Volume 14 | Issue 5 |

 Abstract

This research explores the adaptive reuse of a ruined Portuguese-era church located in the campus of Our Lady of Fatima Convent School, Moti Daman. The project proposes transforming the structure into a children's learning and living centre, combining a heritage library with residential facilities for orphan children. The study focuses on interior design strategies that preserve historical identity while integrating modern functionality. Through literature study, case studies, and site analysis, the research demonstrates how adaptive reuse can serve both heritage conservation and social welfare.

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 Keywords

Adaptive reuse, heritage conservation, children's centre, ruined church, interior design, Daman, Portuguese architecture.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Career guidance is an integral yet underrepresented part of education that helps students decide on what course they want to pursue post-secondary. In India, career guidance for students is either not available or available to only some extent because students do not have structured guidance. Moreover, the traditional career guidance is generic, sporadic, and does not cater to personal preferences of the students. This paper proposes the design and development of an Artificial Intelligence-powered Career Guidance Platform known as AI Guruji. This platform uses various features like AI, psychometric assessment, and conversation-based recommendation engines to offer personalized career recommendations to the users based on their profile and preferences. Some of the technical aspects of this platform include React18 as a frontend, Nodejs Backend, Supabase Database, and Gemini AI-powered recommendation engine. The users go through multidimensional psychometric assessments of interest, aptitude, and unconventional careers along with chatbot-powered career guidance. The user validation test revealed that most of the time students receive no career guidance at school. Moreover, all the respondents expressed an immense desire for AI Career Guidance.

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 Keywords

AI Career Guidance, Psychometric Assessment, Gemini API, NLP, Personalized Recommendation, Conversational Chatbot

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ABSTRACT Background: The COVID-19 pandemic caused significant psychological distress among healthcare workers. Psychological First Aid (PFA) is an early intervention that helps reduce distress and improve coping abilities. Methods: A pre-experimental one-group pre-test and post-test design was conducted among 40 staff nurses on COVID-19 duty in selected hospitals in Bengaluru. Participants were selected using purposive sampling. A structured knowledge questionnaire was used for data collection. After the pre-test, a Structured Teaching Programme (STP) was administered, and a post-test was conducted after 7 days.Results: The mean pre-test knowledge score was 12.3%, which increased to 22.34% in the post-test, showing an improvement of 10.04%. The difference was statistically significant at the 5% level. No significant association was found between pre-test knowledge and socio-demographic variables (P > 0.05). Conclusion: The study revealed inadequate knowledge among staff nurses, and STP was effective in improving knowledge regarding PFA.

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Key words:PFA- psychological first aid, STP-structured teaching programme

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Creative Commons Attribution 4.0 and The Open Definition

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ABSTRACT Background: Covid-19 is really an infectious disease caused by a recently discovered corona virus. The majority of Covid-19 virus-infected individuals will develop mild to severe respiratory disease and recover without the need for special care. The elderly and individuals who have underlying medical diseases such cancer, diabetes, lung disease, and cardiovascular disease are more prone to have serious illnesses. COVID-19 affects different people in different ways. Methods: In this study quantitative approach with pre-experimental research design was used. The setting of the study was selected rural area of Valsad district. Sample size was 30. In this study non-probability sampling technique convenient sampling technique was done. Structured questionnaire was used. Data analysis was done mainly using descriptive statistics paired t -test for hypothetical testing and chi square test was done for association. Result: The present study evaluates the knowledge of post COVID patients regarding prevention of post COVID complications and found that 13.33% of the samples had poor knowledge 80% of people had average knowledge and 6.67% of people had good knowledge in the pre-test. The overall mean percentage of knowledge in pre-test was 9.36 with the standard deviation of 3.52. In the post test the overall mean percentage of knowledge in post-test was 14.5 with the standard deviation of 3.98. Conclusion: Analysis data shows that the post test knowledge score has significantly higher than the pre test knowledge score at p < 0.05 level of significance i.e. mean difference is 5.19. There was a significant improvement in knowledge of post COVID patients. The study revealed that structured teaching programme was observed to be very effective method to improving knowledge on prevention of post COVID complications.

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 Keywords

Knowledge, Prevention, Post, Covid, Complication

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Creative Commons Attribution 4.0 and The Open Definition

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This paper explores the connection between Vincent Van Gogh's painting visual components and emotional expression. Van Gogh, a prominent character in Post-Impressionism, developed a unique style that prioritized subjective experience over accurate portrayal. A qualitative approach based on careful visual inspection of pieces of art is used in the study. It draws attention to the ways that differences in color, texture, and brushstrokes help to express emotional depth. The results show that Van Gogh's artistic approach has had a major impact on the development of modern art and functions as a medium for conveying inner states. Keywords: Vincent van Gogh, Post impressionism, Color dynamics, emotional expression, art analysis.

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Keywords: Vincent van Gogh, Post impressionism, Color dynamics, emotional expression, art analysis.

