Mengying Zhang | Information Technology | Women Researcher Award

Published on by Scientific World Research Awards

Women Researcher Award

Mengying Zhang

Mengying Zhang
Affiliation Anhui University
Country China
Scopus ID 57191031563
Documents 24
Citations 330
h-index 8
Subject Area Information Technology
Event Scientific World Research Awards
ORCID 0000-0002-2789-0459

Mengying Zhang is an academic researcher associated with operational research, supply chain management, pricing strategy, and information technology studies. Her scholarly work includes investigations into online platform systems, cap-and-trade allocation models, opaque selling mechanisms, and probabilistic supply chain structures.[1]

Abstract

Mengying Zhang has contributed to research in information technology and operational research with emphasis on supply chain systems, probabilistic selling, and online marketplace structures. Her publications examine pricing models, capacity allocation mechanisms, and competitive interactions in fashion and digital commerce environments. The Women Researcher Award recognizes her scholarly participation in analytical and technology-driven research addressing modern business operations and decision-making frameworks. Her work reflects interdisciplinary engagement in optimization, platform economics, and strategic operational planning within contemporary information and management systems.[2]

Keywords

Operational Research, Supply Chain Management, Information Technology, Pricing Strategy, Marketplace Systems.

Introduction

Operational research and information technology increasingly influence modern supply chain optimization and digital marketplace systems. Mengying Zhang has participated in research exploring pricing structures, market coordination, and decision-making strategies within technology-driven commercial environments.[3]

Research Profile

Her research profile includes studies on platform supply chains, probabilistic selling, online marketplace systems, and operational optimization models. These publications contribute to analytical approaches in digital commerce and economic decision systems.[1]

Research Contributions

Mengying Zhang has contributed to research concerning cap-and-trade regulations, pricing competition in fashion supply chains, reseller marketplace strategies, and allocation mechanisms within operational systems.[4]

Publications

  • Impact of power structure on probabilistic selling in supply chains
  • Pricing and Capacity Allocation in Opaque Selling
  • Marketplace or reseller? The effect of asymmetric selling cost and demand information

Research Impact

Her research activities contribute to understanding digital commerce structures and operational management systems. The studies provide analytical perspectives applicable to platform economics, resource allocation, and supply chain decision-making processes.[5]

Award Suitability

The Women Researcher Award acknowledges academic engagement, interdisciplinary research participation, and contributions to operational research and information technology studies through peer-reviewed scholarly publications.

Conclusion

Mengying Zhang’s research profile reflects continued scholarly participation in operational research, supply chain systems, and analytical modeling relevant to digital marketplace structures and information technology applications.

References

  1. ORCID. (2026). Mengying Zhang researcher profile and publication record.
    https://orcid.org/0000-0002-2789-0459
  2. Elsevier. (n.d.). Scopus author details: Mengying Zhang, Author ID 57191031563.
    https://www.scopus.com/authid/detail.uri?authorId=57191031563
  3. International Journal of Production Economics. (2026). Impact of power structure on probabilistic selling in supply chains.
    https://doi.org/10.1016/j.ijpe.2026.109940
  4. European Journal of Operational Research. (2024). Pricing and Capacity Allocation in Opaque Selling.
    https://doi.org/10.1016/j.ejor.2024.05.022
  5. International Transactions in Operational Research. (2024). Cap allocation rules for an online platform supply chain under cap-and-trade regulation.
    https://doi.org/10.1111/itor.13268

Jianxi Zhao | Artificial Intelligence | Best Researcher Award

Published on by Scientific World Research Awards

Mr. Jianxi Zhao | Artificial Intelligence | Best Researcher Award

Beijing Information Science and Technology University, China

Mr. Jianxi Zhao is an emerging researcher recognized for his contributions to computational statistics, recurrent event analysis, and advanced statistical modeling. Affiliated with Beijing Information Science & Technology University, he has developed expertise in handling complex quantitative data through innovative analytical methodologies. His scholarly work focuses on improving statistical accuracy in situations involving intermittently observed covariates and dynamic event-driven datasets. With multiple indexed publications and a steadily growing citation record, he has demonstrated academic consistency and research capability within the field of applied statistics. His research activities emphasize methodological precision, mathematical computation, and interdisciplinary problem-solving relevant to modern scientific investigations. Through collaborations with fellow researchers and participation in scholarly publishing, he continues to strengthen his professional visibility and academic impact. Mr. Jianxi Zhao’s dedication to statistical innovation and computational research reflects strong potential for future contributions to global scientific and analytical advancement.

