Diana Al-Nabulsi|Best Research Article Award| Graph Neural Networks

Published on by Network Science

Best Research Article Award

Diana Al-Nabulsi
Western Michigan University, United States

Diana Al-Nabulsi
Affiliation Western Michigan University
Country United States
Scopus ID 59982332800
Documents 6
Citations 21
h-index 2
Subject Area Graph Neural Networks
Event International Research Awards on Network Science & Graph Analytics

Diana Al-Nabulsi recognizes scholarly contributions that demonstrate research quality, originality, methodological development, and relevance to advancing scientific knowledge. This academic recognition profile presents Diana Al-Nabulsi of Western Michigan University, United States, whose research interests are associated with Graph Neural Networks. The profile summarizes the available bibliometric information and outlines the relevance of graph-based learning methods to contemporary network science and graph analytics. The researcher is featured in connection with the International Research Awards on Network Science & Graph Analytics.

1. Abstract

Graph Neural Networks (GNNs) represent an important area of machine learning designed to process information structured as graphs. They enable computational models to learn relationships among interconnected entities, including nodes, edges, and associated features. These methods have applications in network analysis, pattern recognition, recommendation systems, knowledge representation, and complex relational data processing. This article presents the academic profile of Diana Al-Nabulsi, affiliated with Western Michigan University, whose listed subject area is Graph Neural Networks. The available Scopus profile reports six documents, 21 citations, and an h-index of 2. These indicators provide a quantitative overview of the indexed research record and serve as contextual information for academic recognition. [1][2]

2. Keywords

Graph Neural Networks; Graph Machine Learning; Network Science; Graph Analytics; Deep Learning; Relational Data; Computational Intelligence; Artificial Intelligence; Graph Representation Learning; Network Analysis.

3. Introduction

The growing availability of interconnected datasets has increased the importance of computational techniques capable of representing complex relationships. Traditional machine learning approaches frequently operate on structured feature vectors, whereas graph-based methods incorporate relationships and connectivity patterns into the learning process. Graph Neural Networks address this requirement by combining information from connected nodes through computational operations commonly described as message passing and neighborhood aggregation. [3]

Graph-based learning has become relevant across scientific and technological disciplines because many real-world systems can be represented as networks. Examples include communication infrastructures, biological interactions, transportation systems, social relationships, and knowledge graphs. Research in this area investigates how structural information and node attributes can be integrated to improve predictive and analytical capabilities.

Within this broader research landscape, Diana Al-Nabulsi’s listed subject specialization provides a relevant academic connection to the field of graph-based computational methods. Her profile is presented in the context of research recognition associated with network science and graph analytics.

4. Research Profile

Diana Al-Nabulsi is associated with Western Michigan University in the United States. Her identified research subject area is Graph Neural Networks, a field that combines graph theory, machine learning, and computational modeling to study structured relational information.

The supplied Scopus author identifier is 59982332800. The corresponding profile information reports six indexed documents, 21 citations, and an h-index of 2. These bibliometric figures offer a snapshot of the research record represented by the supplied author profile. Citation counts and h-index values can change as publications are indexed and cited, and should therefore be interpreted in relation to the date of database access. [1]

The researcher’s Google Scholar profile provides an additional source for exploring scholarly publications and citation information. Different academic databases may report different totals because of their indexing coverage, document matching, and update schedules. [2]

5. Research Contributions

Graph Neural Networks contribute to the development of machine learning methods that account for relationships among data entities. Their underlying approaches provide a framework for analyzing complex networks while incorporating both local neighborhood information and broader structural patterns. Research in this area includes several important directions:

  • Graph representation learning: Developing numerical representations of graph nodes, edges, and entire network structures for computational analysis.
  • Node classification: Applying graph structure and node attributes to categorize entities within interconnected datasets.
  • Link prediction: Investigating potential relationships between nodes based on existing network structures and learned representations.
  • Graph classification: Developing methods for categorizing complete graphs and structured relational datasets.
  • Network analysis: Examining connectivity, structural properties, and relationships in complex systems.
  • Computational efficiency: Exploring scalable learning methods for large graphs and computationally demanding network applications.

These research directions demonstrate the broader scientific relevance of Graph Neural Networks. Specific contributions attributable to an individual researcher should be established through verified publications and associated research records rather than inferred solely from subject specialization.

6. Publications

The supplied Scopus author information lists six documents associated with the researcher’s profile. These indexed documents provide a starting point for examining the researcher’s scholarly output, subject coverage, and publication activity. [1]

A comprehensive publication assessment should consider the originality of each contribution, the relevance of the research question, the suitability of the methodology, the quality of the publication venue, and the potential contribution to the wider research community. Publication titles, journal details, publication years, and DOI identifiers should be obtained directly from verified bibliographic records.

No individual publication titles or DOI identifiers have been supplied for this profile. Consequently, specific article titles, journal names, and DOI links are not listed here to avoid introducing unverified bibliographic information.

