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Volume 2, Issue 1 - 2026 (April - June 2026)

Volume 2 Issue 1 Cover
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Issue Details:

Volume 2 Issue 1
Published:Apr 6, 2026

Editorial: April - June 2026

Welcome to the 2026 issue of Demo Journal from ScholarJMS. This issue showcases the remarkable breadth and depth of contemporary research across multiple disciplines. From cutting-edge applications of machine learning in climate science to the revolutionary potential of quantum computing in drug discovery, our featured articles demonstrate the power of interdisciplinary collaboration in addressing global challenges.

We are particularly excited to present research that bridges traditional academic boundaries, reflecting our journal's commitment to fostering innovation through cross-disciplinary dialogue. The integration of artificial intelligence with environmental science, the application of blockchain technology to supply chain management, and the convergence of urban planning with smart city technologies exemplify the transformative potential of collaborative research.

As we continue to navigate an era of rapid technological advancement and global challenges, the research presented in this issue offers both insights and solutions that will shape our future. We thank our authors, reviewers, and editorial board members for their continued dedication to advancing knowledge and promoting scientific excellence.

ScholarJMS
Editor in Chief
Demo Journal from ScholarJMS

Articles in This Issue

Showing 6 of 6 articles
Research PaperID: demo21Pages 1-10Closed AccessClosed Access

Artificial Intelligence Readiness Among Small and Medium Enterprises: A Survey Based Assessment Framework

Mr. Darshan Patel (ME CE)

Artificial Intelligence (AI) has become an essential technology for improving business efficiency and competitiveness. However, many Small and Medium Enterprises (SMEs) continue to face challenges in adopting AI due to financial limitations, lack of technical expertise, and uncertainty regarding return on investment. This demonstration study proposes an AI Readiness Framework and presents fictional survey data from 150 SMEs to illustrate how such a study may be structured. The findings indicate that while business leaders recognize the strategic importance of AI, implementation readiness remains moderate, emphasizing the need for affordable AI solutions, employee training, and organizational planning.

Artificial IntelligenceSMEsDigital TransformationBusiness StrategyTechnology Adoption
2,811 views
755 downloads

Contributors:

 Mr. Darshan Patel (ME CE)
Research PaperID: demo210033Pages 1-2Open AccessOpen Access

Artificial Intelligence-Driven Predictive Analytics for Improving Patient Outcomes in Healthcare

Dr. Rahul Mehta

The rapid growth of electronic health records and digital healthcare systems has generated vast amounts of patient data, creating opportunities for data-driven clinical decision-making. This study investigates the effectiveness of machine learning-based predictive analytics in identifying patients at risk of chronic diseases at an early stage. A retrospective dataset comprising 50,000 anonymized patient records was analyzed using supervised learning algorithms, including logistic regression, random forests, and gradient boosting techniques. The proposed framework integrates demographic information, clinical indicators, lifestyle factors, and historical medical records to develop predictive models for disease risk assessment. Performance evaluation was conducted using accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC). Experimental results demonstrated that the gradient boosting model achieved the highest predictive performance, with an AUC-ROC score of 0.92 and an overall accuracy of 89.4%. The findings suggest that machine learning models can significantly improve early disease detection and support healthcare professionals in making timely interventions. The study highlights the potential of predictive analytics to reduce healthcare costs, optimize resource allocation, and enhance patient outcomes while addressing challenges related to data privacy, model interpretability, and ethical considerations.

Artificial IntelligencePredictive AnalyticsMachine LearningHealthcare InformaticsDisease Risk PredictionClinical Decision Support+1 more
2,501 views
739 downloads

Contributors:

 Dr. Rahul Mehta
Research PaperID: demo210039Pages 1-1Closed AccessClosed Access

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ReceivedRevisedAcceptedCitation for the PaperCopyright 2026 International Research Journal of Scientific Reports and Reviews All rights reserved
3,328 views
1,045 downloads

Contributors:

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Research PaperID: demo210076Pages 3-7Open AccessOpen Access

Demo Article

Demo User

Demo abstract For Testing. This is a Demo Description.

DemoKeywordForScholarJMS
2,278 views
718 downloads

Contributors:

 Demo User
Research PaperID: demo210084Pages 8-12Closed AccessClosed Access

Blockchain-Based Supply Chain Management for Enhanced Transparency and Traceability

Dr. Arjun Malhotra, Bhaumik

Modern supply chains involve multiple stakeholders, including manufacturers, suppliers, distributors, logistics providers, and retailers. The increasing complexity of these networks often results in limited visibility, data inconsistencies, and challenges in product traceability. This study proposes a blockchain-based framework for supply chain management that enhances transparency, security, and operational efficiency through decentralized record-keeping. The proposed system integrates blockchain technology with Internet of Things (IoT) sensors and smart contracts to enable real-time monitoring and automated verification of supply chain events. Every transaction and product movement is recorded on a distributed ledger, ensuring data immutability and reducing the risk of fraud, counterfeiting, and unauthorized modifications. A prototype implementation was evaluated using a pharmaceutical supply chain scenario involving multiple stakeholders. Performance analysis demonstrated a 35% reduction in product verification time, a 27% improvement in inventory visibility, and a significant decrease in administrative overhead compared with traditional centralized systems. The findings indicate that blockchain technology can transform supply chain operations by improving trust, accountability, and end-to-end traceability. However, challenges related to scalability, interoperability, regulatory compliance, and implementation costs must be addressed to enable widespread adoption.

BlockchainSupply Chain ManagementSmart ContractsInternet of ThingsTraceabilityLogistics+2 more
2,488 views
891 downloads

Contributors:

 Dr. Arjun Malhotra
,
 Bhaumik

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