Volume 14 | Issue 3

 Abstract

Trauma, when left unaddressed, can significantly shape the psychological and behavioural patterns of an individual. There exists a strong interlink between trauma and masochism. Individuals exhibiting masochistic tendencies frequently have a traumatic childhood of neglect, abuse, and emotional detachment. This paper examines the unresolved trauma stemming from emotionally and physically abusive family dynamics and how it affects identity formation and relationships, with reference to Normal People. Focusing on the character of Marianne, this paper studies how her internalized trauma manifests as psychological masochism, leading her to find solace in toxic, abusive and self-effacing romantic relationships. By situating Marianne's experiences within psychoanalytic and trauma frameworks, the paper argues that her repeated submission to pain is not rooted in desire, but in an unconscious compulsion toward self-punishment and validation.

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 Keywords

Internalized Trauma, Masochism, Identity Formation, Toxic Relationships, Sally Rooney, Normal People

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 Abstract

In modern healthcare and service environments, appointment booking still relies on manual phone calls and physical visits, leading to missed calls, scheduling conflicts, and increased staff workload. This paper presents an AI Voice Agent for Real-Time Appointment Booking System that automates the entire booking process using voice-based interaction. The system integrates a React.js web interface, n8n workflow automation, Twilio Voice API, Retell Ai for to make the voice agent, Google Sheets for data storage, and Google Calendar for scheduling. Upon form submission, an AI voice agent calls the user, collects preferred appointment details, checks real-time availability, and confirms the booking automatically -- with zero human involvement. The proposed system reduces manual effort by 70-80%, enhances accessibility for non-technical users, ensures accurate scheduling, and provides instant real-time confirmation.

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 Keywords

AI Voice Agent, Appointment Booking System, n8n Automation, Twilio API, Real-Time Scheduling, Healthcare Automation, Voice Interaction, Google Calendar Integration, React.js, Workflow Automation, Retell AI Voice Agent, Appointment Booking System, n8n Automation, Twilio API, Real-Time Scheduling, Healthcare Automation, Voice Interaction, Google Calendar Integration, React.js, Workflow Automation,Retell ai

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Brain tumor diagnosis using Magnetic Resonance Imaging (MRI) remains a challenging task due to tumor heterogeneity, complex morphology, and variability across imaging modalities. Recent advances in deep learning, particularly Convolutional Neural Networks (CNNs), have significantly improved automated detection and classification performance. However, conventional CNNs often struggle to capture global contextual dependencies and long-range spatial relationships. To address these limitations, hybrid architectures integrating attention mechanisms--such as channel, spatial, self-attention, and Transformer-based modules--have emerged as state-of-the-art solutions. This paper presents a comprehensive review of hybrid CNN-attention frameworks for brain tumor detection and multi-class classification. The review systematically analyzes architectural designs, datasets, preprocessing strategies, evaluation metrics, and performance trends. A taxonomy of hybrid models is proposed, including CNN + attention modules, CNN + Transformer hybrids, and multi-branch fusion networks. Comparative analysis highlights that hybrid attention-based models consistently outperform standalone CNNs, achieving accuracy levels exceeding 98% on benchmark MRI datasets. Furthermore, this review identifies critical research gaps, including lack of clinical generalization, dataset bias, interpretability limitations, and computational overhead. Future directions focusing on explainable AI, federated learning, and lightweight deployment are discussed. The study aims to provide a structured foundation for researchers developing next-generation intelligent diagnostic systems.

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 Keywords

Brain Tumor Classification, MRI, Hybrid CNN, Attention Mechanisms, Vision Transformers, Deep Learning, Medical Imaging

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This study looks at the HR policies and practices of small-scale businesses, with a focus on how these organizations manage their workforces to promote organizational expansion and enduringness. Small firms often suffer with a lack of resources, financial constraints, and the need for effective HR procedures in order to remain competitive in the market. This research looks at key HR practices, including recruiting, training, performance evaluations, compensation, and employee relations, and assesses how they impact employee satisfaction, productivity, and overall corporate success. Using qualitative and quantitative data collected from a range of small-scale industries, the study illustrates the unique HR practices employed by small enterprises and how they adapt their strategies to match the specific requirements of their operations.

