Volume 13 | Issue 4 |

 Abstract

Abstract: The world economy is becoming progressively knowledge driven, and intellectual capital is considered to be the major driver that affords organizations a competitive lead. In such a situation, turnover among highly-productive, key employees can be costly. Employers often consider various measures for reducing turnover cost which consists of hiring, recruiting and selecting the employees, besides methods of engaging the selected employees meaningfully. Available research provides ample evidence connecting job satisfaction to reduction in employee to turnover. Ali (2009), Kamasak (2010), and Kumar & Gupta (2013) suggest that job satisfaction has a correlation with the turnover intention of the employee. This inverse correlation starts with the thinking of quitting, seeking for a new job, and the last is moving out of the job once the employee has found a better opportunity. Eisenberger et al. (1990) argued that employees who feel that they are being appreciated by their supervisors or managers, not only have an elevated degree of commitment, they are also more conscious of the responsibilities vested on them, have better involvement in the organization, and are more active and innovative. Hence, such appreciation shown by managers will cause lesser turnover intention. Besides job satisfaction, and commitment, contemporary research throws light on the construct of job embeddedness as a determinant of employee turnover. Job embeddedness comprises of the links an employee has with the job and the organization and other factors in the work space, the employee's fit with the organization and community, and how much an employees is ready to sacrifice to change jobs (Lee et al., 2004; Holtom & Inderrieden, 2006). This study aims to investigate and find out the influences of three distinct factors like job commitment, job satisfaction, job embeddedness which are taken as our independent variables on two dependent variables, turnover intent and job search behavior of an employee. We have selected the banking industry as the field of our study in view of the fact that banking being one of the service oriented industries where people are considered to be the most valuable assets, erosion of this asset will tell upon the whole banking sector. Banks with progressive thinking are already looking for ways to leverage their human resources, along with other processes and technology, to reach their objectives. With the gradual change in employee expectations, organizations are forced to place greater emphasis on talent management and practices (Marie-Antoinette van Zyl, 2011). Moreover, with the introduction of special initiatives for financial inclusion and as a post-demonetization effect, employees at both public and private sector banks are exposed to prolonged working hours with work pressure. A primary survey was conducted on the employees of various private as well as public sector banks in and around Kolkata. There are a total of 80 questionnaires selected from 100 questionnaires that were distributed at the chosen organizations on the basis of which the paper has attempted to diagnose any kind of relationship that might exist between the individual factors that are taken into consideration.

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job commitment, job embeddedness, turnover intent, job satisfaction, job search behavior

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This research examines the financial performance of Indian Railway Finance Corporation (IRFC) over a period of five years (2019-2024), highlighting liquidity, profitability, solvency, and activity measures. Ratio analysis was used to explain financial well-being, business efficiency, and long-term sustainability. The findings reflect steady liquidity and profitability with good asset use but excessive reliance on long-term debt. Recommendations are made to improve financial effectiveness and strategic management.

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Financial Performance, Ratio Analysis, IRFC, Liquidity, Profitability, Solvency, Asset Utilization.

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As parents, it's essential to actively monitor your child's hydration. This can mean offering fluids at regular intervals, especially if your child is too young or too distracted to ask for water themselves. Educating your kids about the importance of drinking water and recognizing the signs of dehydration is also crucial.

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DEHYDRATION. PREVENTION. FLUID REPLACEMENT

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The Buddhist community in India, which was once a powerful presence in the spiritual and cultural landscape of the subcontinent, has witnessed a phenomenal resurgence in the new age. The essay discusses the transformation of the Buddhist community in new India, with emphasis on their cultural practice, identity, and social roles. The revival of Buddhism in India has much to do with the works of Dr. B.R. Ambedkar and his utopian idea of a social and political transformation among oppressed people, specifically the Dalits. On the basis of a synthesis of literature review, interviews, and field research, this study seeks to provide a glimpse into the cultural habits of the Buddhist group in contemporary India and how these persistently keep evolving in spite of socio-political hardships. The results show that contemporary Buddhist rituals not only follow conventional rituals but also concentrate on social justice, equality, and group identity.

