Volume 13 | Issue 1

 Abstract

Holistic and multi-disciplinary education has emerged as a transformative approach in modern pedagogy, aiming to nurture well-rounded individuals equipped with diverse skills and competencies. This educational model integrates various disciplines, promoting a comprehensive understanding of knowledge beyond traditional subject boundaries. It emphasizes the development of cognitive, emotional, social, and physical dimensions of learners, fostering creativity, critical thinking, and problem-solving abilities. By combining arts, sciences, humanities and vocational studies, holistic education prepares students to adapt to complex real-world challenges. The National Education Policy (NEP) 2020 in India advocates for a holistic and multi-disciplinary framework to break the silos of compartmentalized learning. This approach encourages flexibility in course selection, experiential learning, and research-driven studies, allowing students to explore their interests and potential. Incorporating co-curricular and extracurricular activities further enriches the learning experience, fostering emotional intelligence, ethical values and social responsibility. Holistic education focuses on personalized learning paths, integrating life skills, environmental awareness, and technological literacy. This creates responsible global citizens capable of contributing to sustainable development. Multi-disciplinary education, on the other hand, encourages cross-functional knowledge, innovation, and collaboration across diverse fields. It cultivates adaptability and lifelong learning habits essential for success in the dynamic global economy. Incorporating holistic and multi-disciplinary education in teacher training programs ensures that educators are equipped with innovative teaching strategies and inclusive pedagogical methods. This paradigm shift addresses the diverse learning needs of students and bridges the gap between academic knowledge and practical applications. Ultimately, this educational approach aims to produce individuals who are not only academically proficient but also emotionally resilient, socially responsible, and environmentally conscious.

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 Keywords

Holistic Education, Multi-Disciplinary Learning, NEP 2020, Cognitive Development, Emotional Intelligence, Critical Thinking, Experiential Learning, Life Skills, Innovation, Sustainable Development, Personalized Learning, Cross-Functional Knowledge, Social Responsibility, Lifelong Learning, Inclusive Pedagogy.

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Varicose ulcers are the most common complication arising from varicose veins, predominantly affecting adults. Treatment for Varicose ulcer involves maintaining sterility by keeping the ulcer clean and dry, frequently dressing the wound, and performing surgery to prevent further complications. Homeopathy, with its holistic approach, offers significant potential for managing venous ulcers by addressing the underlying issues and promoting healing from within. Case summary: A 53-year-old woman presented with an ulcer on the medial aspect of the left leg near the medial malleolus, accompanied by itching and a thin, sticky discharge from the affected area. The patient was on allopathic medication but got no improvement. Patient's signs and symptoms were taken in detail as per homoeopathic principles then analysed the symptoms, and prescriptions were made based on the totality of symptoms, i.e., a holistic approach. The medicine prescribed was Lycopodium Clavatum 200, has showed a remarkable improvement in the patient in reducing the ulcers. This article gives a detailed report on the way to recovery of the patient through homeopathic medicines by following Homeopathy principles.

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Case report, Homoeopathy, Varicose ulcer

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One of the most well-established ways for improving athletic performance, specifically with respect to endurance sports, is through altitude training. Systematic review is synthesized to provide findings from multiple studies that examine the impact of different altitude training models including 'live high, train low' (LHTL) and 'live high, train high' (LHTH) in endurance athletes. However, within and between studies, the outcomes were highly variable and were influenced by both individual variability and genetics, and there was a high degree of consistency across studies in terms of key physiological changes such as improved oxygen transport and ventilatory acclimatization. Most research confirms that altitude training improves endurance performance, but the exact time of exposure and duration, as well as athlete specific responses, are key factors in maximizing its benefit. Altitude training protocols still need further research to refine them and maximize performance outcome in a wide range of endurance sports.

