Volume 14 | Issue 3

 Abstract

This paper explores the concerted relationship between Lucknow's rich culinary heritage and its representation in Indian cinema. The city's iconic cuisine, characterized by kababs, biryani, and sheermal, has been a recurring theme in Bollywood films, showcasing its cultural significance and allure. Films like 'Tanu Weds Manu' and 'Ishaqzaade' have not only highlighted Lucknow's architectural and picturesque settings but also its delectable food, introducing the city's culinary identity to the world. This study examines how cinema has contributed to the popularization of Lucknow's cuisine, promoting a cross cultural understanding and appreciation. Through a critical analysis of film narratives and culinary traditions, this research paper is an attempt towards highlighting the intersection of food and cinema which has played a significant role in shaping Lucknow's cultural narrative, making its cuisine an integral part of India's gastronomic and cinematic landscape.

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 Keywords

Lucknow, cuisine, cinema, Bollywood, gastronomy

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The secure storage of user credentials is a critical requirement for modern authentication systems. This project focuses on analyzing and implementing Argon2id, the recommended variant of the Password Hashing Competition winner, to evaluate its efficacy in secure password storage. Argon2id provides robust resistance against brute-force and GPU-based cracking attacks through its configurable parameters, specifically time cost, memory cost, and parallelism. The main goal of this research is to evaluate how parameter tuning affects both performance and security in real-world environments and to perform a systematic comparative evaluation of Argon2id and established password hashing algorithms such as bcrypt, scrypt, and PBKDF2. To accomplish this, a prototype login and registration system was implemented using Python and Flask, incorporating an automated benchmarking module to measure execution time and memory usage, and overall efficiency. The comparative analysis and performance benchmarking highlight the trade-offs between usability and security under varying configurations. The results demonstrate that Argon2id effectively balances speed and memory hardness, providing strong resistance against brute-force attacks while maintaining practical authentication latencies. Ultimately, this paper provides practical recommendations and guidelines for developers to adopt secure parameter choices in CPU-based web environments while considering real-world system constraints.

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Argon2id, Password Hashing, Parameter Optimization, Memory-Hard Functions, Authentication Systems, Performance Benchmarking.

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Ocean pollution has become a serious environmental issue that threatens marine ecosystems, biodiversity, and human health. Traditional monitoring approaches, such as ship-based surveys and manual interpretation of satellite imagery, are costly, time-consuming, and limited in spatial coverage. To overcome these limitations, this paper presents an automated ocean pollution detection system using satellite imagery analysis and deep learning-based computer vision techniques. The proposed framework utilizes the You Only Look Once (YOLO) object detection model to identify and localize major surface-level pollutants, including oil spills, harmful algal blooms, and floating plastic waste. Prior to detection, satellite images are pre-processed using OpenCV to enhance image quality and ensure compatibility with the detection model. The trained YOLO model is integrated into a Flask-based web application that enables users to upload satellite images and visualize detection results in real time. The system highlights polluted regions using bounding boxes and provides confidence scores for each detection. Experimental evaluation demonstrates that the proposed approach achieves reliable detection performance with fast inference speed, making it suitable for practical environmental monitoring applications. The modular architecture of the system supports scalability and cloud deployment for large-scale ocean surveillance. The results indicate that combining satellite remote sensing with deep learning offers an efficient and cost-effective solution for automated marine pollution monitoring.

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 Keywords

Ocean Pollution Detection, Satellite Imagery, Computer Vision, YOLO, Deep Learning, Remote Sensing, Environmental Monitoring.

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Modern digital ecosystems demand software systems that sustain high performance, reliability, and maintainability as user bases and data volumes grow exponentially. This paper investigates the design and development of a scalable software solution capable of adapting to evolving workloads without compromising service quality. The study synthesizes established architectural principles, distributed-systems theory, and contemporary engineering practices to propose an integrated five-layer framework spanning presentation, application logic, data management, infrastructure, and observability. A mixed-methods research design combines systematic literature analysis with prototype-based experimentation on a cloud-hosted microservices platform. Experimental results demonstrate that the proposed architecture achieves near-linear horizontal scalability up to thirty-two nodes, with a mean response-time increase of less than twelve percent across a tenfold traffic surge. Key findings affirm that scalability is an emergent property arising from coherent architectural decisions, polyglot data persistence, automated infrastructure provisioning, and robust observability pipelines rather than any single technology choice. The paper concludes with practical engineering guidelines and directions for future research in autonomous, self-healing scalable architectures.

