Volume 13 | Issue 4

 Abstract

Nowadays, people are interested in hair preparations and conditioner materials, such as shampoos, hair tonic and conditioner formulations containing herbal extracts. The Aim of present study involves preparation of herbal hair tonic by using Fenugreek leaves, Tulsi leaves, Hibiscus flower, Curry leaves and coconut oil, Almond oil, Clove oil, Eucalyptus Using the Direct Method, oil and a crude medication mixture were made into herbal hair tonic, and their antifungal and antibacterial properties were assessed. Batches F1, F2, and F3 with varying concentrations are used to construct the final preparation of these substances. F1, F2, F3 formulation is tested for antibacterial and antifungal activity. The formulation of different concentrations was characterized for analysis including Moisture content, Acid value, Saponification value, Specific gravity. The formulation exhibits favorable organoleptic qualities, no grittiness, good consistency, good spread ability, homogeneity, appearance, and pH, as well as good antifungal and antibacterial activity. Result was determined and are reported in this work.

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Herbal formulations, anti-fungal activity, anti- bacterial activity, hair tonic, coconut oil and almond oil

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This paper presents the design, development, and evaluation of an intelligent mock interview platform aimed at improving career readiness through real-time, AI-driven simulations. Leveraging Google's Gemini (PALM) large language model, the system dynamically generates domain-specific interview questions and evaluates user responses based on contextual relevance, fluency, and delivery. Built using JavaScript, Tailwind CSS, Firebase, and WebRTC, the platform delivers an immersive, role-based experience simulating real-world interviews. Users select a job role to receive customized sessions, during which their audio or video responses are analysed in real time. Gemini's semantic understanding enables automated feedback across articulation, content depth, confidence, and role alignment, with session data stored securely in Firebase for review and iterative learning. The architecture comprises a user interface layer, an AI services layer powered by Gemini APIs, and a data services layer. Evaluation with 75 participants from fields such as software engineering, marketing, and data science showed a 38% improvement in performance over three sessions, validated through both automated and expert assessments. User feedback emphasized the platform's realism and adaptability. This study demonstrates the effectiveness of large language models in enhancing communication skills and proposes future enhancements including emotion analysis, multilingual support, and longitudinal impact studies.

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Mock Interview, Gemini PALM, Real-time Feedback, Career Development, Audio-video AI, Firebase, Google Gemini PALM, AI-based Interview Training, Job Interview Practice Platform

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India's competition law framework has undergone significant reforms with the Competition (Amendment) Act, 2023, and new regulatory updates that aim to improve penalty structures, streamline merger control, and strengthen enforcement. These changes indicate a shift toward improved dispute resolution procedures, increased regulatory transparency, and stricter control of anti-competitive activities. These reforms align India's competition legislation with worldwide best practices by encouraging fair competition and protecting consumer interests, but they also raise new compliance concerns for businesses. Among the significant changes that will guarantee speedier and more comprehensive regulatory oversight are the Deal Value Threshold (DVT) for M&A transactions, a revised definition of "control," and shortened merger review timelines. The 2024 CCI regulations also introduce settlement and commitment mechanisms, revised turnover-based penalties, and formal recognition of 'hub and spoke' cartels, expanding the scope of anti-competitive conduct enforcement. This paper explores the key regulatory developments, their broader implications for market participants, and the evolving role of the Competition Commission of India in shaping a competitive and transparent economic environment.

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 Keywords

Competition Law, Antitrust Regulation, Market Competition, Merger Control, Abuse of Dominance

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Listened fed people face important challenges in the urban environment, as it is an inability to detect important hearing signs such as vehicle horns, emergency sirens and other traffic -related sounds. Navigating in busy roads without awareness of going to vehicles leads to a serious security risk that limits their dynamics and freedom. To solve the problem, the project suggests an IoT-based Help Smartwatch designed to detect real-time vehicle sounds and provide intuitive alerts through light signals and vibrations.The system includes a sound detection sensor, an ESP32 microcontroller and a machine learning-based classification models, which accurately identify the sound of the vehicle when filtering the background noise. A hybrid CNN- SVM model is used to ensure accurate sound classification, and separates a variety of traffic sounds such as car horns, ambulance sirens and heavy vehicle engines. When you detect a possible danger, the smart watch activates LED indicators and vibrates with different intensity depending on the urgency of the detected sound.

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IoT, Assistive Technology, Hearing Impairment, Smart Wearables, Sound Detection, Vibration Alerts, Urban Safety.

