VOLUME 2, ISSUE 12, DECEMBER 2025

Abstract: Legal language is inherently prone to indeterminacy, presenting significant challenges for drafting, interpretation, and translation. Ambiguity, polysemy, and context-dependent meanings can undermine precision in statutes, contracts, and judicial decisions, leading to divergent interpretations and practical uncertainties. Traditional approaches in legal linguistics and doctrinal analysis, while providing qualitative insights, are limited in their ability to systematically quantify and model semantic vagueness across large corpora of legal texts. This study proposes a computational lexico-semantic model designed to analyse indeterminacy in legal terminology. Leveraging recent advances in natural language processing (NLP), including transformer-based language models (GPT), contextual embeddings, and ontology-driven semantic frameworks, the model provides a probabilistic assessment of term vagueness. A multi-domain corpus comprising constitutional, criminal, and financial law texts was constructed to evaluate the approach. Semantic similarity measures, context-sensitive embeddings, and GPT-derived probability scores were applied to identify and quantify degrees of lexical indeterminacy. Preliminary results demonstrate that the model effectively detects terms with high semantic ambiguity, revealing patterns of indeterminacy that align with expert legal annotations. Visualisations of semantic probability distributions highlight the domains and contexts in which vagueness is most pronounced, offering insights into term usage, frequency, and contextual dependency. The findings have significant implications for legal drafting, automated translation, and AI-assisted legal reasoning. By providing a quantitative, scalable framework for analysing legal indeterminacy, the proposed model contributes to improving the precision, consistency, and interpretability of legal texts, while supporting future research on real-time monitoring of evolving legal terminology.

Keywords: Legal indeterminacy, lexico-semantic modelling, NLP in law, GPT language models, semantic vagueness, ontology-based legal analysis, computational legal linguistics

Abstract: This paper presents the design and implementation of Walsh code generators with lengths of (8), (16), and (32-bits) bits using shift registers on a Cyclone II EP2C20F484C7 FPGA from ALTERA placed on education and development board DE-1. Walsh codes, as orthogonal sequences, enable efficient multi-user direct-sequence spread spectrum (DSSS) communication by minimizing cross-user interference. The core contribution is a shift-register-based generator architecture that outputs Walsh codes in a synchronized manner with the system clock, enabling scalable multi- user support while maintaining low resource usage on the Cyclone II FPGA. The work includes HDL descriptions, synthesis considerations on DE-1, and verification through functional and timing tests, demonstrating reliable code generation across all specified lengths and straightforward extension to additional lengths if required. The results indicate that the proposed approach provides a compact, deterministic, and easily verifiable implementation suitable for education, prototyping, and small-scale DSSS experiments.

Keywords: DSSS, Walsh Codes, PNWCG, FPGA.

Abstract: The Indian financial sector has undergone significant changes over the past two decades, evolving from traditional banking to a technology-driven ecosystem. The introduction of digital banking, mobile payments, Aadhaar- linked transactions, and AI-powered financial services has revolutionised the way people access financial services. A key driver of this transformation has been the Unified Payments Interface (UPI), which has made digital transactions seamless and widely adopted. Additionally, government initiatives such as Jan Dhan Yojana, demonetization, and digital lending regulations have played a crucial role in promoting financial inclusion and fintech adoption in the country. This paper covers the evolution of fintech in India, focusing on its key growth drivers, challenges, and future trends. Despite its rapid expansion, fintech in India faces challenges such as cybersecurity risks, regulatory compliance, financial literacy gaps, and infrastructure limitations. Emerging trends such as AI-driven automation and blockchain technology are expected to drive innovation in the sector. The study highlights how fintech is shaping India’s digital economy and underscores the need for robust security measures, effective regulatory frameworks, and public awareness to ensure its sustainable growth.

Keywords: Fintech, Traditional Banking, Digital Transformation, Blockchain, Artificial Intelligence, UPI.

Keywords: Legal linguistics, English–Uzbek legal terminology, semantic asymmetry, textual interpretation, doctrinal synthesis, norm classification, comparative legal analysis, terminological equivalence.

