Volume 13 | Issue 4 |

 Abstract

A study titled "Life Skills of Senior Secondary Level Students" was conducted on higher secondary school students in Prayagraj city. The research included 318 students from rural and urban schools, selected through stratified random sampling. The descriptive survey method was used, and data were collected using a standardized scale developed by Dr. Chandra Kumara and Ayushi Tripathi. The study focused only on Prayagraj city. The findings revealed significant difference in life skills between male and female students. However, there was a significant difference in life skills between students from rural and urban areas, UP Board and CBSE Board, as well as government and private schools. The study emphasizes the need to improve students" life skills by changing the teaching-learning approach to foster critical thinking, problem-solving, decision-making, self-awareness, and effective communication. Life skills are essential for students' overall development, helping them navigate academic, social, and emotional challenges. The research highlights the importance of student-centered learning, which accommodates different cognitive styles. Integrating life skills into the curriculum can enhance students" analytical thinking, resilience, adaptability, and interpersonal relationships. This study underscores the urgency of life skills training in schools to prepare students for an evolving world. A structured educational framework focusing on life skills can enrich learning experiences and empower students in their personal and professional lives. By understanding students" life skills, teachers can enhance their teaching strategies, making the curriculum more effective and beneficial. The study concludes that incorporating life skills education is essential for developing well-rounded, competent individuals ready to face real-world challenges.

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 Keywords

"LIFE SKILLS OF SENIOR SECONDARY LEVEL STUDENTS."

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 Abstract

Wildlife conservation faces challenges due to deforestation, climate change, and human encroachment, leading to animal-vehicle collisions, habitat degradation, and wildfires. This project introduces an AI and IoT-based system for real-time wildlife monitoring and environmental assessment. It employs YOLO-based AI cameras to detect animal movements, integrating an automatic braking system in vehicles to prevent collisions. In case of an impact, the system instantly alerts the forest department with detailed incident data. To enhance habitat protection, flame and smoke sensors detect wildfires early, while soil sensors monitor moisture, pH, and nutrient levels to assess ecosystem health. An AI-driven leaf analysis module further enables early disease detection and stress assessment in plants. All data is processed in a real-time dashboard, generating automated alerts and insights for conservation authorities. By combining AI, IoT, and predictive analytics, this system provides an efficient, scalable, and automated approach to wildlife protection and habitat conservation.

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AI,IOT,YOLO,Automatic Braking system

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Pain and anxiety from injections are a common problem, especially in children and patients with needle phobia. In this article, the Cool-Tune-Vibe is a compact, non-invasive device that reduces injection-related pain by integrating heat distraction, thermal stimulation, and vibrational feedback. The device works according to the principles of pain theory of Gate Control Theory, using a combination of localized cooling, soothing music, and gentle vibrations, to distract sensory nerves at the site of injection. This multi modal approach can effectively reduce pain sensation, improve patient comfort, and is particularly applicable in clinical, pediatric, and vaccination settings. Experimental studies have shown a significant reduction in perceived pain levels, which demonstrates the potential to improve the patient experience of everyday injections.

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 Keywords

Bluetooth, Arduino Nano, Injection, Needle Anxiety

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 Abstract

The growth in smart home technologies has raised the stakes on energy efficiency research. Most existing smart home energy monitoring systems have been focusing on general energy tracking, machine learning-based energy consumption prediction, and cloud-based monitoring solutions. There is a lack of automated energy-saving mechanisms for specific high-power-consuming appliances such as TVs and ovens that continue to consume electricity when left unattended. This paper introduces an innovative IoT-based smart home energy monitoring system with integration of motion sensors for the functionality of auto-off for TVs and an automated power cut-off mechanism for ovens. The proposed system detects user presence near the TV and automatically turns it off when no movement is detected for a predefined time to prevent unnecessary consumption of power. Besides, the oven power cut-off system ensures that the flow of electricity is stopped as soon as the heating process is over, which saves energy consumption. Both systems are connected to a mobile application for real-time monitoring and manual control. The realization of this system can greatly improve household energy efficiency while maintaining user convenience. Experimental tests and simulations suggest a huge electricity saving as compared to conventional energy monitoring systems 15.

