A Study to Assess the Effectiveness of a Structural Teaching Programme on Knowledge Regarding First Aid and Safety Measures Among School Children at Selected Schools of District Mandi HP
Promoting health, wellness, and disease prevention is increasingly vital in today's changing society. This study aimed to enhance the knowledge of school children regarding first aid and safety measures through a structured teaching programme to prepare them for future emergencies. A quasi-experimental research design was utilized, involving a sample of 100 school children selected via convenient sampling from schools in District Mandi, Himachal Pradesh. Data analysis was conducted using descriptive and inferential statistics. The pre-test mean knowledge scores were comparable between the control (9.68) and experimental (9.9) groups. Following the intervention, the control group’s post-test mean score remained statistically stagnant at 9.46, whereas the experimental group’s mean score significantly increased to 25.5. This massive improvement in the experimental group was statistically significant (t-value = 33.92, surpassing the critical table value of 2.021 at p < 0.05). Additionally, post-test knowledge in the experimental group showed significant associations with age, school type, maternal education, and paternal occupation. The structured teaching programme had a highly significant positive impact on improving first aid and safety knowledge among school children.
Published by: Shephali Walia, Yamini sharma, Pallavi mehra, Priyanka sharma
Author: Shephali Walia
Paper ID: V12I4-1185
Paper Status: published
Published: August 9, 2026
Socio-economic Determinants of Livelihood Security among Street Vendors: Evidence from Paramathi Velur Taluk
Street vending constitutes a significant segment of the informal economy in India by generating employment opportunities and ensuring access to affordable goods and services for urban and semi-urban populations. Despite their substantial contribution to local economies, street vendors continue to experience livelihood insecurity arising from limited institutional support, inadequate infrastructure, restricted access to formal credit, and weak social protection mechanisms. This study examines the socio-economic determinants of livelihood security among street vendors in Paramathi Velur Taluk of Namakkal District, Tamil Nadu. The study is based on primary data collected from 355 street vendors using a structured questionnaire covering demographic characteristics, business profile, income, expenditure, access to financial services, registration status, and workplace conditions. The findings indicate that women constitute the majority of vendors, reflecting the growing participation of female workers in the informal economy. Most respondents belong to economically productive age groups but possess relatively low levels of formal education. A substantial proportion operate without official registration or vendor identity cards, limiting their access to government welfare schemes and institutional credit. Vegetable vending represents the dominant economic activity, while inadequate infrastructure, market uncertainty, and regulatory constraints continue to affect livelihood outcomes. The study concludes that livelihood security is shaped by a combination of socio-economic characteristics, business conditions, and institutional support. Strengthening vendor registration, expanding financial inclusion, improving market infrastructure, and enhancing awareness of welfare schemes would contribute significantly to sustainable livelihoods among street vendors.
Published by: Saravanan R., S. Parvathi
Author: Saravanan R.
Paper ID: V12I4-1179
Paper Status: published
Published: August 9, 2026
Examination Integrity in India: Analysing the Role of Political Influence, Governance Deficits, and Institutional Weaknesses in Examination Paper Leaks
India has one of the world's oldest and largest education systems, where competitive examinations and board assessments play a crucial role in determining academic and career opportunities. However, examination paper leaks, cheating, and the re-conduct of exams undermine the credibility of the education system, affecting deserving aspirants' time, effort, and morale. This study examines how political influence, governance deficits, corruption, and institutional weaknesses contribute to breaches of examination integrity. METHODOLOGY: This study adopts a descriptive and analytical research design based on both primary and secondary sources of data. Primary data were collected through a structured questionnaire administered through Google Forms, with responses from 80 participants. Approximately 62% of respondents were school students, 17–18% were college students from institutions across Delhi NCR, while the remaining participants comprised working professionals from various sectors. Secondary data were collected from government reports, official documents, news articles, policy papers, and published research studies to provide contextual understanding and validate primary findings. RESULT: The findings reveal that most respondents consider examination paper leaks a serious threat to fairness and merit. They identified political influence, corruption, and weak governance as key factors contributing to these incidents. Secondary data further indicate that paper leaks affect millions of candidates, increase public expenditure due to re-conducted examinations, and weaken public trust in the examination system. CONCLUSION: The study concludes that examination paper leaks are not only educational failures but also governance challenges arising from corruption, weak accountability, and institutional shortcomings. Strengthening examination security, transparency, and institutional accountability is essential to safeguard meritocracy, restore public confidence, and protect the integrity of India's education system.
