Volume 13 | Issue 5 |

 Abstract

The Judicial system of India, known for its constitutional strength, is facing a chronic concern of pending litigation. With more than 5 crore pending litigations across several courts, the delay in imparting justice not only burdens the system but also infringes on the fundamental rights of individuals provided under Article 21 of the Constitution of India, i.e., the Right to Life and Personal Liberty. One of the critical approaches of this right is the guarantee of a speedy trial, as highlighted in the landmark verdict of Maneka Gandhi v. Union of India, in which the Hon'ble Supreme Court prolonged the interpretation of Article 21 & included the right to a speedy trial as a part of the fundamental right under Article 21 of the Indian Constitution. The complications of this judicial pendency are profound. The delays in legal proceedings for a long time often resulted in stretched incarcerations without having a trial, directly violating the principles of justice & fairness. For example, in April 2022, In Bihar, the court acquitted an accused for charges of murder after he had been in jail for 28 years awaiting trial. These instances not only pointed to the personal misfortunes of the accused but also reflected the failure on the part of the system which demoralized the confidence of the public in the judiciary. This paper tries to explore the consequences & causes of judicial delays, & their direct effect on Article 21, & tries to propose justifiable reforms as the way forward so that the faith of the public in the judicial system continues to exist. The author tries to explore the paper by adopting the Doctrinal Method of research while dealing with the concern of pending litigation under various Courts in the country.

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 Keywords

Article 21, Indian judiciary, judicial backlog, legal reforms, speedy trial.

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The growing demand for high-speed and efficient digital systems has driven advancements in hardware design methodologies. This paper presents the development of a 32-bit Arithmetic Logic Unit (ALU) using Verilog Hardware Description Language (HDL). The design incorporates modular components for arithmetic, logical, and shift operations, which are integrated into a single functional unit. The ALU, implemented on a Field Programmable Gate Array (FPGA) using the Xilinx ISE Design Suite, demonstrates efficient resource utilization and high performance. This implementation includes simulation, synthesis, and testing phases to ensure accurate functionality and optimization. The paper also discusses key design techniques, challenges, and future research directions, highlighting the ALU's importance in modern computing.

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 Keywords

Verilog HDL, Arithmetic Logic Unit, FPGA, digital design, simulation.

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Acne and inflammatory skin conditions present ongoing dermatological challenges, with conventional treatments often causing adverse effects. In response herbal medicine is emerging as a promising alternative, utilizing plant-derived compounds that offer anti-inflammatory, antimicrobial and antioxidant benefits for managing these conditions. Herbal creams carefully formulated with extracts from plants like neem, tea tree oil and aloe vera can effectively reduce acne symptoms and skin inflammation while minimizing the harsh side effects associated with synthetic drugs, highlighting their potential as a safer more holistic treatment option.

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 Keywords

Acne vulgaris, Skin inflammations, Herbal cream, Herbal formulations, Plant extracts, Anti-inflammatory, Antimicrobial, Topical treatment.

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This paper presents the design and implementation of a Personal AI Assistant (PAA) for Windows using Python, leveraging artificial intelligence and natural language processing (NLP). Inspired by commercial assistants like Siri and Alexa, PAA enables voice-controlled interaction to perform tasks such as browsing, note-taking, retrieving information, and managing applications. By utilizing Python libraries for speech recognition and command execution, the assistant functions locally without cloud dependency, offering privacy and flexibility. The system aims to enhance user productivity, accessibility, and educational engagement while exploring the broader integration of AI in daily digital experiences.

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 Keywords

Python, Natural Language Processing, Speech Recognition, Human-Machine Interaction, Smart Automation.

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This study analyzes the financial performance of the company from the perspective of DuPont analysis and value-based financial ratios from FY2018 to FY2024. The results show a period of high performance from FY2018 to FY2022 with a high return on equity (ROE) of 38.22%, high return on assets (ROA) and net profit margin (NPM), and strong value creation as depicted through Economic Value Added (EVA) and Cash Value Added (CVA).The subsequent decline, with worsening ROE (8.44% in FY2024), ROA (2.97%), and NPM (2.98%), indicates a reduction in operating and financial efficiency. Value measures such as EVA and CVA depicted volatility, with CVA turning negative in FY2024 (-INR4,807 lakh), indicating cost overruns and erosion of shareholder value. Volatile Free Cash Flow to Equity (FCFE) and the Financial Leverage Index (FLI) reflect instability in cash flow management and rising solvency concerns. Although revenue growth has been evident since FY2019, profitability has declined, highlighting inefficiencies in scaling up operations. Overall, the analysis underscores the need for greater focus on cost control, capital management, and strategic use of debt to restore financial health and improve shareholder returns.

