Volume 14 | Issue 4 |

 Abstract

Abstract: Garment construction in fashion design commonly uses two primary techniques: flat pattern making and draping. Flat pattern making involves drafting patterns on paper using body measurements, while draping focuses on shaping fabric directly on a dress form to develop garment structure and fit. Both methods form the foundation of garment development in fashion design and education. An innovative approach that extends these traditional techniques is Transformational Reconstruction (TR), developed by Shingo Sato. In this method, a basic garment block is placed on a mannequin and new style lines are drawn directly onto the garment. The garment is then cut along these lines, and the pieces are manipulated and flattened to create new pattern pieces. This process transforms the original garment structure into innovative designs with sculptural silhouettes, asymmetrical seams, and geometric panels. A critical analysis of TR highlights both its strengths and limitations. The technique promotes creativity, experimental pattern development, and integration of two-dimensional and three-dimensional design approaches. However, it also requires high technical skill, involves time-consuming processes, and may be difficult to adapt for mass production. Despite these challenges, TR remains significant in contemporary fashion design and fashion education.

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 Keywords

Transformational Reconstruction, Garment Construction, Draping Techniques, Flat Pattern Making, Fashion Design Innovation

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Creative Commons Attribution 4.0 and The Open Definition

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The conducted research studies examine and analyse the impact of strategies of green marketing on the consumer's purchase decision. It focuses on the intention-action gap in the Indian context. Primary data was collected from 68 respondents mainly based in Delhi-NCR using a structured questionnaire and analysed using the method of correlation analysis. The findings indicated that eco-labelling had a strong and powerful positive influence on the purchase decisions, while awareness shows only a weak relationship. Green advertising and consumer trust have a minimal impact, whereas price sensitivity acts as a barrier to the adoption of green products. These results confirm that although the consumers are environmentally aware, this does not always convert to the actual purchasing behaviour. The study concludes that effective green marketing requires credible information, transparency and affordable pricing to influence consumer decisions.

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Green Marketing, Ecolabelling, Sustainability, Environmental Awareness, Consumer Behaviour, Green Advertising, Price Sensitivity

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Commonsense question answering demands reasoning over tacit, experiential knowledge that is rarely stated explicitly in text, making it a persistent challenge for artificial intelli- gence systems. Approaches based solely on neural networks demonstrate strong capabilities in linguistic representation and pattern learning, but often exhibit limited transparency and weak explicit reasoning abilities. In contrast, symbolic reasoning frameworks support formal inference and inter- pretability, but face difficulties in handling large-scale data and the variability inherent in natural language. Neuro- symbolic artificial intelligence has emerged as a unifying paradigm that combines neural learning mechanisms with structured knowledge representations and symbolic infer- ence processes. This survey systematically analyzes neuro- symbolic approaches developed for commonsense ques- tion answering, organizing existing methods according to their architectural designs and reasoning paradigms. In ad- dition, it reviews widely adopted datasets and evaluation benchmarks and discusses open challenges, limitations, and promising directions for future research in this domain

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 Keywords

Neuro-symbolic AI, Commonsense Question Answering, hybrid reasoning, symbolic reasoning, neural-symbolic integration, knowledge graph reasoning, symbolic knowledge grounding, contextual reasoning, explainable AI, interpretable reasoning, logical inference, multi-hop reasoning, graph neural networks, transformer-based reasoning, large language models, retrieval-augmented generation, commonsense knowledge integration, semantic reasoning, ontology-based inference, dynamic knowledge retrieval, answe

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Early diagnosis of chronic and life-threatening diseases remains a significant challenge in modern healthcare systems, particularly in rural and resource-constrained environments. Conditions such as cardiomegaly, diabetes, hypertension, and anemia often go undetected until they reach advanced stages due to limited access to diagnostic tools and medical expertise. This paper presents an AI-based multi-modal health screening system that integrates deep learning techniques with clinical parameter analysis to provide a comprehensive and accessible diagnostic solution. The proposed system employs a Convolutional Neural Network (CNN) model to analyze chest X-ray images for the detection of cardiomegaly, while simultaneously evaluating patient health parameters such as blood pressure, blood sugar levels, body mass index, hemoglobin concentration,and creatinine levels to predict multiple diseases. A user-friendly web interface is developed using Streamlit to facilitate seamless interaction, real-time predictions, and clear visualization of results. The system is designed to assist in preliminary screening and does not aim to replace professional medical diagnosis. Experimental evaluation demonstrates that the proposed approach is efficient, scalable, and capable of delivering reliable results, thereby making it suitable for deployment in low-resource healthcare settings.

