Volume 13 | Issue 8 |

 Abstract

In EV and HEV applications, battery optimization has increased. Lithium-ion batteries, in particular, are increasingly used as an energy storage system in green technology applications because of their high power and energy density. Drawbacks are seen in electric car applications during the storage system's charging interval period. Multi-state charging is seen to be the best option in these circumstances. The state charging of lithium-ion batteries and their criteria for charging and discharging for long battery life are discussed in this study using the MATLAB Simulink tool. The state-of-charge (SOC), which is measured and used to assess charging and discharging characteristics, is a crucial factor in determining a battery's performance. Therefore, an accurate SOC estimate is required to safeguard the battery and avoid overcharging and undercharging it. Additionally, by doing this, the battery's lifespan will be extended. Multi-state charging is used for applications that demand greater efficiency.

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 Keywords

MATLAB, Li-ion, state-of-charge, electric car

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Creative Commons Attribution 4.0 and The Open Definition

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Individuals with intellectual disabilities face significant barriers to employment, including limited access to education and training, social stigma, and lack of support. Pre-employability training programs aim to address these challenges by providing skills and knowledge necessary for successful employment. This study investigates the need for pre-employability training among individuals with intellectual disabilities from the perspective of their parents. A survey research design was employed, using a questionnaire to collect data from 30 parents of individuals with intellectual disabilities. The results show that the majority of parents (53%) felt that individuals with intellectual disabilities face no need for pre-employment training programs, while 47% expressed a low need for such programs. None of the parents indicated a medium or high need for pre-employment training programs. The findings suggest that parents generally believe that individuals with intellectual disabilities have either no need or low need for pre-employment training programs. The findings reflect those of some previous studies that parent's perspectives tend to be more optimistic than other stakeholders. Further studies incorporating perspectives other stakeholders such as special education teachers and employers with triangulation need to be done to comprehensively understand the need for pre-employment training programs for individuals with intellectual disabilities.

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 Keywords

Pre-Employability Training, Individuals with Intellectual Disabilities, Parental Perspective

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The curing practice of tribal communities in Kerala Include a deep historical connection of ancient South Indian Medical knowledge . This Study try to find the origin of tribal medicinal knowledge from ancient period subsistence and religious ritual worlds to its complex entanglements with Ayurveda and present day bio-medicine. Focusing on the Kani , Kurichiya , Malayaraya , Paniya , and other tribal groups, it re-constructs continuities in plant lore , diagnosing ideas , Ritual practice, and Ecological ethics. Findings on oral histories , Colonial Ethnographies, and recent Ethnobotanical Studies, the study poinout that tribal medicinal knowledge is a surviving archive preserve health in forest and peasant communities while offering models of ecological reciprocity and community care. The scope, specificity, and durability of this knowledge can be demonstrated by a comprehensive catalogue of medicinal plants, which includes uses, targeted ailments, and historical notes. The Kani are believed to be the most advanced in medicinal knowledge among Kerala's tribes, according to a comparative analysis, while other groups possess notable specializations in bone-setting, fever management, and ritual healing.

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 Keywords

Tribal medicine; Kerala; ethnobotany; Ayurveda; colonial encounters; oral tradition; historical method

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 Abstract

The rapid growth of software systems has led to an increase in the volume and complexity of bug reports, necessitating efficient and accurate prediction models to manage and address these reports effectively. This paper presents a Nature-Based Prediction Model of Bug Reports (NBPMBR) leveraging ensemble machine learning techniques to enhance the prediction accuracy and reliability of bug report classifications. By integrating various nature-inspired algorithms such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO) within an ensemble framework, NBPMBR combines the strengths of these algorithms to improve performance.The model undergoes rigorous training on historical bug report data, using feature extraction methods to capture relevant information such as bug severity, priority, and textual descriptions. The ensemble approach ensures robustness by mitigating the weaknesses of individual algorithms, leading to a more balanced and accurate prediction outcome. Experimental results on benchmark datasets demonstrate that NBPMBR outperforms traditional machine learning models in terms of precision, recall, and overall prediction accuracyThis nature-based ensemble model not only advances the state-of-the-art in bug report prediction but also provides a scalable and adaptable solution for real-world software maintenance and quality assurance processes. By automating the classification and prioritization of bug reports, NBPMBR aids in the efficient allocation of resources, thereby improving the software development lifecycle and enhancing the overall quality of software products.

