Ramakrishna Mission Vidyamandira

The development of a three-axis delta robot, designed with servo motors for high-precision sorting tasks in pick-and-place operations, is presented in this paper. The system integrates at ailored Inverse Kinematics (IK) method and utilizes Model Predictive Control (MPC) to enhance motion control and trajectory accuracy. A comparative analysis of standard kinematics and the proposed IK model is conducted to evaluate improvements in precision. The robot is represented in the Robot Operating System (ROS) through a Unified Robot Description Format (URDF). MPC’s real-time testing underscores improved motion stability, diminished error rates, and appropriate ness for high-velocity industrial settings. The results underscore the promise of this delta robot design to fulfill the increasing demands for high-speed, high-precision sorting and handling tasks in contemporary manufacturing and logistics settings.

Background: The study of ultra-high diluted (UHD) solutions is a compelling and controversial domain in modern science. Despite numerous investigations and hypotheses, there is a lack of rigorous, reproducible empirical evidence supporting the existence of molecular footprints beyond the Avogadro's limit. Objective: This study conducted a detailed investigation of the molecular and electrical properties, focusing on hydrogen bonding and impedance to understand the fundamental nature of UHD solutions. Materials and methods: Combined impedance and infrared spectroscopic investigations were performed to probe the structural behaviour of relevant water networks at the molecular level of UHD alcoholic extract of Arnica montana (AM) and Rhus toxicodendron (RT). Results: At higher potencies (200C and 1M) of both AM and RT, an interesting correlation was observed between impedance and spectral characteristics. An increase in impedance was observed with potencies for both AM (93.9%) and RT (49%), which was attributed to changes in the hydrogen bonding network and molecular reorganisation during potentisation. This correlates with the Mid-IR spectra, showing increased hydrogen bond length and potential weakening of inter-molecular hydrogen bonds with increasing potency. Also, Far-IR spectra indicate a contrast in the system's enthalpic gain with increased potencies for both AM (29.8%) and RT (65.2%). Conclusion: Distinct variations in spectral and electrical parameters have been observed for AM and RT due to potentisation, suggesting that these solutions retain structural and molecular traits despite extreme dilutions. This notion may revisit traditional homoeopathic treatment approaches by addressing conflicting paradigms regarding the presence of molecular footprints in UHD solutions.

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).

Water pollution has become a critical environmental concern due to the discharge of industrial, agricultural and domestic wastewater, which demands sustainable and cost-effective treatment solutions. Phytoremediation is an emerging green technology. It offers an eco-friendly alternative by utilising plants to absorb, accumulate and degrade contaminants from polluted water. This study examines the efficiency of Ipomoea aquatica (Water Spinach) in treating wastewater through a vertical flow constructed wetland (VCW) system at various hydraulic retention times (HRT). Qualities such as rapid growth, high biomass production, and the ability to remove excess nutrients, organic pollutants, and heavy metals from contaminated water sources make it a valuable tool for treating wastewater. The study shows the incorporation of Ipomoea aquatica inside a VCW system for wastewater treatment. Water quality parameters like TDS, TSS, pH, DO., COD, BOD, Total Hardness, Chlorides, and Nutrients (Sulphate, Phosphate, Nitrate, Ammoniacal Nitrogen) were examined on retention of 3HRT, 6HRT, and 9HRT, respectively. Hydraulic retention time (HRT) of 9 days showed best reduction in sewage wastewater of 59.7% TDS, 74.4% TSS, 98.1% BOD, 92.4% COD, 80.0% NH₄⁺–N, and complete removal of NO₃–N while in dairy effluent, removal efficiencies were 16.8% for TDS, 50.2% for TSS, 90.9% for BOD, 91.1% for COD, 60.5% for NH₄⁺, N, and 79.9% for NO₃–N. Research findings indicate that the application of Ipomoea aquatica in VCW enhances water quality and promotes environmental sustainability. This research highlights the potential of combining phytoremediation with constructed wetlands as an environmentally responsible wastewater treatment system, thus harnessing the natural remediation qualities of Ipomoea aquatica.

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.

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.

Abstract Cloud computing isn’t slowing down anytime soon, and that means we need bigger, more powerful data centers to keep up with all the processing, storage, and networking we rely on every day. The catch? These centers burn through a massive amount of electricity. That jacks up bills and makes people worry about the environmental impact. So, making sure we use less energy when scheduling tasks has turned into a real priority. The challenge is to squeeze as much as we can out of these resources without letting energy use spiral out of control—or letting service quality slip.That’s where our study comes in. We built an energy-aware task scheduling framework designed just for cloud setups. The system looks at workloads, chooses hosts in a smarter way, consolidates virtual machines on the fly, and actually models power use to bring it down. We split the whole thing into three parts: monitoring, scheduling, and resource management. This keeps the framework nimble and scalable, even if demand keeps growing.When we put everything through its paces, the results spoke for themselves. Our model dropped energy consumption, improved resource use, and kept the whole system reliable—even as the workload shifted around. Bottom line: if we want cloud computing that’s greener and more sustainable, energy-aware scheduling isn’t just nice to have; it’s essential. Keywords Cloud Computing, Energy Efficiency, Task Scheduling, Virtual Machine Consolidation, Green Computing, SLA Management.

This study illustrates the solution combustion production and characterization of LaMnO3 perovskite powders for use in solar cell applications. Using precursors based on La and Mn, LaMnO3 powders were created using the solution combustion process with two distinct Reducing agents (Citric acid and Glycine). The optical, structural, and microstructural characteristics were assessed using Raman spectroscopy, Fourier transform infrared, X-ray diffraction, scanning electron microscopy, Ultraviolet Visible spectrometer, and high-resolution transmission electron microscope. The bandgap for both successfully synthesized LaMnO3 and the different reducing agents was found to be 1.7 eV. The investigation showed that the heat of synthesis affects the crystalline and particle sizes as well as optical characteristics. The results show potential for the use of LaMnO3 powders in solar cell applications, particularly due to their narrow bandgap, which can improve efficiency and performance.