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The safety of railway transportation systems largely depends on the condition of the tracks. Track damages, especially cracks, can result in severe operational hazards if not detected in time. Traditional inspection techniques often fall short due to their manual nature and inefficiency in covering long distances. With the emergence of image processing and sensor-based technologies, several automated systems have been developed for crack detection. This paper presents a detailed overview of recent innovations in this field, including methods involving LED-LDR sensors, image analysis, infrared and ultrasonic sensors, and data analytics. Each method's architecture, effectiveness, and real-world feasibility are discussed to aid future implementations in railway infrastructure maintenance. Automated crack detection systems significantly reduce the need for human intervention and enable continuous monitoring. These systems offer high accuracy, reliability, and the capability to operate under varying environmental conditions. Sensor-based techniques such as ultrasonic testing and infrared thermography provide real-time data on track integrity, while image processing algorithms enable the identification of micro-cracks that are otherwise difficult to detect. Deep learning and machine learning models further enhance the precision of detection systems by learning from large datasets and identifying complex patterns. The integration of GPS and IoT modules allows for real-time tracking and remote monitoring, further optimizing maintenance schedules and resource allocation. Several experimental and commercial systems have been reviewed, highlighting their design considerations, implementation challenges, and cost-effectiveness. A comparative analysis of different detection techniques is also provided to evaluate their suitability across diverse railway environments. The paper emphasizes the need for hybrid systems that combine multiple technologies for improved accuracy and robustness. Future research directions include the development of lightweight, energy-efficient systems and the integration of AI for predictive maintenance.

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IOT

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The quick demand for electric vehicles (EVs) results in the development of effective, environmentally friendly, and user-friendly charging infrastructure necessary. This study investigates the design and optimization of an EV wireless rapid charging system that uses solar electricity and state-of-the-art wireless power transfer (WPT) technology. Photovoltaic (PV) panels serve as the main energy source in the suggested system, which also includes a high-frequency inductive coupling for wireless charging and an effective power management unit. To improve system performance, a multi-layered optimization technique is used, with a particular emphasis on three important areas: overall system reliability, wireless transmission efficacy, and solar energy harvesting efficiency. Energy conversion efficiency, charging time, system losses, and operating cost are examples of key performance metrics. The findings show that the suggested system can achieve high energy efficiency while lowering reliance on grid electricity with the right optimization, supporting green transportation alternatives. The system's wireless nature also improves user convenience and safety, which makes it appropriate for installation at highway charging stations and smart cities.

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 Keywords

Wireless Power Transfer , Power Management Unit , Solar Powered , Electric Vehicles

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Most of us spend a large part of our lives at work, so it plays a big role in our overall well-being. Because of this, it's very important to have a job that supports both our mental health and general health. Workplaces that support employees' mental health and help those going through tough times usually see their teams work better and get more done. Mental health refers to how someone thinks and feels about themselves and their life. It also includes a person's ability to do their best and stay connected with family, friends, coworkers, and their community (Mental Health Foundation, 2008). Today, one of the main reasons people struggle at work is because of mental health challenges caused by their job. Issues like anxiety,stress and depression are becoming more common in the workplace. In fact, depression is expected to become the second most common health issue in the world, right after heart disease (Murray & Lopez, 1996). This paper aims to explore and highlight the key factors--based on existing research--that are most likely to affect mental health at work.

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mental health, workplace, employees, anxiety, depression

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Attitudes towards different hair, such as hairstyles and hair removal, vary widely across different cultures and historical periods, but it is often used to indicate a person's personal beliefs or social position, such as their age, sex, or religion. As hair is considered as barometer of beauty each one is over conscious about it and is in search for a better remedy for the maintenance and management of their crowning glory. Alopecia areata is characterized by acquired skin aliment that can affect all the hair bearing skin including scalp, beard, which is characterized by localized areas of non-scarring hair loss which can be compared with Indralupta. In Ayurveda literature Acharyas had briefly explain hair and mentioned many more modalities to treat hair loss. Nidana Parivarjana, Prachanna, Shamanoushadhi and externally use of Lepa. Prachanna Karma is a specialized form of Raktamokshana (bloodletting) in Ayurveda, primarily utilized for expelling vitiated blood and treating localized disorders. Indralupta, which according to modern goes with the name as alopecia, a condition characterized by sudden hair loss, is often associated with vitiation of Pitta and Kapha Doshas, leading to obstruction of hair follicles. This research explores the effectiveness of Prachanna Karma in the management of Indralupta, its historical references, procedural methodology, and modern clinical relevance. A thorough review of Ayurvedic texts, along with contemporary findings, is presented to establish the efficacy of this treatment.

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Alopecia areata, Indralupta, Ayurveda, Prachaanakarma, Lepa.

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As we know, many people suffer from unhappiness in today's world, so there is a requirement for a system that recommends music based on human emotions such as anger, sadness, happiness, etc. In this paper, we propose a system named Multimodal Emotion Detection which helps people by suggesting music and movies based on their emotions. We implemented this system using the FER- 2013 dataset, which contains 35,887 images of different emotions such as sadness, happiness, neutral , surprise, disgusted, fearful, and anger. We used Python's TensorFlow framework and Haar Cascade algorithms to recommend movies and music based on human facial emotions. The main concern in existing recommendation systems is manual sorting. To avoid this, we propose this model, which automatically plays music and movies without requiring much browsing time.

