Volume 14 | Issue 3 |

 Abstract

In recent years, the rapid increase in cybercrime, digital fraud, and data breaches has made the secure management of digital evidence a critical requirement in modern legal systems. Traditional centralized evidence management approaches are highly vulnerable to issues such as data tampering, unauthorized access, insider threats, and single points of failure, which can compromise the integrity and reliability of investigations. To address these limitations, this paper presents the TrustChain Evidence Management System (EMS), a decentralized framework that integrates blockchain technology with the InterPlanetary File System (IPFS) for secure and tamper-resistant evidence storage. The proposed system ensures data integrity, transparency, and traceability by recording essential evidence details on a blockchain network, while large digital files are stored efficiently using IPFS. A role-based access control mechanism is implemented through smart contracts on an Ethereum platform, allowing only authorized users such as police personnel and judicial authorities to access and verify evidence. Each evidence record is further protected using cryptographic techniques to prevent unauthorized usage. Smart contracts automate essential processes including user registration, evidence submission, validation, and retrieval, reducing manual intervention and minimizing errors. All transactions are permanently recorded on the blockchain, maintaining a transparent and verifiable chain of custody. The integration of blockchain, IPFS, and secure authentication mechanisms provides a reliable and efficient solution for digital evidence management, enhancing trust, security, and accountability in modern forensic and legal environments.

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 Keywords

Blockchain, InterPlanetary File System (IPFS), Digital Evidence, Evidence Management System, Smart Contracts, Data Security, Chain of Custody.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Geography education plays a crucial role in developing students' spatial thinking, environmental awareness, and understanding of human-environment interactions. However, learning outcomes in high school geography are influenced by multiple interrelated factors. This paper presents a thematic analysis of the key factors affecting students' geography learning, drawing on existing literature. The study identifies seven major dimensions: student-related factors, teacher competency, curriculum and content, teaching-learning resources, school environment, assessment practices, and socio-economic background. Findings indicate that student interest, teacher effectiveness, availability of resources, and relevance of curriculum significantly shape learning outcomes. Additionally, traditional assessment methods and inadequate infrastructural support hinder conceptual understanding. The paper highlights the interconnected nature of these factors and emphasizes the need for a holistic and learner-centered approach. It suggests strengthening teacher training, integrating technology, promoting experiential learning, and reforming assessment practices to enhance the quality of geography education at the secondary level.

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 Keywords

Geography learning, secondary education, teaching-learning factors, student achievement

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Career planning is a crucial yet often confusing process for students, especially those at the school level who are uncertain about the path to achieve their dreams. The GoalGuider project aims to assist such students by generating personalized career roadmaps based on their current academic status and future career goals. Using an interactive chatbot interface, the system collects basic information such as the student's name, present academic level, and desired profession and then provides a step-by-step roadmap outlining what subjects, courses, and skills are needed. Developed using Python and the Django framework with AI API integration, GoalGuider offers a simple and effective digital solution for individualized career guidance, bridging the gap between ambition and actionable planning. The system further incorporates a streak-based progress tracking mechanism to keep users motivated and consistent throughout their learning journey.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Artificial Intelligence, Career Planning, Personalized Roadmap, Chatbot Interface, Python, Django Framework, Streak-based Progress Tracking, Career Guidance System.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The human gut microbiota has emerged as a critical determinant in drug metabolism, influencing both pharmacokinetics and pharmacodynamics. Comprising trillions of microorganisms, the gut microbiome plays a significant role in the biotransformation of various xenobiotics, including therapeutic drugs. These microbial communities possess diverse enzymatic capabilities that can activate, inactivate, or toxify pharmaceutical compounds, thereby affecting drug efficacy and safety. Interindividual variability in gut microbiota composition has been identified as a key factor contributing to differences in drug response, leading to challenges in standard therapeutic regimens. Recent advances in metagenomics, metabolomics, and systems biology have facilitated a deeper understanding of host-microbiome interactions, paving the way for microbiome-based personalized medicine. This review highlights the mechanisms by which gut microbiota influences drug metabolism, discusses clinically relevant drug-microbiome interactions, and explores the potential of microbiota profiling in optimizing individualized therapeutic strategies. Furthermore, current challenges and future perspectives in integrating gut microbiome data into precision medicine are critically evaluated.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Gut microbiota, Drug metabolism, Personalized medicine, Pharmacokinetics, Pharmacodynamics, Microbiome, Xenobiotics, Precision medicine, Microbial enzymes, Host-microbe interaction