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scalable architecture, microservices, distributed systems, cloud computing, Kubernetes, horizontal scaling, fault tolerance, DevOps, CI/CD, container orchestration, event-driven architecture, polyglot persistence, API gateway, observability, autoscaling, performance engineering, CAP theorem, system reliability, load balancing, resilience.

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Literature, in both its poetic and dramatic forms, constitutes one of the most prominent cultural tools in shaping societal consciousness and guiding it towards the values of civilization and progress, particularly during periods of transformation and crisis. This research examines and analyses the role played by British literary production in the post-World War II and post-colonial eras in shaping the intellectual and social structure of British society. This is achieved through a critical reading of several poetic and dramatic works that reflected profound transformations in identity, belonging, power, and social justice. The research focuses on how poetry contributed to framing collective consciousness in relation to crises and human experiences, while theatre contributed to exposing oppressive structures and questioning cultural and political systems. The research adopts an analytical-cultural approach that connects literary texts to their historical and social contexts. It seeks to demonstrate how British literature, through its aesthetic and intellectual tools, contributed to supporting the path of civilizational transformation and establishing a humanistic and resistant discourse based on the values of dialogue, accountability, and renewal. This study confirms that literature is not merely a mirror of reality, but a key player in reshaping it.

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British literature, cultural transformation, British poetry, British theatre, post-crisis, cultural consciousness

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This research project presents the design and implementation of a smoke detection system utilizing Internet of Things (IoT) technology to mitigate the risks of fire accidents, which often lead to significant property loss and fatalities. The primary objective is to develop an interconnected safety system that can identify smoke and hazardous gases without human intervention. The system's methodology centers on a prototype developed within the Tinkercad simulation environment. The core hardware consists of an Arduino UNO R3 microcontroller and an MQ-2 metal oxide semiconductor (MOS) sensor. The MQ-2 sensor is chosen for its ability to detect flammable gas concentrations between 300 and 10,000 ppm, including smoke, LPG, propylene, and hydrogen. Additional components include a piezo buzzer for audible alerts, red and green LEDs for visual status indicators, and an LCD screen for real-time messaging. The system functions by monitoring voltage fluctuations from the MQ-2 sensor, which are proportional to smoke concentrations in the environment. Through an if-else control loop, the system compares live sensor data against a predefined threshold of 400. Results from testing demonstrate that when smoke levels exceed this threshold, the system successfully triggers a red LED and a continuous buzzing alarm, while maintaining a green LED status during safe conditions. The proposed system offers a portable and efficient solution for early fire detection, with future work recommended to improve sensitivity for low-energy flames and further minimize false alarms.

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IoT, Smoke Detection, Arduino UNO, MQ-2 Sensor, Fire Safety, Embedded Systems

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Floral waste generated from temples, religious activities, weddings, and social functions forms a considerable portion of biodegradable waste in India. Due to cultural and religious beliefs, flowers offered during rituals are often disposed of in rivers, ponds, or open areas rather than through organized waste management systems. Such practices contribute to environmental problems including water pollution, foul odour, soil contamination, and emission of greenhouse gases during decomposition. Although floral waste is biodegradable, its slow degradation rate and improper handling make it an environmental concern. At the same time, discarded flowers contain valuable natural components such as pigments, organic matter, and aromatic compounds, which remain largely underutilized.The present study focuses on the utilization of floral waste for the preparation of eco-friendly value-added products, supporting the concept of a circular economy. Floral waste was collected from nearby temples, manually segregated, and processed using simple, low-cost, and environmentally safe methods. Various products such as natural pigments, Holi colours, herbal lip balm, incense (dhoop), cloth tags, seeded paper, handmade paper, diary, and compost were prepared from discarded flowers. The prepared products were qualitatively evaluated based on colour, texture, fragrance, and usability. The findings indicate that floral waste can be effectively reused as a useful bio-resource instead of being treated as waste. This approach not only reduces environmental pollution but also promotes resource recovery, waste minimization, and sustainable waste management practices. The study highlights the potential of small-scale floral waste utilization as a practical step towards circular economy and environmental sustainability.