7. Research Impact

Research impact can be examined through several complementary indicators, including scholarly citations, publication output, research collaboration, methodological contributions, and the application of findings in related disciplines. The supplied Scopus profile reports 21 citations across six documents, with an h-index of 2. These figures provide quantitative context but do not independently establish the quality or practical significance of individual research contributions. [1]

In Graph Neural Networks, research impact may also be reflected in the development of computational techniques that support network prediction, relational learning, and the analysis of complex datasets. The wider field has applications in artificial intelligence, computational biology, cybersecurity, knowledge discovery, and intelligent information systems.

A balanced evaluation should combine quantitative bibliometric indicators with qualitative examination of research originality, reproducibility, methodological soundness, and relevance to scientific or technological challenges.

8. Award Suitability

The Best Research Article Award recognizes research articles that demonstrate originality, scholarly rigor, methodological clarity, and meaningful contributions to their respective disciplines. The recognition is intended to highlight research that advances knowledge and provides a valuable foundation for further academic investigation.

Diana Al-Nabulsi’s identified specialization in Graph Neural Networks establishes a subject-area connection with the International Research Awards on Network Science & Graph Analytics. The supplied bibliometric information also provides an initial reference for reviewing her academic research profile.

A comprehensive award assessment should consider the following criteria:

  1. Originality: The extent to which the research presents new ideas, methods, findings, or interpretations.
  2. Scientific quality: The strength of the research design, methodology, analysis, and supporting evidence.
  3. Research relevance: The significance of the work to Graph Neural Networks, network science, and related academic disciplines.
  4. Publication quality: The scholarly standing, peer-review standards, and relevance of the publication venue.
  5. Research impact: Evidence of scholarly influence, citations, practical relevance, and potential for further development.
  6. Supporting documentation: Verification of authorship, institutional affiliation, publication records, and relevant research achievements.

Final award eligibility and selection should be determined through the official nomination requirements and independent evaluation of the relevant research article. The information presented in this profile does not constitute confirmation of an award decision.

9. Conclusion

Diana Al-Nabulsi, affiliated with Western Michigan University, United States, is associated with the research area of Graph Neural Networks. The supplied academic profile records six Scopus-indexed documents, 21 citations, and an h-index of 2. Her identified subject area is relevant to the expanding intersection of artificial intelligence, graph theory, and network science.

The Best Research Article Award provides a framework for recognizing scholarly work that demonstrates originality, methodological rigor, and meaningful research contributions. Further assessment of individual publications, verified research outcomes, and supporting documentation can provide a more complete understanding of the researcher’s academic contributions in this field.

11. References

    1. Elsevier. (n.d.). Scopus author details: Diana Al-Nabulsi, Author ID 59982332800. Scopus. Bibliometric figures are based on the information supplied for this profile and may change over time.
      https://www.scopus.com/authid/detail.uri?authorId=59982332800
    2. Google Scholar. (n.d.). Diana Al-Nabulsi – Google Scholar Profile. Google Scholar.
      https://scholar.google.com/citations?user=eiaLExkAAAAJ&hl=en&oi=sra
  1. International Research Awards on Network Science & Graph Analytics. (n.d.). Official Award Website.
    https://networkscience-conferences.researchw.com/

Sabir Hussain|Industry Impact Award|Technological Networks

Published on by Network Science

Industry Impact Award

Sabir Hussain

Islamabad Electric Supply Company, Pakistan

Sabir Hussain
Affiliation Islamabad Electric Supply Company
Country Pakistan
Documents 8
Subject Area Technological Networks
Event International Research Awards on Network Science & Graph Analytics
ORCID 0000-0001-9836-386X

Sabir Hussain, affiliated with Islamabad Electric Supply Company, Pakistan, is presented in connection with the Industry Impact Award under the subject area of Technological Networks. His professional affiliation provides an industry context for examining the relationship between technological systems, network infrastructure, and applied engineering practices. His profile lists eight research documents and an ORCID identifier for researcher identification and academic record discovery. The recognition is associated with the International Research Awards on Network Science & Graph Analytics.

Abstract

The Industry Impact Award recognizes the relevance of research, technological innovation, and professional contributions to practical industry applications. This profile presents Sabir Hussain of Islamabad Electric Supply Company, Pakistan, in the subject area of Technological Networks. The available researcher information records eight documents and provides an ORCID identifier for academic identification. The profile highlights the importance of connecting technological research with industrial infrastructure, network operations, and applied engineering. Further evaluation of specific projects, publications, and measurable outcomes would provide additional evidence of individual contributions and their industrial significance. [1]

Keywords

Industry Impact Award; Sabir Hussain; Technological Networks; Network Science; Graph Analytics; Industrial Innovation; Electrical Infrastructure; Applied Technology; Pakistan; Research Recognition.

Introduction

Technological networks are essential components of modern industrial and infrastructure systems. They support communication, information exchange, operational coordination, monitoring, and the management of interconnected resources. In the electricity sector, network-oriented technologies can contribute to infrastructure reliability, operational efficiency, system monitoring, and the integration of digital solutions.

