Volume 14 | Issue 5 |

 Abstract

A Study of Foundational Literacy and Numeracy Status of Primary School Students in the Context of the NIPUN Bharat Mission

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A Study of Foundational Literacy and Numeracy Status of Primary School Students in the Context of the NIPUN Bharat Mission

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Our research dives into the revolutionary shift happening in how we make tough choices, all thanks to Generative AI (GenAI). We're taking a close look at the main players--from the models that create realistic fake data (GANs) and the ones that help design structured ideas (VAEs), right up to the huge chat engines (LLMs) that power today's digital world. The big takeaway is that GenAI is dramatically speeding up decision-making in vital areas like finance and healthcare. However, this power comes with a major catch: these tools are unreliable on their own. They carry the risk of inherited bias and sometimes just confidently make things up ("hallucinate"). Because of this, the key to using GenAI successfully isn't replacing humans, but ensuring robust human-AI teamwork is in place, backed by clear rules on transparency and accountability. This paper reviews how we can properly harness these systems.

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Generative AI, Decision Making, Hallucination, GANs, VAEs, Diffusion Models, Large Language Models, Human-AI Collaboration, Bias Mitigation, Ethical AI

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Aviation brackets are critical structural components that must sustain complex loading conditions while maintaining minimal weight. This study presents the finite element analysis (FEA) and structural optimization of an aviation bracket to improve its performance and efficiency. A 3D model is analyzed under realistic boundary conditions to evaluate stress distribution, deformation, and factor of safety. Critical stress regions are identified, and design modifications are implemented to reduce weight while maintaining structural integrity. The results demonstrate that FEA-based optimization significantly enhances performance and achieves an efficient strength-to-weight ratio, making it suitable for aerospace applications.

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Aviation Bracket, Critical Loading Conditions, Structural Optimisation, stress distribution, Efficient Strength-to-Weight Ratio, Aerospace Application

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The work introduces a smart energy-related consumption system that aims to detect deviations in the pattern and develop a better comprehension of how to use time-series data. This system takes advantage of a multi-perspective analytical perspective to identify both minimal and remarkable consumption patterns deviations whilst being flexible under different data circumstances. The combination of reconstruction-based learning and statistical analysis successfully separates regular operation patterns and abnormal fluctuations in the framework. Also, forecast technologies are included to determine the future consumption trends and enhance accuracy in prediction. The modular design allows effective data processing, application deployment, and easy visualization via interactive interface. Experimental testing shows that there is better consistency and interpretability of detection than as a single model. The proposed solution gives a scalable and real-world energy monitoring application a reliable base to enable proactive decision-making, resource optimization, and enhanced awareness of the working environment in dynamic environments.

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Energy Monitoring, Anomaly Detection, Time-Series Analysis, Consumption Patterns, Predictive Analytics, Intelligent Systems, Data Visualization.

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Abstract The rapid advancement of financial technology (FinTech) has significantly transformed the financial landscape of emerging markets, particularly in enhancing financial inclusion and reshaping consumer behavior. This study examines the role of India's Unified Payments Interface (UPI), developed by the National Payments Corporation of India under the regulatory framework of the Reserve Bank of India, in transforming the digital payment ecosystem, consumer behavior, and economic architecture. Methods: The study adopts a qualitative and analytical research design based on secondary data. Data were collected from reports published by the Reserve Bank of India, World Bank, government publications, and peer-reviewed journals. A conceptual framework integrating digital payment systems, financial inclusion theory, and consumer behavior models (such as TAM and behavioral economics) was used to analyze the impact of UPI. Comparative analysis with global models, including China's digital wallets and Africa's mobile money systems, was also conducted. Results: The findings reveal that UPI has significantly enhanced financial inclusion by enabling real-time, low-cost, and accessible financial transactions, particularly among underserved populations in rural and semi-urban areas. It has reduced transaction costs, increased financial participation, and promoted the formalization of economic activities. From a behavioral perspective, UPI has accelerated the shift from cash to cashless transactions, reduced the psychological "pain of paying," and increased consumption frequency, including micro-transactions. Additionally, UPI has contributed to the growth of the digital economy, expansion of e-commerce, rise of the gig economy, and transformation of banking into digital-first models. Its open and interoperable architecture has fostered fintech innovation and enhanced competition. Conclusion: UPI has emerged as a transformative digital public infrastructure that extends beyond a payment system to become a catalyst for inclusive economic growth and digital transformation. While its scalability and interoperability make it a globally replicable model, challenges such as cybersecurity risks, digital divide, infrastructural constraints, and regulatory complexities must be addressed to ensure sustainable development. The study concludes that UPI plays a critical role in redefining financial ecosystems and offers strategic implications for policymakers, financial institutions, and marketers in emerging markets.