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Human Resource Management, Small-Scale Manufacturing Industry, HR Policies, Recruitment, Training and Development, Performance Appraisal, Employee Satisfaction, Workforce Productivity, Organizational Growth.

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Abstract The Telangana Movement (1969-1975) represents a crucial phase in post-independence India's regional political history. While the agitation for a separate Telangana state has been widely studied, the role of women participants remains underexplored. This paper examines the contributions of women activists, leaders, students, and grassroots participants in the movement. It argues that women were not passive supporters but active agents who shaped the social base, ideological framework, and cultural expression of the agitation. Despite their significant involvement, patriarchal structures and historiographical biases marginalized their contributions. This study attempts to reconstruct women's participation and situate it within broader debates on gender, regional identity, and political mobilization.

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 Keywords

Keywords Telangana Movement, Women Activists, Sadalakshmi, Eswari Bai, Gender History, Regional Politics, Social Movements, 1969 Agitation

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Type 2 Diabetes Mellitus (T2DM) is a major global health concern associated with multiple complications, among which Diabetic Peripheral Neuropathy (DPN) is highly prevalent and disabling. DPN leads to impaired balance, reduced lower extremity muscle strength, and increased anxiety levels, thereby increasing fall risk and reducing quality of life. Exercise therapy is widely recommended as a non-pharmacological intervention to improve both physical and psychological outcomes in individuals with DPN. This randomized clinical trial aimed to compare the effects of Interval Walking Training (IWT) and Nordic Walking (NW) on balance, lower limb muscle strength, and anxiety levels in individuals with DPN. A total of 66 participants aged 45-60 years were randomly allocated into two groups: Interval Walking Training (n = 33) and Nordic Walking (n = 33). Both groups underwent supervised training sessions for a specified duration. Outcome measures included the Berg Balance Scale (BBS) for balance, Manual Muscle Testing (MMT) for lower extremity strength, and the Generalized Anxiety Disorder-7 (GAD-7) scale for anxiety. Statistical analysis was performed using paired t-tests for within-group comparisons and independent t-tests for between-group comparisons. The results demonstrated that both groups showed statistically significant improvements in balance, muscle strength, and anxiety levels following the intervention (p < 0.05). Within-group analysis revealed significant improvements in BBS, MMT, and GAD-7 scores in both groups. However, between-group comparison demonstrated no significant difference in balance (p = 0.945) and anxiety levels (p = 0.531), while Interval Walking Training showed significantly greater improvement in lower extremity muscle strength compared to Nordic Walking (p < 0.05). In conclusion, both Interval Walking Training and Nordic Walking are effective in improving balance and reducing anxiety in individuals with Diabetic Peripheral Neuropathy; however, Interval Walking Training is superior in enhancing lower extremity muscle strength, supporting its inclusion in rehabilitation protocols for individuals with DPN.

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 Keywords

Type 2 Diabetes Mellitus, Diabetic Peripheral Neuropathy, Interval Walking Training, Nordic Walking, Balance, Muscle Strength, Anxiety.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

In modern educational institutions, secure and efficient student entry management has become a significant challenge due to increasing security concerns and large student populations. Traditional methods such as manual attendance registers and physical identity card verification are inefficient, error-prone, and vulnerable to misuse. These systems lack real-time validation, centralized monitoring, and secure digital record maintenance. This research proposes a QR Code Based Secure Student Entry Management System that integrates encrypted QR authentication with real-time server validation and automated digital logging. Each registered student is assigned a unique, time-sensitive QR code generated using secure tokenization techniques. The QR code is scanned at entry checkpoints, and the system verifies its authenticity through a centralized database server before granting access. The system enhances operational efficiency, prevents identity duplication, ensures secure communication using encryption protocols, and maintains tamper-proof digital logs. Experimental evaluation demonstrates significant improvement in processing speed, accuracy, and administrative monitoring compared to traditional entry systems. The proposed solution provides a scalable, cost-effective, and secure framework suitable for academic institutions.