Abstract: This study analyzes the performance and risk characteristics of all 50 Nifty 50 stocks over the five-year period from April 2020 to March 2025. Using historical prices and Nifty 50 benchmark returns, the research evaluates each stock through key measures such as annual returns, volatility, Sharpe ratio, Treynor ratio, and Jensen’s alpha. The results show clear differences across sectors: Industrials, Infrastructure, Pharma, and Telecom delivered stronger risk-adjusted returns, while FMCG and Cement lagged behind the index. Adani Group companies posted the highest overall returns and alpha, though with higher volatility. Financials and IT showed mixed outcomes, with several stocks trailing the benchmark. Overall, the findings highlight how cyclical, high-beta sectors generated higher alpha, whereas defensive sectors provided stability but lower returns—offering useful insights for investors and portfolio managers studying large- cap equity performance.

Keywords: Sharpe Ratio, Treynor Ratio, Jensen's Alpha, Risk-Adjusted Returns, Nifty 50, Portfolio Performance, Systematic Risk, Beta, Financial Metrics, Sector Analysis, Indian Stock Market, Mutual Fund Analysis, Benchmark Comparison, CAPM, Volatility, Return Analysis, Equity Research, Investment Strategy, Performance Evaluation, Financial Modeling

Abstract: This analysis evaluates the financial performance of ten leading Indian IT and Banking companies from April 2020 to March 2025 using historical stock prices and NIFTY 50 benchmark returns. Key metrics including annualized returns, volatility, Beta, Sharpe Ratio, Treynor Ratio, and Jensen’s Alpha were used to assess risk-adjusted performance and market sensitivity. The results show that banking companies, particularly ICICI Bank and SBI, achieved stronger risk-adjusted returns and positive alphas, indicating consistent outperformance. In contrast, major IT firms such as TCS, Infosys, and Wipro experienced higher volatility, lower risk-adjusted returns, and negative alphas, reflecting underperformance during the period. Overall, the dataset provides a solid foundation for equity research, portfolio evaluation, and sectoral comparison within the Indian market.
The content focuses on assessing financial performance through tools like Sharpe, Treynor, Jensen’s Alpha, and Beta. It analyses risk, volatility, and returns using CAPM, compares IT and Banking sector stocks with the NIFTY 50 index, and reviews historical prices to evaluate overall portfolio efficiency.

Abstract: The rapid proliferation of IoT and embedded systems across critical sectors agriculture, energy, and transportation demands a shift toward sustainable computing. These systems must operate with minimal energy overhead while delivering accurate, timely data and control. This paper explores energy-efficient design principles, including low-power sensor architectures, lightweight communication protocols such as Zigbee, BLE, LoRaWAN, and NB-IoT, as well as battery-aware management strategies and edge-level intelligence. Applications in smart farming, grid optimization, and intelligent mobility are examined to demonstrate how embedded platforms enable environmentally responsible computation. The study identifies major gaps in existing designs, such as inconsistent protocol integration and underutilized renewable energy harvesting methods. A battery-aware, event-driven, and modular design approach is proposed, and validated using a case implementation. The results affirm that IoT systems, when thoughtfully designed, can significantly reduce carbon footprint while maintaining performance. Future directions include AI-assisted scheduling and cross-layer protocol optimization.

Keywords: IoT Embedded Systems, Energy Harvesting, Smart Grid, Green Mobility, Sustainable Computing

Abstract: This study evaluates the risk-adjusted performance of India’s top AUM-based large-cap mutual funds over five financial years (2020–21 to 2024–25), integrating Modern Portfolio Theory and the Capital Asset Pricing Model to assess whether leading schemes generate meaningful alpha or simply mirror benchmark behaviour under SEBI’s category constraints. Using Sharpe Ratio, Treynor Ratio, Jensen’s Alpha, Beta, and return volatility, the analysis reveals clear differences in efficiency and managerial skill. Nippon India Large Cap Fund delivers the strongest outcomes (Sharpe 0.845, Treynor 27.10, Alpha 7.69%), followed by ICICI Prudential (Alpha 5.48%) and HDFC Large Cap (Alpha 4.97%), each demonstrating effective risk control. In contrast, SBI Large Cap shows the highest volatility (32.04%) with modest alpha (2.11%), while Mirae Asset exhibits near-benchmark behaviour. With Betas clustered between 0.91–1.00, the findings show that scale advantages are not uniform. These insights offer implications for investors, fund managers, and regulators seeking more nuanced evaluation frameworks.

Keywords: Large-cap mutual funds; Risk-adjusted performance; Sharpe Ratio; Treynor Ratio; Jensen’s Alpha; Systematic risk; Beta; Portfolio efficiency; Mutual fund evaluation; Nifty 100 Index; Asset management; SEBI categorization.