Volume 14 | Issue 5 |

 Abstract

The study focuses on the formulation and evaluation of a compact herbal face pack designed to enhance skin glow and maintain overall skin health. The formulation utilizes natural ingredients such as Neem, tulsi, red lentils, honey, rice which are known for their cleansing, exfoliating, and rejuvenating properties. The ingredients were blended in specific proportions to produce a fine, compact, and easy-to-use powder form that can be activated with rose water or milk before application. The prepared formulation was evaluated for various parameters including organoleptic characteristics, pH, spreadability, smoothness, irritancy test, and stability studies. The results indicated that the formulated face pack exhibited good homogeneity, pleasant fragrance, smooth texture, and an ideal pH suitable for all skin types. It was found to effectively remove excess oil, impurities, and dead cells, thereby improving skin brightness and softness. The study concludes that the developed compact face pack provides a safe, natural, and effective alternative to synthetic cosmetic formulations for glowing skin.

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 Keywords

Compact face pack, herbal formulation, glowing skin, neem, orange peel , sandalwood, multani mitti natural cosmetics, skin care, evaluation, stability study.

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 Abstract

bharatiya shastriya sangeet ka bhavishya

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bharatiya shastriya sangeet ka bhavishya

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Metacognitive awareness represents a critical competency for effective teaching, enabling educators to reflect on their thinking processes and instructional decisions. This research investigates the levels and patterns of metacognitive awareness among pre-service teachers and explores implications for developing reflective teaching practices. Through a mixed-methods study involving 320 pre-service teachers across four teacher education institutions, this research employed the Metacognitive Awareness Inventory (MAI) alongside qualitative reflective journal analysis. The findings reveal that pre-service teachers demonstrate moderate overall metacognitive awareness (mean score 3.42 on a 5-point scale), with significant variations across different metacognitive dimensions. Knowledge of cognition scored higher (3.68) than regulation of cognition (3.21), indicating stronger theoretical understanding than practical application of metacognitive strategies. Notably, 62% of participants showed weak connections between metacognitive awareness and actual teaching practice during field experiences. The research identifies critical gaps in procedural and conditional knowledge that hinder reflective teaching development. These findings have important implications for teacher education programs, suggesting the need for explicit metacognitive instruction, structured reflection protocols, and authentic teaching contexts that promote metacognitive strategy application. This study contributes to understanding how metacognitive competencies develop among novice teachers and provides evidence-based recommendations for strengthening reflective practice in teacher preparation.

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Metacognitive awareness, pre-service teachers, reflective practice, teacher education, knowledge of cognition, regulation of cognition, teaching strategies

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 Abstract

In recent years, generation cost and fuel emission have been essential to minimize to sustain on the planet. Fossil fuel has been enormously utilized for power generation for a long time. It is required to manage the demand of customer and efficiently utilize the fossil fuels. The economic load dispatch problem has been considered to solve the crisis of conventional energy resources. So here, a novel stochastic optimization approach to solve constrained economic load dispatch problem using hybridization of Particle swarm optimization. This review paper shows that by applying PSO algorithm the power demand can be successfully met at minimum generation cost incurring minimum transmission loss.

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 Keywords

ELD - Economic Load Dispatch, PSO - Partical Swarm Optimization

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Sciatica can cause pain and discomfort in the lower back or lumbar spine, buttocks, and legs. This back pain makes it difficult to engage in everyday activities. However, practicing specific yoga poses can help relieve the symptoms, soothe nerve pain, and improve your overall well-being. In this article, we will guide you through a series of beginner-friendly yoga exercises designed to target sciatica, allowing you to find relief and enhance your flexibility and strength. Let's dive into the world of yoga and discover the power it holds in alleviating sciatic pain.

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Keywords : Ayurveda ,Yoga , Iyenger props

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Social media platforms, which offer communication, entertainment, and educational opportunities, have become an indispensable element of students' everyday lives. However, overuse has led to social media addiction, which has a detrimental impact on social interaction, productivity, mental health, and academic achievement. This study examines usage patterns, behavioral trends, screen time, academic impact, and emotional repercussions of social media addiction among teenagers using data analytics. Addiction levels and their impact on student performance are determined using statistical and machine learning methods. The study emphasizes how extended use of social media can lead to anxiety, poor sleep, decreased focus, and poor academic performance. The suggested analysis shows how data analytics may be used to identify addictive behavior and offers preventative strategies for kids to use social media in moderation.

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Social Media Addiction, Data Analytics, Student Behavior, Machine Learning, Academic Performance, Mental Health, Big Data

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Traditional healthcare and supply chain systems operate independently and rely heavily on manual processes, resulting in poor coordination, limited visibility, delayed decision- making, and inefficient resource utilization. In the healthcare sector, this leads to problems such as appointment congestion, lack of remote consultation facilities, delayed access to medicines, and inefficient management of medical records and supplies. Existing systems also lack intelligent support for predicting demand, managing inventory, and monitoring service usage patterns. To address these challenges, this project proposes an integrated digital platform that combines a telemedicine system with an AI- enabled digital supply chain framework. The telemedicine module allows patients to book appointments, consult doctors remotely, receive digital prescriptions, and access medical reports through an online platform. At the same time, artificial intelligence techniques are applied to analyze healthcare usage data, appointment trends, and medicine demand to support better planning and decision-making. AI-based analysis helps in predicting service demand, identifying resource shortages, and improving the availability of medical supplies and services. Compared to the existing system, the proposed solution reduces manual workload, improves accessibility to healthcare services, enhances decision accuracy, and enables efficient coordination between healthcare delivery and supply chain operations. The project demonstrates how integrating telemedicine with AI-driven digital supply chain concepts can improve healthcare accessibility, operational efficiency, and overall system reliability.

