Volume 13 | Issue 4

 Abstract

Self-objectification refers to the tendency to evaluate oneself based on external appearance. At the same time, integrated self-criticism involves internal negative self-evaluations, and self-reassurance reflects the capacity to comfort oneself during distress. This study examines the impact of these three variables on the psychological well-being of amateur and non-dancers. A total of 200 participants aged 20-35 were involved, including 100 amateur dancers and 100 non-dancers. The Self Objectification Beliefs and Behaviors Scale (Lindner & Tantleff-Dunn, 2017) was used to measure self-objectification, the Forms of Self-Criticizing/Attacking and Self-Reassuring Scale (Gilbert et al., 2004) assessed self-criticism and self-reassurance, and the Psychological Well-Being Scale (Ryff, 1995) evaluated various dimensions of well-being. Statistical analysis using Pearson's correlation revealed significant associations between self-objectification and psychological well-being, indicating that higher self-objectification is linked to lower well-being. Additionally, independent t-tests were conducted to compare amateur dancers and non-dancers on self-objectification, self-criticism, self-reassurance, and well-being, revealing that non-dancers reported higher overall well-being. Regression analysis further demonstrated a significant interaction effect of self-objectification, integrated self-criticism, and self-reassurance on psychological well-being, showing that these factors collectively influence well-being differently in each group. This research offers a deeper understanding of the complex relationship between artistic self-perception and psychological health.

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Keywords: self-objectification, integrated self-criticism, self-reassurance, Psychological Well-Being Scale, amateur dancers, non-dancers.

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The automobile business has seen tremendous dis- ruption since the introduction of electric vehicles (EVs), which provide a sustainable substitute for traditional internal com- bustion engine automobiles. However, because EVs are more closely linked to digital technologies, they are more vulnerable to hacking. These attacks compromise the reliability, efficiency, and safety of EVs, putting EV owners and their vehicles in grave peril. This article looks at how machine learning techniques can be used to identify and reduce cyberattacks on electric vehicles. the fusion of unsupervised techniques like autoencoders and isolation forests with supervised machine learning algorithms like Random Forest and Support Vector Machine (SVM). A significant amount of data from different EV components, control units, and communica- tion networks will be gathered and analyzed. The multi-layer detection framework improves the accuracy and dependability of cyberattack detection by utilizing the advantages of each technique. While unsupervised algorithms use anomaly detection to identify new or emerging threats, supervised algorithms are trained on labeled datasets to classify current types of cyber- attacks. The suggested model's performance is assessed using a real dataset. This study emphasizes how important machine learning is to protecting the future generation of electric vehicles from new threats and guaranteeing their safe and reliable operation.

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 Keywords

electric vehicles, machine learning, artificial intelligence, cybersecurity, authentication, and protection.

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This research explores the culture, social structure, and transformation of the Nicobarese tribe, who reside in the Nicobar Islands. The paper examines the tribe's traditional practices, including food, language, religion, and social organization, alongside the impact of modernization and external influences. A significant focus is placed on the effects of the 2004 Indian Ocean tsunami, which displaced communities and introduced new governance and development efforts. The study highlights the tribe's unique food practices, especially the ceremonial pig feast, and discusses the challenges faced by the Nicobarese as they balance traditional lifestyles with modern development. Through secondary sources such as government reports and ethnographic studies, the paper evaluates the shifts in their economy, language, and cultural practices, while raising concerns about the loss of cultural identity. The research advocates for development policies that respect the Nicobarese tribe's traditions, encouraging a balance between modernization and the preservation of their cultural heritage.

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 Keywords

Nicobarese Tribe, Indigenous Communities, Tribal Culture, Andaman and Nicobar Islands, Tribal Transformation, Post-Tsunami Development, Tribal Food, Sustainable Development, Community Identity

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 Abstract

Agriculture forms the backbone of India's economy, with a significant portion of the population reliant on it for livelihood. However, despite its importance, farmers face numerous challenges in optimizing crop selection and fertilizer management to maximize yield rates. This project addresses these challenges by implementing an intelligent system aimed at assisting farmers in making informed decisions regarding crop selection and fertilizer usage. The project also focuses on user interface development, incorporating a user-friendly interface tailored for farmers' ease of use. Through comprehensive testing, validation, and continuous improvement efforts, the system aims to revolutionize agricultural practices, ultimately contributing to increased crop yields, reduced input costs, and improved livelihoods for farming communities.