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Image processing, Facial recognition, Movie Recommendation, Song Recommendation . 2 [1-2]

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Emotional stability plays a vital role in personal well-being and everyday functioning. This research explored the use of data analytics to evaluate and comprehend emotional stability, concentrating on psychological, behavioral, and environmental elements like stress. By examining survey responses, communication trends, and psychological evaluations, the study compared the reliability of the Cronbach and Omega methods. Gated Recurrent Unit (GRU) models were employed to forecast stress levels among student groups. The research developed predictive models for outcomes such as overall emotional stability, the risk of mental health problems, academic satisfaction, perceived social support, and stress levels. The analysis aimed to identify significant connections between input features and predicted outcomes, assess model performance using relevant metrics, and determine the importance of individual attributes in predicting students' well-being. It also offers valuable insights into the quantitative measurement of emotional stability and its variations in different situations.

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Cronbach's Alpha, Omega method, Psychometric Analysis, RNN, LSTM,GRU.

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Databases store and manage critical data, making them prime targets for security threats. various security issues [1], such as SQL injection, unauthorized access, insider threats, and data breaches, pose significant risks to database integrity and confidentiality. Additionally, improper encryption, weak authentication mechanisms, and inadequate access controls can further expose databases to cyberattacks. To mitigate [3] these threats, organizations implement security measures such as role-based access control (RBAC), multi-factor authentication (MFA), and encryption techniques for data at rest and in transit. Firewalls, intrusion detection systems (IDS), and regular security audits help identify and prevent unauthorized access. Moreover, database activity monitoring (DAM) and the use of secure coding practices minimize vulnerabilities like SQL injection. This seminar discusses various security challenges in databases and explores modern solutions, including blockchain-based security [2], zero-trust architecture, and AI-driven anomaly detection. By implementing robust security strategies, organizations can safeguard their databases against evolving cyber threats and ensure data privacy, integrity, and availability.

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Database security, SQL injection, unauthorized access, insider threats, data breaches, encryption, weak authentication, access controls, RBAC, MFA, IDS, firewalls, security audits.

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Neck pain and disability are prevalent in HNC patients, which can be attributed to the diagnosis itself or its treatment. Neck pain and associated disability may cause psychological distress and subsequently decrease the patients' QoL. This longitudinal prospective study has been undertaken to assess neck pain and related functional and psychological concerns and determine the QOL among HNC patients receiving anti-cancer treatment. 35 head and neck cancer patients undergoing cancer treatment will be enrolled in the study. They will undergo a baseline evaluation before the initiation of treatment and will be followed up at 1st month, 2nd month, and 3rd month.

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Anxiety, depression, head and neck cancer, neck disability, neck pain, QOL

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Heart disease remains one of the leading causes of mortality worldwide, necessitating early detection and accurate prediction to improve patient outcomes. Traditional diagnostic methods often require extensive medical expertise and expensive tests, leading to delays in diagnosis. Machine learning (ML) techniques offer a promising solution by analyzing vast datasets to identify patterns indicative of heart disease. This study explores the application of various ML algorithms, including Logistic Regression, Decision Trees, Random Forest, and Support Vector Machines, to predict heart disease based on clinical parameters such as blood pressure, cholesterol levels, and lifestyle factors. The dataset used for this research is sourced from reputable medical repositories, ensuring reliability and robustness. The proposed model achieves high accuracy in disease classification, showcasing the effectiveness of ML-driven predictions. The findings highlight the potential of machine learning in aiding healthcare professionals with early detection, improving treatment plans, and reducing death rates. (CVDs) account for approximately 17.9 million deaths each year, making them the leading cause of mortality worldwide. Early detection and prevention are crucial in reducing the fatality rate and improving patient survival. Conventional diagnostic approaches, such as electrocardiograms (ECG), echocardiograms, and angiograms, require specialized equipment and expertise, making them less accessible in many regions. In recent years, machine learning has emerged as a powerful tool in the healthcare domain, capable of analyzing complex medical data to identify patterns and predict diseases with high accuracy. By leveraging ML algorithms, healthcare professionals can improve diagnostic precision, automate risk assessment, and provide personalized treatment recommendations. This study focuses on applying various machine learning techniques to predict heart disease using clinical datasets. The primary objective is to develop a model that can accurately classify individuals at risk of heart disease based on key health indicators, including age, cholesterol levels, blood pressure, diabetes status, and lifestyle habits.

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Angiograms, Blood Pressure, Decision Tree, Electronic Health Records, ECG, FBS, Heart Disease Prediction, Logistic Regression, Machine Learning Models, Random Forest, Support Vector Machines.