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 Keywords

Smart home automation, energy monitoring, motion sensors, TV auto-off, oven power cut-off, electricity reduction, IoT, mobile app integration.

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Creative Commons Attribution 4.0 and The Open Definition

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The surge in the tourism industry has massively attracted tourists to India on a large scale to the places of dark tourism. Dark tourism is a niche within the tourism industry nowadays that concentrates on visiting places connected to sorrow, tragedy, or death. The darkest eras of human history, effects of war or disaster, impacted communities, dark incidents of infamous crimes, the popularity of social media, and stories of human history behind tragedies have all contributed to the rise in popularity of this type of tourism in recent years. The surge is not only because of the external and mysterious stories and reasons associated with the site it also involves certain internal human factors associated with human behavior and psyche. The paper aimed to study those internal factors like behavioral intention, motivation, and triggers that instill the urge among tourists to visit a destination of dark tourism. Research has tried to focus on the experiences of the tourists and the transition they observe in their feelings and emotions before, after, and during the tour. For serving this purpose narrative inquiry will be performed based on the interviews of those tourists that have visited a site of dark tourism. The results have justified that the tour is initiated as a result of curiosity, past human experiences, some emotional triggers, or a feeling of thrill. Findings have also stated that the emotions and feelings of the tourists can change a lot from curiosity to acceptance towards the negative energies, from thrill to the satisfaction of completing the tour, and much more.

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Dark tourism, Behavior intention, Motivation, Triggers, Emotions and Feelings

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This research paper investigates how Brain Breaks video-based classroom physical activity intervention programs affect the physical fitness, psychological health, and well-being of pre-adolescent children aged 9 to 12 years in India. 110 students were divided into experimental (n = 60) and control (n = 50) groups in a quasi-experimental design. For twelve weeks, the experimental group watched Brain Breaks films, which included two to five minutes of physical activity six days a week during classroom sessions. Assessments were conducted between the two groups using the EUROFIT Physical Fitness Test, the World Health Organisation (WHO) Well-Being Scale, and the Physical Activity Enjoyment Scale (PAES) before and after the intervention. ANCOVA was used to assess within-group and between-group differences in the data. Descriptive statistics (mean and standard deviation) were calculated for each variable. In each variable, the control group showed negligible changes. According to these results, schoolchildren's resilience, mental health, and enjoyment of physical exercise could all be greatly improved by short, planned physical activity interventions like Brain Breaks. The research highlights how these activities could be used in classrooms to encourage holistic development and discourage sedentary behaviour

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Physical activity; EUROFIT; Children; Technology-based video intervention; Psychological scale.

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Tax provisions are always important to government and taxpayers. Out of many tax provisions, provisions related to Capital Gains significantly affect investments in the country. It has a direct relation with investment choices of investors which may increase investments in economy and vice versa. Amendments in tax provisions may make taxpayers feel happy or sad, depending on how it is satisfying or affecting them. Government every year makes amendments in tax provisions to balance the needs of own as well as taxpayers both. Recently, in Union Budget 2024 government has made significant changes in tax provisions related to capital gains such as withdrawal of indexation in case of long-term capital gains, changes in capital gains tax rates, making buyback of shares taxable, increasing exemption limit for long term capital gains in respect of equity-oriented investments. Since taxation always have direct relations with taxpayers, knowing how these amendments related to capital gains tax provisions has affected investors are very important. The current study focuses on the satisfaction level of investors with respect capital gains tax amendments made in Union Budget 2024 and their perception towards it.