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 Keywords

QR Code Security, Access Control, Real-Time Validation, Digital Logging, Student Authentication, Secure Entry System

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Creative Commons Attribution 4.0 and The Open Definition

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Cold chain management plays a crucial role in maintaining the quality and safety of temperature-sensitive products such as vaccines and perishable food items. Improper temperature control during storage and transportation can lead to vaccine degradation and food spoilage, resulting in serious health and economic consequences. This survey focuses on IoT-based cold chain monitoring systems that use sensors, microcontrollers, and communication technologies to ensure continuous temperature and location tracking. Existing solutions using DHT22 temperature and humidity sensors, u-blox NEO-M8N GPS module, Wi-Fi communication, and cloud platforms are studied and analyzed. The survey highlights system capabilities, limitations, and implementation challenges. The paper aims to provide a clear understanding of current cold chain monitoring approaches and supports the development of an efficient, low-cost, and reliable IoT-based monitoring system.

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 Keywords

Cold Chain Monitoring, IoT, Vaccine Transportation, Food Logistics, Temperature Sensors, u-blox NEO-M8N GPS Module, Cloud Monitoring

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Creative Commons Attribution 4.0 and The Open Definition

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The foundational decision regarding software architecture is a critical determinant of a system's long-term success, directly influencing factors such as scalability, maintainability, and operational complexity. The pervasive shift toward distributed computing has fueled a contemporary debate regarding the suitability of traditional monolithic structures versus the emergent microservices paradigm. This research provides a comparative analysis addressing the architectural dilemma faced by organizations when designing modern, resilient systems. We investigate both architectures across key dimensions, including scalability, deployment agility, fault isolation, and organizational alignment, using a structured analytical methodology grounded in established distributed systems theory. High-level findings indicate that while monolithic architectures offer simplicity in initial development and unified data management, they inherently introduce coupling that impedes independent scaling and rapid deployment. Conversely, microservices facilitate superior decoupling, enabling independent deployment and specialized scaling, yet introduce significant complexity concerning operational overhead, inter-service communication, and distributed data management. The analysis concludes that the optimal architectural choice is highly contextual, relying on a systematic evaluation of specific business requirements, organizational maturity, and system complexity rather than the adoption of a universally superior model

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Monolithic Architecture, Microservices, Software Scalability, DevOps, Fault Isolation, CAP Theorem, Conway's Law, Distributed Systems, Deployment Agility, System Design.

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 Abstract

The The need for intelligent systems that can extract pertinent information from many documents has increased due to the increasing growth of digital textual data. Conventional transformer-based language models perform well in natural language comprehension, but they are constrained by hallucination problems and context window limitations. A Retrieval-Augmented Transformer framework for question- answering and multi-document summarization is proposed in this study. Document chunking, transformer-based response generation, similarity-based retrieval utilizing FAISS, and dense vector embeddings are all integrated into the system. The model increases scalability and factual grounding by only accessing pertinent contextual information prior to generation. Compared to standalone transformer models, experimental findings show increased reaction accuracy and less creation of extraneous material. By retrieving only relevant contextual information before generation, the model improves factual grounding and scalability. Experimental observations indicate improved response accuracy

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Retrieval-Augmented Generation(RAG), FAISS,Multi-Document Summarisation, Large language model, Multi-format Document Processing

