Volume 13 | Issue 11 |

 Abstract

This study investigates the status of implementation of RTE Act norms in the district of Guntur in the state of Andhra Pradesh based on primary data gathered from 90 schools of different management types. This study follows a dual methodology and is based on structured questionnaires that were delivered to school principals and teachers in order to determine whether they meet the requirements concerning infrastructure, teacher qualifications, the student-teacher ratio and the facilities under the Act. Data analysis indicates substantial differentiation in levels of implementation between government and aided schools, private unaided and Municipal schools. Of these, although the government schools have 67.2% ENSURE overall compliance to RTE norms, private unaided schools have 71.8% adherence, especially in school infrastructure and facilities. Critical teacher shortages remain, with 34.5% of schools surveyed reporting pupil-teacher ratios above the stipulated maximum. The research reveals resource allocation, monitoring systems and community involvement as the common factors that influenced successful implementation. Results suggest that full implementation of the RTE is hindered by practical realities, such as lack of funds, lack of adequate infrastructure, and administrative roadblocks, although the RTE law clearly provides for such implementation. This research provides empirical evidence from a regional perspective and offers recommendations on policies for enhancing the implementation mechanisms in Andhra Pradesh and other similar states across India.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Right to Education Act, Guntur district, Primary education, Implementation compliance, School infrastructure, Educational policy

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Generalised anxiety disorder involves excessive worry in day-to-day life. The case report shows the usefulness of the homoeopathic remedy of the Lilium tigrinum for managing the symptoms of the generalised anxiety disorder[1]. A 31-year-old male diagnosed with generalised anxiety disorder. Presented with the symptoms of public anxiety, fear of ghosts, dark imaginary thoughts, dead bodies, overthinking, loss of confidence, delusion frequent change of thought. A person in both religious and sexual mania. He also had physical illness of hypothyroidism and pain in the right knee joint, trembling and numbness in both legs. The patient was administered Lilum tigrinum in the 1M Potency. The case was assessed with the Hamilton Anxiety Rating Scale (HAM-A)[2]. To note the public anxiety, enhance the self-esteem, in reduce the fear and increase life satisfaction.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Generalized Anxiety Disorder, GAD-7, HAM-A, Lilium tigrinum, Homoeopathic Psychiatry, Religious mania, Sexual mania, Hypothyroidism.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Through this project, we aim to develop a Raspberry Pi-based smart home automation system that intelligently controls appliances by analyzing environmental conditions, occupancy status, and time of day using machine learning techniques. The system monitors indoor climate and evaluates ambient lighting conditions using environmental and light intensity data. For occupancy detection, the system replaces traditional IR sensors with a camera-based approach using Convolutional Neural Networks implemented via OpenCV, enabling more accurate and real-time detection of human presence within the monitored space. Additionally, time-based features including hour of the day and day/night classification are incorporated to enhance decision-making accuracy. A machine learning model is trained on historical data encompassing temperature, humidity, occupancy status and temporal features to predict optimal control actions for household appliances such as lights, fans, and air conditioners. The Raspberry Pi serves as the central controller, interfacing with sensors and the camera, and executing automation decisions in real time

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Smart home automation, IoT, AI, ML, Raspberry Pi, CNN, Occupancy detection, environmental monitoring, OpenCV, computer vision, sensor fusion, dynamic appliance control, energy efficiency, predictive automation, real-time decision making, temperature and humidity sensors, light intensity detection, home appliance control, time-based feature analysis, and adaptive control system.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Fundamental building blocks in the field of autonomous vehicle perception, which has seen significant progress in recent years, are object detection and classification. These innovations are essential for giving cars the ability to sense and comprehend their environment, which promotes safe navigation and well-informed decision-making. Even with these advancements, present-day perception algorithms sometimes struggle to remain dependable under harsh weather situations like dimly lit rooms, intense sunlight, reflecting glare, or obscuring fog. These circumstances have the potential to seriously impair perception system performance, raising safety concerns. As we move closer to the objective of complete autonomy, it is critical that we create a robust perception system--a system that can confidently manage these various and demanding external circumstances. With the help of such a system, autonomous cars will be able to function consistently safely and precisely, even in unpredictable real-world situations. Achieving this degree of perception system complexity is not just a goal, but also a prerequisite for the development of fully autonomous cars that have the potential to completely transform the way we travel. Our study uses the CNN method, which is a powerful tool for deep learning, to take on this difficulty head-on. The CNN is being trained to be exceptionally good at identifying cars in bad circumstances, such as fog and rain. The objective is to provide our system with the capacity to surpass the constraints of hazy images and accomplish high-fidelity, real-time vehicle recognition. This results in a notable advancement in AV safety, guaranteeing their ability to confidently traverse even the most difficult weather situations. We're laying the groundwork for a time when autonomous cars will be able to drive on roads with greater safety and efficiency in all weather conditions by fusing the capabilities of deep learning and WDS. In this research, we are detecting vehicles in inclement weather by utilizing an algorithm based on YOLO.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Object Detection; Vehicle Detection; YOLO; Deep Learning; Adverse Weather Conditions; Weather-Degraded Images; Image Enhancement; Intelligent Transportation Systems (ITS); Autonomous Vehicles; Robust Perception; Transfer Learning; Real-Time Detection; CNN-Based Models; Visibility Restoration; Performance Benchmarking.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This project presents an AI-driven dermatology application designed to assist users in identifying skin diseases through image-based analysis. By leveraging advanced algorithms, the app provides accurate diagnostic predictions along with relevant educational content and preliminary treatment suggestions. A key feature is the integrated Severity Analysis and Risk Assessment module, which categorizes conditions as mild, moderate, or severe and evaluates personalized risk based on factors such as skin type, age, medical history, and UV exposure. This enhances clinical relevance and supports better decision-making for users, especially in underserved regions with limited access to dermatologists. Overall, the system aims to bridge the gap between technology and professional skincare by offering a reliable, accessible tool for early detection and monitoring.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

