Sri Ramakrishna Institute of Paramedical Sciences

This study focuses on the analysis of the financial performance of Tata Consultancy Services Ltd for the period 2020-2021 to 2024-2025 concentrates on the profitability, solvency, Liquidity and trend analysis of the company. Tata Consultancy Services Ltd was a division of Tata Sons Limited originally named Tata Computer Systems by providing various services like Digital Transformation, Automated tools, etc. The company shows significant increase in the overall financial performance in the period of study. This study aims to give advices to the investors for investing in the best performing IT company.

This study focuses on the impact of using electronic waste (E-waste) as partial replacement for gravels in the Self-Compacting Concrete (SCC) with the mixing of 10% Alccofinite, and 20% fly ash as supplementary cementitious materials. To assess performance in both fresh and hardened states, the study examined thirteen distinct SCC formulations, incorporating E-waste in 2.5% increments ranging from 0% up to 30%. The experimental data show that the values of the slump flow vary between 652.5mm and 809.2mm, the values of V-funnel times range from 6.3s to 10.3s, and the value of L-box ratio is between 1.023 and 0.787. Maximum compressive strength, split tensile strength, flexural strength, modulus of elasticity, density, and bond strength of the composite are 38.9MPa, 3.6MPa, 5.4MPa, 29,833.3MPa, 2406.7kg/m3, and 11.33MPa, respectively. The results show that the mechanical performance reaches its optimum with a 15% E-waste replacement; however, higher contents of E-waste reduce the workability and strength. Mechanical strength prediction using four machine learning algorithms (Linear Regression, Support Vector Regression, Random Forest, and XGBoost) was carried out by using fresh concrete properties and mix variables. It is interesting to note that the XGBoost model provided the best prediction, as it delivered an R2 value of more than 0.93 and an RMS prediction error of less than 0.8MPa. Six 1.5m long beams of 100mm × 200mm dimensions is subjected to two-point loading to determine strength, deflection and failure modes. The results show the 15% E-waste mix reached a maximum flexural strength of 40.50MPa, which is a + 12% increase in load capacity over the 10% mix. The study thus confirms a moderate addition of E-waste (15%) improves the sustainability of SCC while maintaining good structural integrity.

1. Abstract Driving through fog is one of the most dangerous experiences a motorist can face, and yet most vehicles still rely on headlights designed for clear-sky conditions. The “Fog Adaptive Smart Headlight and Automation Speed Control System” was developed to directly address this gap — offering an intelligent, embedded solution that responds in real time to deteriorating visibility. A significant share of highway accidents can be traced back to what researchers call “veiling luminance”: when standard headlights scatter off tiny fog droplets and bounce the light right back at the driver, creating a blinding white wall effect. Rather than expecting drivers to react manually under these stressful conditions, this project proposes a two-layer automated safety response. The first layer is the Adaptive Lighting Layer — an Arduino Uno paired with an LDR and IR/Fog sensor that continuously reads the surrounding atmosphere. When fog is detected, it triggers Pulse Width Modulation (PWM) to intelligently reduce LED brightness and commands a Servo Motor to physically angle the beam downward toward the road surface. The second layer is the Automation Speed Layer — which monitors vehicle RPM through a Hall-effect sensor and steps in to limit speed whenever the system detects a hazardous condition. Together, these two layers form an affordable, practical safety net that brings smart vehicle protection to everyday cars, without the price tag of premium automotive systems. 2. Keywords Adaptive Front-lighting System (AFS), Arduino Uno, Fog Density Sensing, Pulse Width Modulation (PWM), Backscattering Mitigation, Hall-Effect RPM Monitoring, Servo-based Beam Steering, Embedded C, Automotive Automation, LDR (Light Dependent Resistor).