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Background: India's judicial system manages over 50 million pending cases with average disposition times exceeding 15 years, creating severe delays in justice delivery. Artificial Intelligence and Machine Learning offer potential solutions through automated legal document analysis and case outcome prediction. Objectives: This research investigates Retrieval-Augmented Generation (RAG) combined with Large Language Models (LLMs) for supporting legal document analysis, case outcome prediction, and explainable reasoning in the Indian judicial context. Six research questions guide the investigation: (RQ1) effectiveness of document retrieval methods, (RQ2) answer accuracy and factual grounding, (RQ3) pipeline synergy, (RQ4) user trust and explainability impact, (RQ5) multimodal interface accessibility, and (RQ6) responsible deployment safeguards. Methodology: A three-tier evaluation framework was employed: (i) retrieval tier evaluating hybrid (semantic + keyword) retrieval on 600 Indian court judgment documents, (ii) generation tier assessing LLM output quality on 30 test questions with 5 expert evaluators, and (iii) user-centered tier with 15 legal professionals (lawyers, judges, legal researchers) using System Usability Scale and user trust questionnaires. Results: The RAG system achieved 83.3% exact match accuracy (vs. 71% for LLM-only baseline), with hallucination rate of 5.1% (vs. 14.2% baseline, 64% reduction). Citation accuracy reached 94.7%, indicating strong source grounding. User trust improved by 41% (2.9 -> 4.1 on 5-point scale) with source transparency. Retrieval precision was 0.72 (Precision@5) and 0.84 (Recall@10). System Usability Score: 76.3 (Good category). Speech-to-text achieved 88.3% overall accuracy but showed accent bias (83.5% Indian English vs. 97% standard English). Performance varied by case type (Criminal: 85.2%, Appeals: 80.3%). Deployment Readiness: Core functionality is production-ready. Critical challenges include hallucination mitigation (target: <2%), Indian English ASR fine-tuning (target: 92%+), bias detection system development (current: 62.5%, target: 85%+), and regulatory framework establishment. Conclusions: RAG systems achieve superior accuracy and factual grounding compared to LLM-only approaches. Source transparency dramatically increases user trust, validating explainability-by-design approaches. Legal AI deployment requires domain-specific methods, careful bias auditing, clear regulatory frameworks, and stakeholder collaboration. The research provides evidence-based guidance for AI-assisted legal research systems and contributes to responsible AI deployment frameworks for high-stakes domains.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Retrieval-Augmented Generation, Explainable Artificial Intelligence, Legal AI, Natural Language Processing, Machine Learning, Indian Judiciary, Case Outcome Prediction, Responsible AI, Multimodal Interfaces Conflict of Interest

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Shirpur, a modest town in Maharashtra's Dhule district, is gradually emerging as a promising candidate for tourism development, owing to its integrated strengths in water management, agro-industrial prominence, educational ecosystem, and cultural-spiritual heritage. This research aims to assess Shirpur's untapped potential through a critical review of secondary data from official reports, case studies, and academic literature. Key variables explored include tourism infrastructure, environmental sustainability, regional economy, and public-private participation. The findings suggest that Shirpur, with the right strategic investment and branding, could evolve into a model for sustainable, rural-industrial tourism in Maharashtra.

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Shirpur, rural tourism, eco-tourism, sustainable development, industrial visits, Maharashtra

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This paper examines how contemporary Carnatic composer Padma Veera Raghavan has employed the medium of devotional composition to document, preserve, and transmit the living ritual heritage of Tiruvinnagar (Oppiliappan Kovil), one of the 108 Divya Desams of Sri Vaishnava tradition located in Thirunageswaram, Tamil Nadu. Through close textual and musicological analysis of eight compositions--spanning themes of the Unjal and Ratnangi utsavams, Brahmotsavam, Vimana Seva, Kalyana Utsavam, Tiruvonam festival, and the Rathotsavam--this study demonstrates how Raghavan systematically encodes liturgical practice, iconographic description, theological doctrine, mythological narrative, and temple topography within the formal structures of Carnatic kirtana. The compositions are analysed for their citation of specific ritual sevas (Muththangi, Ratnangi, Vimana Seva), Alvar devotional literature (Tiruvaymozhi, Pallandu), temple mythology (the Markandeya-Bhudevi narrative), and the distinctive theology of Oppiliappan Kovil as a centre of prapatti (surrender). The paper argues that Raghavan's compositional corpus constitutes a form of ethnomusicological and cultural documentation that bridges academic musicology, living temple tradition, and devotional practice, offering a model for the role of composition in heritage preservation within South Indian classical music.