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Floral waste Value-added products Circular economy Sustainable waste management Eco-friendly materials Resource recovery

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 Abstract

Background: Proper evaluation of pulp vitality is essential for accurate diagnosis and effective endodontic treatment planning. Dental students and interns must have sufficient knowledge and awareness of pulp vitality testing methods to ensure appropriate clinical decisions. Aim: To evaluate the knowledge, attitude, and awareness related to pulp vitality tests among dental students and interns. Materials and Methods: A cross-sectional survey was carried out among 262 dental students and interns, including third-year students, final-year students, and interns. A structured questionnaire consisting of demographic information and 15 multiple-choice questions was used to assess knowledge of pulp vitality tests. Each correct response was given a score of 1, and incorrect responses were scored as 0, with a maximum possible score of 15. Descriptive statistics were used to summarize the data, and a one-way Analysis of Variance (ANOVA) test was applied to compare knowledge scores among the three groups. A p-value of less than 0.05 was considered statistically significant. Results: The overall findings indicated moderate to good knowledge among participants. Final-year students showed slightly higher average knowledge scores compared to third-year students and interns. However, the difference between the groups was not statistically significant (F = 1.71, p = 0.183). Conclusion: The study demonstrated that dental students and interns possess an acceptable level of knowledge regarding pulp vitality tests, with no significant variation among the academic groups. Continued clinical training and educational programs are recommended to improve awareness and practical skills.

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 Keywords

Pulp vitality test, Dental students, Interns, Knowledge, Awareness, Attitude

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Creative Commons Attribution 4.0 and The Open Definition

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Second-generation immigrant literature increasingly portrays identity not as an automatic inheritance but as something produced through continual negotiation between memory, culture, and everyday experience. This study investigates hybrid identity in On Earth We're Briefly Gorgeous by Ocean Vuong and White Teeth by Zadie Smith, focusing on how migrants' children shape their sense of self within spaces shaped both by family histories and contemporary national life. Whereas first-generation narratives usually emphasize movement and adaptation, second-generation works address inherited displacement -- a condition in which people feel cultural tension despite being born in the receiving society. Identity therefore develops not from migration itself but from its psychological and cultural consequences.

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Second-generation immigrant literature increasingly portrays identity not as an automatic inheritance but as something produced through continual negotiation between memory, culture, and everyday experience.

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Social media platforms generate massive volumes of data every second, making it difficult for individuals and organizations to manually analyze public opinion and engagement trends. Twitter (X), being one of the most influential microblogging platforms, plays a major role in shaping public sentiment, brand perception, and digital communication. This project presents an AI-Driven Twitter Analytics Dashboard built using Python, Streamlit, Plotly, and Natural Language Processing (NLP) techniques. The system analyzes Twitter posts and provides real-time insights into user engagement and public sentiment. It processes tweet data and extracts key performance metrics such as likes, retweets, replies, views, and engagement rate. Using TextBlob sentiment analysis, the application automatically classifies tweets into positive, neutral, and negative categories. The dashboard visualizes results through interactive graphs including sentiment distribution, engagement histograms, and performance comparison charts. The platform also supports data export in CSV and Excel formats, enabling further offline analysis. This project demonstrates how artificial intelligence and data visualization can be combined to create an intelligent, user-friendly analytics tool for social media monitoring, brand tracking, and trend analysis

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AI-Driven Analytics, Twitter Analytics Dashboard, Sentiment Analysis, Natural Language Processing, Data Visualization, User Engagement Metrics, Social Media Monitoring

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Abstract The provision of basic amenities is a key resource to rural development with an impact on not only material life conditions but also in other larger socioeconomic and spatial transformation processes. The current research questions how the rural change in Banda District was determined by the primary facilities such as drinking water, sanitation, electricity, education, healthcare, and transport. The district is located on the semi-arid Bundelkhand area and is marked by the ecological strain, agrarian reliance and preexisting infrastructural imbalances, which makes it a salient area regarding developing effects of the implementation of basic services. Using a critical geographical approach, the paper will focus on the issue of spatial-religion of amenities in the countryside settlement and its implication to the livelihoods, social processes and pattern of settlements. It is also found that expansion of rural livelihoods has been pooled over time by improvements in basic amenities, heightened mobility and quantifiable growths in health and educational achievements. However, the services of infrastructural development are also not evenly distributed, with those villages that are located close to transports and administrative centres enjoying more benefits in compared to those that are located in distant or periphery areas.

