Volume 13 | Issue 9

 Abstract

Manipur, a state renowned for its rich diversity of languages and cultures, faces significant challenges in managing its library systems effectively due to the multilingual makeup of its population. The presence of numerous indigenous languages and cultural practices calls for libraries that cater to the specific needs of various communities. In this scenario, Artificial Intelligence (AI) offers a promising solution to enhance library services by facilitating better access to information, addressing language barriers, and supporting the preservation of native languages and cultural traditions. AI technologies can streamline information searches, provide personalized learning experiences, and foster inclusivity by bridging the gap between different linguistic groups. This paper delves into the potential applications of AI in Manipur's libraries, focusing on overcoming language barriers, ensuring cultural inclusivity, and safeguarding local knowledge. Drawing on relevant case studies and existing research, the paper emphasizes the importance of developing AI systems that are culturally sensitive and customized to meet the needs of Manipur's unique demographic. It discusses the practical aspects of implementing AI, such as creating language models for local dialects, promoting equal access to digital content, and involving local communities in the AI development process. The paper concludes by arguing that AI can revolutionize library services in Manipur, making them more inclusive and accessible while preserving the state's cultural heritage for future generations. However, the successful integration of AI into these systems requires a deep understanding of Manipur's cultural context and careful consideration of the challenges posed by its multilingual population.

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AI

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India has hosted successive refugee movements for more than seven decades--from Partition (1947) and Tibetans (1959) to Sri Lankan Tamils (1980s-2000s), Afghans, Chakma-Hajong communities, and, most recently, Rohingya from Myanmar. Yet India has neither acceded to the 1951 Refugee Convention and its 1967 Protocol nor enacted a national refugee law. Protection, therefore, relies on a patchwork of constitutional guarantees, ordinary immigration statutes, executive practice, UNHCR's limited mandate operations, and case-by-case judicial interventions. This paper critically examines India's refugee policy through a human-rights lens. Methodologically, it synthesizes constitutional doctrine, statutes (especially the Foreigners Act, 1946), Supreme Court and High Court decisions, UNHCR practice in India, and recent executive measures, including the rules operationalizing the Citizenship (Amendment) Act, 2019 (CAA). It finds persistent structural gaps--legal uncertainty around status determination, risks of detention and deportation, uneven access to socio-economic rights, and gendered vulnerabilities--exacerbated by a securitized approach to "illegal migration." Results indicate that while Articles 14 and 21 of the Constitution have provided a floor of protection, their application remains contingent and inconsistently enforced across refugee groups. The paper argues that adopting a rights-based, uniform refugee framework--aligned with India's constitutional values and international obligations--would reconcile humanitarian commitments with legitimate security concerns. It concludes with actionable reforms: a comprehensive refugee statute codifying non-refoulement, procedural safeguards against arbitrary detention, clear roles for UNHCR and domestic institutions, calibrated use of the CAA to avoid discrimination or statelessness, and regionally coordinated burden-sharing.

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Refugee protection, Non-refoulement, Securitized migration policy, Detention and deportation, UNHCR in India

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India being most populous nation in the world, with a population of approximately 1.428 billion people where in notably, over 40% of this population falls under the age group of 25, placing India with a median age of 28 years. This growth has contributed significantly to the nation's GDP which enabled India to secure the world's fifth-largest global economy with an estimated 4.0 trillion USD (Time- 2023, pewresearch -2023). Contrary to the demographic advantage, there are persistent scarcity of skills for employments. According to a modern study, only 45% graduates are employable with required skill set which indicates a huge gap between the academic curricula and industry demands. Recruiters are reluctant to recruit Gen Z candidates as they lack in teamwork, communication skills and managing conflicts at workplace due to inefficiency of applicability skills to incorporate acquired theoretical knowledge into practice. (time.com, 2024) In order to bridge this gap between the industry expectations and academic supply of Gen-Z, it is crucial to adapt activity-based classroom teaching pedagogy to empower the Gen-Z with employability skills. Addressing this challenge aligns with SDG 4 framed by United Nations General Assembly, 2015 which emphasizes the inclusive and equitable quality education and promote life-long learning opportunities for all (UN- DESA-SDG-2015). The framework of bloom's taxonomy theory, popularly accepted model across the educational bodies to deliver the curriculum efficiently. On the other hand, the levels of its applicability have impacted over the employability skills acquired by the students. The present study aims to understand the importance of Activity Based Classroom teaching strategies using the bloom's taxonomy framework towards the development of employability skills among Gen-Z using the statistical tool JAMOVI version 4 application and inferential statistical test.

