Volume 13 | Issue 10 |

 Abstract

Abstract Background : Arthritis affects one or multiple joints characterized by inflammation, pain and stiffness. Two most common types of arthritis are osteoarthritis and rheumatoid arthritis. Osteoarthritis is a degenerative disease of the joints Rheumatoid Arthritis is an autoimmune disease causing inflammation in the joints. Objective of the study was to evaluate the effect of nurse led interventions on knowledge of patients with Rheumatoid Arthritis. Methods: Quasi-experimental non-equivalent time series design was used in the study. The sample consisted of 106 patients who are diagnosed as Rheumatoid Arthritis who are recruited by purposive sampling technique. Results: There was statistically significant difference in the mean pre-test and post-tests scores of knowledge among patients with rheumatoid arthritis in the intervention group (p < 0.001) Conclusions: Study concluded that the nurse led interventions were effective in enhancing the knowledge of patients with Rheumatoid arthritis.

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Keywords: Effect, Nurse led interventions, Knowledge and Rheumatoid arthritis

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A Quantitative research approach, descriptive study was conducted to assess the level of knowledge on benefits of Amla among adolescent girls in St. Antony's Matriculation Higher Secondary School at Kumbakonam in Thanjavur (DT). The Purposive sampling technique was used. Data collected by structured questionnaire from 50 samples. The results showed that 50% of them had moderately adequate knowledge, 26% of them inadequate knowledge and 24% of them having adequate knowledge.

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Knowledge, Benefits of Amla, Adolescent Girls

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This research paper investigates the dynamic relationship between stock returns, trading volume, and volatility in the Indian stock market, with a focus on the Nifty 50 index from 2018 to 2023. Employing advanced econometric models such as quantile regression and Granger causality tests, the study explores how changes in stock returns influence trading volumes and volatility, and vice versa. Findings reveal an asymmetric effect where negative returns significantly increase volatility, while positive returns tend to elevate trading volume. The study further highlights a unidirectional causality from implied volatility to stock returns, suggesting that volatility indexes can serve as effective hedging tools for investors. These insights contribute to understanding market microstructure and can assist investors in making informed decisions by anticipating market fluctuations and managing risk effectively.

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Indian stock market, stock returns, trading volume, volatility, Nifty 50, quantile regression, Granger causality, market microstructure, implied volatility, risk management.

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The accelerating threat of climate change demands innovative and data-driven solutions. Artificial Intelligence (AI), powered by machine learning and deep learning, has emerged as a transformative tool to address both climate change mitigation and adaptation. This research paper explores how AI-enabled "smart models" can analyze massive and complex datasets, enhance predictive capabilities, and optimize resource utilization for sustainable development. Applications range from renewable energy forecasting and precision agriculture to disaster prediction and carbon monitoring. The paper highlights both existing work and future opportunities, while also addressing key challenges such as energy costs, algorithmic bias, and data inequities. By proposing an integrated framework that emphasizes inclusivity, energy efficiency, and explainability, this work contributes toward shaping a sustainable and equitable AI-driven climate future.

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Artificial Intelligence (AI), Climate Change, Smart Models, Machine Learning, Climate Mitigation, Climate Adaptation, Renewable Energy, Precision Agriculture, Sustainable Development.

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Kazuo Ishiguro's fiction persistently interrogates the tensions between home, identity, and displacement, rendering exile not merely as a geographical dislocation but as a psychological and moral condition. This article explores the thematic and narrative dimensions of exile in When We Were Orphans (2000) and The Remains of the Day (1989), examining how Ishiguro reimagines the concepts of home and self through fragmented memory, emotional repression, and moral introspection. Both novels reveal that exile is as much internal as external, a state of estrangement from one's past, identity, or emotions. The protagonists, Christopher Banks and Stevens, embody the exilic consciousness that negotiates belonging in the absence of rootedness, illustrating Ishiguro's preoccupation with the ethical complexities of remembering and forgetting. Through his distinctive narrative minimalism and introspective prose, Ishiguro transforms exile into a site of moral inquiry where silence, memory, and selfhood intersect. The paper argues that Ishiguro's characters do not merely experience exile; they inhabit it as a mode of being, thereby redefining the modern literary imagination of home and identity.

