Volume 14 | Issue 2

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Abhiyan Hi Nayi , Rastriya Chetna Hai Adhyatmak

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Abhiyan Hi Nayi , Rastriya Chetna Hai Adhyatmak

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The aims to examine parents awareness, perception, and buying behaviour regarding organic baby care products. The study focuses on understanding the factors influencing purchase decisions, the level of awareness among parents, and the impact of demographic variables such as age, gender, education, and income on their purchasing patterns. The research is based on primary data collected from 100 respondents in Coimbatore City using a structured questionnaire. Statistical tools such as percentage analysis, Chi-square test, and One-Way ANOVA were employed to analyze the data. The findings reveal that the majority of respondents are young female parents below 25 years with diploma-level education and income below INR30,000. Although awareness about organic baby care products is high, the level of detailed knowledge remains moderate. Natural ingredients and environmental friendliness are the major factors influencing purchase decisions, while high price, limited availability, and lack of clear information act as significant barriers. The study also indicates that income significantly influences the type of organic baby care products purchased, and education plays a significant role in shaping parents' reasons for usage and their belief that organic products contain fewer harmful chemicals. The study concludes that while there is strong market potential for organic baby care products in Coimbatore City, improving affordability, accessibility, and product awareness is essential to enhance purchase behaviour and encourage long-term adoption among parents.

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Parents Satisfaction, Parents Experience, purchase behavior, product awareness and satisfaction level of parents.

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The agricultural productivity is highly dependent on the climate variability, soil conditions and the management of the resources. Application of conventional decision making has led to the wrong choice of crops, poor yield forecasting and high risk of financial losses among the farmers. To solve these dilemmas, the proposed paper introduces a smart Farm Decision Support System (FDSS) that has been modeled based on the machine learning algorithm, the random forest algorithm. The suggested model is a multi-dimensional analytics system that analyzes farm data such as the soil characteristics, rain distributions, temperature fluctuations, seasonal shifts, the use of fertilizers and past records of crop yield. The classification and regression tasks are performed with the help of the Random Forest algorithm to suggest appropriate crops, predict the performance of yield, predict the amount of fertilizer required, and recommend the profit potential. Because of an ensemble learning, Random Forest improves the predictive accuracy, reduces overfitting, and increases the robustness of the model in different environmental conditions. An easy-to-use dashboard system allows the farmer and the agricultural officers to enter the parameters of the fields and get real-time and data-driven recommendations. The effectiveness of experimentation proves the better accuracy of predictions and more reliable decisions, in comparison to traditional approaches. The proposed system helps in precision agriculture by ensuring that resources are utilized in the most efficient way, less risk is taken, and sustainable farming methods are ensured using artificial intelligence-based analytics.

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Precision Agriculture, Farm Decision Support System, Machine Learning, Random Forest Algorithm, Crop Recommendation, Yield Prediction, Fertilizer Optimization, Profit Estimation, Sustainable Agriculture, Data-Driven Farming

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Kamala Markandaya's Nectar in a Sieve occupies a central place in Indian English fiction for its profound depiction of destitution, famine, and the dislocation of rural communities. Told through the voice of Rukmani, a poor village woman, the novel reveals how entrenched social inequalities, environmental volatility, colonial-era economic pressures, and gendered responsibilities shape the fragile realities of survival. This study explores how the text represents poverty and hunger as more than economic hardships; they function as existential forces that shape identity, dignity, interpersonal bonds, and emotional endurance. By situating the story within the historical and socio-economic conditions of mid-twentieth-century India, the paper illustrates how Markandaya foregrounds the subtle, private anguish of ordinary individuals to expose the pervasive nature of systemic oppression. The analysis argues that the novel's lasting influence lies in its ability to humanize deprivation while simultaneously exposing its devastating effects, offering insights into resilience, moral ambiguity, and the quiet forms of resistance that hunger produces. Through literary interpretation, historical context, and critical engagement, this paper provides a detailed examination of how poverty and hunger operate both literally and symbolically in Nectar in a Sieve, highlighting the novel's enduring significance as a powerful commentary on social inequity.