Professional Profile

Education

Jianxi Zhao has established a solid academic background in statistics, computational mathematics, and data-oriented scientific research. Associated with Beijing Information Science & Technology University, he has developed expertise in advanced statistical methodologies, recurrent event analysis, and mathematical modeling. His educational foundation emphasizes quantitative reasoning, analytical computation, and applied statistical interpretation, enabling him to address complex research challenges effectively. Through continuous academic engagement, he has strengthened his understanding of survival analysis, time-varying coefficient models, and intermittently observed covariate techniques. His scholarly preparation reflects dedication to methodological precision and scientific innovation. The combination of theoretical knowledge and computational capability has supported his contributions to statistical sciences and interdisciplinary analytical studies. His educational journey highlights a commitment to rigorous research practices, academic discipline, and the advancement of modern computational statistics for practical and scientific applications.

Professional Experience

Mr. Jianxi Zhao has gained valuable academic and research experience through active involvement in computational statistics and analytical modeling studies. His professional activities include conducting statistical investigations, contributing to scholarly publications, and collaborating with researchers in quantitative science disciplines. Working within the research environment of Beijing Information Science & Technology University, he has participated in projects focusing on recurrent event data, predictive modeling, and applied statistical methodologies. His experience reflects competence in handling complex datasets, developing mathematical frameworks, and interpreting analytical outcomes for scientific purposes. He has also contributed to collaborative research networks involving multiple co-authors and interdisciplinary perspectives. Through publication activities and academic engagement, he has strengthened his professional reputation within computational and statistical research communities. His growing experience demonstrates dedication to scientific inquiry, problem-solving, and the application of innovative statistical techniques in contemporary research environments.

Research Interest

The research interests of Jianxi Zhao primarily focus on computational statistics, recurrent event analysis, survival data modeling, and time-varying coefficient methodologies. His scholarly attention is directed toward developing advanced statistical approaches capable of addressing incomplete or intermittently observed covariate information in complex datasets. He is particularly interested in improving analytical accuracy and predictive reliability within biomedical statistics, longitudinal data interpretation, and mathematical computation. His work explores innovative techniques that enhance the understanding of event-driven data structures and dynamic statistical relationships. In addition, he demonstrates interest in interdisciplinary applications where computational modeling supports scientific and technological advancements. His research orientation combines theoretical development with practical implementation, contributing to the evolution of modern statistical science. By investigating sophisticated analytical frameworks, he aims to provide meaningful solutions for complex quantitative challenges across academic and applied research domains.

Award and Honor

Mr. Jianxi Zhao has earned academic recognition through his impactful research contributions in computational statistics and applied data analysis. His scholarly publications, citation record, and collaborative research activities reflect growing recognition within the scientific community. With indexed publications and measurable citation impact, he has demonstrated the quality and relevance of his research work in statistical modeling and recurrent event analysis. His contributions have strengthened his professional standing as an emerging researcher in computational and mathematical sciences. Participation in collaborative academic studies and publication in recognized scientific platforms further highlights his dedication to research excellence. Although publicly available information regarding formal awards remains limited, his academic performance, research productivity, and methodological contributions represent significant professional achievements. His growing citation influence and consistent engagement in advanced statistical research position him as a promising contributor to future scientific innovation and scholarly development within the international research landscape.

Conclusion

Mr. Jianxi Zhao demonstrates strong potential in computational statistics through impactful research, scholarly dedication, and analytical expertise. His growing academic influence and innovative statistical contributions support continued success in advanced scientific research.

Publications Top Noted

  • Title: A time-varying coefficient rate model with intermittently observed covariates for recurrent event data
    Authors: Jianxi Zhao et al.
    Year: 2025

Maliki Moustapha | Computer Science | Best Researcher Award

Published on by Scientific World Research Awards

Dr. Maliki Moustapha | Computer Science | Best Researcher Award

PhD | Erciyes University | Turkey

Dr. Maliki Moustapha, an accomplished researcher from Erciyes University, is recognized for his expertise in Artificial Intelligence (AI), Deep Transfer Learning, and Data Engineering, with a strong focus on the integration of intelligent algorithms and data-driven models to address real-world computational challenges. His academic background is rooted in computer science and engineering, where he developed advanced skills in machine learning, neural networks, data mining, and smart systems design. Professionally, Dr. Moustapha has been actively engaged in both research and academic mentorship, contributing to the development of innovative solutions in AI-powered automation, pattern recognition, and intelligent monitoring systems. His major research interests encompass computer vision, deep learning model optimization, spatiotemporal data analysis, and Internet of Things (IoT)-based smart healthcare systems. Among his most cited contributions is the publication titled “A Novel YOLOv5 Deep Learning Model for Handwriting Detection and Recognition” in the International Journal on Artificial Intelligence Tools (2023), which demonstrates superior accuracy and efficiency in image recognition. He has also published influential works on spatial and spatiotemporal clustering algorithms and IoT-based patient monitoring, bridging the gap between data intelligence and applied computing. His research skills span across Python programming, neural network modeling, big data analytics, data preprocessing, and model training for intelligent systems. Though early in his academic journey, Dr. Moustapha has earned recognition for his impactful work, showing promising potential in advancing AI technologies. According to Scopus and Google Scholar, he has achieved 9 citations, an h-index of 1, and several published documents reflecting growing international recognition. Dr. Moustapha’s research continues to contribute meaningfully to the fields of artificial intelligence and computational intelligence. In conclusion, his innovative approach, interdisciplinary mindset, and technological vision position him as a forward-thinking researcher committed to shaping the next generation of intelligent data systems and AI-driven innovations.