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Dalit, Transformation, Religion, Culture, Society

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Anemia is a major health problem throughout the world with an annual prevalence of 400 million. The prevalence rates are higher in developing countries like India, especially affecting toddler, adolescents and women in reproductive age group. Further women of child bearing age are more susceptible to have anemia because of various factors. The current study throws a light on the same issue. This descriptive study was conducted "A study to assess the effectiveness of structured teaching program on knowledge regarding iron deficiency anemia among women of reproductive age group in selected urban area of Ratia (Fatehabad." With having objectives like to assess the knowledge of women of reproductive age group regarding iron deficiency anemia . secondly to evaluate the effectiveness of STP in terms of knowledge regarding IDA and to find the association between selected demographic variables with pretest and post test knowledge score of women of reproductive age group . Materials and Methods: Study was conducted using quantitative approach and Pre- Experimental one group pretest and posttest design on 60 women of reproductive age group in selected area of Ratia (Fatehabad) by using purposive sampling technique. Structured interview was used to collect the relevant data from sample regarding iron deficiency anemia. Results: Descriptive and inferential statistics was used for data analysis. The result showed that out of 60reproductive age group women. The overall mean value of pre-test score was 12.81 and posttest knowledge score was 21.71. The obtained't' value for knowledge is -27.76. The chi square test used to see the association between the knowledge and selected demographic variables. In the pretest chi square test were 4.72 (age), 8.7 (education) , religion (12.2), type of family (1.7),family income (0.85), education of husband (3.5), type of home (0.96), prior exposure to STP regarding anemia (0.96) which shows that there is significant association with selected demographic variable with pretest. Working area (8.65, occupation (14.28) which show that there is no any significant association with selected demographic with pretest. In posttest chi square test were 1.9 (age), (20.8) education , religion (3.39), type of family(0.17), family income (0.8), education of husband (5.2) which shows that there is significant association with selected demographic variable with post test. Working area (12.41), occupation(37.96), type of home (0.8), prior exposure (24.77) which shows that there is no any significant association with selected demographic with post test. Conclusion: The study concludes that effectiveness of STP has improved the post test knowledge as compared to pretest knowledge score of reproductive age group women .The following conclusions were made on the basis of findings of the study. There was lack of knowledge regarding iron deficiency anemia. The study concludes that structured teaching on iron deficiency anemia was found effective in increasing their knowledge. Women of reproductive age group had a significant gain in knowledge regarding iron deficiency anemia. The written prepared material by the investigator in the form of structured teaching programme helped women of reproductive age group to improve their knowledge regarding iron deficiency anemia. Hence structured teaching programme was effective to improve the knowledge regarding Iron deficiency anemia of reproductive age group women .

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 Keywords

Effectiveness , Assess,Iron Deficiency Anemia , Reproductive Age , Structured

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Abstract Coronary artery disease is the most common among cardiac clients. Coronary artery disease is the global threat. Many people are dying due to heart problem. Once the client diagnosed as cardiac problems, they have to continue the medicine for life long. Some of the patients may not have economic stability to continue the medicine lifelong. Some people are unable to cope up the treatment. Cardiac clients develop anxiety and depression due to expenses, limitation of adult daily activity and frequent hospitalization. The mind and body plays an important role for the recovery of the cardiac clients. In order to have a good prognosis, the clients should be free from anxiety and depression. In the present study the researcher would like to assess the anxiety and depression on cardiac clients. Objectives: To assess the hospital anxiety and depression among cardiac clients. Design: Descriptive research design adopted for the study. Setting: The study was conducted in a tertiary care multi-specialty hospital, Coimbatore. Population: Cardiac clients who fulfill the inclusion criteria were participated in the study. Methods: The study design was Quantitative descriptive research design was used for the study. The non-probability convenient sampling technique was used to select the sample. The data were collected by administering the structured questionnaire of hospital anxiety and depression scale. The structure questionnaire consists of 14 items with four option score. Results: The study depicted that 84% of the clients belong to male. 37% of the clients were between 50 and 60 years of age. Majority of the clients were normal. 15%, 8% of clients had abnormal anxiety and depression. Conclusion: Coronary artery disease is the major health concern with a leading morbidity, mortality in developed and developing countries. Coronary artery disease is a major burden to the family members because of the medical expenses. It leads to increased stress, anxiety and depression.

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Anxiety, Depression, Hospital, Cardiac, Clients, Coronary artery disease.

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The objective of this study is to develop a robust machine learning pipeline for heart disease prediction using an ensemble of K-Nearest Neighbors (KNN), Support Vector Classifier (SVC), and Decision Tree (DT) models, with hyperparameter tuning to improve accuracy. Early and precise detection of heart disease is vital in the medical field for better treatment outcomes. The process begins with importing necessary libraries and loading the dataset, followed by data preprocessing, which includes exploratory data analysis, handling missing values, reducing memory usage, and describing features. Outliers are identified using boxplots to ensure data quality. Visualization is carried out through univariate and bivariate analysis using scatter plots to explore relationships between features such as age, chest pain, and exercise. After applying MinMax scaling, the data is split into training and test sets. The models are then built and optimized using hyperparameter tuning. Finally, performance is evaluated using accuracy and classification reports, with a comparison of the models to determine the most effective one. This complete pipeline offers a scalable and accurate solution for heart disease diagnosis.

