Volume 14 | Issue 3 |

 Abstract

Bioanalytical method validation is an important process used to ensure that analytical methods provide reliable and accurate results for the determination of drugs and their metabolites in biological samples such as plasma, serum, blood, and urine. The development of a bioanalytical method involves selecting appropriate analytical techniques, optimizing sample preparation procedures, and establishing suitable chromatographic conditions. Common extraction techniques include protein precipitation, liquid-liquid extraction, and solid-phase extraction to improve analyte recovery and reduce matrix interference. Method validation evaluates key parameters such as accuracy, precision, selectivity, linearity, recovery, limit of detection, limit of quantification, and stability. These parameters confirm that the method is consistent, reproducible, and suitable for routine analysis. Bioanalytical methods play a crucial role in pharmacokinetic studies, drug discovery, and clinical research. Proper validation ensures the quality and reliability of analytical data and supports regulatory requirements in pharmaceutical development.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Bioanalysis, Plasma, Validation, Chromatography, Precision, Selectivity, Drug Discovery, Stability, Pharmacokinetics

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The Voice and Perspective of Women in the Works of Bhikhari Thakur: A Philosophical Analysis

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

The Voice and Perspective of Women in the Works of Bhikhari Thakur: A Philosophical Analysis

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Healthcare data is one of the most sensitive personal pieces of information that a person creates - it captures not only physical statuses but mental health background, reproductive status, genetic formation, and patterns of behaviour that, when revealed, may lead to discrimination, financial abuse, damaged reputation, and severe abuse of personal dignity. The healthcare industry in India with its fast digitalization and the adoption of electronic health records, telemedicine platforms, insurance portals, and government health schemes like Ayushman Bharat are taking a more appealing form to cybercriminals. There has been a significant increase in the number of data breaches of hospitals, health-tech firms, and state health databases in the country, but the legal framework concerning the protection of health data is fragmented, under-enforced, and, until the recent adoption of the Digital Personal Data Protection Act, 2023, does not include a specific data protection law. In this paper, a thorough review of the Indian healthcare data breach problem is performed using the prism of a cybercrime law and a data protection law. It examines the character and extent of healthcare data breaches, charts out the current legal context such as the Information Technology Act, 2000, the DPDP Act, 2023, and the industry-specific regulations, and critically assesses how these tools are sufficient to prevent breaches and safeguard patients. The paper contends that India is at a crossroad: the IT infrastructure of digital health is being developed on a massive scale and the legal framework of protecting the information that infrastructure produces must be developed equally seriously.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

healthcare data breach, cyber offences, data protection, DPDP Act 2023, Information Technology Act, patient privacy, India, digital health

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Background: Lumbar spondylosis is a common degenerative condition of the spine associated with pain and functional disability. Physiotherapy interventions such as motor control exercises and conventional back exercises are widely used to manage symptoms. However, evidence comparing their effectiveness in lumbar spondylosis remains limited. Objective: To compare the effectiveness of motor control exercises and conventional back exercises in reducing pain and functional disability in patients with lumbar spondylosis. Methodology: A pre-post experimental study was conducted on 44 patients diagnosed with lumbar spondylosis. Participants were divided into two groups (n=22 each). Group A received motor control exercises and Group B received conventional back exercises for 4 weeks (5 days per week). Pain and functional disability were measured using the Numerical Pain Rating Scale (NPRS) and Back Performance Scale (BPS). Results: Both groups showed significant improvement in pain and functional disability after intervention (p<0.05). However, the motor control exercise group demonstrated greater improvement compared to the conventional exercise group. Conclusion: Motor control exercises are more effective than conventional back exercises in reducing pain and improving functional disability in patients with lumbar spondylosis.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Lumbar spondylosis, Motor control exercises, Conventional back exercises, Low back pain, Physiotherapy.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Lions, Himalayan Ecosystem, Feeding Behaviour, Living Patterns, Environmental Changes, Habitat Fragmentation, Climate Change, Prey Availability, Human-Lion Conflict, Wildlife Conservation.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

As the global tourism industry undergoes a digital transformation, the traditional reactive model of travel safety--relying on emergency calls after an incident--is becoming obsolete. This paper proposes "Tourist Safe India," a pioneering, proactive digital ecosystem designed to transform the travel experience through an AI-driven "Digital Guardian" framework. Unlike standard navigation tools, this platform orchestrates a multi-layered defence strategy integrating Advanced Global Positioning System (GPS) tracking, lightweight Machine Learning models for real-time risk assessment, and an automated Incident Response System.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