Profiles: ORCID | Google Scholar

Featured Publications

1. Moustapha, M., Taşyürek, M., & Öztürk, C. (2023). A novel YOLOv5 deep learning model for handwriting detection and recognition. International Journal on Artificial Intelligence Tools, 32(04), 2350016.

2. Moustapha, M. (2024). Spatial and spatiotemporal clustering algorithms in data mining. In Proceedings of the 3rd International Conference on Data and Electronics and Computing (ICDEC).

3. Moustapha, M. (2019). Alternative approach of patient monitoring system based on Internet of Things. In Proceedings of the II. International Science and Academic Congress (INSAC).

Lina Alnaddaf | Nanotechnology | Best Researcher Award

Published on by Scientific World Research Awards

Assoc. Prof. Dr. Lina Alnaddaf | Nanotechnology | Best Researcher Award

Associate Professor | Homs University | Syria

Assoc. Prof. Dr. Lina M. Alnaddaf is an accomplished academic and researcher in the Department of Biotechnology and Molecular Biology at Homs University, Syria. With a strong foundation in plant molecular genetics and biotechnology, Dr. Alnaddaf has built a distinguished career dedicated to advancing sustainable agriculture and crop improvement through molecular and nanobiotechnological approaches. She earned her doctoral degree in Biotechnology and Molecular Biology, focusing on the genetic diversity and molecular breeding of major cereal crops such as wheat and Aegilops species, which are crucial for food security and genetic conservation. Over the years, Dr. Alnaddaf has served as an educator and researcher, mentoring students and leading innovative projects that integrate molecular breeding, genomics, and nanotechnology for improving plant tolerance to abiotic stresses. Her primary research interests include molecular breeding strategies, plant-derived nanomaterials, genetic variability, crop stress physiology, and the application of nanotechnology in agriculture. She possesses advanced research skills in DNA sequencing, molecular marker analysis, nanoparticle biosynthesis, and plant genetic transformation, reflecting her interdisciplinary expertise. Dr. Alnaddaf has authored numerous influential publications, including chapters in Springer Nature and Elsevier volumes, on topics such as nanomaterial interactions with plant cellular mechanisms, nanofertilizers, and green synthesis of nanoparticles. Her research achievements are recognized globally, earning her academic distinction and participation in international scientific collaborations. According to google Scholar, she has  accumulated 200 citations, and holds an h-index of 9, reflecting her consistent scholarly impact. Dr. Alnaddaf has been honored for her contributions to nanobiotechnology and molecular breeding, particularly in developing sustainable strategies for crop resilience and productivity. In conclusion, Assoc. Prof. Dr. Lina Alnaddaf exemplifies a forward-thinking scientist whose pioneering work at the intersection of molecular biology and nanotechnology continues to inspire advancements in modern agricultural biotechnology and sustainable food production.

Profiles: Scopus | ORCID | Google Scholar

Featured Publications

Abu-Ellail, F. F. B., Salem, K. F. M., Saleh, M. M., Alnaddaf, L. M., & Al-Khayri, J. M. (2021). Molecular breeding strategies of beetroot (Beta vulgaris ssp. vulgaris var. conditiva Alefeld). In Advances in Plant Breeding Strategies: Vegetable Crops, Volume 8: Bulbs. [Citations: 32]

Saleh, M. M., Alnaddaf, L. M., Almuhammady, A. K., Salem, K. F. M., & Alloosh, M. T. (2021). Applications of plant-derived nanomaterials in mitigation of crop abiotic stress. In Nanobiotechnology: Mitigation of Abiotic Stress in Plants (pp. 201–238). [Citations: 20]

Al-Khayri, J. M., Alnaddaf, L. M., & Jain, S. M. (2023). Nanomaterial interactions with plant cellular mechanisms and macromolecules and agricultural implications. Springer Nature. [Citations: 19]