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DuPont Analysis, Value-Based Financial Ratios, Return on Equity (ROE), Return on Assets (ROA), Net Profit Margin (NPM), Economic Value Added (EVA), Cash Value Added (CVA), Free Cash Flow to Equity (FCFE), Financial Leverage Index (FLI), Operating Efficiency, Financial Efficiency, Revenue Growth.

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This study proposes a dual-layered stock selection methodology applied to the NSE 500 universe. The first layer involves a multifactor model combining quality, value, momentum, and low volatility factors. The top 50 multifactor scoring stocks are further filtered through correlation matrix analysis, eliminating stocks with high pairwise correlations. Among each correlated pair (r > 0.50), the stock with the lower Sortino Ratio is removed. The final 25-stock portfolio is constructed using equal weighting. The study evaluates the portfolio's performance against market benchmarks using risk-adjusted metric i.e., sharpe ratio. Results indicate superior diversification and downside risk optimization through this systematic selection process.

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Multifactor investing, NSE 500, stock selection, correlation filtering, Sortino ratio, portfolio construction, sharpe ratio.

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The increasing demand for energy and the environmental impact of traditional fossil fuels have led to a growing interest in renewable energy sources like solar power. However, solar energy is highly dependent on weather conditions, causing fluctuations in power output and instability in energy supply. This project aims to address this challenge by integrating supercapacitors as an energy storage solution in solar power systems. Supercapacitors offer advantages over conventional batteries, including high power density, fast charge-discharge cycles, and longer lifespans, making them ideal for handling fluctuations in solar power generation. The proposed system uses supercapacitors to store excess energy during periods of high solar radiation and discharge it when sunlight decreases, ensuring a stable power supply. A boost converter is integrated to regulate the voltage from the supercapacitor, maintaining consistent power delivery to the load. An energy management system (EMS) optimizes the charging and discharging cycles, while power electronics components such as MOSFETs and DC-DC converters enhance power conversion efficiency. This approach offers several benefits, including fast response times, high cycle life, environmental sustainability, and improved system reliability. By studying meteorological data, the project also examines the impact of solar radiation variations on energy storage, ultimately improving the performance of solar power systems for applications such as off-grid power solutions, electric vehicle charging stations, and rural electrification.

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 Keywords

Solar Power Optimization, Supercapacitor, Energy Storage.

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 Abstract

Assistant allows users to control their systems through spoken commands. The system becomes increasingly accurate and tailored to individual needs by learning from user behavior and preferences using machine learning algorithms. With more usage, it improves speech recognition, anticipates actions, and generates relevant responses, enhancing overall usability. Unlike conventional voice control systems, adaptive assistants use sophisticated technologies like Machine Learning (ML) and Natural Language Processing (NLP) to understand diverse speech patterns and user habits. This improves accessibility and productivity by allowing hands-free control of applications, file management, reminders, and system configurations.

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Machine Learning Algorithm(ML), Natural Language Processing (NLP)

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International pharmaceutical companies are responsible for the present change in marketing strategies. Leading companies use high-end development rather than adaptable development, new innovation-based businesses are expanding, and businesses that don't innovate their processes and products are finding it hard to compete with businesses that are adapting. inventiveness. The pharmaceutical sector is extremely complex. A good marketing strategy will help in survival of pharmaceutical companies in the stiff competition not only from the domestic companies but also from their foreign counterparts. This research paper will focus on development of appropriate marketing strategies. The responses have been collected from various stakeholders in pharmaceutical industry.

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Pharmaceutical, marketing strategies, innovation-based business

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Mobile phones are being increasingly targeted by advanced malware, making conventional detection techniques less efficient. This paper introduces a hybrid malware detection system in an Android Studio WebView-based app that incorporates the VirusTotal API, machine learning, and real-time threat intelligence. The system integrates signature-based, heuristic, and behavioral analysis to identify malicious behavior and adds IP/domain analysis for network threat evaluation. A machine learning model that learns on an ongoing basis improves detection of polymorphic and zero-day malware. The software allows users to scan files, inspect URLs, and inspect network traffic. Performance indicates enhanced detection accuracy, minimized false positives/negatives, and efficient real-time threat response. This paper proves the capability of AI-driven, hybrid strategies in improving mobile security.

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 Keywords

Malware Detection, Android Security, VirusTotal API, Machine Learning, Threat Intelligence, Hybrid Detection, Mobile Security.

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