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 Keywords

Harvest Helper - Agriculture System , Centralized Database , Central Government Websites Access , Crop Recommendation , Interface Between Farmers And Digital Crop Recommendation.

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The rising demand for sustainable water management has led to increased adoption of solar water pumping systems over diesel-powered alternatives. This study analyzes the cost-effectiveness of solar pumps using MATLAB for modeling and simulation. Key cost components, including investment, operational, and maintenance expenses, are evaluated. Findings highlight significant long-term savings, reduced fossil fuel reliance, and environmental benefits. Comparative analysis shows solar pumps outperform diesel pumps in efficiency and life cycle costs. Hybrid solar-diesel configurations address energy variability for continuous operation. IoT-based monitoring enhances performance optimization and predictive maintenance. Government incentives and subsidies improve economic feasibility and adoption rates. Advancements in photovoltaic and battery storage further enhance system efficiency. Community awareness programs educate stakeholders on transitioning to renewable energy solutions.

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Pic16f887, Rectifier, Inverter, PWM, Relay, Lithium Battery.

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Vehicle black box systems play a crucial role in accident analysis and road safety enhancement by recording crucial vehicular data before, during, and after incidents. This research presents a cost-effective black box system designed for vehicles, integrating sensors, GPS, and realtime data logging to improve accident investigation and driver accountability. The proposed system continuously records vehicle speed, impact force, location, and driver behavior, ensuring valuable insights for accident reconstruction. With advancements in IoT and cloud connectivity, the system enables remote monitoring and data retrieval, aiding authorities and insurance companies in ensuring fair assessments and improved safety standards.

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Keywords--Vehicle black box, accident analysis, data logging, IoT, GPS tracking, road safety

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 Abstract

Smart Car revolutionizes autonomous mobility by offering both Auto Mode for self-driving and Manual Mode with Bluetooth control. It integrates advanced sensors for real-time obstacle, pothole, and slope detection, adjusting speed and direction for enhanced safety. Designed to be cost-effective and scalable, Smart Car ensures a seamless driving experience, making intelligent transportation accessible and reliable.

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 Keywords

Self-driving Technology , Autonomous Vehicle , Safer Mobility , Driverless Car, Intelligent Transportation Systems, Collision Avoidance , Navigation Algorithms

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With the growing demand for smart home solutions, this paper presents a versatile, scalable, and user-friendly home automation system that enables seamless control of household appliances via a combination of touchscreen, smartphone app, and IR remote interfaces. The system is based on Arduino Mega, which acts as the central controller, and utilizes a Bluetooth module for wireless communication with an Android app built using MIT App Inventor. A TFT touchscreen provides a direct, tactile interface for local control, while an IR remote offers a traditional manual control option. The system uses a 4-channel relay module to control appliances such as lights and fans, ensuring reliable and real-time response across different control methods. The aim is to enhance user convenience by offering multiple control options in one integrated solution, addressing the limitations of single interface systems commonly found in home automation. Future enhancements could include support for more devices, energy monitoring, and AI-based voice recognition.

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Arduino Mega, Touchscreen, Home Automation, Bluetooth, Relay Module.

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 Abstract

" Comparative study of the level of educational achievement in Social Science subject among students studying in class 6 in council and private higher primary schools of district Pilibhit."

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" Comparative study of the level of educational achievement in Social Science subject among students studying in class 6 in council and private higher primary schools of district Pilibhit."