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deprivation, resilience , moral ambiguity , resistance , social inequity.

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Bacteriocins are considered a blessing for the food industry. They act as a preservative to inhibit the growth of bacteria in the food items during the production or preservation process. These bacteriocins are produced by the bacteria, which reduces the growth of closely related bacterial strains. Food safety has become a serious issue in the present era for which bacteriocins, an antimicrobial compound, can effectively fight against food-pathogenic bacteria. Different views of the actions of bacteriocins have been elaborated. Bioengineered bacteriocins, or the hurdle approach to bacteriocins, can increase the efficacy of preservation and reduce the resistance effect against bacteriocins. They are mostly produced by LAB (Lactic acid bacteria), which is naturally present in most food. They can also be combined with antimicrobial proteins, agents, and natural phenolic compounds. The combined approach can act as a barrier to developing bacteriocin-resistant strains. It can also be combined with physical treatments like pulse electric fields and high-pressure processing by which foods can be more effectively preserved. This chapter summarizes and focuses on the importance and potential applications of bacteriocins as a food preservative in the food industry.

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Bacteriocins, Bio preservative, Lactic acid bacteria, Hurdle technology

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In recent years, social media platforms have emerged as critical sources of data, providing rich and diverse in formation about public opinions, behaviors, and emotions. Sentiment analysis and emotion detection in social media text have gained considerable attention, serving as essential tools for understanding user sentiments and emotional responses. This survey explores the advancements and methodologies employed in sentiment analysis and emotion detection, focusing on various techniques including rule based, machine learning, and deep learning approaches. It highlights the challenges in here not in analyzing informal and context-rich social media language, such as slang, emojis, and multi lingual content. Additionally, the survey discusses the role of pre-processing strategies, feature extraction methods, and the effectiveness of different models like LSTM, BERT, and Transformer based architectures in capturing complex emotional nuances. The paper also examines the applications of these techniques in diverse domains such as brand monitoring, crisis management, and mental health analysis. By providing a comprehensive overview of the existing literature and emerging trends, this survey aims to guide future research directions and improvements in sentiment analysis and emotion detection in the dynamic context of social media text.

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LSTM,CNN,NLP,SENTIMENT ANALYSIS

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Diabetes mellitus is a chronic metabolic disorder that continues to pose a major global health challenge due to its rising prevalence, long-term complications, and significant economic burden. While traditional therapies such as oral hypoglycemic agents and insulin have long served as the foundation of diabetes management, recent advancements in pharmacological innovation, technology-driven monitoring, and predictive analytics have transformed modern care. Emerging drug classes--including SGLT2 inhibitors, GLP-1 receptor agonists, and next generation insulin analogues--offer improved glycemic control along with cardiovascular and renal protection. Complementary technological innovations such as continuous glucose monitoring systems, insulin pumps, and closed-loop artificial pancreas devices enhance precision, reduce glycemic variability, and promote patient-centred care. Additionally, machine learning-based predictive models support early detection, risk stratification, and personalized treatment planning. Despite these improvements, challenges remain regarding cost, accessibility, long-term safety, and technological literacy. This review highlights recent advances in pharmacological, technological, and analytical strategies, discusses their limitations, and outlines future perspectives in achieving personalized, efficient, and equitable diabetes management.

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Diabetes Mellitus, Type 2 Diabetes, SGLT2 Inhibitors, GLP-1 Receptor Agonists, Insulin Therapy, Continuous Glucose Monitoring, Insulin Pumps, Artificial Pancreas, Machine Learning, Predictive Analytics, Pharmacological Advances, Diabetes Technology, Precision Medicine, Glycemic Control, Non-Pharmacological Interventions.