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 Keywords

Keywords: Essential facilities, Urbanization in rural regions, Spatial inequality, Human geography, Banda District.

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This research investigates the dynamic association between gold prices, silver prices, and movements in the Indian stock market by employing contemporary machine learning methodologies. Financial markets in India have experienced heightened volatility driven by economic disruptions, inflationary pressures, and global uncertainties. During such turbulent episodes, investors characteristically redirect capital toward precious metals, particularly gold and silver, viewing them as protective stores of value and inflation hedges. This study utilizes daily historical price data for gold, silver, and a prominent Indian stock index spanning the period from 2019 to 2026. Following rigorous data preprocessing and transformation, both statistical and machine learning models were constructed to identify underlying patterns and generate short-term market movement forecasts. Model effectiveness was assessed using standard validation metrics. The empirical findings reveal distinct volatility profiles for gold and silver and illustrate their differential predictive relevance for equity market movements.

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Gold price, Silver price, Indian stock market, Machine learning, Forecasting, Time-series analysis

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Folk songs are one of the most conservative and articulate forms of cultural memory in traditional societies. Transmitted orally, folk songs function not only as artistic expressions but also as repositories of historical, moral, emotional, and familial values. This paper presents a thematic analysis of a few folk songs of the Kangra region based on the thematic analysis of seven representative compositions that are Hari Singh Raajjey, Suliya Tangoi Gayi Meri Jaan, Prithvi Singh Inderdeyi, Pind Deya Lambda, Sithniyan and Ghodi. The paper examines how these songs convey justice, heroism, tragedy, loss, regional pride, nostalgia, the voice of women, social denunciation, and rites of celebration through close textual analysis and interpretive reading. The paper concludes that Kangra folk songs serve as an alternative historical narrative and emotional archives to support the social values, gendered experiences and regional identity. Through the process of collective memory, these folk compositions ensure cultural continuity and provide insight into the moral imagination of the people.

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Folk literature, Kangra folk songs, oral tradition, regional identity, justice, tragedy, women's voice, ritual culture

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Creative Commons Attribution 4.0 and The Open Definition

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This paper examines hybridity as a survival mechanism in Amitav Ghosh's The Hungry Tide, arguing that hybrid identities function not merely as postcolonial conditions but as necessary tools for survival. Set in the ever-changing Sundarbans delta, characters like Piya Roy, Fokir, Moyna, and the Morichjhapi refugees navigate cultural, linguistic, and geographical displacements by developing new identities that challenge fixed notions of belonging. The novel presents hybridity as a transformative process that creates possibilities while acknowledging the violence and loss accompanying displacement.

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Hybridity, Survival, Displacement, Postcolonial Identity, Amitav Ghosh, The Hungry Tide

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The drug approval process requires submission of large volumes of regulatory documentation in the electronic Common Technical Document (eCTD) format. Preparation, validation, and regulatory review of these dossiers are traditionally time-consuming and prone to human error. Artificial Intelligence (AI) is emerging as a transformative technology that can accelerate both eCTD submission and regulatory review processes. AI tools can automate document generation, perform real-time compliance checks, validate data integrity, and assist regulatory authorities in faster evaluation of submissions. The integration of AI technologies in regulatory systems improves accuracy, reduces submission deficiencies, and enhances the efficiency of digital drug approval pathways. This review highlights the application of AI in eCTD preparation, automated validation, and AI-assisted regulatory review, demonstrating how digital innovation can significantly accelerate global drug approval processes.

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Artificial Intelligence, eCTD, Regulatory Affairs, Digital Drug Approval, Machine Learning, Regulatory Review

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Women's empowerment is a multidimensional process aimed at enhancing the social, economic, political, and legal strength of women. While India has made significant strides through legal reforms, affirmative action, and grassroots-level participation (such as through Panchayati Raj), women continue to face persistent challenges. This paper explores the structural, cultural, institutional, and psychological barriers that hinder women's empowerment in India, particularly in rural and marginalized settings. It also offers policy recommendations for addressing these issues in a holistic manner.