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Artificial Intelligence, Deep Learning, Cardiomegaly Detection, Health Screening, Convolutional Neural Networks, Clinical Decision Support

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Creative Commons Attribution 4.0 and The Open Definition

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Abstract: Aim: The study aims to find the effectiveness of information booklet on knowledge regarding oral health hazards of tobacco among adolescent. Problem statement: "A study to assess the effectiveness of information booklet regarding knowledge on oral health hazards of tobacco among adolescent in selected area''. Primary objective: the primary objective of study was to find out the effectiveness of information booklet on knowledge regarding oral health hazards of tobacco among adolescent. Secondary objective: 1.To assess the existing knowledge regarding oral health hazards of tobacco among adolescent. 2.To find the effectiveness of information booklet on knowledge regarding oral health hazards of tobacco among adolescent.3. To determine the association between the pre test knowledge scores of adolescent with selected demographic variables. Method: Quasi- experimental pre experimental with one group pre test and post test research design use for the study. It was conducted over 60 people by using convenient sampling technique. Result: Study reveals that in post test 6(10%) had Inadequate knowledge, 14(23%) had Moderately Adequate knowledge and 40(67%) had Adequate knowledge. Study reveals that that the pre-test mean score of the adolescent was 4.5 and post -test of the mean score was 12.7. And Standard deviation was 19.50. The obtained 't'-value was 1.542 was greater than the table value (1.02) at 29 degree of freedom at 0.05 level of significance. Therefore it was concluded that there was significant gain in knowledge. Therefore H1 was accepted .Interpretation and conclusion: On the basis of the findings of the study the below said conclusion were drawn. It also brings out the limitations of the study in picture. The implication are given on the various aspect like Nursing Practice, Nursing Education, Nursing Administration and Nursing Research and it also give insight in the future studies.

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effectiveness, information booklet, adolescents, tobacco

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Creative Commons Attribution 4.0 and The Open Definition

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Background: Acetabular fractures following road traffic injuries are managed conservatively or surgically depending on fracture stability and displacement. Coccygeal fracture-dislocation can cause severe pain and functional limitation. In Ayurveda, trauma-induced fractures are correlated with Asthi Bhagna and aggravated Vata Dosha, where Basti therapy is considered the principal line of management. Purpose: To evaluate the role of Mahasneha Basti in the conservative management of bilateral acetabular and coccygeal fractures following road traffic injury. Case Presentation and Methods: A 28-year-old male presented with severe right lower limb pain, low back pain and inability to stand following a fall from a scooty. MRI of hip joint revealed a comminuted undisplaced fracture involving the anterior wall of the right acetabulum and an undisplaced fracture of the anterior wall of the left acetabulum with marrow edema. X-ray of the sacrococcygeal region showed fracture-dislocation of the second and third coccygeal segments with incurving. The patient was managed conservatively with pelvic stabilization and integrative Ayurvedic therapy with Mahasneha Anuvasana Basti administered over three months in a structured tapering regimen. Supportive therapies included Sthanik Pichu, Bashpa Sweda, internal medications, and physiotherapy. Results: Gradual reduction in pain, improved weight-bearing capacity and enhanced mobility were observed during follow-up. Radiographic evaluation after one month demonstrated callus formation, indicating progressive fracture healing. Conclusion: Mahasneha Basti,as part of integrative conservative management, may contribute to pain reduction, functional recovery and fracture healing in stable trauma-induced Asthi Bhagna. Further clinical studies are warranted to validate these findings.

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Mahasneha Basti,Asthi Bhagna,Acetabular fracture, Coccygeal fracture, Road traffic injury, Basti therapy, Integrative management, Vata Dosha

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Creative Commons Attribution 4.0 and The Open Definition

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The rapid growth of cloud-based applications has increased the demand for scalable, secure, and energy-efficient computing solutions.Traditional cloud infrastructures often rely on continuously running servers, leading to higher operational costs and unnecessary energy consumption. To address these challenges, this paper proposes a secure serverless architecture deployment that promotes green innovation and sustainable digital infrastructure.The proposed system leverages serverless computing services from multiple cloud providers, including AWS and Google Cloud, to dynamically allocate resources based on demand. By eliminating idle server instances, the architecture significantly reduces energy usage and operational overhead while improving scalability and fault tolerance. Security is enhanced through cloud-native identity management, encrypted data storage, and event-driven access control mechanisms. The multi-cloud design improves system reliability and avoids vendor lock-in, enabling efficient workload distribution across platforms. Experimental evaluation demonstrates reduced response latency, cost efficiency, and improved resource utilization compared to traditional server-based models. The results highlight how serverless and multi-cloud technologies can contribute to sustainable growth by minimizing carbon footprint and optimizing computational resources. This work demonstrates that secure serverless multicloud architectures are a viable solution for building environmentally sustainable, scalable, and resilient cloud applications, supporting the goals of green computing and sustainable technological development.