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BUDGET, CAPITAL GAINS, INVESTOR, SATISFACTION, TAX AMENDMENTS

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This paper presents the design, development, and implementation of a bionic arm controlled using servo motors and an Arduino microcontroller. The proposed system aims to replicate human hand movements with a focus on precision, efficiency, and affordability. A tendon-driven mechanism is integrated with servo actuators to ensure smooth articulation of fingers and wrist motion. The system incorporates forward and inverse kinematics for accurate positioning, along with real-time embedded control algorithms. Performance validation through experimental testing highlights the effectiveness of the bionic arm in replicating grasping and manipulation tasks. The research contributes to the field of prosthetics and assistive robotics by offering an open-source, low-cost alternative to commercial bionic limbs.

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Bionic arm, servo motors, Arduino, kinematics, embedded control, prosthetics.

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An experimental evaluation of the solar photovoltaic (PV) system's depletion under the weather.investigating how blurring affects the PV system with different solar radiation levels four PV solar module panels (20W, Manf: Usha Solar) subject to parallel (SP) weather are used in this investigation--cross-sectional rate-arrested (TCT) and forecast. To evaluate the PV system's performance under artificial lighting fixtures, the test set includes electrical performance rating devices with a variable load. Furthermore, performance comparisons between PV modules programmed into the SP and TCT modified PV modules were conducted under the shading I-II test case.A thorough examination is aided by the anomalous behaviour of the current-voltage (V-V) and power-voltage (P-V) curves when numerous power points, such as local maximum power points (LMPP) and ground mass points (GMPPT), are present. Furthermore, performance indicator measures such as GMPP power, low power loss, and fill factor (FF) were evaluated under both PV power assumptions in distinct shadow scenarios.

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Solar energy, series-parallel connection. Total-cross-tied connection, power losses, power, fill factor

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 Abstract

The advancement of intelligent systems has significantly impacted the healthcare industry, enabling the development of automated diagnostic tools for early disease detection and monitoring. Respiratory diseases, particularly in children, are among the most common health concerns, requiring timely and accurate diagnosis. Traditional methods rely on clinical evaluations and laboratory tests, which can be time-consuming and resource-intensive. In this study, we introduce RESPITECH, a deep learning-based framework for automated cough sound classification. By leveraging audio signal processing techniques and machine learning, RESPITECH aims to differentiate between healthy and pathological coughs in individuals, with a focus on respiratory conditions such as asthma, upper respiratory tract infection (URTI), and lower respiratory tract infection (LRTI), COPD, Pneumonia, Bronchitis and also Healthy lungs. Our approach employs a Convolutional Neural Network model (CNN) trained on Mel-Frequency Cepstral Coefficients (MFCCs) extracted from a carefully curated dataset of cough sounds labeled by clinicians. The CNN model is designed to capture temporal dependencies in cough sounds, enhancing classification performance. When trained to distinguish between healthy and pathological coughs, the model achieves an accuracy exceeding 84%, aligning closely with physician diagnoses. To improve classification performance for specific respiratory pathologies, multiple cough epochs per subject are aggregated, resulting in an overall accuracy surpassing 91% for detecting asthma, URTI, and LRTI. However, classification performance declines when distinguishing between four separate cough categories, as certain pathological coughs share overlapping acoustic characteristics, leading to misclassification. A detailed analysis of the MFCC feature space through a longitudinal study indicates that pathological coughs, regardless of the underlying condition, exhibit similar acoustic patterns, making it challenging to differentiate between specific respiratory diseases using MFCCs alone. This suggests that while MFCCs provide valuable spectral information, additional features or multimodal data integration may be necessary for finer disease discrimination. Despite these challenges, RESPITECH demonstrates strong potential as an automated, non-invasive screening tool for respiratory disease monitoring. Future work will explore advanced feature extraction techniques, multimodal learning approaches, and real-world deployment strategies to enhance diagnostic accuracy and clinical applicability.

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 Keywords

Deep Learning,Neural Networks,Convolutional Neural Networks (CNNs),Long Short-Term Memory (LSTM),Feature Extraction,Data Representation,Audio Classification.

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