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comprehensive review on performance of lithium-ion batteries under high-altitude, sub-zero environmental conditions

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The Bone Age Prediction System is a web based tool that was developed to improve the management of bone age determination within a clinical setting. Typical measurement techniques utilize a laborious process that calls for manual interpretation of X-ray photographs combined with extensive subjective judgment by experts in order to derive the final evaluation of bone age. Using Firebase for backend functionality, including authentication and cloud based data storage, and using Streamlit as the front end user interface, the proposed tool gives a secure digital method of overcoming the limitations. The proposed system consists of three main modules, Administration, Patient, and User. Using the User Module, authorized healthcare staff can securely log into the database to manage patient information and maintain records of bone age. Using the Patient Module, patients can receive electronically generated PDF reports automatically and also access information regarding patients individual reports. Administering of the User Module is through the Administration Module. The additional support within the tool for visual analytics and data monitoring can assist with healthcare decision support. Firebase provides scalable, cloud based storage and allows data to be synchronized in real time while supporting secure authentication, providing for efficient and dependable operation of the proposed tool. The Bone Age Prediction System demonstrates how modern web based technology has the potential to improve workflow for healthcare data systems, particularly with the potential to implement machine learning based automatic prediction of bone age as well as cloud based remote.

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CNN, Deep Learning, Hand Wrist X-ray, Medical Image Analysis, Bone Age Prediction

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Environmental risk has emerged as a critical factor influencing credit rating assessments globally. As climate change intensifies and sustainability becomes central to financial decision-making, credit rating agencies are increasingly integrating environmental metrics into their evaluations. This paper explores the relationship between environmental risk and credit rating dynamics using secondary data and literature analysis. It highlights how environmental performance, carbon emissions, and climate vulnerability affect creditworthiness across sectors and regions.

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Environmental Risk, Credit Rating, ESG, Climate Vulnerability

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The rapid expansion of blockchain technology and decentralized finance (DeFi) has significantly increased the adoption of smart contracts, particularly on platforms such as Ethereum. Smart contracts are autonomous, self-executing programs deployed on blockchain networks to enforce agreements without intermediaries. Despite their advantages in transparency and automation, vulnerabilities in smart contract code can lead to severe financial losses and system compromises. Historical security incidents, including the The DAO exploit, demonstrate the critical importance of proactive security auditing before deployment. The Smart Contract Vulnerability Auditor is an automated analysis framework designed to detect and mitigate security flaws in blockchain-based smart contracts. The system employs static code analysis, dynamic testing, and pattern-based vulnerability detection techniques to identify common threats such as reentrancy attacks, integer overflow and underflow, access control misconfigurations, unchecked external calls, gas inefficiencies, and timestamp dependencies. By integrating with popular development environments and blockchain testing frameworks, the tool enables real-time vulnerability assessment during the development lifecycle The proposed solution generates detailed audit reports that categorize vulnerabilities based on severity levels and provide remediation suggestions aligned with secure coding standards. This approach reduces reliance on time-consuming manual audits, minimizes human error, and enhances overall contract reliability. Additionally, the system supports scalability to audit multiple contracts efficiently and can be extended to support other blockchain platforms. Overall, the Smart Contract Vulnerability Auditor strengthens decentralized application security by providing an efficient, reliable, and automated auditing mechanism for secure smart contract deployment.

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Blockchain, Ethereum, DAO, Dumb, Gesture recognition, Deaf, Flex.