The Industry Impact Award is associated with recognizing the practical relevance of technological knowledge and its potential contribution to industrial development. The award profile of Sabir Hussain places his professional affiliation with Islamabad Electric Supply Company within the broader context of technological networks and industry-oriented research.

Research Profile

Sabir Hussain is affiliated with Islamabad Electric Supply Company in Pakistan. His profile is associated with Technological Networks, a multidisciplinary subject area encompassing interconnected technical systems, communication infrastructure, network management, and industrial applications.

The supplied profile information identifies eight research documents and an ORCID researcher identifier. ORCID provides a persistent digital identifier that helps distinguish researchers and connect their scholarly contributions across academic platforms. [2]

  • Researcher: Sabir Hussain
  • Institution: Islamabad Electric Supply Company
  • Country: Pakistan
  • Research documents: 8 (as supplied)
  • Subject area: Technological Networks
  • Research identifier: ORCID 0000-0001-9836-386X

Research Contributions

Research and professional activity in technological networks can address challenges involving connected infrastructure, information systems, network performance, and industrial technology implementation. Within the electricity sector, these themes are relevant to the development and operation of interconnected energy systems.

Potential areas of industry-oriented contribution associated with this subject include:

  • Network infrastructure: Understanding interconnected technical systems and their operational requirements.
  • Digital transformation: Exploring technological approaches to improve industrial processes and information management.
  • System reliability: Examining network monitoring, fault identification, and infrastructure performance.
  • Industrial applications: Connecting technological research with practical engineering and operational challenges.
  • Network analytics: Applying data analysis and network-based methods to understand complex technological systems.

These areas describe the broader relevance of the subject area and should not be interpreted as verified individual achievements without supporting project records or publications.

Publications

The supplied researcher information lists eight documents. These records provide an initial indication of documented research activity. Individual publication titles, journal details, publication years, co-authors, and DOI identifiers were not included in the supplied information and therefore cannot be independently detailed in this article.

A complete publication record should include the following information wherever available:

  1. Full publication title and author list.
  2. Journal, conference, or publisher information.
  3. Publication year and volume or issue details.
  4. DOI or other persistent publication identifier.
  5. Evidence of the research contribution and its relevance to technological networks.

Research Impact

The industrial impact of technological research can be assessed through its practical application, adoption, measurable operational outcomes, and contribution to infrastructure development. In electricity-related environments, relevant assessment areas may include system reliability, efficiency, monitoring capabilities, digital integration, and improved technical decision-making.

The Industry Impact Award provides a framework for considering how research and professional experience may connect with industry requirements. A comprehensive assessment of Sabir Hussain’s individual impact would require verified evidence such as implemented projects, technical reports, documented improvements, patents, collaborations, or independently measurable outcomes.

Award Suitability

The Industry Impact Award recognizes the relationship between professional expertise, technological innovation, research activity, and practical industrial relevance. Sabir Hussain’s affiliation with Islamabad Electric Supply Company and the supplied research profile in Technological Networks provide a relevant professional context for consideration within this award category.

A complete award evaluation may consider the following criteria:

  • Professional experience: Relevant experience and responsibilities in an industrial or technological environment.
  • Research record: Quality, relevance, and documented significance of research outputs.
  • Industrial application: Evidence of practical implementation of research or technical solutions.
  • Innovation: Documented contributions to technological development or process improvement.
  • Professional collaboration: Evidence of partnerships, interdisciplinary work, and knowledge exchange.
  • Supporting documentation: Verified publications, project records, certificates, patents, and professional achievements.

The information presented establishes the supplied profile details but does not independently confirm a final award decision or specific industrial achievements.

Conclusion

Sabir Hussain’s profile brings together an industry affiliation with Islamabad Electric Supply Company, Pakistan, and a research subject area associated with Technological Networks. The supplied record of eight research documents and an ORCID identifier provides a starting point for reviewing his professional and scholarly background. Through the Industry Impact Award category, the International Research Awards on Network Science & Graph Analytics highlight the importance of connecting research, technological development, and practical industrial applications. Further verified evidence of projects, publications, and measurable outcomes would strengthen a comprehensive assessment of his contributions.

References

  1. International Research Awards on Network Science & Graph Analytics. (n.d.). Industry Impact Award and award information. Award website.
    https://networkscience-conferences.researchw.com/
  2. ORCID. (n.d.). ORCID researcher record: Sabir Hussain, ORCID 0000-0001-9836-386X.
    ORCID.https://orcid.org/0000-0001-9836-386X

Rami Ahmad El-Nabulsi | Network Science Excellence Award | Quantum Optical Networks

Published on by Network Science

Network Science Excellence Award

Rami Ahmad El-Nabulsi
CESNET, Czech Republic

Rami Ahmad El-Nabulsi
Affiliation CESNET
Country Czech Republic
Scholar ID 2LDk56EAAAAJ
Documents 100
Citations 76
h-index 47
Subject Area Quantum Optical Networks
Event International Research Awards on Network Science & Graph Analytics