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Heart Disease Prediction, Machine Learning, KNN, SVM, Decision Tree, Hyperparameter Tuning, Medical Diagnosis, Classification Models, Health Informatics, Predictive Analytics.

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The increasing focus on the safety of lithium-ion batteries (LIBs) in electric vehi- cles (EVs) necessitates early detection of soft short circuits (SCs) to prevent severe faults such as fire or thermal runaway. This paper proposes an on-board soft SC fault diagnosis method using the extended Kalman filter (EKF). The EKF adjusts a gain matrix based on real-time measured voltages to estimate the state of charge (SOC) of the faulty cell. The SOC difference is then utilized for soft SC fault detection, with identified soft SC resistance values indicating the severity of the fault. Experimental validation on a series-connected battery pack confirms the method's effectiveness in promptly detecting soft SC faults and accurately estimating their resistance While lithium-ion batteries offer advantages such as high energy density and quick charging, concerns about their thermal stability hinder their widespread use due to potential fire and explosion risks. This manuscript comprehensively reviews the thermal runaway phenomenon and fire dynamics in both single LIB cells and multi-cell battery packs. It discusses potential fire prevention measures, emphasizing the chal- lenges associated with ensuring the safety of LIB applications in electric vehicles and energy storage systems. Additionally, the paper provides an overview of fault detection methods for critical EV components, including Permanent Magnet Synchronous Motors (PMSMs) and lithium-ion battery packs, stressing the importance of accuracy, speed, sensitivity, and cost-effectiveness in fault detection approaches, with a focus on the latest research developments.

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Anomaly Detection, Electric Vehicles, Internal Faults, Predictive Maintenance, Machine Learning, Real-time Monitoring, Fault Diagnosis, Vehicle Safety, Reliability, IoT, Sensor Data, Advanced Driver Assistance Systems (ADAS), Automotive Safety, Fault Detection, Vehicle Health Monitoring.

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Traditional Ecological Knowledge (TEK), popularly known as indigenous knowledge, regained its significance and political recognition of indigenous rights with the adoption of Agenda 21 during the UN Conference on Environment and Development held in Rio de Janeiro in 1992. For the first time, the Convention on Biological Diversity (CBD) established international protocols for protecting and sharing national biological resources, specifically addressing issues of traditional indigenous knowledge. Additionally, the Nagoya Protocol (2010) provides a legal framework for the effective implementation of the relevant objectives of the CBD. Traditional Ecological Knowledge (TEK) plays a crucial role in environmental conservation, particularly in the Sikkim Himalaya, by providing valuable insights into local ecosystems and resource management practices. This knowledge, passed down through generations, often incorporates a deep understanding of local flora, fauna, and ecological interactions, influencing resource use, soil and water management, and diversification strategies. The "Sacred" aspect of the environment, revered in local cultures, further reinforces conservation efforts by promoting respect for natural resources.

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Traditional Ecological Knowledge (TEK), Resource Management, Environmental Conservation, Sikkim Himalaya

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The widespread adoption of digital transactions has amplified the threat of credit card fraud, presenting significant challenges to financial institutions and customers. One major hurdle in fraud detection lies in the severe imbalance between legitimate and fraudulent transactions, causing traditional models to underperform. This study presents a hybrid deep learning strategy that merges the Synthetic Minority Over-Sampling Technique (SMOTE) with a Deep Neural Network (DNN) to better identify fraudulent transactions. Utilizing a publicly available dataset of 284,807 transactions--only 492 of which are fraudulent--SMOTE was used to rebalance the classes by generating synthetic minority instances. The DNN model, built with multiple hidden layers and dropout mechanisms to prevent overfitting, was trained on the balanced dataset. Evaluation using metrics such as accuracy, precision, recall, and F1-score revealed that the system achieved an accuracy of 97.55%. These results show that integrating SMOTE with DNN significantly improves fraud detection performance, offering a robust and scalable solution for real-time financial fraud prevention.

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Credit Card Fraud Detection, Deep Neural Networks, SMOTE, Class Imbalance, Fraud Prevention, Machine Learning.

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