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 Keywords

Keywords: Carnatic music composition; Oppiliappan Kovil; Divya Desam; utsavam documentation; Padma Veera Raghavan; temple heritage; Sri Vaishnava; Bhudevi; musical ethnography

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This study examines the investment awareness and attitude toward stock market investment among women workers in Tirupur. The main objective of the study is to analyse how factors such as investment awareness, income level, financial literacy, and social influence affect the attitude of women workers toward stock market investment. The study adopts a descriptive research design and data were collected from 125 respondents using a structured questionnaire through the convenience sampling method. The collected data were analysed using percentage analysis, correlation, and chi-square statistical tools. The findings reveal that most respondents have moderate awareness of investment options and maintain regular saving habits, but their knowledge of stock market investments is limited. The correlation analysis indicates a significant positive relationship between investment awareness and interest in learning about stock market investments, while the chi-square analysis shows no significant association between age and opinion toward stock market investment. The study concludes that improving financial literacy and investment awareness can enhance women workers' participation in stock market investments and contribute to their financial empowerment.

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Investment Awareness Stock Market Participation Women Investors Women Workers Financial Literacy Investment Behavior Investor Attitude

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Investment behaviour is very important in establishing how individuals utilize their financial resources to various investment opportunities in order to gain financial security as well as wealth generation. The analysis report seeks to investigate the investment behaviour and decision-making pattern of the employees working in the IT sector in Trichy City. This research is aimed at determining the interest in investment among IT professionals and examining the interest influences on their investment. The decent study used descriptive research design with a structured questionnaire as primary instruments of data collection of 384 employees in IT sector by applying the convenient sampling method. The analysis was done on the demographic factors, the preferred investment strategies and the main criteria used to make the investment decision including expected returns, risk, financial literacy, liquidity and tax. The findings of the research indicate that the vast majority of the respondents represent the young and middle-aged group and can show the knowledge of the diverse investment directions. It is identified that mutual funds and investments in the stock markets are the most favored investment avenues by the employees in IT because the investments will have higher returns. The results further suggest that expected return and risk level rank as the most important in making investment decisions and then there is financial knowledge. The research also finds that the employees working in the IT industry in Trichy have positive investment attitude and that the share of participation in market-related financial instruments is on the rise. The results of the current study can guide financial institutions, investment advisors, and policy makers in the creation of appropriate monetary products and enhancement of financial literacy among the salaried professionals.

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Investment behaviour, investment decision-making, IT sector employees, financial literacy, risk tolerance, investment preference, Trichy City

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Creative Commons Attribution 4.0 and The Open Definition

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This study examines the concept of brand synergy in tech-fashion collaborations, with specific reference to the partnership between Ray-Ban and Meta and the positioning of Meta smart sunglasses in the Indian market. The research aims to examine how the integration of advanced technology into an established fashion brand influences consumer awareness, perceptions, and purchase intentions. The study focuses on key factors such as brand awareness, perceived value, pricing sensitivity, privacy concerns, and barriers to the adoption of smart wearable products. A primary research approach was adopted, collecting data from 100 respondents through a structured questionnaire. The survey captured insights into consumer familiarity with smart glasses, attitudes toward wearable technology, and willingness to adopt products that combine fashion and functionality. Additionally, a qualitative interview with a store manager provided practical insights into customer reactions, common queries, and challenges faced during instore promotion of Meta smart sunglasses. The findings indicate that while Ray-Ban's strong brand recognition drives initial interest, awareness and understanding of Meta's technological integration remain limited among Indian consumers. High pricing is identified as a major barrier, with many respondents perceiving the product as expensive relative to its utility. Privacy concerns, particularly regarding built-in cameras and data use, further constrain purchase intention. Despite these challenges, the collaboration is perceived positively for innovation and brand appeal, indicating potential for growth through improved awareness and pricing strategies. The study concludes that addressing privacy concerns, improving consumer education, and adopting more competitive pricing are essential for increasing adoption in the Indian market.