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The present study was aimed at diabetic studies with orally administered aqueous leaf extract of Ocimum sanctum Linn (OSE). In this study four groups of mice (n=6/group) were tested, in which the first group the normal group, the second group diabetic control group was injected with 70 mg/kg bw alloxan, and the third and fourth groups of diabetic mice were orally treated with doses of 100 and 200 mg/kg bw of O. sanctum for 28 days, and a blood glucose study was taken. Treatment with OSE did show some changes in their blood glucose.

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O. sanctum, Antidiabetic, Alloxan, Glucose , Aqueous, Antioxidant

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A straightforward framework for creating wireless network protocols incorporates localized broadcasting and routing. The basic foundation is predicated on the cost-benefit ratio of specific choices, such cutting back on the number of distances. In this wireless network application, one of the main networks is the routing protocol. These are some of the most often used routing strategies in network layers. In the distributed routing in every node determines if routing through a certain neighbor is less expensive than using nodes that are already in use.

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Packet Delivery, Routing Protocols, Network Layer, IPv4, IPv6, ICMP, RIP, and Network Efficiency

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Artificial intelligence (AI) within financial technology (FinTech) platforms creates radical changes in customer-related activities and operational performance while adjusting service delivery practices. This research examines AI-based service solutions that affect user interactions through evaluations of service quality as well as trust and personalization, and sustainability. A quantitative research method involved gathering survey information from people who actively use FinTech platforms that incorporate artificial intelligence technologies. Through AI technology, financial service providers enhance customer satisfaction by simplifying delivery systems and providing better ease-of-use options. The level of trust users have in AI encounters problems because of their worries about data protection, together with their need to understand how algorithms work. The research demonstrates why FinTech companies must use AI properly through visible system operations along with security protocols and adherence to rules. The research links technical innovation with user expectations to supply FinTech companies with tactics that optimize Artificial Intelligence applications for better interactions with customers. This study advances the financial service AI knowledge base by presenting applicable solutions for sustainable ethical development of FinTech innovation.

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The analysis incorporates Artificial Intelligence, FinTech, customer Experience and Service Quality, User Perception, Convenience, and Sustainability as important terms.

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This paper rethinks translation studies through an ecological framework, proposing an integrated approach that considers language, culture, and environment as mutually constitutive systems. It moves beyond the traditional source-target text dichotomy to examine how translations function within broader ecological networks of meaning, power, and survival. While translation studies have made significant strides in various interdisciplinary directions, a notable vacuum persists in addressing environmental literature, particularly at the theoretical level. This gap is critical given the rising global concern for sustainability and ecological consciousness. The paper undertakes a critical survey of how environmental thinking has evolved within translation discourse and identifies emerging paradigms that align translation practice with ecological ethics. By highlighting the necessity of an ecologically informed translation theory, and calls for a reorientation of translation studies toward sustainability, responsibility, and interconnectedness.

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Eco-translation, Ecolinguistics, Translation Ethics, Sustainability, Environmental Humanities

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The aim of the present study is to investigate mathematical mistakes related to definite integration committed by Yashwantrao Chavan Polytechnic students at First Year Diploma Level in their academic. For this study 69 students from first year diploma classes were chosen randomly. The samples of this study were collected from the test attempted by the participants. The following are the primary difficulties that students face while solving definite integration problems; didn't get the basics, had trouble choosing how to solve, relied on memory, didn't do enough practice, time was a problem

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Definite Integration problems, Problem-Solving, Conceptual Understanding, Time Management.

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In India Parliamentary Privileges have become the "Democles swords" which hang over those whose business it is to write on proceedings of the legislatures. The Non codification of privileges causes confusion and violates the freedom of speech and expression. There are several instances after independence of India, It has been seen that parliamentary privileges has been misused for example: In 2017, two senior scribes of Kannada tabloids were jailed for the breach of privileges of the state assembly. Other instances is The Blitz case (1951) The search light case (1959), Kesav singh case and the hindu case (2003) etc. The concept of parliamentary privileges has been taken from UK but it is quite incumbent to know that the situation of UK is very different. Firstly in UK Parliament is Sovereign but in India Sovereignty lies in the people of India, In Britain Law can be enacted through simple majority only and there is no as such judicial review but In India Judicial review is the basic structure of the constitution and there are certain laws enacted through special majority and special majority with ratification. Through this research paper Researcher is going to analyse the concept of Parliamentary Privileges of India, UK, Australia and New Zealand.

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Privileges of Member of Parliament in India: A comparative Study with UK, Australia, New Zealand and Canada.

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 Abstract

Suryabala Ki Kahaniyo Me Nari Savedna : Ek adhayan

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Suryabala Ki Kahaniyo Me Nari Savedna : Ek adhayan

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