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Altitude, Endurance, Athletes, Physiological Adaptations, Performance

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Leveraging Computer Simulations for Ethical and Efficient Drug Testing The reliance on animal experimentation in drug testing faces increasing scrutiny due to ethical, scientific, and economic challenges. Computer simulations, powered by advancements in artificial intelligence (AI), provide a transformative alternative to traditional methods by offering precision and innovation in drug development. This project explores the development of predictive models that leverage comprehensive datasets--spanning chemical compositions, biological activities, and human reactions--to forecast drug efficacy and safety with accuracy. Beyond predictive modeling, computer simulation in drug testing delivers a comprehensive and critical examination of current and emerging research on designing and developing advanced drug delivery systems. These systems focus on physicochemical principles which are engineered to address specific therapeutic needs in both experimental and clinical settings. By illustrating the pivotal role of a multidisciplinary approach, this project underscores the integration of biology, chemistry, and physics in optimizing drug delivery systems. By minimizing animal testing, accelerating drug development timelines, and enhancing safety profiles, this project provides a more ethical and efficient framework for drug testing. The combination of cutting-edge simulations and multidisciplinary expertise ensures the delivery of safer and more effective therapeutic solutions, revolutionizing the future of drug development.

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Artificial intelligence (AI), Drug testing, Ethical alternatives , Predictive modeling, Animal testing alternatives

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The essence of guidance is to help a person understand himself and understand his abilities and limitations . With the help of guidance, a person is able to work at his level. With the help of guidance, a person can solve his problems and prepare for the problems that lie ahead.

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Mean Consept process importance of Direction

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Abstract In Indian culture morality is inextricably related with dharma and take the form of dharmaniti. Dharma is that which upholds the man, the society and above all the universe. On the other hand, morality is not just a bundle of few rules or principles designed for living a life with material or sensual pleasure, rather a behavioural pattern or style for healthy, disciplined and righteous life mingled with a feeling of empathy and compassion. The prime aim of dharmaniti is to serve the most and absolute well-being of each and every member of this universe and any violation of this dharmaniti, provoked by evil inclinations, leads to unwanted harmful adverse effects. Basically, In Indian context dharma designated as right conduct, following which morality is fully revealed within a man. In this article, I am trying to deal with certain aspects of the very concept of this dharma as it has been referred in Indian philosophical tradition.

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Keywords -Dharma, morality, sad?c?ra, ?ta, peace, co-existence.

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Transforming India's banking landscape is underway. The Reserve Bank of India (RBI) envisions payment banks as a solution to serve low income households and small businesses by offering low-cost savings accounts and remittance services. The goal is to help individuals reliant on cash transactions transition to formal banking. While opening branches in every village might be expensive for traditional banks, the widespread availability of mobile phones offers a cost-effective way to deliver basic Banking services to rural areas. This initiation is also accelerate India's move towards a cashless economy. The domestic remittance market estimated at around Rs. 800-900 billion, is growing and mobile based money transfers could capture a significant portion, particularly for migrant workers. Payment banks will also support the Government's direct benefit transfer program by channeling subsidies for healthcare, education and gas directly into beneficiaries' accounts. Notably, this marks the first time private business groups have received RBI approval for banking services since nationalization. This paper explores the objectives of payment banks, the opportunities they present, and the challenges facing India's evolving banking system.

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Banking services, Cashless economy, Nationalization, Traditional banks

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Drones, otherwise known as unmanned aerial vehicles, are revolutionizing the field of agriculture, logistics, disaster management, and environmental monitoring. Such systems come with advanced AI-driven technologies for real-time data analysis and precision operations alongside autonomous navigation capabilities unseen before while offering unprecedented efficiency and reliability. Recent trends in developing obstacle avoidance systems, edge computing, hybrid drones, and swarming capabilities have highly enhanced the functionality of drones. With these advancements, drones can now be used for crop health monitoring and precision spraying, medical supply delivery, or search-and-rescue operations. Yet, most of these problems remain with drones: limited battery life, regulatory obstacles, and security issues. Lighter materials, alternative sources of energy, and secure communication protocols are on the horizon and could help solve most of these issues. This paper explores the current progress, implementations, and difficulties related to the advancement of drone technology. It looks at its revolutionary role in serving the demands of the present and opening new paths for growth and integration.

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Applications of Drones, AI-Guided Drone Navigation, Drone Environmental Stability, Multimodal Systems.

