprehensive Review of IoT, Intelligent Systems, and Computing Applications in Enhancing Renewable Energy Sources
This chapter provides a thorough examination of the application of the Internet of Things (IoT), intelligent systems, and advanced computing in enhancing the effectiveness and sustainability of renewable energy sources such as wind, ocean, hydro, and solar energies. This study explores the incorporation of real-time monitoring, predictive maintenance, and energy forecasting facilitated by the Internet of Things (IoT) and intelligent systems. The integration of artificial intelligence (AI)-based analytics and cloud computing methodologies significantly improves the process of decision-making, grid management, and optimization of energy storage. This analysis highlights the significant impact of recent technological breakthroughs and case studies on the transformation of renewable energy generation and management, ultimately contributing to the development of a sustainable and intelligent energy landscape. 2026 River Publishers. -
AI-Driven Early Diagnosis of Acute Liver Failure: A Machine Learning Perspective
The liver performs a valuable role in operating proper metabolism. This organ in the human body is responsible for maintaining and preserving overall health and well-being. However, when it fails to function optimally, it can cause severe and significant health complications. Liver diseases are multifactorial conditions that can be challenging to diagnose and treat. Early detection of any disease is beneficial for effective treatment and diagnosis of patients' conditions. Machine Learning algorithms create a great platform for analyzing medical data that helps improve disease detection procedures. This paper aims to get a better understanding of ML algorithms for detecting diseases associated with the liver. The paper tries to explore various machine learning techniques for predicting accurate liver diseases. It uses various parameters as symptoms and calculates ALF (Acute Liver Failure) based on the parameters and ALF predicts in-case the person is suffering from a Liver disease or not. Accuracy was calculated with various ML techniques i.e. Logistic Regression Classification, KNN Classification, Decision Tree, Random Forest and Support Vector Machine. Among all these, Logistic Regression was found to be most effective in identifying and predicting the outcome of the dataset compared to other algorithms. SVM has a higher cross-validation score but Accuracy, precision and recall are very low thus, cannot select this model. 2025 IEEE. -
Cloud Job Scheduling Using Deadline-Based Task Optimisation Algorithm in Internet of Things
The cloud-based Internet of Things (IoT) gadgets are becoming increasingly significant in todays current environment. Thoroughly examining the ever-changing relationship among these two domains, this literature review sheds light on how the field of research is developing and how important both domains are to defining our digital future. The analysis delves into the various uses of cloud-based computing in conjunction with IoT devices, highlighting how these two technologies have the combined power to transform companies, improve productivity, and improve user experiences. Blending cloud-based resources with IoT has become essential for advancement, from connected houses to industrial automation. The article provides a detailed overview of the complexities involved in this merger, highlighting the importance of computing in the cloud in tackling issues like data protection, immediate analysis, and resource optimisation. This study also points out significant gaps in current understanding, highlighting the need for more investigation to fully realise the promise of cloud computing when combined with IoT devices. Essentially, this analysis of the literature highlights the critical role in determining the integration of cloud technology and IoT devices by giving a more efficient and optimal scheduling Deadline-Based Task scheduling algorithm, which has proved to have the least average waiting time of five units when compared to all the scheduling algorithms taken into consideration. The beginning of a new era characterised by connectivity and data-driven decision-making, and the key to realising the full potential of IoT applications is to comprehend and leverage the power of cloud technology. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Artificial Intelligence-Driven Perspectives on Maternal Health: Revealing Important Aspects and Improving Pregnancy Results via Machine Learning
A number of factors, including genetic, environmental and social ones, affect the intricate biological process of pregnancy. The developing foetuss health as well as the mothers must be maintained in the necessary secure equilibrium of these variables. The mothers health, which encompasses her mental as well as physical health, lifestyle decisions, money, social support systems and educational attainment, will determine whether the pregnancy ends well. Medical research has changed as a result of the long-awaited tools for processing for complicated datasets that have been made possible by recent advancements in machine learning models. These models have the ability to identify correlations between characteristics that are difficult for traditional analytical techniques to uncover. Therefore, scientists can improve their understanding of the elements influencing conception and create diagnostic tools by utilizing machine learning technology for timely intervention and customized treatment. Machine learning encompasses various techniques, such as logistic regression, linear regression, random forest, K-Nearest Neighbours and gradient boosting classifier. While Random Forest is an effective way to handle big databases with multiple dimensions and interactions, KNN classifiers are excellent for more organic, data-driven cluster finding of relevant instances and association investigation between various parameters and pregnancy outcomes. Logistic regression only explains the ways in which individual factors affect pregnancy outcomes; it cannot handle binary outcomes as well as linear regression does. We will look for significant determinants of pregnancy outcomes and assess each models performance. Important elements will also be expanded upon. Pregnant patients care, professional practice and improved program decisions may all benefit from this information. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Modeling and computational fluid dynamic analysis on a non-AC bus coach system
The main objective of this paper is to reduce the drag force and enhance the uniform airflow inside an existing non-air-conditioning bus coach system. The redesigning of an existing bus carried out by considering the forces that reduce the moment of the bus. Modeling and meshing was carried out using solid works and Hypermesh software, respectively. Finally, the problem is simulated using Ansys fluent software and analysis is carried out for different bus models. The noteworthy findings state that the air resistance of the vehicle is found to be 812.74 N and coefficient of drag is 0.67 are less as compared to existing bus model. 2020 Wiley Periodicals LLC