Volume 13 | Issue 4 |

 Abstract

Background: Breast cancer is one of the most prevalent forms of cancer affecting women globally, and early detection plays a crucial role in reducing morbidity and mortality. Breast Self-Examination (BSE) is a simple, cost-effective method for early detection. Aim: This study aims to assess the awareness and knowledge regarding BSE among women in selected rural areas of Haryana. Methodology: A descriptive research design was adopted. The study included 60 women selected through non-probability purposive sampling. Data was collected using a structured questionnaire and analyzed using descriptive and inferential statistics. Results: Among 60 participants, 16.7% had poor knowledge, 55% had average knowledge, and 28.3% had good knowledge. Factors such as education and previous exposure to information on BSE showed a significant association with awareness levels. Conclusion: The study reveals that while awareness exists among some women, the overall knowledge regarding BSE remains limited. Targeted health education and community-based interventions are essential to empower women with life-saving knowledge and practices.

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 Keywords

Awareness, Knowledge, Breast Self-Examination

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Creative Commons Attribution 4.0 and The Open Definition

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The exponential rise of online shopping has significantly influenced consumer behavior and led to the widespread adoption of digital wallets as a preferred payment method. This study explores the impact of e-commerce growth on the adoption and usage of digital wallets among Indian consumers aged 18 to 55. By employing both qualitative and quantitative research methods, including surveys and secondary data analysis, the study identifies key factors driving digital wallet usage--such as convenience, security, faster checkout, and promotional offers like cashback. Findings reveal that while a majority of users appreciate the speed and ease of use provided by digital wallets, concerns around security, privacy, and limited merchant acceptance remain notable barriers to adoption.The research also highlights the growing trend of digital wallets being used for impulse purchases and daily transactions. Based on the insights gained, the study offers suggestions to enhance digital wallet features, promote trust, and expand their usability. Ultimately, the study underscores the transformative role digital wallets play in shaping the future of online shopping and digital payment ecosystems

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 Keywords

Digital wallets, online shopping.

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 Abstract

Abstract Longitudinal Submerged Arc Welded (LSAW) steel pipes are critical in high-pressure applications, and their manufacturing quality directly influences safety and reliability. This paper explores the integration of digital manufacturing technologies and artificial intelligence (AI) to enhance the precision, efficiency, and defect detection capabilities in LSAW pipe production. Leveraging machine learning (ML) algorithms, such as convolutional neural networks and object detection models like YOLOv5, combined with sensor-based monitoring systems, enables real-time quality prediction and process optimization. This approach significantly reduces the risk of welding defects, improves operational efficiency, and supports adaptive control throughout the welding lifecycle. The results indicate that incorporating AI-driven quality monitoring in digital manufacturing frameworks can substantially elevate the quality and consistency of welded steel pipes, paving the way for autonomous, intelligent production lines.

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 Keywords

LSAW pipes, digital manufacturing, artificial intelligence, machine learning, weld quality, defect detection, real-time monitoring

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 Abstract

The economic decline of France in the 18th century was intricately tied to its aggressive military expenditures, which severely strained the state's finances and laid the groundwork for revolutionary upheaval. Major conflicts such as the war of the Spanish succession (1701-1714), the war of the Austrian succession (1740-1748), the seven years' war (1756-1763), and the American war of independence (1775-1783) cumulatively contributed to an alarming rise in public debt, estimated to have reached 4 billion livres by 1789, which would approximate EUR100 billion in contemporary economic terms. This fiscal crisis was exacerbated by a regressive tax system that exempted the clergy and nobility while overwhelmingly burdening the peasantry and urban poor. Despite the efforts of reformist finance ministers such as Turgot, Necker, and Calonne, attempts at restructuring state finances were systematically blocked by entrenched aristocratic interests. The objective of this study is to investigate the causal connection between war-induced debt, socio-economic instability, and revolutionary sentiment in late 18th-century France. Using a qualitative methodology to historical research, the paper examines primary financial documents, official records, and secondary scholarly analyses. The study focuses on six important questions concerning fiscal mismanagement, unequal taxation based on social class, the reasons behind the failure of reform efforts, and the influence of military funding in sparking revolutionary transformations. The results indicate that the downfall of the French monarchy was not solely due to poor financial management, but rather a consequence of continuous military overextension, feudal privileges, and resistance to economic progress within the institutions. By anchoring the discussion within the broader frameworks of fiscal sociology, political economy, and revolutionary theory, this paper concludes that the revolution of 1789 was fundamentally precipitated by an unsustainable fiscal regime shaped by war, privilege, and failed reform. This case also provides a broader perspective on how unresolved financial crises in autocratic systems can serve as catalysts for political revolution and systemic transformation.

