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

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

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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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 Abstract

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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Creative Commons Attribution 4.0 and The Open Definition

 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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Creative Commons Attribution 4.0 and The Open Definition

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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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 Keywords

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

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

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Creative Commons Attribution 4.0 and The Open Definition

 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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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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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 Keywords

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

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Creative Commons Attribution 4.0 and The Open Definition

 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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Creative Commons Attribution 4.0 and The Open Definition

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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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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Diabetes,LSTM,Deep Learning,Soft Max

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Creative Commons Attribution 4.0 and The Open Definition