Rami Ahmad El-Nabulsi  associated with the analysis, modeling, and development of interconnected systems. Rami Ahmad El-Nabulsi, affiliated with CESNET in the Czech Republic, is presented in this academic recognition context for work associated with Rami Ahmad El-Nabulsi  research area that connects principles of quantum information with network architectures and communication systems. Network-based approaches provide a framework for examining complex relationships among physical, computational, and informational components, while quantum networking extends these concepts toward communication protocols involving quantum states and entanglement. [1][3]

Abstract

This article presents an academic recognition profile for Rami Ahmad El-Nabulsi of CESNET, Czech Republic, in connection with the Network Science Excellence Award. The stated research subject is Quantum Optical Networks, an interdisciplinary field involving network science, quantum information, optical communication, and distributed information systems. The profile records 100 documents, 76 citations, and a reported h-index of 47 as supplied for the recognition record. The award is associated with the International Research Awards on Network Science & Graph Analytics and emphasizes scholarly activity relevant to network-oriented research.

Keywords

Network Science; Quantum Optical Networks; Quantum Networking; Graph Analytics; Optical Communication; Quantum Information; Complex Networks; Network Research; CESNET; Research Excellence.

Introduction

Network science studies systems through entities, relationships, topology, dynamics, and measurable patterns of interaction. Its methods have been applied across technological, biological, social, and information systems. Foundational network research established mathematical and empirical approaches for characterizing connectivity and structural organization. [1][2] Quantum networking introduces additional considerations because information can be encoded in quantum states and because network functionality may depend on phenomena such as entanglement and quantum measurement. [3]

Quantum optical networks are particularly relevant to the intersection of network science and photonic communication. Optical components can provide physical channels for transmitting quantum information, while network-level architectures address routing, synchronization, resource management, security, and interoperability. The field therefore requires coordination between theoretical models and practical communication infrastructures. [3][4]

Research Profile

Rami Ahmad El-Nabulsi is identified in the supplied recognition information as a researcher affiliated with CESNET in the Czech Republic. The stated subject area is Quantum Optical Networks. The supplied scholarly indicators comprise 100 documents, 76 citations, and an h-index of 47. These figures are presented as profile data supplied for this article and may change as bibliographic databases are updated.

The research profile is situated within a broader interdisciplinary environment in which network topology, communication technologies, quantum information, and graph-based analysis can be studied together. Such an orientation is consistent with contemporary research that considers quantum networks as distributed systems requiring both physical-layer technologies and network-level protocols. [3][4]

Research Contributions

The stated specialization in Quantum Optical Networks places the research profile at the intersection of quantum communication and network science. Important research dimensions in this field include the representation of interconnected quantum nodes, characterization of communication resources, analysis of network structures, and development of methods for reliable transmission across optical channels. [3] These themes contribute to the broader scientific objective of understanding how quantum technologies can be organized into scalable communication infrastructures.

  • Analysis of network structures relevant to quantum and optical communication environments.
  • Interdisciplinary consideration of quantum information and network architecture.
  • Application of network-oriented perspectives to distributed communication systems.
  • Research activity contributing to the conceptual development of quantum networking.

Publications

The supplied profile records 100 documents. Because individual publication titles, journals, years, and bibliographic identifiers were not provided in the input data, this section does not assign specific publications to the researcher without verification. The document count is therefore treated as a profile-level indicator rather than a detailed publication bibliography. General scholarship in network science and quantum networking provides the academic context for the stated subject area. [1][3]

  1. The supplied research record reports 100 documents.
  2. The supplied citation record reports 76 citations.
  3. The reported h-index is 47 according to the primary profile data supplied for this article.

Research Impact

The supplied bibliometric indicators provide one quantitative description of scholarly visibility, while the broader significance of research in quantum optical networks is linked to the development of future communication infrastructures. Quantum networking research addresses challenges involving scalable architectures, quantum resources, interfaces, and reliable communication. [3][4] Network science contributes complementary analytical tools for understanding connectivity, resilience, and system-level behavior.

The reported citation count and h-index should be interpreted in relation to the databases, dates, disciplinary coverage, and counting methodologies used by individual indexing services. Consequently, bibliometric measures are most appropriately considered alongside the quality, reproducibility, relevance, and technical significance of individual research outputs.

Award Suitability

Based on the supplied information, the profile is relevant to a Network Science Excellence Award because its declared subject area, Quantum Optical Networks, directly concerns interconnected communication systems and the application of network-oriented concepts to quantum technologies. The recorded scholarly indicators also provide evidence of an established publication and citation record. Final award decisions, however, should be based on the official evaluation process, verified publications, research quality, originality, and documented contributions rather than bibliometric indicators alone.

Conclusion

Rami Ahmad El-Nabulsi is presented in the supplied award information as a CESNET-affiliated researcher from the Czech Republic whose subject area is Quantum Optical Networks. The profile records 100 documents, 76 citations, and a reported h-index of 47. The research area occupies an interdisciplinary position between network science, quantum information, and optical communications. In this context, the Network Science Excellence Award provides a framework for recognizing research activity connected with the analysis and development of advanced networked systems.