Published by: Divit Batra, Aadit Batra, Amit Gurjar
Author: Divit Batra
Paper ID: V12I4-1176
Paper Status: published
Published: August 2, 2026
Automated Video Generation using Deep Learning
The increasing utilization of multimedia content in education, marketing, and digital communication has created a growing demand for efficient video generation systems. This paper presents an Automated Video Generator that converts textual input into narrated videos using a modular deep learning-based pipeline. The proposed system generates a narrative, retrieves context-relevant images, synthesizes speech using text-to-speech technology, and composes synchronized videos through a web-based Django framework. Experimental results demonstrate improved efficiency, stable performance, and audio-visual synchronization, making the system suitable for educators, marketers, and content creators while reducing manual effort and production time. ``` Batch11 Automated Video Generator Using Deep Learning.pdf
Published by: V. Shiva Kumar, K. Eshwar, M. Raja Laxmi, V. Kavya Sri
Author: V. Shiva Kumar
Paper ID: V12I4-1173
Paper Status: published
Published: August 2, 2026
Strengthening Hospital Disaster Preparedness at District General Hospital Matara, Sri Lanka: A Case Study of Preparedness Assessment and Improvement Strategies
Hospital disaster preparedness is essential for ensuring continuity of healthcare services and minimizing the impact of emergencies and disasters. Despite the availability of national disaster preparedness guidelines, hospitals face challenges in maintaining comprehensive and up-to-date preparedness systems. This study evaluated disaster preparedness at District General Hospital Matara, Sri Lanka, identified key operational challenges, and proposed evidence-based strategies to strengthen organizational resilience. A descriptive case study design was employed using methodological triangulation. Data were collected through document review, key informant interviews, direct observations, and assessment using a structured checklist adapted from the World Health Organization Hospital Emergency Response Checklist and national disaster preparedness guidelines. The checklist comprised 43 indicators across 12 preparedness domains. A study-specific Composite Hospital Disaster Preparedness Score (CHDPS) was developed to summarize preparedness across organizational domains. Operational challenges were prioritized using the Nominal Group Technique, and the highest-priority problem was further analysed through root cause analysis to identify underlying organizational factors. The overall CHDPS was 74.4% (64/86). The highest preparedness scores were observed in Recovery and Termination (100%), External Coordination (83.3%), Security and Access Control (83.3%), and Notification and Activation (80.0%). Lower preparedness scores were identified in Staff Mobilization and Welfare (60.0%), Triage and Patient Management (62.5%), and Debriefing and Quality Improvement (66.7%). The principal operational challenge was an outdated Hospital Disaster Preparedness and Response Plan, with root causes related to governance, organizational processes, human resources, and monitoring mechanisms. The study demonstrates that structured preparedness assessment combined with quality improvement methodologies can effectively identify institutional strengths and operational gaps. The proposed CHDPS provides a practical framework for monitoring hospital disaster preparedness and supporting continuous organizational improvement. The findings offer practical guidance for strengthening disaster preparedness and enhancing health system resilience in Sri Lanka and other comparable resource-constrained
Published by: Sasikumar S, Thotagamuwa T.W.A.N, Maithily B
Author: Sasikumar S
Paper ID: V12I4-1164
Paper Status: published
Published: July 27, 2026
Pain Recognition with Physiological Signals using Hybrid Models
The pain measurement is a very vital aspect of healthcare, but the traditional approaches offer extensive dependence on manual feature extracting through physiological measurements and clinical judgement. These conventional methods involve expert knowledge, they are time consuming and have a tendency to fail to be generalized effectively nor across various groups of patients. To overcome these shortcomings, this paper comes up with a proposal of an automated pain recognition model that processes physiological signals through a hybrid deep learning model. The model combines Convolutional Neural networks, Bidirectional Long short term memory networks, and Gated Recurrent Units with which hierarchical spatial and time representations of raw signals are learned automatically. The architecture captures the local signal properties as well as the long-range contextual dependencies which enhance the difference between pain and no-pain states. Further, a stacking classifier ensemble is used to improve the prediction robustness and generally improve its performance. The proposed method has a high level of reliability as experimental assessment shows that this method is accurate at the level of 99. The system offers scalable and intelligent capability of objective pain monitoring which aids in supporting the making of better clinical decisions and patient care within the contemporary healthcare settings.
Published by: A. Harsha Vardhan Reddy, Dr. Burla Srinivas, Venkata Lakshmi
Author: A. Harsha Vardhan Reddy
Paper ID: V12I4-1172
Paper Status: published
Published: July 27, 2026