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This paper explores the growing landscape of caste, class, and social justice in West Bengal, focusing on the period under Mamata Banerjee's leadership. The state has long been a site of complex sociopolitical dynamics, with entrenched caste hierarchies and socioeconomic disparities shaping its political discourse. This study delves into the state's reservation policies and examines their role in addressing the needs of marginalized communities, including Scheduled Castes (SCs), Scheduled Tribes (STs), and Other Backward Classes (OBCs). Since 2011, West Bengal's approach to social justice has developed from class-based policies under the CPI (M) to a more caste-conscious framework under the Trinamool Congress (TMC). Mamata Banerjee's government has expanded reservation benefits for OBCs, especially Muslim communities, because of her political benefits in the election as there are no constitutional provisions for religious based reservation in India. A total of 97 Muslim castes were included in the list of OBCs in Categories A and B. The paper also highlights the legal complexities surrounding these policies, including the 2024 Calcutta High Court ruling that invalidated the OBC status of Muslim communities, which sparked intense political debate. West Bengal's reservation system fosters inclusion; however, a new study reveals its limitations and contradictions, particularly the conflict between economic equality and caste-based justice. West Bengal's complex interplay of caste, political power, and social justice is further examined through key legal and political milestones. The evolving trajectory of social justice in West Bengal, a product of political and legal influences, highlights valuable lessons on the intricate relationships between caste, class, and policy-making in modern India.

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Marginalized Castes, Other Backward Classes (OBCs), Social Justice, Reservation Policy, Politics, Muslims, Calcutta High Court, Supreme Court of India, Mandal Commission. Trinamool Congress (TMC), Bhartiya Janta Party (BJP), CPI(M), Indian National Congress (Indira).

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Viral diseases pose a significant threat to global health, with a vast array of viruses infecting humans and animals, causing widespread morbidity and mortality. This review aims to provide a comprehensive overview of the laboratory investigation of viral diseases, highlighting the importance of understanding the biology and pathogenesis of viruses. Viruses, consisting of genetic material encased in a proteinaceous capsid, rely on host cells for replication and spread of disease. This review will delve into the diverse range of viral diseases, including arboviruses, bunyaviruses, cytomegalovirus, papillomavirus, and many others, which affect various organ systems and manifest in different clinical presentations. The laboratory investigation of viral diseases is crucial for diagnosis, surveillance, and prevention of outbreaks. This review will summarize the current laboratory methods and techniques used in the detection, identification, and characterization of viruses, as well as discuss the challenges and limitations faced in the laboratory investigation of viral diseases. The review will also highlight the significance of laboratory investigation in informing public health policy and guiding the development of effective vaccines and treatments.

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Genetic material, Detection, Identification, Public health policy

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The Purpose of this study was to compare the achievement level in science subject among the students of Government Prishadiya Upper Primary Schools and students of Aided Junior High School. The sample size group is 300 students for Government Parishadiya Upper Primary Schools and 300 students of Aided Junior High School. A test of 20 marks is applied on each student . Stratified random sampling method used for collection of data.Then after calculating the answer and analysing the result by statistical tools and t-test ,the result of the study shows that there is no significant difference in the achievement level in science subject among the students of Parishadiya Upper Primary Schools and students of Aided Junior High School. It is Possible that the teachers of Government Parishadiya Upper Primary Schools and Aided Junior High School have similar interest in teaching or the learning level of the students of Government Parishadiya Upper Primary Schools and students of Aided Junior High School is same.

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Comparative study of achievement level in science subjects among the students of Government Parishadiya Upper Primary Schools and students of Aided Junior High Schools of district Pilibhit.

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Facial expression recognition (FER) is an important component in improving human computer interaction through the ability of machines to recognize human emotions. The new improved FER systems have been using deep learning techniques, especially Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). Real time emotion recognition is something that CNNs excel at as their spatial feature extraction powers them to be very good at extracting spatial features from static images. On the other hand, RNNs are aptly suited for temporal sequences, that is, for capturing of emotion progression with time that is crucial for video based FER. In this paper, we offer a comparison of CNNs and RNNs, with respect to their strengths, limitations, and where we think they are best applied. It also examines hybrid models that combine both structures, and thus presents a complete approach to exploiting the combined capabilities of both architectures. Our findings suggest that CNNs are well suited for fast, static image recognition, while RNNs are better suited for dynamic, sequence based tasks. The next step in future research should extend to making models more robust and integrating multimodal data to make more adaptive and accurate FER systems.