References

  1. Newman, M. E. J. (2003). The structure and function of complex networks. SIAM Review, 45(2), 167–256. https://doi.org/10.1137/S003614450342480.
  2. Barabási, A.-L., & Albert, R. (1999). Emergence of scaling in random networks. Science, 286(5439), 509–512. https://doi.org/10.1126/science.286.5439.509.
  3. Wehner, S., Elkouss, D., & Hanson, R. (2018). Quantum internet: A vision for the road ahead. Science, 362(6412), eaam9288. https://doi.org/10.1126/science.aam9288.
  4. Kimble, H. J. (2008). The quantum internet. Nature, 453, 1023–1030. https://doi.org/10.1038/nature07127.

 

Anastasia Soultati | Technological Networks | Research Excellence Award

Published on by Network Science

Dr. Anastasia Soultati | Technological Networks | Research Excellence Award

National Centre for Scientific Research (NCSR) “Demokritos” | Greece

Dr. Anastasia Soultati is an Associate Researcher specializing in perovskite, organic, and hybrid optoelectronic materials, with a strong focus on photovoltaic devices, neuromorphic components, and photonic applications. She is currently affiliated with the Department of Electrical and Computer Engineering at Hellenic Mediterranean University, Greece, and Ajman University, UAE. Her research spans bifacial and transparent perovskite photovoltaics for greenhouse and building integration, perovskite-based memristors for neuromorphic computing, and advanced perovskite laser devices. Dr. Soultati has extensive experience in project management, device fabrication, and materials characterization, including thin films, crystals, and photosensitive materials. She has led and contributed to multiple national and international research projects, supported by competitive personal funding and postdoctoral fellowships, addressing sustainable energy, green semiconductor processing, smart packaging, and climate-resilient materials.

Citation Metrics (Scopus)

2100
800
500
100
0

Citations
2050

Documents
70

h-index
25

Citations

Documents

h-index


View Scopus Profile
 View Orcid Profile
 View Google Scholar Profile

Featured Publications

Strain Relaxation and Multidentate Anchoring in n-type Perovskite Transistors and Logic Circuits
– Nature Electronics, 2024 · 25 Citations
Aggregation-Driven Photoinduced α-C(sp3)-H Bond Hydroxylation/C(sp3)-C(sp3) Coupling of Boron Dipyrromethene Dye in Water Reported by Near-Infrared Emission
– Journal of the American Chemical Society, 2024 · 3 Citations · Open Access
Plasmonic Enhanced OLED Efficiency upon Silver–Polyoxometalate Core-Shell Nanoparticle Integration into the Hole Injection/Transport Layer
– Scientific Reports, 2024 · 3 Citations
Highly Robust Double Memristive Device Based on Perovskite/Molybdenum Oxide-Sulfide Compound Heterojunction System
– Advanced Electronic Materials, 2025 · 1 Citation
Sulfur-Doped ZnO as Cathode Interlayer for Efficient Inverted Organic Solar Cells
– Materials, 2025 · 1 Citation

Arriel Makembi Bunkete | Biological Networks | Research Excellence Award

Published on by Network Science

Dr. Arriel Makembi Bunkete | Biological Networks | Research Excellence Award

Kinshasa University Congo | Democratic Republic of the

Dr. Arriel Makembi Bunkete is a highly qualified nephrologist specializing in internal medicine, clinical nephrology, dialysis, and renal transplantation, with extensive international experience in clinical research, teaching, and complex patient care across mainland France, French Guyana, and the Democratic Republic of Congo. Since January 2025, he has served as a nephrologist and deputy renal transplant coordinator at the University Hospital Centre (CHU) of French Guyana, overseeing nephrology and transplantation care, dialysis programs, and medical and paramedical teams. He previously coordinated kidney transplant programs at Franck Joly Hospital and has been actively involved in research and teaching as deputy coordinator of the scientific research, teaching, and innovation pool for the nephrology sector at Guyana University Hospital. As the National Representative for the Young Nephrologist Platform of the European Renal Association, he contributes to European-level nephrology initiatives. Dr. Bunkete has also trained healthcare professionals as a lecturer at the Institut Supérieur des Sciences Infirmières PPPLUS and gained extensive clinical experience in France at Hôpital Pitié-Salpêtrière, Hôpital Foch, and the Association for the Use of Artificial Kidneys (AURA). His early career includes residency in internal medicine and nephrology at the University Clinics of Kinshasa, DRC, managing complex kidney diseases and intensive hospital care. Throughout his career, he has demonstrated expertise in coordinating multidisciplinary teams and advancing scientific research in kidney disease and integrated care approaches.

Profiles: Scopus | Orcid | Google Scholar

"Home Hemodialysis in French Guiana: Transforming logistical challenges into healthcare opportunities", AM Bunkete Bulletin de la Dialyse à, Domicile 8 (2), 2025.

