Volume 14 | Issue 5 |

 Abstract

The purpose of the present study was to examine the relationship between personality traits and life satisfaction among sports persons. For this study, a total of 100 sports persons were selected as subjects. The age of the subjects ranged between 18 to 30 years and they were selected through simple random sampling technique. Personality traits were assessed using a standardized personality questionnaire and life satisfaction was measured using the Life Satisfaction Scale. The collected data were tabulated and subjected to statistical analysis. Descriptive statistics such as mean and standard deviation were calculated and Pearson's correlation coefficient was used to determine the relationship between personality traits and life satisfaction. The results of the study revealed that personality traits have a significant relationship with life satisfaction among sports persons. It was concluded that positive personality traits contribute to higher life satisfaction in athletes.

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 Keywords

Personality traits, life satisfaction, sports persons, psychology, athletes

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 Abstract

Alzheimer's disease (AD) is a leading cause of dementia, characterized by progressive cognitive decline and memory loss, and remains a major public health challenge worldwide. Current therapeutic options primarily address symptoms and offer limited efficacy, necessitating the exploration of innovative treatment strategies. This review focuses on multi-drug targeting approaches, which aim to address the multifactorial nature of AD by intervening at multiple pathogenic pathways simultaneously. Multi-drug targeting strategies encompass the use of drug combinations, polypharmacology (single molecules with multiple targets), and the integration of pharmacological treatments with biological agents such as monoclonal antibodies. These approaches strive to modify disease progression by reducing amyloid-beta plaques, preventing tau aggregation, mitigating neuroinflammation, and preserving synaptic function. Incorporating the "One Health" perspective, this review emphasizes the interconnectedness of human, animal, and environmental health in the context of AD. A holistic approach considers environmental factors, lifestyle, and co-morbidities, advocating for comprehensive interventions that include lifestyle modifications, dietary adjustments, and environmental controls alongside pharmacological treatments. The objectives of this review are to provide a detailed analysis of multi-drug targeting strategies for AD within the "One Health" framework, evaluate their efficacy and safety, and discuss the potential benefits of integrating broader health determinants in therapeutic approaches. The review covers the pathophysiology of AD, current therapeutic limitations, detailed discussion of multi-drug strategies, and how environmental and lifestyle factors can enhance treatment outcomes. Through this comprehensive analysis, the review aims to propose a more effective and holistic strategy for combating Alzheimer's disease.

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 Keywords

Alzheimer's Disease, Multi-Drug Targeting, Combination Therapy, Polypharmacology, One Health

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Green Bonds Renewable Energy Financing Sustainable Finance Climate Finance Green Investment Environmental Finance Renewable Energy Infrastructure Decarbonization ESG Investing

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With the rapid increase in the popularity of learning platforms on the Internet, there are huge amounts of educational content available. It can be a big problem for users who wish to choose proper courses based on their own needs. There are a variety of platforms like Coursera, Udemy, or edX which provide a large amount of content related to different courses. The keyword-based searching approach used by these platforms does not provide enough accuracy for the recommendation of courses that fit perfectly into users' goals, skills, or interests. Therefore, this paper describes an intelligent course recommender system using a machine learning algorithm, which will help learners discover appropriate courses much more efficiently. In this research, a content-based filtering approach is adopted to recommend courses. First, course descriptions are transformed into numeric vectors using Term Frequency-Inverse Document Frequency (TF-IDF). Then, cosine similarity is applied to measure the similarities between courses. Such a system can recognize courses that are similar in topic to each other. The application is implemented in Python using the Flask framework. Evaluation results show that the suggested recommender system indeed works better in discovering new courses than existing approaches.

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 Keywords

: Online Learning, Course Recommendation System, Machine Learning, TF-IDF, Cosine Similarity, Personalized Learning, Content-Based Filtering, Artificial Intelligence, Natural Language Processing

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The quick growth of digital technologies and the widespread use of online banking have made financial fraud more frequent. As more people use credit cards, mobile banking, and online shopping, it's harder for financial companies to identify fake transactions. Traditional systems that depend on fixed rules aren't effective against new and changing fraud techniques, which often causes too many false alerts and less efficient processes. In recent years, machine learning and deep learning have become key tools for detecting fraud. These methods can learn from large amounts of transaction data and adapt to new fraud tactics. This paper provides a detailed overview of different approaches used in financial fraud detection, including traditional methods like Logistic Regression, Naive Bayes, Decision Trees, and Random Forest, as well as more advanced models like Artificial Neural Networks (ANN) and Long Short-Term Memory (LSTM) networks. The study compares these methods based on their performance, scalability, and ability to handle real-time situations. It also discusses the advantages of combining different models, especially ANN-LSTM setups, which are good at learning features and identifying patterns over time. Some major challenges mentioned include unbalanced data, high computing requirements, rapidly changing fraud trends, and the need for fast processing. The review shows that while traditional methods are simple and easy to understand, deep learning models are better at handling complex and evolving fraud situations. This paper is intended to help researchers and professionals by summarizing current techniques and identifying areas for future improvement in developing stronger and more effective fraud detection systems..