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 Keywords

Digital Supply Chain, Telemedicine Platform, Artificial Intelligence, Healthcare Management System, Demand Forecasting, Inventory Optimization, Digital Healthcare.

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 Abstract

The implementation of car damage detection automation has become a pivotal development in the automotive industry, offering substantial improvements in safety, efficiency, and cost-effectiveness. This paper provides a thorough insight into the progress and methodologies associated with automating the car damage detection process, leveraging a variety of technologies including deep learning, computer vision, and transfer learning. Conventional approaches to car damage assessment frequently hinge on manual inspections conducted by experts, introducing subjectivity, time inefficiencies, and the possibility of human errors. To address these challenges, automated systems incorporating computer vision, machine learning, and artificial intelligence methodologies have been devised. These systems amalgamate image processing algorithms and deep learning models to effectively discern and categorize various car damages, including dents, scratches, cracks, and structural deformations. The integration of advanced Convolutional Neural Networks (CNNs) enhances the system's ability to detect even minor imperfections under varying lighting and environmental conditions. Three CNN architectures -- MobileNet, ResNet50, and InceptionV3 -- are evaluated and compared on a curated vehicle damage dataset. Results demonstrate that ResNet50 achieves the highest validation accuracy of 94.1%, significantly outperforming manual inspection benchmarks. Cloud-based deployment and mobile integration further extend the system's practical reach, enabling real-time damage evaluation for insurance companies, repair shops, and individual vehicle owners.

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Deep Learning

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 Abstract

Globally, road accidents have been among the major causes of death and severe injuries, which has been a major challenge to the mode of transport and the safety of the people. The driver fatigue has been cited as one of the major causes of these accidents and this may be underrated. Fatigue causes a loss of alertness to a driver, decreases his or her reaction time, deteriorates judgment and predisposes the driver to making errors. Other factors like travelling long distance, lack of sleep, abnormal working hours, and tedious driving conditions further increase drowsiness, which causes the drivers to be at risk of accidents.To adequately deal with this urgent problem, this project suggests the development of a highly-developed Driving Management System which is expected to improve the road safety by continuously and real-time monitoring of the condition of the driver. The system employs sensor-based technologies and intelligent methods of monitoring to monitor physical and behavioral patients of fatigue. These parameters are the rate of blinking of the eyes, the time of closing of the eyes, the pattern of movement of the head (like nodding) and the facial expression. The system is able to identify these indicators in order to alert against early cases of drowsiness before it turns into a hazardous caseThe main characteristic of the suggested system is that it is able to offer instant feedback and warning systems. As a signal of fatigue is detected, the system automatically sets off warning systems like buzzers, vibrations or audio messages to capture the immediate attention of the driver. This is a proactive measure, which assists in avoiding accidents through ensuring that corrective measure is undertaken at the appropriate time.The system is also meant to be inexpensive, user friendly, and easily adaptable to the needs of various kinds of vehicles such as the public transport (buses), commercial vehicles (trucks, taxis) and the personal cars. It is an easy to install, and effective to use solution and thus can be used in practical implementations. Future technologies like IoT and AI can also be combined with the system to be more precise and intelligent in data analysis.In sum, the suggested Driving Management System can play a vital role in ensuring the safety of the road setting, reducing the number of risks associated with fatigue, increasing the driver awareness, and protecting the lives of the drivers, passengers, and pedestrians. This project reiterates the need to incorporate transport systems with technology to make the driving experience a safer and more reliable one

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 Keywords

Driver Fatigue Detection, Driving Management System, Road Safety, Drowsiness Monitoring, Automatic Alert System, Sensor-Based Technology, Real- Time Monitoring.

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Creative Commons Attribution 4.0 and The Open Definition

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The present research was conducted with the objective of understanding and assessing the emotional intelligence of secondary-level students. Through this study, an attempt was made to determine how students understand and control their emotions and how they behave toward the emotions of others. Under this, the emotional intelligence of the students was assessed through standardized tools and questionnaires included in them. For the study, 200 students were selected from secondary schools in the district of Lucknow. The researcher used the Emotional Intelligence Scale developed by A. K. Singh and Shruti Narain, which is a standardized tool, to measure the emotional intelligence of the students. In this study, the survey method was used, and the results revealed that students with a higher level of emotional intelligence appeared more balanced in their behavior, relationships, and decision-making. On the other hand, students with comparatively lower emotional understanding faced difficulties in stress management, anger control, and adjustment. The study concludes that family, school environment, peer group, and teachers’ behavior influence the emotional intelligence of students. If students are provided with proper guidance, support, and opportunities to express their emotions, their emotional intelligence can be further developed.

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 Keywords

Education, Holistic Development, Emotional Intelligence, Self-Control, Guidance.

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Creative Commons Attribution 4.0 and The Open Definition