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Facial Expression Recognition, Convolutional Neural Networks, Recurrent Neural Networks, Deep Learning, Emotion Recognition

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Abstract Ecofeminism is a critical theory and movement that examines the relationship between the exploitation of the environment and the oppression of gender. Drawing on feminist and ecological thought, they emphasize the interconnections between patriarchal systems and human domination of nature. In this paper, we discuss the historical trajectory, a short list of principles, along with the contributions of ecofeminism in literature, activism, and contributions to sustainable development. Through the lens of prominent ecofeminist theorists and authors, the research highlights its applicability in tackling contemporary environmental problems and gender inequalities about land ownership, environmental protection, and climate change.

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: Ecofeminism, Gender, Environment, Patriarchy, Climate Justice, Environmental Literature

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The Banarasi saree, a culturally significant and beautiful garment, faces challenges due to technological changes and global fast fashion. This has affected local artisans, craftsmen, and entrepreneurs, who must adapt to current market trends to promote handlooms and handicrafts. The proposed paper aims to identify traditional aesthetics and the need for contemporary design in the Banaras saree, highlighting the gap between traditional and modern design aesthetics. The paper evaluates whether design intervention can revive the craft and explores innovative ways to generate livelihoods in this struggling craft. The paper will be a review article, analyzing secondary data and suggesting an innovative approach for craftspeople to expand their craft vocabulary and access contemporary market trends.

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Indian culture, Banaras handloom, Banaras saree, Design aesthetics, Materials & Techniques, Trends In design, Contemporary design, Sustainability, Design intervention.

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The integration of artificial intelligence (AI) and machine learning into fuzz testing, termed AI Fuzzing, presents a promising approach to enhancing software security by automating the identification of vulnerabilities through systematic input generation. However, traditional fuzz testing methods face significant limitations, particularly in their inability to encompass various attack types, leading to a critical gap in software protection prior to deployment. This study aims to explore the concept of AI Fuzzing, specifically focusing on the combination of coverage-guided and behavioral fuzzing techniques. The research employs a structured methodology utilizing OSS-Fuzz's Fuzz Introspector tool to identify under-fuzzed code segments, followed by an evaluation framework that generates and tests new fuzz targets using a large language model (LLM). The framework iteratively adjusts the fuzz targets based on observed outcomes, enhancing code coverage. The findings reveal prevalent software vulnerabilities, including memory leaks, injections, sensitive data exposure, insecure deserialization, buffer overflows, use-after-free errors, data races, and software crashes. These results underscore the effectiveness of AI-driven fuzz testing in revealing security flaws that traditional methods may overlook. The implications of this research highlight the potential for developers to leverage a variety of open-source fuzz testing tools, as well as enterprise solutions, to improve software security in diverse environments, particularly within larger development teams and DevOps settings. This study emphasizes the complementary nature of static code analysis and dynamic fuzz testing in identifying vulnerabilities, advocating for a holistic approach to software security.

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AI Fuzzing, software security, vulnerability detection, fuzz testing, machine learning, dynamic analysis code coverage

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Machine learning (ML) a type of Artificial Intelligence (AI) that allows systems to learn and improve from data without being explicitly programmed. It uses artificial neural networks and deep learning to analyse large amounts of data, identify patterns, and make predictions. ML is used in many areas of our lives, including banking, online shopping, social media, healthcare, and entertainment. A pituitary tumour is an abnormal growth of cells in the pituitary gland, a small gland located in the brain that produces hormones that regulate many bodily functions. Classifying brain tumours accurately is crucial for treatment and prediction. In the proposed work, the ML models, including k-nearest neighbour (KNN), decision trees, logistic regression and Support Vector Machine (SVM algorithms, are used to classify pituitary brain tumours. MRI dataset of Brain Tumour Classification, available in Kaggle, is used. It contains 105 images of no tumour and 74 images of pituitary tumour. From the empirical analysis, it is found that the CNN Classifier has exhibited an accuracy of 88%. ML algorithms exhibit strong performance in classifying brain tumors, with near-maximum area under the curves, sensitivity, and specificity.

