Volume 13 | Issue 10 |

 Abstract

The rapid advancement of artificial intelligence (AI) has revolutionized the field of electronics, enabling the development of intelligent devices and systems with unprecedented capabilities. AI-driven electronics are transforming various sectors including healthcare, automotive, communication, and manufacturing. This paper explores the integration of AI with electronics, focusing on edge computing, smart sensors, neuromorphic circuits, and low-power hardware accelerators. It also discusses the future prospects and challenges of AI-based electronic systems, emphasizing security, energy efficiency, and ethical concerns.

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 Keywords

Artificial Intelligence, Electronics, Edge Computing, Smart Sensors, Neuromorphic Circuits, Low Power Design

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 Abstract

Advertising has been identified as one of the most dynamic and strategic components within current business administration, notably affecting consumer behavior, building brand heritage, and enhancing overall marketplace effectiveness. For both mature organizations and new entrepreneurial ventures, advertising goes beyond mere promotion; it constitutes a long-term investment in publicity, credibility, and customer retention. Within a rapidly fluctuating global economy driven by advances in science and information technologies and driven by digitalization, effective advertising enables organizations to clarify their unique value propositions, differentiate themselves from the competition, and interact productively with specialized audiences. This paper explores advertising as a strategic tool for business and entrepreneurial growth, emphasizing its role as a source of innovation, longevity, and competitive differentiation. It underscores the significant impact of advertising throughout both conventional and digital media outlets, while addressing the issues faced by resource-limited entrepreneurs. Furthermore, the study examines real-world scenarios and empirical research to demonstrate how strategic advertising contributes to brand growth, customer maintenance, and long-term profitability. The findings indicate that carefully conceptualized, ethical, and dynamic advertising schemes are essential to facilitating entrepreneurial growth and ensuring sustainable business growth throughout the current marketplace.

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Advertising, Business Growth, Entrepreneurship, Brand Awareness, Marketing Strategy, Digital Media, Consumer Engagement.

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By speeding up research, cutting expenses, and opening up new treatment approaches, artificial intelligence (AI) is revolutionizing the field of drug discovery and development. To find pharmacological targets, create lead compounds, forecast pharmacokinetics and toxicity, and improve clinical study designs, AI-driven methods make use of enormous datasets from genomes, proteomics, metabolomics, clinical trials, and empirical data. Cloud computing and big data infrastructures facilitate scalable operations, while machine learning, deep learning, and natural language processing improve the effectiveness of virtual screening, de novo molecule creation, and structure-activity relationship modeling. From hit detection to regulatory filing, end-to-end innovation is made possible by combining AI with automation, robots, and high-throughput experimentation. Notwithstanding its potential, issues with data quality, interpretability, moral application, and regulatory approval of insights generated by AI still exist. To fully realize AI's promise in precision and personalized medicines, interdisciplinary cooperation between academia, industry, and regulatory agencies is necessary, as are ongoing developments and explainable AI.

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 Keywords

Artificial intelligence (AI),Drug discovery and development,Precision medicine,Personalized medicine,Machine learning (ML),Deep learning (DL),Natural language processing (NLP),Big data,Cloud computing,Genomics, Proteomics,Metabolomics,Clinical trials,Virtual screening, De novo drug design,Structure-activity relationship (SAR) modeling, Pharmacokinetics prediction,Toxicity prediction,High-throughput screening (HTS),Automation and robotics, Data quality, Model interpretability,Explainable AI (XAI)

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Wind power has become a pillar of the worldwide transition to renewable energy. Yet, the very nature of wind as being variable and intermittent creates serious challenges for assured grid integration, energy storage, and electricity market operations. Reliable forecasting of the wind speed, especially on short and medium time horizons, is thus imperative. Classic models like ARIMA and persistence methods, though interpretable, are unable to handle the non-linearities and the huge volumes of contemporary high-frequency wind data. Deep learning architectures like LSTM offer better accuracy but require large computational resources and huge amounts of training data, which restrict their applicability in real-time environments.This paper suggests a pattern sequence similarity-based forecasting model applied in a big data setting with Hadoop and Spark. Building on Dynamic Time Warping (DTW) and Euclidean distance, the approach locates preceding periods with maximal similarities under current circumstances and projects them forward to estimate upcoming wind speed. Empirical verification was done on an empirical database consisting of 34,080 observations taken every 15 minutes spanning 2022. Cleaning, normalization, and lag feature extraction were the preprocessing activities.The model was compared with ARIMA and LSTM. Results show that the similarity-based method is better than these models, improving RMSE by about 15-20% and MAE by 10-12%, and especially performing well in short-term predictions (1-6 hours ahead). In addition to better accuracy, the approach proves scalable and efficient, essential for real-time use.The research theoretically contributes by generalizing sequence similarity methods to renewable energy prediction, and practically contributes by providing a strong, scalable, and precise method to enable wind power scheduling, smart grid operation, and energy trading platforms.

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 Keywords

Wind speed prediction, time series, big data, pattern sequence similarity, renewable energy, machine learning, smart grid.