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 Keywords

XG Boost classifier, Support Vector Machine (SVM), Logistic Regression, Random Forest, Propose Voting Classifier Confusion Matrix, Extension XGBoost Confusion Matrix

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Creative Commons Attribution 4.0 and The Open Definition

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This study investigates the financial literacy of MGNREGA workers in Karnataka, with a focus on savings, investment, and insurance practices. Adopting a descriptive research design, data were collected from 200 participants using a structured Likert-scale questionnaire. The responses were analysed using SPSS with t-tests to measure the literacy levels across domains. The results reveal moderate financial literacy in savings and investment, with mean scores significantly higher than neutral levels. However, insurance awareness remains low and statistically insignificant, indicating a critical gap in risk management knowledge among the workers. The study underscores the urgent need for targeted financial education programs, especially in the domain of insurance. It recommends collaborative efforts between local self-help groups, NGOs, and policymakers to design and implement financial literacy initiatives tailored to rural communities. Strengthening these capacities is vital for enhancing financial empowerment and ensuring sustainable livelihoods in rural Karnataka.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Financial Literacy, MGNREGA workers, savings awareness

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

(Abstract: Christian Missionaries set their foot on the soil of Assam for the first time in early 19th century and gradually spread to the rest of the other north eastern states. The British official encouraged missionary activities in these regions to improve the backwardness of indigenous population and to spread the gospel of Christianity. However, it was in the later part of 19th century when frontier tribes of Arunachal Pradesh got connected with missionary activities. The paper will enlighten the advent of Christianity in North Eastern region of India.)

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(Key Words: Abor, Christianity, Kampti, Khasi, Missionary, Sadiya, Singpho,)

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Creative Commons Attribution 4.0 and The Open Definition

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This review examines the influence of social media on tourism behaviour, synthesising recent studies to explore how platforms like Instagram, TikTok, and Facebook shape destination choices, tourist engagement, and marketing strategies. The literature is organised thematically, covering the impact of social media content, electronic word-of-mouth (e-WOM), influencers, destination image, psychological factors, and regional contexts. Findings highlight that visually engaging content, credible peer reviews, authentic influencer narratives, and culturally resonant strategies drive tourist decisions, with variations across demographics and regions. Gaps in research, particularly in regional and socio-economic contexts, are identified, suggesting directions for future studies. This review provides insights for destination marketing organisations (DMOs) to leverage social media effectively.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Social media, tourism behaviour, destination choice, electronic word-of-mouth (e-WOM), influencers, destination image, psychological factors, regional contexts, destination marketing.

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Creative Commons Attribution 4.0 and The Open Definition

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The growth of self-financing colleges in Kerala has expanded educational opportunities but has also raised questions about the working conditions of teachers employed in these institutions. This study investigates the level of job satisfaction and economic well-being of self-financing college teachers, while also examining the professional challenges they encounter. Primary data were collected from 135 teachers across different self-financing colleges in the Malappuram district using a structured questionnaire. The finding reveals that job satisfaction among self-financing teachers is moderate, with significant differences between Arts and Science and Engineering faculty. Economic well-being scores indicate that while most teachers can meet household expenses, low and irregular salary payments, limited savings capacity, and dissatisfaction with income contribute to economic insecurity. Professional challenges such as lack of job security, heavy workload, and restricted career advancement opportunities further intensify these difficulties.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Job satisfaction, Economic well-being, Professional challenges, Self-financing colleges

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

this paper is about a unique printmaking technique called color Intaglio: reduction process which is only developed and practices in the college of Fine Arts in Andhra University, Visakhapatnam. This particular process evolved from the scarcity of materials, students started reusing the zinc plates said by Shree T.Sudhakar Reddy Rtd. Professor from printmaking, Fine Arts Department at Andhra University, Visakhapatnam.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Printmaking, Intaglio, etching, color Intaglio, reduction process, zinc plate, burnishing, re use.

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Creative Commons Attribution 4.0 and The Open Definition

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Abstract Rheumatoid Arthritis (RA) is an autoimmune disorder, which can lead to joint deformation and which can lead to permanent disability that cannot be treated in a timely manner. Early diagnosis is thus imperative for effective treatment. In this paper, we present an end-to-end RA detection system with the help of Convolutional Neural Networks (CNN) for hand X-ray images. The model is trained on a balanced 200-image dataset (100 RA, 100 Normal) with extensive pre-processing and augmentation techniques which is used to enhance performance in the lack of large datasets. The CNN architecture was here we used is chosen for its ability to extract hierarchical features from medical images without manual feature engineering. The trained model was deployed using a Flask-based web application, with real-time prediction supported by confidence scores and easy-to-use interaction.This Experiment gives the results that show high reliability, with test cases reporting 96.24% confidence for Normal x-ray image and 92.27% for RA-positive cases.There is another approach where the numerical clinical dataset is used which contain details such as age, gender, ESR, CRP, RF, and Anti-CCP levels. Here the Deep Neural Network (DNN) was trained after preprocessing and scaling the data, demonstrating high predictive performance compared to traditional Machine Learning models. This model was implemented using a Flask-based web application, which will work with real-time prediction and confidence scores and simple interaction. The system can be potentially integrated into clinical workflows, especially in regions where specialist availability is scarce.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

CNN, DNN, Rheumatoid Arthritis, Medical Imaging, Flask, Deep Learning, X-ray Analysis CNN, Rheumatoid Arthritis, Medical Imaging, Flask, Deep Learning, X-ray Analysis.

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Creative Commons Attribution 4.0 and The Open Definition