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Brain Tumour, machine learning, Convolutional Neural Networks, Decision Tree Algorithms, KNN, SVM

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Brain-Based Learning (BBL) is an innovative educational approach rooted in neuroscience, emphasizing teaching strategies that align with the brain's natural learning processes. This paper explores the theoretical underpinnings, practical applications, and challenges of implementing BBL in diverse educational contexts. Key components, such as neuroplasticity, emotional regulation, multisensory engagement, and memory enhancement, are analyzed to illustrate how BBL promotes critical thinking, creativity, and academic achievement. The integration of technology, including AI, is discussed as a means of personalizing learning experiences and addressing diverse student needs. Additionally, the study examines emotional engagement's role in fostering a supportive classroom environment, enhancing memory retention, and building resilience. Practical strategies for implementing BBL, supported by evidence-based examples, are presented to provide actionable insights for educators. The study concludes by highlighting the benefits, limitations, and future directions of BBL in transforming education.

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Brain-Based Learning (BBL) Neuroscience in Education Emotional Engagement Neuroplasticity Multisensory Learning Memory Retention Educational Technology Critical Thinking Personalized Learning Classroom Strategies

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This research proposal looks into the impact of AI on Indian businesses, focusing on its applications, benefits, challenges, and future potential. It analyses 10 studies on AI adoption across sectors like e-commerce, banking, manufacturing, and hospitality and identifies key themes such as AI's role in enhancing operational efficiency, the challenges of infrastructure and skills shortages, and the opportunities for growth in AI-driven industries. A comparative analysis depicts the contrast of AI adoption in various sectors. It reveals that banking and e-commerce lead, while others like education take a longer time in adopting due to infrastructural and resource limitations. It provides a good source for businesses planning to incorporate AI into their processes. The analysis proposes that AI integration is the practice in banking and e-commerce, while all other sectors are somehow still behind in line due to infrastructural and resource constraints. Future advancements in AI, together with the intersection of AI and many other growing technologies such as blockchain and IoT, are promising enough to transform the Indian industries. However, an organized effort involving government and the private sector in approaching issues on how to overcome the barriers and arrange robust digital infrastructure is very important. This research highlights essential sector-specific AI applications, thereby enabling industries to make informed decisions and optimize their processes to secure a competitive advantage in the changing digital front.

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Artificial Intelligence, India, E-commerce, Banking, Manufacturing, Hospitality, AI Adoption, Operational Efficiency, Challenges, Future Potential.

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Rainfall prediction plays a vital role in agriculture, disaster preparedness, and water management. Conventional methods, such as statistical and physics-based models, often struggle to achieve the desired accuracy due to the nonlinear nature of weather patterns. Long Short-Term Memory (LSTM) networks, which are a form of Recurrent Neural Network (RNN), have emerged as an effective machine learning technique for solving these difficulties. This paper reviews various studies focusing on the application of LSTM networks in rainfall prediction. Key methodologies, case studies, and model comparisons are presented, emphasizing the strengths of LSTM in capturing temporal dependencies. Challenges, future directions, and the potential of hybrid models are also discussed. The review underscores LSTM's transformative role in improving prediction accuracy and reliability.

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Rainfall Prediction, Long Short-Term Memory, Machine Learning, Time-Series Analysis, Hybrid Models

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Gas chromatography (GC), also known as gas-liquid chromatography (GLC), is a widely utilized analytical technique in both academic and industrial research settings, particularly for quality control and the identification and quantification of components within mixtures. This paper aims to evaluate and describe the various stages involved in developing and validating GC methods. GC is recognized for its sensitivity, accuracy, repeatability, and flexibility, making it an essential tool for analyzing complex mixtures, including pharmaceuticals and medicinal compounds. However, its application is limited to molecules that can be derivatized into volatile, thermally stable compounds. The development and validation of GC methods are critical to discovering, developing, and producing pharmaceuticals. Method development involves demonstrating that an analytical technique is suitable for accurately measuring the concentration of an active pharmaceutical ingredient (API) in a specific dosage form. This allows for streamlined procedures to confirm that the method will consistently deliver precise measurements of the API in pharmaceutical preparations. The validation of these methods is crucial and is rigorously assessed for various parameters, including robustness, system suitability, linearity, accuracy, precision, range, and detection limits.

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 Keywords

Gas Chromatography (GC), Gas-Liquid Chromatography (GLC)Analytical Method Development, Analytical Chemistry, Quality Control.

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