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Menstrual hygiene management (MHM) continues to be a serious health concern that impacts millions of women and girls around the world. This is a systematic review of the contemporary practices, issues, and interventions in menstrual hygiene on the basis of 13 current publications that were published in 2023-2024. The review summarizes the evidence on menstrual hygiene practices in various groups, pinpoints the major barriers to proper MHM and assesses the efficacy of diverse interventions. Most important results are that 39 percent of schools worldwide teach menstrual health education [1], where there are great differences in access to hygienic menstrual products and the proper facilities. The review identifies the complexity of the topic of menstrual hygiene issues in its socio-economic, cultural, educational, and infrastructural dimensions [7,8,11]. There is evidence to believe that interventions that are holistic, involving education, product delivery, and infrastructure are potentially useful in improving menstrual hygiene practices [8,14]. The results highlight the importance of combined efforts that would focus on the short-term requirements and systemic obstacles that would guarantee menstrual dignity among everyone.

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Menstrual Hygiene, Menstrual Health, Adolescent Girls, Public Health, Hygiene Management

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Background: Ayurveda emphasizes the concept of 'Ahara' (diet) as one of the key pillars of life (Trayopasthambha). Among the various dietary guidelines, the avoidance of 'Viruddha Ahara'--incompatible food combinations--plays a vital role in maintaining physical and mental health. Modern nutritional science similarly recognizes harmful food interactions that lead to metabolic and inflammatory disorders. Aim: To explore the Ayurvedic concept of Viruddha Ahara, its types, effects on health, and preventive and therapeutic measures, while correlating it with modern biomedical perspectives. Methods: Classical Ayurvedic texts such as Charaka Samhita, Sushruta Samhita, and Ashtanga Hridaya were reviewed, along with relevant modern scientific literature on food incompatibility and gut physiology. Comparative analysis was performed to bridge Ayurvedic principles with contemporary nutrition science. Results: Eighteen types of Viruddha Ahara were identified, categorized by factors like place, time, quantity, potency, and preparation. Consumption of incompatible food combinations can lead to doshic imbalance, metabolic derangement, and systemic diseases such as skin disorders, gastrointestinal disturbances, infertility, and autoimmune conditions. Detoxification (Vamana, Virechana) and Ahara Vidhi (dietary discipline) are the mainstay of prevention and treatment. Conclusion: The Ayurvedic understanding of Viruddha Ahara offers a holistic framework for dietary compatibility that aligns with modern insights into food synergy, gut microbiota, and inflammatory pathophysiology.

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Viruddha Ahara, Ayurveda, Food Incompatibility, Dosha, Gut Health, Dietetics

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India's digital revolution has fulfilled consumer expectations and encouraged economic progress. Digital retailers are hoping to boost consumer retention, increase business success, and enhance FMCG consumers' online purchasing experience. Therefore, the study aims to look at the main factors impacting consumer attitude toward online FMCG retail and then how those factors impact consumer intention to make online purchases. This investigation has employed a combination of methods. Tiruppur City and Coimbatore City were hosts to the study. For research purposes, 310 data were collected. The relevant techniques were applied to the data analysis. Research found that consumer attitude had a significant negative impact on consumer online purchase intention (COPI) towards FMCG retail, whereas product and service facilities had a significant impact on consumer attitude (CA) toward online FMCG retail. Online experience has a significant impact on COPI toward FMCG retail. The paper highlights the significance of consumer perception, operational excellence, and user-centric online experiences for FMCG businesses operating online.

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Consumer Attitude, Consumer Online Experience, E-commerce, E-retail, Fast-Moving Consumer Goods (FMCG), Online Shopping Behaviour

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Creative Commons Attribution 4.0 and The Open Definition

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ABSTRACT: The manufacturing industry is facing many challenges today. From higher expectations from customers, to sustainable business practices, the manufacturing industry is dramatically transforming. Now, the adoption of new technologies is imperative for businesses to stay competitive in the age of increased global competition, elevated customer expectations, acute labour shortages, and ever-changing regulations. And, having access to real-time data and the insights it can provide can help avoid risks and prepare businesses for the future. With so much innovation taking place in the manufacturing industry, it is important for businesses to get more insight into what transformative technologies are driving the modern manufacturing movement.

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Keywords To study the effectiveness of Digitalization in Manufacturing Industry. To study the organizational opportunities in capturing the market value using Digitalize Technology.

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The evolution of e-commerce in India has given rise to a new segment known as Quick Commerce (Q-commerce), which emphasizes ultra-fast delivery of essential goods within a short time frame, typically between 10 and 30 minutes. Driven by urbanization, changing consumer behavior, and technological innovation, Q-commerce platforms such as Blinkit, Zepto, Swiggy Instamart, and Dunzo have transformed the retail landscape. This paper explores the emergence, opportunities, and challenges of Q-commerce in India, using secondary data from recent studies, industry reports, and scholarly journals. Findings indicate that while Q-commerce offers immense convenience and market potential, it faces challenges related to sustainability, profitability, and workforce welfare. The study concludes by providing insights and recommendations for enhancing operational efficiency and ensuring long-term viability in the sector.

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Quick Commerce, E-commerce, Retail Innovation, India, Delivery Platforms, Market Growth

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This article presents a comprehensive study of A.S. Ponnammal, the first and only woman elected as an independent member to the Tamil Nadu Legislative Assembly, highlighting her political journey, social impact, and legislative contributions from 1962 to 2000. Born in a marginalised community, Ponnammal rose to become a powerful advocate for the downtrodden, women, farmers, and students. The study emphasises her role as a fearless legislator, voicing concerns on caste discrimination, women's rights, prohibition of alcohol, and rural development. Despite her later political setbacks, Ponnammal's legacy as a pioneering, independent, and people-centric leader continues to hold a unique place in Tamil Nadu's political history.

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Independent Member, Legislative Assembly, Caste-Based Reservation.

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