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Diabetic Retinopathy (DR) represents a primary microvascular diabetes complication which results in global blindness that medical treatment cannot prevent. The process of vision loss prevention requires both detection and precise assessment methods. Optical Coherence Tomography (OCT) enables researchers to obtain detailed retinal layer images which show all pathological conditions. This paper presents a deep learning-based OCT image classification system integrated with an automated clinical interpretation module. A Convolutional Neural Network (CNN) model uses data from the Kaggle OCT dataset to train for image classification into CNV DME DRUSEN and NORMAL categories. The proposed system achieves 97% validation accuracy. The system creates an AI-based diagnostic summary which provides structured information that resembles a preliminary ophthalmic report. This system improves clinical understanding while it enables early screening tests in areas with limited medical resources.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Diabetic Retinopathy, Optical Coherence Tomography (OCT) ,Deep Learning Convolutional Neural Network (CNN), Medical Image Classification ,Kaggle OCT Dataset ,Retinal Image Analysis ,AI-based Diagnostic System

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Kidney stones are different types of mineral salts that mix with the organic matrix that forms in the urinary tract. When stones move from the kidneys to the ureters, symptoms of renal colic can occur and lead to urinary tract infections and/or infections. In fact, renal stone pain is one of the leading causes of emergency room visits. Incidence of kidney stones has more than doubled in the United States (and many developed countries) in the past four years; about 11% of men and 7% of women have kidney stones. Unless the underlying cause of the stone formation is adequately addressed, the recurrence rate of kidney stones is about 50%. Most kidney stones consist of calcium (calcium oxalate and/or calcium phosphate), either pure or combined with uric acid. Pure uric acid stones account for 8% to 10% of all stones, but are more common in older stones with type 2 diabetes and/or metabolic disease. Uric acid stones are usually caused by too much acid in the urine. Cystine stones are caused by inactivating mutations in genes that code for tubular transporters that reabsorb the amino acid cysteine. Lifestyle changes (increased fluid intake, decreased salt intake, increased calcium and animal protein intake) as well as drug therapy for metabolic abnormalities may cause severe symptoms and side effects. Stones that form in the urine, kidneys, ureters, and bladder are called urolithiasis. Urinary stones are quite common. Approximately 5-15% of the population is affected by urinary stones, so its occurrence is higher than the above stone formation. The treatment of renal stones with Lycopodium Clavatum in homoeopathy is described in this succinct case report. According to homeopathic principles, the remedy was chosen based on the patient's particular symptoms and unique constitution. Lycopodium Clavatum, when used in low doses, significantly reduced pain, elevated wellbeing, and improved urine function. This instance highlights the effectiveness of homoeopathy as a non-invasive, individualized strategy for managing kidney stones, promoting more research in this area and emphasizing its potential.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Renal calculi, Pathophysiology, Homoeopathy, Lycopodium, Renal colic, individualization

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Eco-friendly pigments are gaining importance in textile applications because they reduce environmental pollution and are safe for human health. The present study focuses on the performance evaluation of eco-friendly pigments used in durable fabric painting. Natural and environmentally safe pigments were prepared and applied on selected fabric samples using suitable painting techniques. The painted fabrics were then subjected to different tests to evaluate their quality and durability. Fabric thickness was measured to understand the structural properties of the material before and after painting. Fabric weight was also analyzed to determine the effect of pigment application on the fabric. A pH test was conducted to identify the acidity or alkalinity level of the treated fabric and to ensure skin safety. In addition, colour fastness to crocking tests (both dry and wet) were carried out to evaluate the resistance of colour transfer during rubbing. The results of these tests helped to determine the stability and performance of eco-friendly pigments on fabric surfaces. The findings indicate that eco-friendly pigments provide satisfactory colour appearance and acceptable durability. These pigments also support sustainable textile practices by reducing the use of harmful synthetic chemicals. The study highlights the potential of eco-friendly pigments as a suitable alternative for durable fabric painting. Thus, eco-friendly pigment application can contribute to environmentally responsible textile production.