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Diabetes mellitus remains a pervasive public health issue, particularly among certain ethnic groups such as the Pima Indian community. This study leverages deep learning methodologies to forecast the risk of Type 2 diabetes in Pima Indian women using the well-established Pima Indian Diabetes dataset. Employing Artificial Neural Networks (ANNs), including multilayer perception architectures, the model identifies intricate, nonlinear patterns in clinical data such as glucose levels, BMI, and age. Results indicate that the deep learning model achieves superior accuracy and robustness compared to traditional machine learning methods, underscoring its potential as a viable tool for early detection and personalized intervention strategies in diabetes.

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Diabetes,LSTM,Deep Learning,Soft Max

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Keeping track of LPG cylinder levels and ensuring safety can be a real hassle. This project tackles these issues head-on by introducing an intelligent monitoring system for LPG cylinders. The system uses a load sensor to constantly track the weight of the cylinder, providing real-time data on the remaining gas. When the gas level dips below a set point, the system springs into action, automatically placing a refill order through a connected platform. Furthermore, the system incorporates advanced gas sensors to vigilantly watch for any leaks, ensuring immediate alerts and preventative measures are taken to safeguard the surroundings. Currently, managing LPG often relies on manual checks and estimations, which can lead to unexpected depletion and potential service interruptions. Existing leak detection methods, if present, may not be automated or may lack the ability to take immediate preventative action. These limitations can result in inconvenience for users and, more importantly, pose significant safety risks in the event of a gas leak. To overcome these drawbacks, this project proposes a comprehensive system that automates both gas level monitoring and safety protocols. By continuously tracking the cylinder's weight, the system ensures timely refills, eliminating the risk of running out of gas. Moreover, the automated gas leak detection, coupled with the immediate shut-off of the regulator and power sources, along with ventilation activation, significantly enhances safety and minimizes potential hazards. This proactive approach offers users a convenient, reliable, and, most importantly, safe way to manage their LPG supply.

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 Keywords

LPG monitoring system, Gas leak detection, Load sensor, Real-time tracking, Automated refill system, Safety automation, IoT-based solution, Smart home safety, Weight-based monitoring, Regulator shut-off, Power source control, Ventilation activation.

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 Abstract

The Bright Kids Academy Website project was designed to establish a dynamic, intuitive, and interactive digital platform tailored to the educational requirements of students, parents, and educators. This document details the systematic process of designing, developing, and deploying the website, emphasizing its core functionalities, technological framework, and transformative effects on the academy's operations. Serving as a centralized resource, the website facilitates information sharing, online education, communication, and administrative management, delivering a cohesive experience for all users. Prioritizing accessibility, scalability, and security, this initiative sets a benchmark for educational institutions aiming to strengthen their digital infrastructure.

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 Keywords

Interactive Lessons, Personalized Learning, Parent & Educator, Offline Accessibility

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Underground cables are widely used in urban areas for power distribution due to their safety, aesthetic, and space-saving advantages. However, locating faults in these cables is challenging and time-consuming because the cables are not visible. This project proposes an Underground Cable Fault Locator System using a Microcontroller, which can detect the exact location of a fault in the underground cable. The system operates based on the Ohm's Law principle, where the cable's resistance is proportional to its length. A DC power supply is applied to the cable line, and in the event of a short circuit or open circuit, the voltage drop across the cable changes. The microcontroller measures this voltage drop and calculates the distance to the fault using pre-calibrated values. The core components of the system include a microcontroller, voltage sensing circuit, LCD display, and relay driver circuitry for safety control. The calculated fault distance is displayed on the LCD in real-time, helping maintenance teams quickly locate and fix the fault without unnecessary excavation

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Microcontroller, LCD Dispaly, Buzzer

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This paper explores the design and implementation of the Healthify Bottle, a smart hydration monitoring system that leverages IoT and cloud computing to promote healthier lifestyles and improve daily water intake habits. The system integrates sensors, wireless communication, and a mobile application to monitor water consumption in real time, send hydration reminders, and provide personalized insights. By utilizing technologies such as Bluetooth, capacitive touch, and cloud-based data analytics, the Healthify Bottle ensures accurate tracking, user engagement, and secure data management. Experimental results demonstrate the system's effectiveness in real-world usage, with enhanced user convenience and health awareness. This innovation not only addresses hydration-related health concerns but also lays the foundation for broader applications in smart healthcare and wellness ecosystems.