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Brand Synergy, Tech-Fashion Collaboration, Privacy Concerns, Price Sensitivity, Smart Wearables

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Creative Commons Attribution 4.0 and The Open Definition

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Abstract Adolescence is a critical period characterized by physical, emotional, and social changes. During this period, some people experience heightened self-consciousness and shyness. These psychosocial changes can profoundly impact shyness and self-worth, particularly among adolescent girls. Research indicates that social support is vital in enhancing self-worth and mitigating the adverse effects of shyness (Monteiro et al., 2021). The present study tries to explore the protective function of social support in the relationship between shyness and self-esteem among adolescent girls. The research aims to provide insights into fostering supportive environments that promote psychological well-being during this formative stage. In addition, the study tries to explore the mediating role of social support in the relationship between shyness and self-worth among adolescent girls. 100 female students, aged 18 to 25, from University College, participated and completed self-report scales measuring shyness, social support, and self-worth. Correlation analysis revealed a significant positive relationship between social support and self-worth (R = .272*, p<0.01), suggesting that higher social support is associated with greater self-worth. However, no significant correlation was found between shyness and social support, and between shyness and self-worth. Mediation analysis indicated that social support significantly predicted self-worth (B = 0.762, SE = 0.278, t = 2.739, p = .0073), highlighting its protective role in adolescent well-being. However, the direct effect of shyness on self-worth was non-significant, suggesting that being shy does not necessarily lower self-worth. Additionally, the indirect effect of shyness on self-worth through social support was also non-significant.

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Keywords: Shyness, Social Support, Self-Worth.

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Creative Commons Attribution 4.0 and The Open Definition

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This project proposes a blockchain-based digital evidence system to ensure secure storage, integrity, and transparent chain of custody. It uses cryptographic hashing and an immutable ledger to prevent tampering and unauthorized access. The system improves reliability and trust in digital evidence for law enforcement and judicial processes.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Blockchain, Digital Evidence Verification, Chain of Custody, Evidence Integrity, Flask Framework, Digital Forensics, Tamper Detection.

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Creative Commons Attribution 4.0 and The Open Definition

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Abstract: Tripura, situated in Northeast India, is a beautiful state. The Sangrai festival is one of the most important cultural and religious events for the Mog community in Tripura. This study is about that festival. In April, the Mog people, followers of Buddhism and connected to the Arakan region, celebrate Sangrai as their New Year festival. The goal of the research is to learn about the festival's history, customs, and cultural significance. Interviews and other available literature have been used as both primary and secondary sources. The study shows that Sangrai is more than just a religious event; it also shows social values, beliefs, and traditions. Rituals like prayer, sharing food, sprinkling water, and dancing help people feel more connected to their culture and keep their rich history alive.

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Keywords: Mog community, Sangrai festival, Water festival , Sangrai Dance, Mog Festival.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Water-repellent cement is an advanced construction material engineered to improve durability by minimizing moisture penetration. Despite its technical benefits, its adoption in India remains limited. This study examines the primary factors driving adoption among Individual House Builders (IHBs), with a focus on behavioral and market-based determinants. Primary data were gathered from 101 respondents and analyzed using regression techniques in SPSS. The results demonstrate that both attitude (? = 0.380, p < 0.01) and perceived value (? = 0.257, p < 0.01) have a significant positive impact on purchase intention. Conversely, price fairness (? = -0.320, p < 0.01) and digital content exhibit negative or insignificant effects, whereas demonstrations have no significant impact. The study concludes that adoption is primarily driven by perceived value and trust rather than promotional or digital interventions. These findings offer practical guidance for cement manufacturers to transition toward value-driven communication and trust-building approaches.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Water-repellent cement, consumer behaviour, adoption drivers, awareness, price-value perception, construction materials, IHB market.

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Creative Commons Attribution 4.0 and The Open Definition