AI-driven dermatology, Skin disease detection, Image-based analysis, Severity assessment, Risk evaluation, Early diagnosis, Personalized skin health monitoring

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Study of social issues in the present times

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Study of social issues in the present times

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Music has long been understood as a powerful medium capable of influencing human behaviour, perception, and development. In teacher education, its value extends beyond artistic appreciation, becoming a multidimensional tool that strengthens emotional intelligence, cognitive functioning, and social competence among student teachers. This paper provides an extensive scholarly analysis of the role of music in shaping the holistic readiness of teacher trainees, who must navigate demanding academic environments, interpersonal classroom relationships, and complex pedagogical responsibilities. Drawing from psychological theories, educational frameworks, and neuroscientific findings, the study employs a qualitative descriptive methodology to investigate how structured musical engagement impacts self-regulation, memory processes, communicative ability, empathy, collaboration, cultural sensitivity, and professional identity formation. The discussion demonstrates that music enhances the internal and external capacities required of future educators and supports the transformation of student teachers into well-rounded, emotionally grounded, intellectually alert, and socially responsive professionals. Recommendations include curriculum integration, professional awareness programmes, pedagogical innovations, and research-intensive music modules within teacher education institutions.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Music Education, Emotional Intelligence, Cognitive Skills, Social Development, Teacher Training, Neuroscience of Music, Pedagogical Competence, Holistic Development

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Tooth autotransplantation, the surgical relocation of a tooth from one site to another within the same individual, is a biologically sound and effective treatment for tooth replacement, especially in young patients. This procedure offers significant advantages, including the preservation of alveolar bone, natural aesthetics, and the maintenance of proprioception. Success depends on key factors: selecting a donor tooth with incomplete root formation, meticulous surgical handling to preserve the periodontal ligament (PDL), and minimizing extraoral time. Technological advancements like cone-beam computed tomography (CBCT) and 3D-printed replicas have refined preoperative planning and surgical execution, reducing trauma and improving outcomes. Reported success rates range from 75% to over 95% with proper technique. While potential complications exist, autotransplantation remains a predictable and cost-effective alternative to conventional prosthetic options and implants, offering a natural and functional solution for missing teeth.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Autotransplantation; Periodontal ligament; Tooth replacement; Pulp revascularization; Dental surgery

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

In the modern digital era, one of the greatest challenges faced by the primary education system is learners drifting away from academic engagement. The influence of mobile phones, unrestricted internet access, short video formats, reels, and algorithm-based content consumption has created a generation of easily distracted learners. Instead of engaging with textbooks and classroom-based interaction, children are increasingly drawn into fast-paced digital stimuli that reduce sustained attention span and weaken deep cognitive processing.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Primary Teacher Training, Child Development, Learning Engagement, Attention and Concentration, Digital Distraction, Screen Addiction, Play-Based Learning, Activity-Based Learning (ABL), Storytelling Pedagogy, Positive Reinforcement,

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This study investigated primary school teachers' attitudes toward six classroom strategies that enable inclusive education: cooperative group teaching, peer teaching and peer influences, social skills training, parental involvement, self-regulated learning, and collaborative teaching. A survey of Karnataka teachers (N = 396) using a 22-item Likert subscale showed uniformly positive attitudes (M = 3.47-3.52) with high reliability (? = .93). Exploratory factor analysis supported a four-factor solution, and an 18-item refined scale maintained strong reliability (? = .92) and explained 60.73% variance. Gender differences were trivial, but urban teachers showed more favorable views of cooperative group teaching, social skills training, and parental involvement (d ? .21-.29). No differences emerged by age, qualification, or education division. All domains were strongly correlated, as teachers viewed these strategies as interconnected. Regression showed a small effect of teaching experience and urban location. Overall, positive and coherent attitudes highlight the need for school-based professional development and improved rural resources to support inclusive practices.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Inclusive Education; Teacher Attitudes; Primary Education; Strategies; Karnataka

  License

Creative Commons Attribution 4.0 and The Open Definition