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SMART IOT

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 Abstract

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Right to privacy

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Herbal plants contain various biologically active compounds that are useful for life enhancement, and they are the The main origins of modern and conventional herbal medicine for disease prevention. The nature of different kinds of life-supporting voters of plants that have been studied by scientists for their applications. The purpose of the research was to devise an herbal mosquito repellent candle that would include lemon oil, cloves, cinnamon, lavender oil, marigold, neem, and rose water. The candle was tested for flammability, burning time, and repellency against mosquitoes. It may be attributed to the presence of the different active constituents of the oils. The test showed that the manufactured polyherbal candle had become more effective, cheaper, and nonpoisonous than the candle repellents currently available on the market based on chemicals in fighting mosquitoes.

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Mosquito Repellent Candle, Mosquitoes.

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This study explores consumer insights and preferences regarding Starbucks in Coimbatore City, Tamil Nadu. Utilizing a descriptive research design and a sample of 127 respondents, the study examines consumer demographics, behaviour, brand perception, and satisfaction. Key findings indicate a strong preference for quality coffee, ambiance, and brand experience, especially among students and young professionals. While Starbucks enjoys a premium brand image, concerns regarding pricing and limited reward program engagement were also identified. This study offers actionable insights for Starbucks to enhance its customer engagement and market positioning in emerging urban markets.

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Starbucks, Consumer Behavior, Brand Perception, Coimbatore, Coffee Chains, Customer Satisfaction, India

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The Legal Education and Assistance Portal (L.E.A.P) is designed to bridge the gap between complex legal systems and public understanding by offering accessible legal resources and tools. Through a user-centric design approach, L.E.A.P integrates advanced features such as the Legal Pegasus model for document summarization, Indiankanoon API for efficient document retrieval, and a Gemini API chatbot trained on Bhartiya Nyay Sanhita. This review evaluates the platform's design strategies, technological frameworks, and their collective role in enhancing legal literacy and accessibility. The paper highlights the importance of user-focused design in legal technology and underscores L.E.A.P's potential to empower individuals by simplifying access to critical legal knowledge

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Legal technology, Artificial Intelligence, User-Centered Design, Legal Literacy, Document Summarization, Bhartiya Nyay Sanhita

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 Abstract

The project titled "Automated Drain Cleaner System" addresses a major challenge in urban and semi-urban areas -- the clogging of open drains due to plastic, debris, and solid waste. Traditional cleaning methods involve manual scavenging, which is hazardous and unhygienic. This project aims to design a low-cost, safe, and efficient device to automate drain waste removal. The system includes a motor-driven shaft with rotating collector arms that lift floating waste from the drain and deposit it into a tray. It is built using mild steel angles, 20mm diameter shafts, four bearings, and a 12V DC gear motor. The setup is lightweight, durable, and ideal for narrow to medium-sized open drains. By reducing human contact with waste, it ensures better hygiene, prevents water blockages, and supports national cleanliness initiatives like the Swachh Bharat Abhiyan. This system demonstrates the integration of mechanical design and automation to solve a practical, real-world civic issue.

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Automatic Drainage Cleaning System, Urban Drainage Management, Sustainability, Innovation.

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This study investigates the impact of global events on cryptocurrency price fluctuations with a specific focus on Bitcoin, Ethereum, and Litecoin during the pre-pandemic (2019), pandemic (2020), and post-pandemic (2021) periods. Using statistical methods such as correlation and regression analysis on secondary data, the research highlights how global disruptions--like lockdowns, government stimulus, and vaccine rollouts--correlate with volatile cryptocurrency price movements. Key findings suggest that market capitalization significantly influences price, while trading volume has a mixed impact. The study provides vital insights for investors, researchers, and policymakers seeking to understand and navigate the crypto market during global crises.

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Cryptocurrency, Price Volatility, Global Events, Bitcoin, Ethereum, Regression Analysis, Market Capitalization, COVID-19 Impact

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