AI Risk Avoidance , o Digital Guardian , Emergency Radar , Smart Crowd sourcing

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Artificial intelligence is increasingly shaping the lives of today's youth influencing how they learn, work and interact socially this paper explores the multifacested impact of artificial intelligence on the new generation through a comprehensive review of literature and empirical analysis. It examines how AI is transforming education by enabling personalised learning and equiping students with skills relevant to future workforce. The study investigates the impact of artificial intelligence technology's on youths learning process and academic performance with a focus on that perceptions and the challenges associated with AI adoption. The findings reveal that AI offers significant benefits including improved academic outcomes and enhanced students engagement. This study focuses on examining the impact of artificial intelligence on the youths of Tumkuru City by analyzing their awareness, usage patterns, perceived advantages, and challenges associated with AI technologies. The research aims to provide insights that can help educators, policymakers, and society ensure that AI is used responsibly and effectively for the overall development of young individuals.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Artificial Intelligence, AI Awareness, AI in Education, AI Adoption, Career Opportunities, Digital Literacy, AI usage Pattern.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The Inventory Management System (IMS) is a web-based application developed to streamline and automate the process of managing inventory in small and medium-scale businesses. In today's competitive market environment, efficient inventory control plays a critical role in ensuring smooth business operations, reducing operational costs, and improving customer satisfaction. Manual inventory management methods are time-consuming, error-prone, and inefficient, leading to issues such as stock mismanagement, overstocking, understocking, and inaccurate record maintenance. The proposed system provides a centralized platform that enables businesses to track, manage, and control stock levels in real time. The system is designed using modern web technologies with a user-friendly interface for administrators and authorized users. It includes essential modules such as product management, stock tracking, supplier management, sales and purchase records, report generation, and user authentication. The backend system ensures secure data storage and retrieval, while the frontend interface provides an interactive dashboard for monitoring inventory status. The system automatically updates stock quantities after every transaction, thereby minimizing human errors and maintaining data accuracy. Additionally, the system generates analytical reports that help business owners make informed decisions regarding restocking and demand forecasting. The Inventory Management System improves operational efficiency by reducing paperwork, saving time, and enhancing accuracy in inventory tracking. It also supports scalability, allowing businesses to expand their operations without major structural changes to the system. This project demonstrates how digital transformation can optimize inventory operations and support better business management practices. The implementation of this system contributes to improved productivity, transparency, and profitability for organizations.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Inventory Management, Stock Control, Web Application, Database Management, Automation, Business Optimization.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The increasing use of social media platforms has introduced digital environments structured around reward-based feedback such as likes, comments, shares, and notifications. These mechanisms create reward loops that encourage repeated engagement and may influence psychological processes such as instant gratification and self-regulation. The present study aimed to examine the relationship between social media reward loops, instant gratification, cognitive regulation, and emotional regulation among young adults. A quantitative research design was adopted, and data were collected from 100 participants aged between 18 and 25 years using self-report questionnaires. Descriptive statistics and Pearson correlation analysis were used to analyze the data. The results revealed a significant relationship between reward loops and instant gratification. However, no significant relationship was found between reward loops and cognitive or emotional regulation, nor between instant gratification and regulatory variables. A significant relationship was observed between cognitive regulation and emotional regulation. The findings suggest that while digital reward mechanisms may encourage tendencies toward instant gratification, they may not significantly impair cognitive or emotional regulation among young adults.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Digital dopamine, Social media reward loops, Instant gratification, Cognitive regulation, Emotional regulation

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Cardiovascular diseases remain one of the leading causes of mortality worldwide, making early detection and risk prediction essential for effective clinical intervention and preventive healthcare. In recent years, machine learning techniques have demonstrated significant potential in assisting clinicians by analyzing complex medical datasets and identifying hidden patterns associated with cardiovascular conditions. However, many existing prediction models suffer from limitations such as insufficient feature representation, lack of robustness, and limited interpretability, which restrict their practical adoption in clinical environments. To address these challenges, this study proposes a Transformer-Enhanced Ensemble Learning framework for interpretable cardiovascular disease prediction. The proposed approach integrates transformer-based feature representation with ensemble machine learning models to improve prediction accuracy and model reliability. The transformer encoder captures complex relationships among clinical attributes using attention mechanisms, while the ensemble classification strategy aggregates predictions from multiple classifiers to enhance generalization performance. Furthermore, explainable artificial intelligence techniques are incorporated to provide transparency and interpretability by identifying the most influential clinical risk factors affecting prediction outcomes. Experimental evaluation conducted on benchmark cardiovascular disease datasets demonstrates that the proposed framework achieves superior performance compared with conventional machine learning models in terms of accuracy, precision, recall, F1-score, and area under the ROC curve. The interpretability analysis further reveals clinically relevant features that contribute significantly to cardiovascular risk prediction. The results indicate that the integration of transformer architectures, ensemble learning, and explainable AI provides a reliable and transparent framework for cardiovascular disease prediction and can support clinicians in early diagnosis and decision-making processes.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

cardiovascular disease prediction; Transformer models; Ensemble learning; Explainable artificial intelligence; Machine learning; Clinical decision support systems; Healthcare analytics.

  License

Creative Commons Attribution 4.0 and The Open Definition