"Hydroxyurea and Regression of Sickle Cell Nephropathy: Open Clinical Trial in a Pediatric Population in DR Congo", DB Kabasele, AM Bunkete, G Mbayabo, P Lumbala, O Matondo, M Aloni, Kidney360, 2025.

"Peritoneal dialysis in an infant with severe acute renal failure: dialysis performed in flight during a medical evacuation in French Guiana – A case report", AM Bunkete, M Belgrine, M Sidibe, S Bellony, T Davodun, I Djiconkpode, Home Dialysis Bulletin 8 (3), 2025.

"Extramembranous glomerulonephritis in French Guiana: from the primitive form to autoimmune atypical presentations (a report of 5 cases)", AM Bunkete, M Belgrine, M Sidibe, MM Diallo, GN Timem, T Davodoun, PAMJ Clinical Medicine 19 (5), 2025.

"Evaluation of the Tolerability and Efficacy of the Main Hemodialysis Techniques in Intensive Care", T Mabiala, KA Muamba, VHT Ha, G Makeya, R Ngwizani, D Betukumesu, Open Journal of Nephrology 15 (1), 2025.

Yunjie Zhao | Biological Networks | Research Excellence Award

Published on by Network Science

Prof. Yunjie Zhao | Biological Networks | Research Excellence Award

Central China Normal University | China

Prof. Yunjie Zhao is a distinguished biophysicist who earned his Ph.D. in Biophysics from Huazhong University of Science and Technology in 2012, where he conducted pioneering research on computational prediction of non-coding RNA structures under the mentorship of Prof. Yi Xiao, following his B.S. in Physics from the same institution. He advanced his expertise as a Postdoctoral Research Scientist at The George Washington University with Prof. Chen Zeng, contributing significantly to de novo protein design and bionetwork modeling. Since 2016, he has been a faculty member at Central China Normal University, becoming a full Professor in 2019, and has taught a wide range of courses including Machine Learning, Biophysics, Bioinformatics, Computational Biology, and Physics. Prof. Zhao is an active member of several professional societies, including the Chinese Physical Society and the Biophysical Society of China, and previously the American Physical Society. He serves as an editorial board member for leading journals such as Briefings in Bioinformatics, iScience, Pharmaceuticals, Life, Biomolecules, International Journal of Molecular Sciences, and Scientific Reports, and is a frequent reviewer for top-tier publications including Nature Biotechnology, Nature Reviews Genetics, Nature Machine Intelligence, Nature Communications, and Physical Review Letters. His research contributions are further reflected in multiple granted patents spanning protein–ligand binding site prediction, RNA contact prediction, plant lectin polymerization peptide inhibitors, and protein drug binding pocket prediction, underscoring his impactful contributions to computational biology and biophysics.

Profiles: Scopus | Google Scholar

Featured Publications

"RNA-protein interaction prediction using network-guided deep learning", H Liu, Y Jian, C Zeng, Y Zhao, Communications Biology 8 (1), 2025.

"Advances and Mechanisms of RNA–Ligand Interaction Predictions", C Zhuo, C Zeng, H Liu, H Wang, Y Peng, Y Zhao Life 15 (1), 2025.

"Decoding the effects of mutation on protein interactions using machine learning", W Xu, A Li, Y Zhao, Y Peng, Biophysics Reviews 6 (1), 2025.

"Advances in Methods for Accurate Prediction of RNA–Small Molecule Binding Sites: From Isolated to AI-Integrated Strategies", J Gao, C Zhuo, C Zeng, H Liu, Y Zhao, Pharmaceuticals 18 (10), 2025.

"Ab initio HIV-1 Tat–TAR interactions study using hybrid scoring-enhanced molecular modeling across subtypes", C Zeng, H Wang, J Gao, H Liu, C Zhuo, Y Zhao, Physical Chemistry Chemical Physics 27 (27), 2025.

Yingzi Yang | Biological Networks | Research Excellence Award

Published on by Network Science

Prof. Yingzi Yang | Biological Networks | Research Excellence Award

Harvard School of Dental Medicine | United States

Prof. Yingzi Yang is a distinguished developmental biologist whose career spans leading institutions including Harvard Medical School and the National Institutes of Health. She earned her BS in Biology from Fudan University in 1988 and completed her PhD in Molecular Biology at the Sloan-Kettering Institute and Weill Medical College of Cornell University in 1996, followed by postdoctoral training with Andrew P. McMahon at Harvard University. She joined the NIH in 2000 as an Investigator and rose to Senior Investigator before being appointed Professor of Developmental Biology at Harvard Medical School in 2015, where she also serves as Principal Faculty of the Harvard Stem Cell Institute and a member of the Dana-Farber/Harvard Cancer Center. Widely recognized for her contributions to Wnt signaling, skeletal biology, and developmental mechanisms, Prof. Yang has received numerous honors including the AAAS Fellowship, ASBMR Fellowship, the IADR Distinguished Scientist Award, multiple NIH Awards of Merit, and the Vincent du Vigneaud Award of Excellence. She has held major leadership roles such as President of the Chinese Biological Investigators Society (2022–2024), member of the NIH SBDD Study Section, and keynote speaker at national scientific meetings. Her extensive service includes high-level Harvard committees, NIH central tenure committees, Gordon Research Conference leadership, international symposium organization, and major grant review panels for NIH, NSF, and global scientific agencies. A respected scholar and reviewer for top-tier journals including Cell, Nature family journals, Science family journals, and Development, Prof. Yang is recognized worldwide for advancing fundamental understanding of developmental and stem cell biology and their implications for human disease.