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Serverless Computing, Multi-Cloud Architecture, Cloud Security, Function-as-a-Service (FaaS), Event-Driven Architecture, Fault Tolerance, High Availability, Scalability, Vendor Lock-in Avoidance, Identity and Access Management (IAM), API Security, Data Encryption, Cost Optimization, Green Computing, Sustainable Computing

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Creative Commons Attribution 4.0 and The Open Definition

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Multiple AI tools in one platform. all in one ai that have tools like Generate articles, blog title generation, image generation , background removal , remove objects , email writing , resume review and the community page where multiple user can share there AI creations and multiple users can see and like the creations

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Creative Commons Attribution 4.0 and The Open Definition

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Multiple AI tools in one platform

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Creative Commons Attribution 4.0 and The Open Definition

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Modern software systems, especially in e-commerce and microservice architectures, face challenges in predicting the impact of code changes due to complex interdependencies. Even minor modifications can cause cascading failures, affecting performance and reliability. Traditional approaches such as manual analysis and static dependency mapping often fail to capture dynamic behaviors and hidden dependencies. This paper proposes a lightweight, data-driven framework for offline change impact prediction that preserves data privacy. The system integrates Git-based change extraction, Abstract Syntax Tree (AST) analysis, dependency graph construction, and machine learning models (Random Forest and Logistic Regression) trained on historical data to estimate risk with probabilistic confidence scores. It also incorporates pattern mining to detect recurring failure trends. The framework consists of five modules: Change Input, Dependency Mapping, History & Pattern Learning, Risk Prediction, and Risk Advisory. It identifies affected components, classifies risk levels (Low, Medium, High), and provides actionable mitigation guidance. Experimental results show that the system delivers accurate risk predictions and practical decision support while operating on standard hardware without cloud dependencies. Unlike existing solutions, it ensures data sovereignty and is suitable for resource-constrained environments, addressing key gaps such as probabilistic prediction, project-specific learning, runtime awareness, and lightweight deployment.

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Change Impact Analysis, Data-Driven Risk Prediction, Machine Learning, Dependency Mining, Microservices, Software Maintenance, Offline Deployment, Privacy-Preserving Analysis, Lightweight Machine Learning, Risk Advisory, Probabilistic Forecasting, Software Evolution, Startup-Friendly Tools, Code Analysis, Historical Pattern Discovery.

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Non-communicable chronic diseases like hypertension and diabetes demand prolonged adherence to prescribed medicines. But poor adherence has been observed, especially among Indians. Socio-cultural beliefs and psychological aspects have played a significant role in determining the health behaviors of patients. The current study was carried out to investigate the impact of socio-cultural beliefs and psychological factors like belief in myths, locus of control externally oriented towards health, denial, and fear on medication adherence in patients of hypertension and diabetes. Descriptive and correlational research methodology was utilized in this study, using a sample size of 100 people (50 with hypertension and 50 with diabetes). A structured questionnaire, which comprised the Myth Belief Inventory developed by the researcher himself and questions related to medication adherence, external health locus of control, denial, and fear, was used to collect data. Descriptive statistics, Pearson's correlation, multiple regression analysis, and independent samples t-test were used to analyze the data collected. The results showed that myth beliefs were moderately prevalent among the participants. There was an important positive correlation between myths and medication adherence (r = 0.53, p < 0.001), suggesting a complicated interplay of beliefs and behaviors. An external locus of control was also positively correlated with myths (r = 0.50, p < 0.001). The regression analysis found that denial (? = 0.59, p < 0.001) and fear (? = 0.42, p < 0.01) were strong predictors of myths; denial had the highest coefficient among them. Almost a significant difference was seen between the hypertensive and diabetic samples. This study underlines the importance of tackling psychological aspects to improve compliance with treatment regimens. Programs that help eliminate misconceptions, overcome emotional obstacles, and correct perceptions are vital.

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 Keywords

Medication Adherence, Myth Beliefs, External Health Locus of Control, Psychological Factor

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Creative Commons Attribution 4.0 and The Open Definition