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Women's Empowerment, Gender Equality, Panchayati Raj, Structural Barriers, Rural Women, India, SHGs

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Influencers on social media are ushering in a new era of marketing and advertising by significantly changing public consumer behavior.Although brand ambassadors, paid promotions, free trips, discounts, and other benefits come with endorsing products or services, how can individuals actually get affected by social media influencers with a simple post on their platforms?Before the advent of social media, goods and services were advertised on television, in newspapers, and in pamphlets. Today, we witness social media influencers travelling to different locations to publicise products and reap the rewards of doing so on websites like Instagram, Facebook, Youtube, and others. The goal of this study is to determine how social media influencers affect customer behavior.The objective of this study is to analyse the impact of consumer behaviour in influence marketing, to study the negative effects of social media influencers towards a product and to understand the legal stance about the guidelines of social media influencers marketing.The present research is based on an empirical study. The information was gathered from individuals in south India utilizing helpful examining techniques. The sample size is 200 and the sampling method was a convenient sampling method. The independent variables are Age, Gender, Occupation, Educational Qualification and Place of living.The suggestions of the study would be that the social media influencers should always provide content which is honest and real even if it is a paid promotion by the brands.Since the majority of people utilise social media, it can be concluded that individuals are greatly influenced by social media influencers. As a result, new rules regulating social media influencers must be developed in order to protect consumers and their behaviour.

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 Keywords

KEYWORDS: Brands, Consumer Behaviour, Influencer marketing, Paid promotions, Social Media, Social media influencers.

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The financial performance of garment companies plays a crucial role in determining their sustainability, competitiveness, and growth in the global textile industry. The garment sector is highly dynamic and influenced by factors such as production costs, market demand, export opportunities, labor efficiency, and technological advancements. This study examines the financial performance of garment companies by analyzing key financial indicators, including profitability, liquidity, solvency, and operational efficiency. The primary objective of the study is to evaluate the financial health of garment firms and identify the factors that influence their financial outcomes. The research is based on secondary data collected from the financial statements of selected garment companies over a specified period. Financial ratios such as gross profit ratio, net profit ratio, return on assets, current ratio, debt-equity ratio, and inventory turnover ratio are used to assess performance. These indicators help in understanding the companies' ability to generate profits, manage working capital, meet short-term and long-term obligations, and utilize resources effectively. The findings of the study reveal that financial performance in the garment industry is significantly affected by cost control, efficient inventory management, labor productivity, export market fluctuations, and effective financial planning. Companies that adopt modern production techniques, maintain optimal capital structures, and focus on quality improvement tend to perform better financially. On the other hand, firms facing high production costs, poor financial management, or declining market demand experience reduced profitability and liquidity challenges. The study concludes that strong financial management practices, technological adoption, and strategic planning are essential for improving the financial performance of garment companies. It also highlights the importance of government support, export incentives, and skill development initiatives in strengthening the sector.

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Financial performance, garment industry, profitability, liquidity, solvency, financial ratios, operational efficiency, textile sector, cost management, capital structure.

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Creative Commons Attribution 4.0 and The Open Definition

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Accurate analysis of motion parameters is essential in various engineering applications involving rotating mechanical systems. This study presents a numerical approach for analyzing non-linear two-dimensional rotational motion using the Newton-Gregory forward interpolation method. The research considers time and corresponding angular displacement data of a wind turbine blade to determine the unknown motion parameters such as angular velocity and angular acceleration. The mathematical formulation is validated through computational implementation using the Java programming language to improve numerical accuracy and computational efficiency. The results obtained from the computational model show strong agreement with the classical numerical method, with minimal percentage error, demonstrating the reliability of the proposed approach. The study highlights the effectiveness of combining numerical interpolation techniques with computational tools for real-time engineering motion analysis and data-driven system modeling.

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Newton Gregory Interpolation, Non-Linear Motion Analysis, Angular Velocity, Angular Acceleration, Wind Turbine Blade Motion, Numerical Methods, Java Programming, Computational Modeling, Engineering Motion Analysis.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Uttaraandhra maandalikam- varna vignaanam

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Uttaraandhra maandalikam- varna vignaanam

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