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 Keywords

French Revolution, livres, wars, debt, fiscal, economic collapse

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 Abstract

Tendinitis is a common occupational health issue among housekeeping staff due to repetitive physical tasks and improper ergonomic practices. This study evaluates the effectiveness of a structured teaching programme aimed at preventing tendinitis among housekeeping personnel in Rajindra Hospital, Patiala. A pre-experimental one-group pre-test post-test design was employed, involving 60 housekeeping staff selected through purposive sampling. A structured questionnaire was used to assess knowledge before and after the intervention. The post-test scores showed a statistically significant improvement, indicating the success of the teaching programme. The study concludes that health education can play a pivotal role in preventing work-related musculoskeletal disorders like tendinitis

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Tendinitis, Structured Teaching Programme, Housekeeping Staff, Occupational Health

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Skin diseases are among the most common health concerns worldwide, affecting millions of people every year. Early detection plays a crucial role in preventing complications and ensuring timely treatment. This project, titled "Enhancing Healthcare Decision: Image Processing Techniques for Detection of Skin Diseases," aims to build a web-based system for automated skin disease classification using Convolutional Neural Networks (CNN). The front-end of the system is developed using React JS, providing users with an intuitive interface to upload skin lesion images. Flask is used as the backend framework to handle API requests and communicate with the CNN model, which processes the images and predicts the likelihood of various skin diseases. The model is trained on a large dataset of skin lesion images and utilizes advanced image processing techniques to enhance prediction accuracy. Our system aims to assist healthcare professionals by providing quick and reliable diagnostics, reducing the burden on clinical resources. This project demonstrates the effectiveness of deep learning in medical applications and highlights the potential of integrating machine learning models with modern web technologies.

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Skin Disease Detection, Convolutional Neural Network, Image Processing, React JS, Flask, Deep Learning, Healthcare Technology, Medical Diagnostic

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The evolution of digital recruitment has significantly transformed the landscape of human resource management, particularly in large multinational corporations like Amazon. This study investigates the impact of digital recruitment processes on employees at Amazon's Chettipalayam warehouse in Coimbatore. With the widespread use of AI-based screening, online assessments, and automated on boarding systems, recruitment has become more efficient but not without challenges. The research aims to understand employee perspectives regarding digital hiring, the difficulties they face, and its effect on job satisfaction and organizational inclusion. The study is based on primary data collected from 80 employees using structured questionnaires and analyzed using statistical tools like percentage analysis, Likert scale, rank correlation, chi-square test, and t-test. Findings show that although digital recruitment improves hiring speed and accessibility, it can lead to digital exclusion, lack of personalized communication, and increased pressure due to algorithm-based evaluations. The study recommends integrating human interaction with digital tools to enhance inclusivity and employee satisfaction.

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Recruitment Process, Human Resource Management, Employees

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This study investigates the attitudes of parents in Coimbatore towards sales promotions of confectionery products for children. It explores the factors that influence parents' purchasing decisions, their purchasing behaviour, and their concerns regarding the impact of sales promotions on children's consumption. A survey of 120 parents was conducted. The findings indicate that while parents are influenced by sales promotions, particularly price-based incentives, they also have significant concerns about the health implications of confectionery products, especially concerning sugar content and dental health. The study concludes that confectionery marketers need to consider these dual influences to develop effective and responsible promotional strategies.

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Confectionery product

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Energy Consumption Forecasting, Deep Learning, LSTM, Transformer, Hybrid Model, XGBoost, Time Series Analysis, JERICHO-E-usage Dataset, Sustainable Energy, Machine Learning

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Abstract-- Effective quality control in luxury leather goods manufacturing requires objective assessment methodologies. This paper introduces an automated leather bag quality analysis system with a three-layer architecture: input for image preprocessing, processing for defect detection, and output for results visualization. By integrating threshold analysis with edge analysis, the system achieves comprehensive defect identification across varying leather textures. Results demonstrate the system's ability to reliably detect material authenticity and surface defects while quantifying quality metrics with high precision. We conclude that approximately 95% of manual inspection inconsistencies can be eliminated through this approach. This methodology offers potential applications beyond leather goods to other natural material products requiring automated quality assessment.

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 Keywords

Keywords: computer vision; defect detection; quality control; image processing; leather manufacturing

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Creative Commons Attribution 4.0 and The Open Definition