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 Keywords

Keywords: Eco-friendly pigments, Fabric painting, Sustainable textiles, Colour fastness, Fabric thickness, pH test, Durable textile finishing.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This paper proposes a hybrid intrusion detection system combining transformer-based feature extraction with a Double Deep Q-Network (DDQN) to effectively detect zero-day cyber attacks. The model uses prioritized replay and dynamic rewards to improve accuracy, adaptability, and real-time performance compared to traditional methods.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Zero-Day Attack Detection, Intrusion Detection System (IDS), Reinforcement Learning, Double Deep Q-Network (DDQN), Transformer Model, Prioritized Experience Replay, Meta-Adaptive Learning, Cybersecurity, Network Traffic Analysis, Hybrid Deep Learning Framework.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

"Psychological Determinants of Procrastination: The Role of Self-Efficacy and Emotion Regulation among college students" Abstract *Dr. Kanupriya Procrastination is a common behavioral issue that can reduce productivity, efficiency, and the overall quality of work in personal as well as in professional environments. In educational settings, procrastination among students may affect learning, class and school responsibilities, and the timely completion of academic tasks by the students. Present study was planned to focus on two important psychological variables that may influence procrastination are self-efficacy and emotion regulation. Self-efficacy refers to an individual's belief in their capability to successfully perform tasks, while emotion regulation involves the ability to manage and respond to emotional experiences effectively. A purposive sample of 100 college students was taken in present study. A correlational research design was used to examine the role of self-efficacy and emotion regulation in procrastination among college students. College Academic Self-Efficacy Scale (CASES) developed by Owen & Froman (1988), Emotion Regulation Questionnaire (ERQ) by Gross & John (2003) and Tuckman Procrastination Scale (TPS; 1991) were used in present study. Correlational results of the study shows that high level of self-efficacy and Cognitive Reappraisal are positively associated with less academic procrastination tendencies among college students. Expressive suppression positively correlated with academic procrastination behaviour among college students. Regression coefficient analysis exhibits that expressive suppression (a maladaptive emotion regulation strategy) emerged as a strong negative and self-efficacy as a positive predictor of procrastination behaviour among college students means low suppression and high self-efficacy leads to low procrastination among college students; they are less likely to delay tasks or avoid work-related duties. It highlights that self-efficacy act as a cognitive factor, while emotional regulation acts as an affective factor, and both jointly predict procrastination. Present study suggests that procrastination is best predicted and reduced by improving both self-efficacy (as a cognitive factor) and emotion regulation (affective factor) not just time management. *Dr. Kanupriya, Assistant Professor, Dept. of Psychology, G. G. J. Govt. College, Hisar.

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 Keywords

Self-Efficacy, Emotion Regulation and Procrastination

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 Abstract

ABSTRACT The review looks at how to create and assess herbal chocolate that includes Limoniaacidissima (wood apple) extract for its heart and lung health benefits. Limoniaacidissima is recognized for its high levels of flavonoids, tannins, and coumarins. These compounds have antioxidant, cholesterol-lowering, and bronchodilator effects. The herbal chocolate was made with cocoa powder, cocoa butter, dark chocolate, stevia, vanilla, and wood apple extract using a traditional melting and mixing method. This was followed by tempering and molding. We evaluated the formulation based on its physical and chemical properties, taste, and antioxidant levels. The product showed appealing sensory characteristics and strong antioxidant potential, thanks to the bioactive compounds it contains. Therefore, herbal chocolate can serve as both a nutritional aid and a tasty option for promoting heart and lung health.

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 Keywords

LimoniaAcidissima, Wood apple, Herbal chocolate, Bronchodilator effect, Antioxidant activity, Nutraceutical, Method of preparation, Respiratory health, Lung health, Functional food.

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Creative Commons Attribution 4.0 and The Open Definition