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In the modern digital era, operating systems serve as the backbone of all computing devices, enabling users to interact with hardware and software resources efficiently. Despite continuous advancements in computing technology, most conventional operating systems still rely heavily on manual user interaction. Users are required to perform repetitive tasks such as opening applications, managing files, configuring system settings, and searching for information through traditional input methods like keyboard and mouse. These repetitive actions consume time, increase cognitive load, and reduce overall productivity, especially for non-technical users. With the rapid growth of artificial intelligence, intelligent personal assistants have emerged as an effective solution to simplify human-computer interaction. This paper presents Nexus, an intelligent personal OS assistant designed to enhance user experience by integrating artificial intelligence directly within the operating system environment. Nexus enables users to interact with the system using voice or text-based commands, allowing tasks to be executed quickly and efficiently without complex manual operations. The proposed system uses AI-based command processing techniques to understand user intent and perform appropriate system-level actions. Nexus acts as a smart intermediary between the user and the operating system, translating natural language commands into executable operations. The system focuses on simplicity, usability, and accessibility, making it suitable for both technical and non-technical users. By automating common tasks, Nexus reduces manual effort, minimizes errors, and improves overall system efficiency. This paper discusses the design and working of the Nexus system, including its architecture, command processing mechanism, and core functional modules. The system architecture highlights the interaction between the user interface, command processing module, AI engine, system control module, and output module. Each component plays a crucial role in ensuring accurate command interpretation and smooth task execution. The paper also explores the key features, advantages, and practical applications of Nexus in personal computing, educational environments, and office automation. In addition, the paper outlines the future scope of Nexus, emphasizing enhancements such as machine learning-based personalization, emotion recognition, multilingual support, cloud integration, and Internet of Things (IoT) connectivity. These advancements can transform Nexus into a more adaptive, context-aware, and intelligent assistant capable of operating across multiple platforms and environments.

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Intelligent Personal Assistant, Artificial Intelligence, Operating System, Automation, Virtual Assistant, Human Computer Interaction

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The modern personal digital ecosystem remains under a strain of the exponentially growing multimedia content base, which leads to the unorganized media library, inefficient retrieval processes, and growing concerns about the privacy of the data and the power of the user. Classical image management systems are largely based on cloud systems in which personal media are sent to distant servers to be processed and stored, which has brought with it risks of data breach, reliance on the internet, latency, and loss of data sovereignty. The SmartMedia framework offers an Edge-AI-driven privacy-safe framework of intelligent offline media organization by fulfilling the entire media analysis pipeline on the machine of the user. The system combines a quantized Vision-Language Model that is optimized to run on the device to do automated caption, object detection, scene understanding, facial analysis and semantic tagging without depending on external APIs. The structured metadata and AI-generated descriptors are stored safely in a local SQLite database, whereas high-dimensional embeddings are indexed with FAISS to be able to provide the rapid and precise search of semantic similarity. An Electron-Python hybrid architecture will make sure there is efficient isolation between the presentation layer and computational backend which will guarantee quick user interactions as well as the management of resources. The framework will ensure that data privacy is fully achieved, the latency is minimized, and the offline functionality is not interrupted by eliminating cloud dependency and implementing secure local storage with controlled access mechanisms. The empirical testing of the prototype developed indicates that the semantic retrieval is performing high with low-computational costs on consumer-grade equipment, which confirms that it is possible to deploy advanced multimodal AI models at the edge. The suggested system defines a scalable, secure and privacy-conscious system of smart media organization in the contemporary digital contexts.

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Edge AI, Offline Media Organization, Vision-Language Models, Semantic Image Retrieval, On-Device Inference, Data Privacy

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The convergence of cybersecurity and financial auditing represents a critical frontier in organizational risk management. As digitalization expands, vulnerabilities in cyber systems increasingly affect financial integrity, governance, and compliance. This review synthesizes multidisciplinary perspectives to propose a unified framework that integrates cybersecurity assurance and financial audits within enterprise risk management (ERM) principles. Through systematic review and empirical insights, the study identifies significant relationships between cybersecurity audit maturity, unified assurance adoption, and financial-audit quality. Experimental analysis across 46 organizations reveals that integrated assurance practices improve risk-mitigation efficiency by 22 % on average. The proposed Unified Cyber-Financial Audit Assurance Model emphasizes governance alignment, control assessment convergence, and continuous improvement feedback loops. Future research should explore automation, AI-driven audit analytics, and cross-sector adoption to enhance the predictive and preventative potential of integrated assurance systems.