Salem, K. F. M., Alloosh, M. T., Saleh, M. M., Alnaddaf, L. M., & Almuhammady, A. K. (2021). Utilization of nanofertilizers in crop tolerance to abiotic stress. In Nanobiotechnology: Mitigation of Abiotic Stress in Plants (pp. 261–289). [Citations: 18]

Alnaddaf, L. M., Almuhammady, A. K., Salem, K. F. M., Alloosh, M. T., & Saleh, M. M. (2021). Green synthesis of nanoparticles using different plant extracts and their characterizations. In Nanobiotechnology: Mitigation of Abiotic Stress in Plants (pp. 165–199). [Citations: 15]

Nassim Bout | Computer Science | Best Researcher Award

Published on by Scientific World Research Awards

Mr. Nassim Bout | Computer Science | Best Researcher Award

Senior Central Officer |  Hassan II University of Casablanca | Morocco

Mr. Nassim Bout is an accomplished adjunct professor and researcher in computer engineering, healthcare information systems, and artificial intelligence, recognized for his innovative contributions to AI-driven healthcare solutions, digital hospital services, and bioinformatics applications. He holds a Ph.D. in Engineering Sciences (Computer Engineering) from ENSEM, Hassan II University, Casablanca (2021–2024), and a Research Master’s in Management and Modeling of Complex Information Systems from ENSIAS, Mohammed V University, Rabat (2019–2021), reflecting a strong academic foundation in both technical and management aspects of complex information systems. Professionally, he has served as Senior Central Officer at the Ministry of Interior, coordinating digital transformation and IT services, as well as Product Owner at Netopia Solutions, where he led IT ecosystem studies and consulting for healthcare institutions. He has also contributed to Afrihealth Solutions as a software engineer, implementing hospital information systems across multiple regions. His research interests include AI integration in patient-centered healthcare, enterprise architecture in oncology, bioinformatics, and healthcare digital transformation. Mr. Bout possesses advanced skills in artificial intelligence and machine learning (deep learning, NLP, computer vision), algorithms and programming (C, C++, Python, JavaScript, PHP, Django), system design and architecture (TOGAF, UML, ArchiMate), project management frameworks (Agile, SCRUM, KANBAN), database management (SQL, PostgreSQL, Oracle DB), and bioinformatics tools, alongside strong communication and organizational abilities. He has an extensive publication record in reputed journals and conferences, including Discover Internet of Things, IJECE, Springer Lecture Notes, and ISDA proceedings, with citations by 1 document, 1 document, and an h-index reflecting his scholarly impact. He actively participates in professional societies as a reviewer for TELKOMNIKA, Digital Health, Cyber-physical Systems, and Network Modeling Analysis in Health Informatics and leads social inclusion initiatives through e-MOBADARA. Awards and honors include international conference recognitions and certifications in project management, scientific publishing, and bioinformatics training. In conclusion, Mr. Bout demonstrates exceptional interdisciplinary expertise, leadership, and scholarly influence, with strong potential to advance global research in AI-driven healthcare systems, mentor emerging researchers, and contribute to high-impact publications, international collaborations, and societal development through innovative healthcare technologies.

Profiles: Scopus | ORCID | Google Scholar

Featured Publications

  1. Bout, N., Khazaz, R., Azougaghe, A., El-Hfid, M., Abik, M., & Belhadaoui, H. (2021). Implementation of the business process model and notation in the modelling of patient’s clinical workflow in oncology. International Conference on Intelligent Systems Design and Applications, 576–586. Citations: 2

  2. Bout, N., Moukhliss, G., Belhadaoui, H., Afifi, N., & Abik, M. (2025). Integrating emotional AI, IoT, and robotics for patient-centered healthcare: Challenges and future directions. Discover Internet of Things, 5(75), 18. Citations: 1

  3. Bout, N., Azougaghe, A., Belhadaoui, H., El-Hfid, M., & Khazaz, R. (2022). Business process model and notation implemented in the hospital, any use? Case of the patient clinical workflow. Journal of Network and Innovative Computing, 10, 8–8. Citations: 1

  4. Bout, N., Ouzayd, F., & Retmi, K. (2025). Erratum for Role of Hospital Digital Services in Improvement of Clinical Regime of Patients [Health Tech Asmnt Act. 2021; 5(1)]. Health Technology Assessment in Action.

  5. Bout, N., Belhadaoui, H., Afifi, N., Abik, M., El-Hfid, M., & Azougaghe, A. (2025). Towards a standardized enterprise architecture: Enhancing decision-making in oncology multidisciplinary team meetings. International Journal of Electrical and Computer Engineering (IJECE), 15(2), 2224–2236.