Profiles: Scopus | Google Scholar

Featured Publications

"Disrupting bile acid metabolism by suppressing Fxr causes hepatocellular carcinoma induced by YAP activation", Y Liu, J Zhu, Y Jin, Z Sun, X Wu, H Zhou, Y Yang, Nature Communications 16 (1), 2025.

"Fibrous dysplasia/McCune-Albright syndrome: state-of-the-art advances, pathogenesis, and basic/translational research", B Palmisano, C Berry, A Boyce, JF Charles, MT Collins, A Corsi, FA Fierro, Orphanet Journal of Rare Diseases 20 (1), 2025.

"Piezo1-mediated mechanotransduction controls osteocyte maturation and dendrite development via a YAP-CCN-Src signaling axis", YJ Hu, X Wu, F Wang, Y Jin, Y Jin, Y Liu, Q Cong, Y Yang, Nature Communications, 2025.

"Injury-induced niche factors Cxcl12 and Shh/Ihh coordinate suture stem cell activation during calvarial bone regeneration", B Li, T Ouchi, J Liu, Y Yang, Science Signaling 18 (909), 2025.

"Neutralization of Receptor activator of nuclear factor-κB ligand reduces fibrosis and promotes osteoblast differentiation in a mouse model of fibrous dysplasia driven", RT Ormsby, Y Zhang, C Hodys, LA Wake, SM Perez, K Tsang, Y Yang, JBMR plus 9 (10), 2025.

Peter Ikechukwu Nwaichi | Technological Innovation | Best Researcher Award

Published on by Network Science

Dr. Peter Ikechukwu Nwaichi | Technological Innovation | Best Researcher Award

University of Malaysia, Pahang | Malaysia

Dr. Peter Ikechukwu Nwaichi is an accomplished professional with an MSc in Safety, Health, and Environment from Universiti Teknologi Malaysia and a PhD (in view), bringing strong academic depth to his diverse technical expertise. His research interests span Risk Assessment and Risk Management, Oil Well Analysis, Data Analysis, and Waste Management, reflecting a commitment to advancing safety and operational efficiency within industrial environments. Dr. Nwaichi has gained extensive hands-on experience through his work with Weafri Well Services Company, where he contributed to cementing, pumping, corrosion control, and coiled tubing operations. He has also served in key roles at ACM of Nigeria as a Safety Officer, Material Coordinator, and Designer, demonstrating versatility and a strong track record in ensuring safe, efficient, and sustainable work processes across the energy and industrial sectors.

Profiles: Google Scholar

Featured Publications

"Numerical simulation of microbial biohydrogen production under high-pressure, high-temperature conditions for enhanced recovery from depleted reservoirs", D Abutu, PI Nwaichi, C Umunnawuike, F Nyah, B Money, HW Yussof, Petroleum Research, 2025

"Nanoparticles transport in porous media during EOR operations: A Wide-Ranging review of mechanisms, mathematical models, influencing parameters, challenges, and prospects", EN Ngouangna, IB Dzulkarnain, MZ Jaafar, JO Oseh, FA Afolabi, Arabian Journal for Science and Engineering, 2025.

"Biohydrogen production for sustainable energy transition: a bibliometric and systematic review of the reaction mechanisms, challenges, knowledge gaps and emerging trends", C Umunnawuike, SQA Mahat, PI Nwaichi, B Money, A Agi Biomass and bioenergy, 2024.

"Recent advances and prospects on retarder application in oilwell cement: A review", PI Nwaichi, N Ridzuan, EO Nwaichi, C Umunnawuike, A Agi Geoenergy Science and Engineering 241, 2024.

"Comprehensive review on the role of salinity on oil recovery mechanisms during chemical flooding", F Nyah, N Ridzuan, PI Nwaichi, C Umunnawuike, A Agi Journal of Molecular Liquids 415, 2024.