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Keywords: Digital Payments, UPI, Financial Inclusion, Consumer Behavior, FinTech, Emerging Markets

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Acne vulgaris is a common chronic inflammatory skin disorder affecting the pilosebaceous unit and is associated with microbial infection, excess sebum production, and inflammation. The present study focuses on the development and in vitro evaluation of an anti-acne gel formulated using Psidium guajava (guava) leaf extract, known for its antimicrobial, antioxidant, and anti-inflammatory properties. The guava leaves were collected, authenticated, shade-dried, and subjected to hydroalcoholic extraction using the maceration technique. Preliminary phytochemical screening confirmed the presence of bioactive constituents such as flavonoids, tannins, and phenolic compounds. The extract was incorporated into a gel base using Carbopol 934 to prepare three formulations (F1, F2, and F3) with varying concentrations of the extract. The formulated gels were evaluated for physicochemical parameters including physical appearance, pH, spreadability, viscosity, washability, and stability. All formulations showed acceptable organoleptic properties, with pH in the range suitable for skin application (6.0-7.0). Spreadability and viscosity studies indicated that formulation F1 exhibited optimal performance with good consistency and ease of application. Stability studies revealed that F1 remained stable under both room temperature and refrigerated conditions, while F2 and F3 showed moderate variations. The antimicrobial activity was evaluated against Cutibacterium acnes using the agar well diffusion method. The formulation exhibited minimal antibacterial activity compared to the standard drug, indicating the need for further optimization. In conclusion, the study demonstrates that Psidium guajava leaf extract can be successfully formulated into a topical gel with acceptable physicochemical properties, though enhancement of antimicrobial efficacy is required for improved therapeutic potential.

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Anti-acne gel, Psidium guajava, Guava leaf extract, Acne vulgaris, Carbopol 934, Herbal formulation, Antimicrobial activity, Cutibacterium acnes, Topical drug delivery, Phytochemical screening

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Abstract In the digital era, the increasing reliance on data-driven technologies has intensified tensions between individual privacy and state surveillance. While cyber security measures are essential for safeguarding national security, they often conflict with civil liberties, particularly the right to privacy. This paper critically examines India's evolving data protection framework, focusing on the Digital Personal Data Protection Act 2023 and its implications for state surveillance practices. It analyses the constitutional dimensions of privacy under Article 21, with special reference to the landmark judgment in K.S. Puttaswamy v. Union of India, which recognized privacy as a fundamental right. The study highlights the legal and ethical challenges posed by surveillance mechanisms such as interception, monitoring, and data retention. It further evaluates whether current laws strike an appropriate balance between security imperatives and individual freedoms. The paper argues for stronger safeguards, transparency, and accountability in surveillance frameworks, emphasizing the need for proportionality and judicial oversight. Comparative insights are also drawn from global practices to suggest reforms for a more rights-oriented approach in India.

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Cyber security, Data protection, State Surveillance, Right to privacy, National security

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Health and fitness is being given utmost importance these days and a simple system is needed that will allow a user to track nutrition more effectively. This paper proposes a Smart Meal Tracker that helps the user to get meal plans personalized, check nutrition values of the meals and get notified on time. The system collects basic details from user such as age, height, weight, gender and fitness goals that will be utilized to compute BMI, calorie intake for the day and protein needed. Based on these inputs the system generates the diet plan for a day with the complete nutrient composition and nutrition values of the food that needs to be eaten. One of the highlighting features of this application is that it works on the basis of location-based price of the food and makes meal suggestions based on what the user is comfortable spending in his/her region. The smart reminder feature works by providing browser notifications which is another added advantage that will prompt the user on the time of his meals to track diet more consistently. It is designed using HTML, CSS and JavaScript for the display of data to make it responsive and user-friendly and without a back-end.

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Smart Meal Tracker, Personalized Nutrition, Meal Planning, BMI Calculation, Calorie and Protein Estimation, Location-Based Pricing, Reminder System, Web Application, Local Storage, User Engagement

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The study provides an examination of the ensemble machine learning algo rithms alongside the techniques of uncertainty measures on nonlinear economic forecasting. We have an extremely large simulation experiment: we have 12 data generating processes (DGPs), which are threshold effects, smooth transition autoregressive models, regime switching dynamics, and chaotic systems. Together with conformal prediction and quantile regression forests, there are three ensemble methods, which include bagging, boosting, and stacking, that pro vide powerful prediction intervals. We perform performance appraisal of Monte Carlo replications of forecasting with sample sizes of 250 to 2000 observations and signal-noise ratio of 10,000 and nonlinearity of the signal of different levels with signal-noise ratios. Results have depicted stacking with conformal prediction to have great coverage probabilities (93.2-96.8) and competitive point forecast accuracy. Boosting performs optimally in high signal to noises circumstances, but loses coverage when nonlinearity is severe. Bagging achieves good baseline performance of all DGPs. Our analysis provides the outcome of the strong dominance regions of both ensembles techniques and provides effective suggestions of quantifying uncertainty in applying economic forecasting techniques. The study is also relevant methodologically by hypothetically proposing an adaptive ensemble selection framework and empirically by defining performance limits at both extremes within the non-linearity spectrum.

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Ensemble learning, uncertainty quantification, conformal prediction, quantile regression, nonlinear forecasting, Monte Carlo simulation

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This study explores how phonetic awareness contributes to the development of English language skills in multilingual classroom environments. Learners who come from varied linguistic backgrounds often face challenges related to pronunciation, sound recognition, and reading accuracy. The paper argues that an improved awareness of speech sounds supports better articulation, listening ability, and decoding skills. A descriptive qualitative research design was adopted, with data collected through classroom observations, structured questionnaires, and diagnostic assessments. The findings suggest that students with stronger phonetic awareness demonstrate greater fluency, experience less interference from their first language, and show increased confidence in communication. The study emphasizes the need to incorporate systematic phonetic instruction into language teaching practices to promote comprehensive language learning.

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This study explores how phonetic awareness contributes to the development of English language skills in multilingual classroom environments. Learners who come from varied linguistic backgrounds often face challenges related to pronunciation, sound recognition, and reading accuracy. The paper argues that an improved awareness of speech sounds supports better articulation, listening ability, and decoding skills. A descriptive qualitative research design was adopted, with data collected through cla

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