1. ABSTRACT The interview process is a critical component of recruitment; however, it is often affected by human bias, subjectivity, and time limitations. This project presents an Artificial Intelligence– based interview insights system that automates the analysis and evaluation of candidate interviews. The proposed system utilizes Machine Learning (ML), Natural Language Processing (NLP), sentiment analysis, and speech and facial expression analysis to assess candidates’ communication skills, emotional state, confidence level, and behavioral patterns. Audio, video, and textual interview data are processed to extract meaningful features and generate objective performance scores. By providing data-driven insights, the system assists recruiters in making fair and consistent hiring decisions while improving efficiency and scalability. This AI-driven approach enhances transparency in recruitment and supports modern, technology-enabled human resource management. Keywords: Artificial Intelligence, Interview Insights, Machine Learning, Natural Language Processing, Sentiment Analysis, Speech Analysis, Facial Expression Analysis, Recruitment Automation, Candidate Evaluation.

Autonomous Digital Process Twins (ADPTs) represent an advanced paradigm in intelligent computational systems that extends traditional digital twin frameworks by incorporating real-time analytics, artificial intelligence, and autonomous decisionmaking capabilities. Conventional digital twins are primarily designed for monitoring, visualization, and offline simulation; however, emerging complex environments require systems capable of adaptive reasoning and real-time optimization. This paper proposes a comprehensive software-centric architecture for an Autonomous Digital Process Twin that enables continuous synchronization between dynamic process models and intelligent decision engines. The proposed architecture integrates virtual process modeling, distributed real-time analytics, reinforcement learning-based decision intelligence, and simulation- driven control evaluation within a scalable computational framework. A mathematical formulation based on dynamic state-space modeling and Markov decision processes is introduced to support predictive reasoning and adaptive optimization. Extensive simulation experiments demonstrate improvements in decision accuracy, latency reduction, and scalability under high computational workloads. The results indicate that the architecture provides a robust foundation for next-generation intelligent systems capable of autonomous adaptation in complex digital environments. This research contributes to the advancement of intelligent digital twin ecosystems by emphasizing algorithmic autonomy, scalable architecture design, and realtime decision intelligence.

Land registration systems are fundamental for maintaining legal ownership records and ensuring property rights. However, conventional land registration systems rely on centralized authorities and manual record management, making them vulnerable to fraud, unauthorized modification, corruption, and data inconsistency. Manipulation of land records often results in ownership disputes, delayed transactions, and loss of public trust in land administration authorities. This paper proposes a Blockchain-Based Secure Land Registration System that provides transparency, immutability, and integrity for land ownership records. The system records land transactions in a blockchain ledger where each transaction is cryptographically linked using the SHA-256 hashing algorithm. Smart contracts are employed to automate ownership transfer and enforce predefined rules, reducing reliance on intermediaries. The proposed framework ensures tamper detection, verifiable ownership history, and secure record management. The system is lightweight, cost-effective, and suitable for institutional and governmental land administration. The implementation demonstrates that blockchain technology significantly enhances trust, transparency, and reliability in land registration processes.

Renewable-powered hydrogen microgrids require coordinated control of electrolyzer operation, battery state-of-charge (SoC), and power balance under intermittent solar/wind generation and time-varying demand. This paper presents a reproducible multi-objective optimization framework that integrates fractional-order proportional–integral–derivative (FO-PID) control with genetic algorithms (GAs) and reinforcement learning (RL) to improve hydrogen production efficiency while reducing operational losses and preserving battery-friendly SoC behavior. The proposed approach is evaluated on a renewable-powered H2 microgrid model using five strategies: rule-based control, conventional order proportional–integral–derivative (PID), FO-PID, FO-PID tuned by GA (FO-PID + GA), and FO-PID with RL-based decision support (FO-PID + RL). Performance is assessed using hydrogen utilization, power-loss ratio, and a SoC stability index (SSI), where deviations outside the recommended 20%–90% operating window are penalized to discourage deep cycling and boundary stress. Results demonstrate that FO-PID + RL achieves the best overall trade-off among the objectives. Compared with the proportional–integral–derivative (PID) baseline, FO-PID + RL increases hydrogen utilization from 2.8 to 4.5 kg/h (≈60.7%) and reduces the power-loss ratio from 12.4% to 5.9% (≈52.4%), while improving SSI from 0.72 to 0.91, indicating smoother SoC trajectories and reduced stress. These outcomes confirm that combining fractional-order control with Pareto-based optimization and learning-based scheduling can enhance both efficiency and operational reliability in renewable-powered hydrogen microgrids.