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SDG-4, ABC teaching, employ-ability skills and Blooms Taxonomy, Sustainable Strategy.

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Abstract-- In order to enhance signal processing, this work explores the integration of neural networks (NN) at very-large-scale integration (VLSI) levels. In terms of SNR, processing time (latency), energy consumption, and accuracy of signal processing tasks, we suggested and implemented a neural-network-based VLSI based on NNs and compared it with a traditional DSP. Our findings show that, in comparison to DSP techniques, the NN-based circuit efficiently boosts SNR by up to 7 dB. According to latency measurements, the NN-based circuit outperforms the DSP, which has average latencies of 3.5 and 6.2 microseconds for single and complex jobs, respectively, with average latencies of 1.2 and 2.8 microseconds. Additionally, with a 45 mW power usage, the NN implementation reduces the average power by 40%. In contrast, DSP uses 75 mW. With 98.2% accuracies for signal filtering, 97.5% for denoising, and 99.1% for pattern recognition, NN-based circuits outperform DSP, which has accuracies of 95.7%, 92.3%, and 96.8%, respectively, according to accuracy study. These results highlight the better precision, throughput, and energy efficiency that the NN-based circuit can provide. In conclusion, neural network implementation on VLSI can offer notable benefits over DSP-based implementation, making it seem like a promising technology for advanced signal processing that demands high precision, high efficiency, and high performance. By using NN-based circuits in general signal processing applications like communications, imaging, and real-time data analysis, this study lays the groundwork for future advancements in VLSI.

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Networks (NN), Very-Large-Scale Integration (VLSI) Circuits, Digital Signal Processing (DSP), Signal-to-Noise Ratio (SNR), Latency and Processing Speed.

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The present study was conducted to investigate the profile characteristics of Soil hea;th card holders Khandwa (M.P.). One hundred twenty SHC Holders from Khandwa and Punasa were selected for the present study. The following conclusion has been drown from the finding of the study. Most the respondents who fall under the category of middle age group, educated Intermediate, medium level of farming experience, medium size of land holdings, Agri+ Animal Husbanry occupaied, medium level of annual income, medium level of social participation , medium level extension contact, mass media exposure, scientific orientation and innovativeness. The secondary data were obtained from various government offices, soil test laboratory, Krishi Vigyan Kendra and available publications. Data was analyzed with the help of suitable statistical tools. Analyzed data was tabulated and presented in the form of tables and charts. Percentage were used in the study .

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SHC, Socio-Economic, Respondents

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Titanium dioxide (TiO?) has emerged as one of the most promising photocatalysts for industrial wastewater treatment due to its exceptional properties including chemical stability, non-toxicity, and high photocatalytic activity. This review comprehensively examines the various synthesis methods for TiO? catalysts, their characterization techniques, and applications in removing diverse industrial pollutants. The paper discusses recent advances in TiO? modification strategies to enhance visible light activity and addresses the challenges and future perspectives in scaling up photocatalytic systems for industrial applications. Recent developments focus on overcoming TiO?'s inherent limitations such as low efficiency under visible light and rapid recombination of electron-hole pairs.