Probal Banerjee | Biological Network | Best Researcher Award

Published on by Network Science

Dr. Probal Banerjee | Biological Network | Best Researcher Award

The College of Staten Island (City University of New York) | United States

Dr. Probal Banerjee is a distinguished biochemist and neuroscientist whose academic and research career spans over four decades of excellence in bioorganic chemistry, molecular neuroscience, and cancer research. He earned his B.Sc. in Chemistry (Hons) and M.Sc. in Organic Chemistry from Jadavpur University, Calcutta, followed by a Ph.D. in Bioorganic Chemistry from the Indian Institute of Science, Bangalore in 1984. He completed postdoctoral research at the University of Notre Dame in Biochemistry and Enzymology and at the University of Chicago in Molecular Neuroscience. Currently a Professor of Chemistry and Coordinator at the Center for Developmental Neuroscience, College of Staten Island, CUNY, Dr. Banerjee has made pioneering contributions to understanding neurodevelopmental signaling and immune-modulating cancer therapies. His numerous honors include the Children’s Research Foundation Grant (1989), Brain Research Foundation Award, and two Dolphin Awards for Academic Excellence and Teaching. A sought-after speaker, he has delivered invited lectures at leading institutions worldwide on topics ranging from serotonin signaling in brain development to multi-target cancer therapeutics. Dr. Banerjee is also an inventor with several patents on curcumin-based anticancer agents and therapeutic approaches for Fragile X Syndrome. His expertise is widely recognized through his service on multiple NIH grant review committees, reflecting his enduring impact on neuroscience, pharmacology, and translational cancer research.

Profiles: Orcid 

Featured Publications

"Novel Taxol-Derivative, STO-1, Induces Selective Anti-Tumor Immunity and Sustained Remission of Glioblastoma Without Triggering Autoimmune Reactions", Shubhasmita Mohapatra; Adrian Guerrero; Neha Rahman; Khondoker Takia Zaman; Jing Wu; Callistus Onyeagba; Chanyue Hu; Matteo Pellegrini; Jayaram Vankudoth; Seiya Kitamura et al., Cells, 2025.

"One-Pot Synthesis of Phenylboronic Acid-Based Microgels for Tunable Gate of Glucose-Responsive Insulin Release at Physiological pH", Prashun G. Roy; Jiangtao Zhang; Koushik Bhattacharya; Probal Banerjee; Jing Shen; Shuiqin Zhou, Molecules, 2025.

"Biocompatible Anisole-Nonlinear PEG Core–Shell Nanogels for High Loading Capacity, Excellent Stability, and Controlled Release of Curcumin", Jing Shen; Jiangtao Zhang; Weitai Wu; Probal Banerjee; Shuiqin Zhou, Gels 2023.

"The G Protein-Coupled Serotonin 1A Receptor Augments Protein Kinase Cε-Mediated Neurogenesis in Neonatal Mouse Hippocampus-PKCε-Mediated Signaling in the Early Hippocampus", Sreyashi Samaddar; Sudarshana Purkayastha; Souleymane Diallo; Subramanyam J. Tantry; Ryan Schroder; Pranavan Chanthrakumar; Michael J. Flory; Probal Banerjee, International Journal of Molecular Sciences 2022.

"Phytosomal curcumin causes natural killer cell-dependent repolarization of glioblastoma (GBM) tumor-associated microglia/macrophages and elimination of GBM and GBM stem cells", Sumit Mukherjee; Angela Fried; Rahman Hussaini; Richard White; Juliet Baidoo; Sri Yalamanchi; Probal Banerjee, Journal of Experimental & Clinical Cancer Research, 2018.

Milton Mondal | Mathematical Modelling | Best Researcher Award

Published on by Network Science

Dr. Milton Mondal | Mathematical Modelling | Best Researcher Award

Amrita Vishwa Vidyapeetham, Coimbatore | India

Dr. Nikhil Sudhir Sathe (PhD), currently associated with Laboratory Animal Research Services (LARS), is a distinguished researcher with over 20 years of expertise in drug discovery and preclinical evaluation of new chemical entities. His extensive professional experience encompasses in vitro genotoxicology, cell and microbial culture (aerobic, anaerobic, and viral systems including rabies virus), and small laboratory animal studies. Throughout his career at Reliance Life Sciences, Vimta Labs, Dabur Research Centre, and Wockhardt Research Centre, Dr. Sathe has demonstrated exceptional proficiency in GLP-compliant laboratory practices, including bacterial reverse mutation, chromosomal aberration, micronucleus, ELISA, cytotoxicity, and microbial monitoring assays. His early research contributions involved innovative in vitro models for evaluating the efficacy of antimicrobial and cosmetic products. Academically, he holds a Master’s and Bachelor’s degree in Microbiology from Shivaji University, Kolhapur, both with distinction. Dr. Sathe has contributed significantly to scientific literature with publications in reputed journals such as Vaccine, Microbial Pathogenesis, Infectious Medicine, and BioMed, focusing on bacteriophage therapy and vaccine development against multidrug-resistant pathogens and SARS-CoV-2. His scholarly achievements are complemented by active participation in national conferences and workshops, reflecting his deep commitment to advancing microbiological and biomedical research.

Profiles: Scopus | Orcid | Google Scholar

Featured Publications

"Dynamics of a phytoplankton–zooplankton model with three-dimensional patch under the impacts of hydrodynamic conditions", M Mondal, CS Rao, T Zhang, Mathematics and Computers in Simulation, 2025.

"Bloom dynamics under the effects of periodic driving forces", M Mondal, T Zhang, Mathematical Biosciences, 2025.

"Mathematical Modelling and Analysis of Harmful Algal Bloom", M Mondal, Bulletin of the Australian Mathematical Society, 2024.