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Creative Commons Attribution 4.0 and The Open Definition

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The number of elderly people in society is increasing rapidly, and this has created new challenges related to their safety, health, and overall well-being. Many elderly individuals today prefer to live independently, but this also makes them more vulnerable to risks such as falls, medical emergencies, and delayed help. This study focuses on smart safety bracelets as a possible solution to these problems and tries to understand how people perceive and accept such devices. The research is based on secondary data collected from reports, articles, and existing studies related to elderly care and wearable technology. It mainly looks at how consumers, especially family members and caregivers, think about these devices and what factors influence their decision to buy them. Things like ease of use, cost, trust in technology, and usefulness play an important role, along with emotional factors like safety and peace of mind.

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 Keywords

o HEALTH o SAFETY o ELDERLY o BRACELET o EMERGENCY o WATCH

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Distributed Denial of Service (DDoS) anomaly detection in Software Defined Networks (SDNs) plays a crucial role in safeguarding network infrastructure from malicious attacks. In this study, The CICIDS dataset is employed to assess the performance of different machine learning and deep learning techniques in identifying DDoS-related anomalies.The techniques explored include The research incorporates time-dependent neural architectures, notably Recurrent Neural Networks, Long Short-Term Memory models, and Gated Recurrent Units.Bidirectional LSTM (BiLSTM), Convolutional Neural Networks (CNN), CNN+BiLSTM, Support Vector Machines (SVM), Random Forest, AdaBoost, XGBoost, Decision Trees, Logistic Regression, KNearest Neighbors (KNN), and a Voting Classifier. Among Among the evaluated models, the Voting Classifier achieved the best results, delivering higher accuracy and surpassing other techniques in precision, recall, and F1-score. The findings emphasize the strength of ensemble learning in improving DDoS anomaly detection., thereby providing a robust solution for network security in SDNs. The findings suggest that the Voting Classifier represents a strong candidate for future investigations in SDN-based anomaly detection.

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 Keywords

distributed denial of service, Anomaly detection, software defined networks, deep leaning, machine learning, CICIDS dataset, voting classifier.

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Creative Commons Attribution 4.0 and The Open Definition

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U-clamps are widely used mechanical components for supporting and securing pipes, tubes, and cylindrical structures in various engineering applications. Despite their simple geometry, they are subjected to complex loading conditions that may lead to stress concentration, deformation, and possible failure. Therefore, a detailed structural investigation is necessary to ensure safe and efficient performance. In this study, a U-clamp is analysed using the Finite Element Method to evaluate its structural behaviour under different loading conditions. The analysis includes static structural analysis to determine stress distribution and deformation, along with modal analysis to study the vibration characteristics of the component. Two different cases are considered by varying geometric parameters such as diameter and mass to observe their effect on structural response. The results indicate that maximum stress occurs at the curved region of the U-clamp due to bending effects, while deformation increases with higher applied loads and lower stiffness. In modal analysis, the natural frequency is found to be dependent on the ratio of stiffness to mass, where an increase in stiffness raises the natural frequency, and an increase in mass reduces it. The mode shapes represent relative vibration patterns rather than actual displacement under loading. Overall, the study provides a clear understanding of the mechanical behaviour of U-clamps and highlights the importance of proper design selection. The findings can be used to improve structural strength, reduce failure risk, and optimize the design for better performance in real-world applications.

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 Keywords

FEA, U- CLAMP, OPTISTRUCT, Modal Analysis,static analysis.

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Creative Commons Attribution 4.0 and The Open Definition

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Campus security is a major concern in educational institutions due to the increasing number of students, staff, and visitors. Traditional manual gatepass systems are time-consuming, error-prone, and difficult to monitor effectively. This project proposes a Digital Gatepass Automation System that replaces the manual process with a secure, efficient, and paperless solution. The system allows students and visitors to request gatepasses digitally, which are approved by authorized personnel and verified at entry and exit points using QR codes or unique IDs. Real-time data storage, access control, and automated logs help administrators track movements within the campus. The proposed system enhances security, improves transparency, reduces human effort, and ensures better monitoring of campus activities. Keywords: Digital Gatepass, Campus Security, QR Code, Automation System, Access Control, Smart Campus

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Creative Commons Attribution 4.0 and The Open Definition

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Digital Gatepass, Campus Security, QR Code, Automation System, Access Control, Smart Campus

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Creative Commons Attribution 4.0 and The Open Definition

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This study explores the perceived challenges and motivational strategies of university students in English language learning environments. Focusing on the development of core language skills reading, writing, listening, and speaking the research adopts a mixed-methods design to investigate how students navigate instructional, affective, and environmental barriers in English preparatory and ELT programs. Quantitative data, gathered from 122 students through a standardized difficulty scale, revealed an overall low level of perceived challenge; however, specific difficulties were reported in vocabulary acquisition, pronunciation, and productive skills such as speaking and writing. Qualitative responses provided further insight into learner motivation, emphasizing the role of future goals, enjoyment, and peer interaction. Students frequently referenced self-initiated strategies, including the use of digital tools, music, and conversations, to supplement classroom instruction. The findings highlight the importance of creating supportive, engaging, and learner-centered environments that address both instructional and emotional dimensions of language learning.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

English language learning, learner perceptions, learning environment, speaking anxiety, motivation, vocabulary, university students

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Creative Commons Attribution 4.0 and The Open Definition