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TiO? photocatalyst, industrial wastewater, synthesis methods, photocatalytic degradation, environmental remediation

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The rapid growth of online financial transactions has increased the prevalence of fraudulent activities, posing significant challenges for banking and financial institutions. Traditional fraud detection systems often rely on static rule-based models that fail to adapt to evolving fraud patterns. This paper presents an advanced credit card fraud detection framework integrating state-of-the-art machine learning and deep learning techniques for improved accuracy and robustness. The proposed approach utilizes the publicly available Credit Card Fraud Dataset, which contains real-world anonymized transaction data with highly imbalanced class distribution. Multiple algorithms, including Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Artificial Neural Networks (ANN), were implemented and evaluated. Various data preprocessing strategies were applied, including feature scaling, data balancing using SMOTE (Synthetic Minority Oversampling Technique), and outlier removal. The comparative analysis reveals that ensemble-based methods, particularly XGBoost, achieved superior classification accuracy and F1-scores while maintaining low false positive rates. Furthermore, the study emphasizes the importance of handling data imbalance, hyperparameter optimization, and evaluation metrics tailored to fraud detection. The proposed framework offers a scalable, adaptive, and efficient solution for real-time fraud prevention in financial systems.

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Credit Card Fraud Detection, Machine Learning, Deep Learning, Random Forest, XGBoost, Support Vector Machine, Artificial Neural Network, SMOTE, Class Imbalance, Financial Transaction Security.

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Anger, one of the seven deadly sins in the Bible, is a recurrent motif in the myths of other cultures as well. Indian epics are rife with examples of overwhelming rage. This paper analyses the psychology and actions of Amba, a relatively lesser-known character in the Mahabharata, whose anger at her mistreatment is so intense that she chooses to carry it to her next birth. Amba voluntarily undergoes severe penances and then immolates herself to hasten her rebirth to seek revenge that will assuage her unappeasable wrath. The root of Amba's self-destructive fury deserves a closer study, which has been attempted in this paper.

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Rage, revenge, Amba, Bhishma, The Mahabharata, svayamvar, marriage, gender

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ABSTRACT: The Integration of Artificial Intelligence (AI) into Knowledge Management (KM) systems is reshaping how organizations manage intellectual capital, enhance decision-making, and foster innovation. This paper aims to present the results of a systematic literature review (SLR), based on 745 peer-reviewed articles retrieved from ScienceDirect between 2000 and 2024, to investigate the evolving relationship between AI and KM. The review followed a structured methodology, including a predefined search strategy, selection criteria, and data analysis procedures, to ensure a comprehensive and unbiased assessment of current trends. Articles were selected based on their relevance to key AI technologies and their applications in KM processes, including knowledge creation, retrieval, and dissemination. Our analysis revealed that AI significantly enhances KM by automating knowledge processes, improving the conversion of tacit knowledge into explicit knowledge, and fostering collaborative learning and innovation (Williams & Li, 2023). AI-powered KM systems also enable the processing of large volumes of unstructured data, providing real-time, actionable insights that enhance decision-making and organizational agility (Jones et al., 2021). Furthermore, while AI can augment decision-making, there are limitations in how it captures the nuances of human tacit knowledge and cultural context. We also addressed future research directions, such as the development of more transparent and ethical AI frameworks and the need for further empirical studies that examine AI's role in KM across diverse cultural and industry contexts. By offering a comprehensive synthesis of recent literature, this study provides valuable insights into the opportunities and challenges of integrating AI into KM. The paper contributes to the understanding of AI's transformative potential in organizational learning and innovation while acknowledging the limitations and ethical considerations that must be addressed for sustainable AI-KM integration.

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Key words: Artificial Intelligence, Knowledge Management, Sustainable Growth, Augmented Productivity.