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Cybersecurity Audit, Financial Audit, Unified Assurance, Governance, Enterprise Risk Management (ERM), Assurance Analytics.

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This study examines the postcolonial identity and cultural hybridity within the subsequent writings by Salman Rushdie's Midnight Children and The Satanic Verses. In this manner, this article investigates how Rushdie challenges the traditional view that identity and culture are static. Some consistent categories that are shaped by history and the universe. To examine how Rushdie's characters struggle with the complexity of their identities in the postcolonial context, the article utilizes postcolonial theory. The research concentrates on the formation of identity in Rushdie's works and the ways his characters persist and influenced by colonial and worldwide factors. Likewise, it assesses Rushdie's approach to hybridity alongside other postcolonial authors to demonstrate his contribution to the domain via the implementation of magical realism and storytelling methods. The current study contributes to the current field of postcolonial studies by offering new viewpoints on the portrayal of culture, history and identity in Rushdie's novels. They stress the significance of hybridity as a notion for the examination of postcolonial identity within the framework of contemporary global issues. The piece concludes with an identification of paths for additional investigation, with the author urging for additional investigation into the ethical and political dimensions of Rushdie's portrayal of hybridity and its significance in today's globalized society.

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Postcolonialism, cultural hybridity, global issues, Salman Rushdie

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Abstract: Urban Morphology is the study of the human settlements and the process of their formation and transformation. The study mainly emphasized on the discussion of the spatial structure and characteristics of the metropolitan area, city or town by examine the patterns of its component part and the process of its development. Here also involve the analysis of physical structure at different scales as well as the patterns of movement, land use, ownership and occupation. Special attention is given to how the physical form of city changes through time. Land is very important physical component in the urban morphology. Land is the basic natural resources which provides space and many raw materials for various development and other activities. But in the present context this component is use in a frequently manner due to increasing population. To satisfy the objectives of this study, mainly descriptive statistics have been incorporated. Decadal growth rate, population density, sex ratio, work participation rate, literacy rate, satisfaction index and some others techniques have been used. Other hand, different types of relevant cartographic techniques and presentation have been applied. Kanchrapara is a small municipal town, its distance 48 km. from Kolkata. Kanchrapara Municipality belongs to the Kolkata Metropolitan District Area (KMDA). It's situated in the Bizpur Police Station within the Barrackpore-I subdivision, under the North 24-Parganas in West Bengal

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Keywords: Municipality, Sustainable, Development and Demography.

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Education plays vital role in the development of the children. In these modern trends we all required to use the technology in the school and classrooms. National education policy emphasis on the use of technology and made various changes in the education, evaluation and structure and also more focusing on the skills development of the typically developed and special need children. Technology and digitalization in education have been introduced and many school colleges are using the technology for providing accessibility in the education and academic achievement. Teacher is a crucial part of education management and in the school. Proper awareness and training are required to implement the technology in the education. Trained teacher and heads of the schools can make difference in the holistic development of the child. Government run the NISHTHA programme for the teachers and heads of the schools for achieving the foundation literacy and numeracy skills. The present study examines the classroom level implementation of the National Initiative for School Heads' and Teachers' Holistic Advancement (NISHTHA) with more emphasis on the Foundational Literacy and Numeracy (FLN). A survey method used to study the implementation of NISHTHA in classroom practice. A researcher made tool based on a five-point rating scale was developed by the researcher for data collection which consisted of four areas with five statements in each area and total statements were twenty. The areas assessed were: (A) Foundational Pedagogy and Classroom Environment, (B) Foundational Literacy and Numeracy Implementation, (C) Assessment and Remedial Teaching, and (D) Holistic Development and Inclusivity. Trained teachers were the sample of the data collection process and analysed by using descriptive statistics such as mean and standard deviation. Cronbach's alpha was used to check reliability of the tool. The findings conclude that teacher effectively implementing supportive classroom environments and promoting holistic and inclusive practices. It suggests the need for continuous professional support, training, short term training programme for the upgradation knowledge of the teacher also monitoring, and follow-up training to strengthen the classroom impact of NISHTHA foundational literacy and numeracy skill initiatives. The results offer valuable insights for policymakers and educators to enhance the effectiveness of FLN programs in schools.