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Kazuo Ishiguro; exile; memory; home; identity; displacement; silence; When We Were Orphans; The Remains of the Day; reimagined self.

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Family businesses are the foundation of economic stability, are instrumental in long-term value creation, and contribute to 70-90% of global GDP, comprising over 60% of employment base. In India, they account for more than 70% of GDP, driving growth across sectors and creating enduring social impact. From small enterprises to global conglomerates such as Tata, Reliance, Samsung, and Walmart, these organizations combine entrepreneurial spirit with intergenerational vision, making them critical to national and global prosperity. Their success stems from long-term orientation, strong governance, and shared family values that promote stability during economic uncertainty. Yet, succession planning, generational transition, and governance complexity remain persistent challenges. To address these, many businesses have institutionalized family councils and created governance frameworks that balance family unity and professional management. The modern evolution of family offices marks a significant shift--transforming traditional family enterprises into institutional investors and diversified wealth managers. With rising interest in private equity, technology, ESG, and impact investing, next-generation leaders are redefining growth through innovation and sustainability. This transition ensures not only business continuity but also broader economic resilience. Family businesses and offices today stand at the intersection of legacy and modernization--serving as economic powerhouses and custodians of enduring values. Their ability to combine trust, stewardship, and professional governance makes them principal catalysts in shaping the future of both the Indian and global economy. Future Trends : Family offices are professionalizing and scaling rapidly. Over the next 3-10 years the biggest shifts will be: heavier allocations to private/real assets and direct deals; widescale adoption of technology, data & AI for investment and ops; meaningful entry into digital assets and tokenization; growth in impact / ESG and mission-aligned strategies; and consolidation via multi-family platforms, plus stronger governance and talent models. These forces reshape risk, access, reporting and operational models for families and office teams.

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FamilyBusiness FamilyOffice Investment FutureTrend ArohiTiwary

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Handwritten digit recognition is an important application in computer vision, often utilized in banking, postal services, and educational tools. This paper presents a deep learning-based method that makes Convolutional neural network (CNN) use to appropriately classify the handwritten numbers. MNIST is one of the reference datasets used to train the model, which contain diverse samples of handwritten numerals. The CNN architecture includes multiple convolutional and pooling layers,Then come layers that are entirely connected and Regularization of dropouts to avoid overfitting. Techniques like data augmentation and transfer learning are applied to improve generalization and reduce computational load. Performance was assessed by comparing and implementing several deep learning models and machine learning models. With precision, recall, and F1-score values of 0.99%, the CNN model also earned the maximum accuracy of 99.25%. These outcomes show how reliable and efficient CNNs are in recognizing handwritten digits. The system exhibits enormous potential for Empirical world uses that require precise and quick digit categorization.

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Computer Vision,Deep Learning,MNIST Dataset ,Data Augmentation, Model Generalization, Digit Categorization, Precision ,Recall ,Classification Accuracy.

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In recent years, the recognition of mental health as a critical component of overall employee well-being has surged. Companies worldwide are increasingly implementing mental health initiatives to foster a supportive work environment, enhance productivity, and reduce absenteeism. This report analyzes the impact of such initiatives on employee well-being by exploring various dimensions including employee satisfaction, productivity, turnover rates, and overall mental health outcomes. Through comprehensive data analysis and visualization, this report aims to provide insights on the effectiveness of mental health programs and their correlation with employee performance and organizational success.

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Mental Health,Employee Well Being,Workplace Wellness Supportive Work Environment

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This project focuses on applying an end-to-end data analytics workflow to solve a key business problem providing sales teams with a clear, data-driven understanding of regional sales performance to optimize resource allocation and identify growth opportunities.

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Data Analytics, Python (for EDA and Data Wrangling),Power BI, MySQL, Exploratory Data Analysis (EDA),Data Cleaning / Data Wrangling ,Feature Engineering

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Creative Commons Attribution 4.0 and The Open Definition

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Creative Commons Attribution 4.0 and The Open Definition

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Digital Natives, Millennials, Generation Z and Alpha, Technology adoption

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Creative Commons Attribution 4.0 and The Open Definition