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This paper presents the Adaptive Dream Journey Analyzer (ADJA). This advanced AI platform leverages Natural Language Processing (NLP) and adaptive algorithms to interpret and analyze user-described dream experiences with remarkable contextual accuracy. In the modern era, where understanding The subconscious mind is growing in both clinical and personal significance. ADJA distinguishes itself by enabling dynamic, human-like interpretation of highly subjective dream narratives. [1] The system is engineered to comprehend, process, and generate insightful feedback on dream content, facilitating automated analysis for psychological insight, creativity enhancement, or digital self-reflection. ADJA employs sophisticated NLP techniques, including intent recognition (to understand the user's reflection aims), named entity recognition (extracting dates, characters, and settings from dreams), sentiment analysis (capturing the emotional undertone), and advanced contextual modeling, which preserves narrative coherence throughout a session. [2] personalized recommendations, and automating routine tasks. Blending state-of-the-art machine learning with rule-based interpretation patterns, ADJA adapts to unique user profiles and personal symbolism, delivering an evolving and personalized interpretation experience over time. Its applicability spans clinical psychology (assisting therapists with dream content exploration), creative industries (stimulating story generation and art), wellness technology (aiding with self-awareness and nightly mood tracking), and consumer-facing dream journaling tools. [3] (keeping the conversation coherent). The system combines machine learning models and rule-based logic, creating a hybrid approach that improves its adaptability and performance over time This work demonstrates how combining adaptive AI and modern NLP expands our potential to decode dream experiences, making subconscious analysis more accessible, engaging, and actionable than ever before. This project shows how using modern AI and NLP can improve user interaction and engagement through advanced automated dialogue systems.

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 Keywords

Adaptive Dream Journey Analyzer, Artificial Intelligence (AI), Natural Language Processing (NLP), Dream Analysis, Conversational Agents, Adaptive Systems, Machine Learning (ML), Deep Learning, Intent Recognition, Named Entity Recognition (NER), Sentiment Analysis, Emotion Detection, Personalization, Contextual Understanding, Natural Language Understanding (NLU), Natural Language Generation (NLG), Human-Computer Interaction (HCI), Automated Interpretation, Dialogue Management, Psychological Asses

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Sitakund, Rishikund and Rajgir hot spring site is a sulphur containing hot spring site. Bacteriological water analysis is a method of analyzing water to estimate the numbers of bacteria present. It is a micro biological analytical procedure which uses sample of water and form these samples determine the concentration of bacteria. The Biological parameters were analyzed as per WHO, APHA. The bacteriological parameter included SPC (standard plate count), MPN (most probable number). Bacterial contamination of drinking water sources is the most common health risk. The water sample were collected from three selected hot spring site Rishikund, Sitakund&Rajgir during period (October 2014 to June 2016) within and interval of four month. All the results were compared with WHO, APHA, BIS and found that water is potable.

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APHA, BIS, WHO, SPC, MPN

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Unexpected pavement breakdown issues, such as potholes, unevenness, cracks, etc., have been reported by a number of road agencies. This results in a poor understanding of roads and their lifespan. Conversely, rubbers, polymers, and other materials are being added daily. Waste materials such as plastic bottles, polymers, mugs, and used tires can be recycled by grinding them up or mixing them with cementers. They can also be used to cover asphalt and bitumen using any heating method.In this work, we employed crumbed rubber and plastic as binding agents in relation to bitumen and total. We employ bitumen, strength, and total (of various colored sizes) as accessories on bituminous roads. This study includes vivid tests on bitumen penetrate value, stiffness, and softening point in addition to summations like crushing value, impacting value, bruising value, and particular graveness. This paper uses a lot of sequences.Sustainability is a pressing requirement in the construction sector globally, and in order to lessen the impact on the natural environment, the usage of waste materials in road building is being reduced. To establish their suitability for the design, building, and preservation of the pavements, several apparent accessories and technologies have been built into the trace structure.Among them are rubber and polymers. Due to modifications to lifestyle and growing populations, the amount of plastic garbage in external solid waste is increasing. Additionally, the majority of tires, particularly those found on motor vehicles, are made of synthetic rubber. Getting rid of both is a major issue.At the exact same time, the constant rise in automobile traffic highlights the need for greater engineering and road condition. The desired mechanical properties for a certain road mixture can be improved by partially substituting the traditional material with this waste polymer and rubber.