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NISHTHA, Foundational Literacy and Numeracy, Teacher Training, School

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Objective: The present study aimed to examine the biopsychological influence of menstrual cycle phases on mood state and performance consistency among elite female precision shooters. Specifically, the study investigated whether phase-wise variations in mood are associated with changes in shooting performance across different shooting disciplines. Method: A repeated-measures correlational research design was employed involving 61 national-level female shooters representing rifle, pistol, and shotgun events at the Madhya Pradesh Shooting Academy, Bhopal. Mood state was assessed using the Profile of Mood States (POMS), while shooting performance scores were recorded during the follicular and luteal phases of the menstrual cycle. Performance evaluation followed the International Shooting Sport Federation (ISSF) competition standards, and scores were normalized according to official event scoring systems (10 m Air Rifle = 654.0 decimal scoring scale, 10 m Air Pistol = 600 points, Skeet Shooting = 125 targets). Statistical analysis included descriptive statistics, paired sample t-tests, and Pearson product-moment correlation to examine phase-wise differences and relationships between mood and performance variables. Conclusion: The findings revealed significant phase-wise variations in mood state and normalized shooting performance; however, mood fluctuations did not significantly predict performance consistency among athletes. These results suggest that menstrual cycle phases may influence psychological and performance outcomes independently, indicating the presence of adaptive mechanisms that help stabilize performance in elite female precision shooters.

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Keywords: menstrual cycle, mood state, precision shooting, female athletes, biopsychology, performance consistency.

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Depression is a common and important health problem affecting the lives of many children and young adults. Depression is the leading cause of health-related disability. A proportion of depression cases begin in childhood and increase dramatically during adolescence. This systematic review aimed to estimate the global prevalence of depression or depressive symptoms in children and adolescents. Depression manifests with increasing frequency as early adult life approaches and represents an escalating set of impairments across personal, family, social and educational life of children and young people. This article explain that childhood depression is a serious mood disorder causing prolonged sadness, irritability, or hopelessness that impacts a child's relationships and activities, and it is treatable with talk therapy and medication. Risk factors include genetics, adverse life events, family stress, and co-occurring conditions like anxiety.

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Lingers, sadness, child depression , mental disorder ,persistent depressive sadness.

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The present study was conducted to assess the effectiveness of a structured teaching program regarding type-2 diabetes mellitus among nursing students in selected nursing colleges in Bareilly, Uttar Pradesh. In the present study, an evaluative approach was selected. One group pre and post-test design was adopted. The Structured Knowledge Questionnaire was developed to collect the data. The sample size of 60 among nursing students, selected by a simple random sampling technique and the data collected were analysed and interpreted based on descriptive and inferential statistics. As a result, the overall knowledge score of students revealed that a majority of student nurses, 83.3%, had moderate knowledge, 13.33% had inadequate knowledge, and only 3.33% students had adequate knowledge. The pre-test analysis revealed that most of them had some knowledge about type 2 diabetes mellitus. During post-test analysis, it was found that most of them scored in the category of adequate knowledge and post test score mean was 21.96, and the mean difference of pre-test and post-test was 7.38. post-test assessment significant difference (p<0.05) was found between pre-test and post-test score was demonstrated by using 't' test; it was found that the structured teaching program was effective. Keywords- structured teaching program, structured teaching program, nursing students, Structured Knowledge Questionnaire.

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"A Study to Assess the Effectiveness of Structured Teaching Program Regarding Type-2 Diabetes Mellitus Among Nursing Students in Selected Nursing Colleges Bareilly, (U.P)"

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