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 Keywords

Fraud Detection, Machine Learning, Deep Learning, ANN, LSTM, Financial Security

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 Abstract

Peptic ulcer disease (PUD) is a disorder where the lining of the stomach or duodenum develops open sores due to acid related damage. These ulcers occurred due to an imbalance between the harmful factors such as gastric acid and protective mechanisms of the mucosa. The common etiological included infection with Helicobacter pylori and prolonged use of non-steroidal anti-inflammatory drugs (NSAIDs) (1). Peptic ulcer disease which includes both stomach and duodenal ulcers, mainly a significant portion of people seeking surgical advice to the world. A peptic ulcer is a develops on the stomach or duodenal lining. Peptic ulcers are caused by factors like hydrochloric acid (HCL), pepsin, refluxed bile, leukotriene's (LTs), reactive oxygen species (ROS), and defensive factors like the mucus bicarbonate barrier, prostaglandins (PGs) (2). the last one-hundred-year written history on a peptic ulcer disease and the treatment options are most common complications. Perforation The reason for reviewing the literature was evaluating most common ideas on how to treat for peptic ulcers (PPU) in general opinions treatment and surgical treatment. Peptic ulcer disease (PUD) is a mucus secretion, bicarbonate production, mucosal blood flow, and prostaglandins. PUD remains a significant global health contributing to morbidity and health care burden, particularly in developing countries. The primary etiological factors include h pylori infection and prolonged use of Non-steroidal anti-inflammatory drugs (NSAIDs), inhibit the prostaglandin synthesis and compromise mucosal defense. Additional risk factors include smoking, alcohol consumption, stress, and genetic predisposition. Clinically, patients with PUD may present with Epigastria pain, bloating, nausea, vomiting, and in severe cases, complications such as gastrointestinal bleeding, perforation, and gastric outlet obstruction. Diagnosis is typically established through endoscopic examination, along with non-invasive tests such as the urea breath test, stool antigen test, and serological assays for H. pylori detection or surgery Management of PUD focuses on the H. pylori infection, reduction of gastric acid secretion, and prevention of the PUD. Pharmacological therapy includes proton pump inhibitors (PPIs), H2 receptor antagonists, antibiotics and Cytoprotective agents. Eradication therapy, or other therapy of a combination of antibiotics and reduce rates of PUD. Lifestyle modifications and avoidance of NSAIDs are also required to management. Advances diagnosis technology and treatment, challenges such as antibiotic are early diagnosis therapy, and patient education is reducing disease burden and improving clinical safety.

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Etiology, Causes, Symptoms, Pathophysiology, Complication, Diagnosis, Radiology (3).

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This research inspects the dynamic and evolving nature of aesthetics, tracing its development from the functional focus of ancient Greek art to the contemporary emphasis on innate artistic value. Through in-depth interviews and assignments with art students, the study explores the subjective experience of beauty, revealing how personal experiences, artistic influences, and emotional connections shape individual perceptions. The diverse interpretations of a flower's beauty, for example, highlight the manifold nature of aesthetic appreciation. This study concludes that beauty is not only inherent in the object but also shaped by the individual's perspective, illuminating the critical role of individual interpretation and cultural context in influencing our understanding of art and aesthetics.

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subjectivity, beauty, perception, everyday objects, art education, Philosophy.

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This research paper explores the concept of financial nihilism and its impact on the spending patterns of Generation Z. Financial nihilism refers to the belief that long-term financial goals such as home ownership and wealth accumulation are unattainable. As a result, many young individuals focus on short-term consumption. The study uses secondary data and a small scale primary survey (n=50) conducted through online questionnaire to analyse spending behaviour. The findings suggest that while Gen Z is aware of financial concepts, their spending behaviour is driven by immediate gratification rather than long-term planning. The paper also highlights theoretical frameworks and practical implications for improving financial discipline among youth.

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Financial Nihilism, Generation Z, Spending Behaviour, Social Media Influence, Financial Literacy.

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Oral health is a vital component of overall well-being, influencing nutrition, communication, and quality of life. In Ayurveda, Mukhaswasthya (oral health) is considered essential as the oral cavity serves as the gateway to the body and reflects systemic health. Tambul Sevan, described by Acharya Vaghbhata, is a traditional practice recommended for maintaining oral hygiene and supporting digestion. It involves chewing betel leaves along with selected herbal ingredients that possess cleansing, refreshing, and stimulatory properties. This review explores classical Ayurvedic descriptions and relates them to contemporary scientific understanding of oral health. The analysis suggests that properly practiced Tambul Sevan may help maintain oral hygiene and prevent disorders, while inappropriate use can lead to adverse effects.

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Ayurveda; Tambul Sevan; Mukhaswasthya; Oral Hygiene

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Psoriasis is a chronic inflammatory skin disorder characterized by multiple clinical subtypes and diverse visual manifestations, making early and accurate diagnosis a challenging task. This paper presents a web-based diagnostic system for automated detection and classification of psoriasis using advanced deep learning techniques. The proposed system allows users to securely register, upload images of affected skin regions, and receive predictive results along with accuracy scores and basic medical suggestions, enabling early-stage assessment. The core component of the system is a Convolutional Neural Network (CNN) model implemented using TensorFlow, which is trained on a dataset of labeled skin images representing various types of psoriasis, including Erythrodermic, Guttate, and Plaque psoriasis. The model performs feature extraction and classification, supported by image preprocessing techniques such as resizing, normalization, and noise reduction to improve accuracy and consistency. Additionally, the platform includes features such as result history tracking, user profile management, secure authentication, and an administrative panel for monitoring system operations and managing users and datasets. The system is designed to be efficient, scalable, and user-friendly, providing a fast and accessible solution for preliminary diagnosis. By leveraging deep learning and web technologies, the proposed platform aims to bridge the gap between patients and early diagnostic support, particularly in remote or underserved areas, thereby improving awareness, reducing diagnostic delays, and supporting timely medical intervention.

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 Keywords

Psoriasis Detection, Deep Learning, Convolutional Neural Network (CNN), Image Classification, Artificial Intelligence in Healthcare, Image Preprocessing

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