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Plastic, Rubber, Pavement, Bitumen

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Ayurveda, the ancient Indian system of medicine, emphasizes preventive healthcare and the therapeutic use of natural resources. Value-added Ayurvedic products such as Amla sharbat, Amla syrup, Amla candy, Ale pak, Green chilli pickle, Amla supari, and Mixed fruit jam were prepared in the college laboratory by undergraduate students under faculty supervision. The study followed a hands-on, skill-oriented methodology including selection of raw materials, extraction, cooking, preservation, drying, packaging, and storage. Organoleptic and stability evaluations revealed that the products were acceptable in quality, with Amla-based preparations retaining significant nutritional value, especially Vitamin C.??The initiative not only demonstrated the medicinal and nutritional potential of traditional Ayurvedic products but also served as a practical educational model in alignment with NEP 2020. Students developed skills in product formulation, quality assessment, hygiene, and entrepreneurship while integrating with botany knowledge. This approach highlights the importance of experiential learning in higher education, promoting self-reliance, entrepreneurship, and preservation of traditional knowledge.?

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Ayurveda, Amla, Value-added products, NEP 2020, Experiential learning, Skill development

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Unmanned Aerial Vehicles (UAVs) have gained significant importance in defence, disaster management, and security applications due to their versatility and ability to operate in hazardous environments. This research presents the design and development of a multifunctional surveillance drone integrated with landmine detection capabilities and a robotic arm for remote handling tasks. The system is intended to enhance safety for military personnel by reducing direct human exposure to life-threatening conditions. This project is about a special drone that can move both in the air and on the ground as it work like rover. The proposed drone is built on a quadcopter platform equipped with gear motor and wheels to work like rover, a high-resolution camera for real-time aerial surveillance. For landmine detection, the system uses a metal detection module, enabling the identification of explosive threats from a safe distance. A robotic arm mounted on the drone provides additional functionality, allowing for object manipulation such as picking up suspicious materials, collecting samples. The drone is remotely operated through a wireless communication link, with an onboard flight controller managing navigation. The project demonstrates a hybrid approach that combines aerial mobility with ground-focused sensing, offering a cost-effective and scalable solution for security forces. Field testing showed promising results in detecting metallic landmine simulants, maintaining flight stability during arm operation, and providing continuous live feed for surveillance. This work contributes to the growing field of UAV-based defence applications by proposing a multipurpose platform that improves operational safety, and supports in reducing demining human efforts. Future developments will focus on improving detection accuracy and extending flight endurance.

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Aerial Surveillance, Ground Surveillance, Operational safety, Landmine detection, Material manipulation, GPS Tracking, Wireless Telemetry

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Role of Women in Premchand's Novels: A Socio-Cultural Perspective

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Role of Women in Premchand's Novels: A Socio-Cultural Perspective

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Nanotechnology has introduced groundbreaking advancements in oncology by revolutionizing how anticancer agents are delivered. Conventional chemotherapy faces challenges like poor selectivity and severe systemic toxicity. Nanocarrier-based drug delivery systems--including liposomes, dendrimers, polymeric particles, micelles, and metallic nanoparticles--enable site-specific drug accumulation, enhance bioavailability, and minimize adverse effects. This review comprehensively examines key nanotechnology principles in cancer treatment, types of Nano systems, FDA-approved nano formulations, and emerging innovations, underscoring their transformative role in targeted cancer therapy.

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Cancer therapy, Nanotechnology, Nanocarriers, Targeted drug delivery, Liposomes, Chemotherapy, Oncology

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Girls in India confront a complex web of interrelated challenges that undermine their health, education, safety and life chances. This paper synthesizes national surveys, government reports, NGO analyses and peer reviewed studies to examine seventeen core issues affecting girls, including skewed sex ratios, period poverty, anaemia, limited healthcare access, mental health crises, sexual violence, dowry and domestic abuse, child marriage, school dropout, employment and political underrepresentation, superstition driven stigma, cyber harassment and the pressures of non-traditional family choices. Despite these adversities, Indian girls demonstrate remarkable resilience, emotional intelligence and leadership potential. Drawing on evidence from NFHS, NCRB, UNICEF, MOSPI, NITI Aayog and leading research articles, this study highlights both structural failures and promising programmatic responses. The review concludes with targeted policy priorities to strengthen health services, expand educational retention, improve legal redress and community norms, and invest in girls' economic and civic empowerment.

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Gender Inequality, Child Marriage, Anaemia, Mental Health Challenges, India, Women's Empowerment, Adolescent Girls, Superstitions, Resilience, Education, Social Stigma

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Creative Commons Attribution 4.0 and The Open Definition

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This paper presents a Secure Pattern-Based CAPTCHA-Enhanced Resource Booking System designed to safeguard online resource reservation portals from automated attacks. The system integrates a unique pattern-based CAPTCHA mechanism to improve security while maintaining user convenience. Implemented within a college facility management context, this system ensures that only legitimate users can book resources such as auditoriums and sports facilities. Experimental results demonstrate the system's robustness against bot attacks and enhanced user experience compared to traditional CAPTCHA systems.

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Secure CAPTCHA, Pattern-Based CAPTCHA, Resource Booking System, Bot Prevention, College Facility Management, User Authentication

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Creative Commons Attribution 4.0 and The Open Definition

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This study evaluates and compares the nutritional properties of pearl millet flour (Bajra) from three commercial brands--Smita Grah Udhyog, Patanjali, and Manan. Parameters assessed include moisture content, total ash, water-soluble and insoluble ash, alkalinity of water-soluble ash, and calcium concentration. Smita Grah Udhyog samples recorded the highest total ash and calcium content, suggesting superior mineral density, while Patanjali showed better mineral bioavailability due to higher water-soluble ash. Natural indicator China rose was used to assess alkalinity. These findings provide insight into the nutritional quality of pearl millet flour, guiding consumer choices and contributing to food security.

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Keywords: Pearl millet flour, ash content, calcium, alkalinity, moisture, nutritional quality, commercial brands

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Collagen serves as both scaffold and signal in mammalian tissues, providing tensile strength while encoding biochemical cues in its triple helical sequence. A central question in matrix biology is how cells convert this motif level information into adhesion, migration, differentiation, and repair. In skin and many soft tissues, fibrillar collagens I and III dominate the mechanical landscape; cells interpret these fibers largely through collagen binding integrins (?1?1, ?2?1, ?10?1, ?11?1) and the discoidin domain receptors DDR1/DDR2. Systematic Collagen Toolkits devised in Richard Farndale's laboratory (University of Cambridge) mapped short triple helical peptides and defined key motifs - GFOGER/GxOGER for integrin ?2?1 and related ?I containing integrins, GLOGEN/GROGER for ?1?1 preference, and GVMGFO for DDRs, thus revealing how cells "read" collagen at amino acid resolution. Integrating structural, biochemical, and functional data, this review synthesizes how fibroblasts, keratinocytes, endothelial cells, chondrocytes, immune cells, and platelets use these receptors to extract meaning from collagen, with special attention to the HUVEC study by Hunter et al. (2022) that disentangles integrin usage during adhesion, migration, and tube formation on collagen. Finally, I discuss how motif aware insights now inform biomaterial design, wound care, dermatology, and aesthetics, where engaging or modulating specific collagen-receptor axes may improve outcomes.

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Collagen; Integrins ?1?1/?2?1/?10?1/?11?1; Discoidin domain receptors; Collagen Toolkit; GFOGER; GLOGEN; Endothelial cells; HUVEC; Mechanotransduction

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