Jeslin Jebish G P | Machine Learning on Databases | Innovative Research Award

Published on by International Database Scientist Awards

Innovative Research Award

Jeslin Jebish G P — Scott Christian College (Autonomous), Nagercoil, India

Jeslin Jebish G P
Affiliation Scott Christian College, Nagercoil
Country India
Google Scholar IUCgka8AAAAJ
Documents 8
Citations 6
h-index 1
Subject Area Machine Learning on Databases
Event International Database Scientist Awards
Scopus 59938914400

Jeslin Jebish G P is an early-career Physics researcher associated with Scott Christian College, Nagercoil, whose work spans nanotechnology, functional materials, photocatalysis, photoelectrochemical water splitting, hydrogen generation, and renewable-energy systems. His documented academic record includes a submitted Ph.D. thesis, indexed publications, conference presentations, peer-review activity, faculty development, and interdisciplinary research involving computational and machine-learning approaches to materials discovery.

Abstract

Jeslin Jebish G P is a Physics researcher whose documented work focuses on titania nanotubes, nanostructured functional materials, photocatalysis, photoelectrochemical water splitting, and green hydrogen generation. His research record also includes work connecting density functional theory and machine learning with materials discovery, providing an interdisciplinary basis for considering computational approaches within emerging energy-materials research. [1]

Keywords

Machine Learning on Databases; materials discovery; nanotechnology; TiO₂ nanotubes; photocatalysis; photoelectrochemical water splitting; green hydrogen; renewable energy; functional materials; electrochemical analysis; computational materials science; scientific data analysis; sustainable energy.

Introduction

The research profile of Jeslin Jebish G P is situated primarily within experimental Physics and materials-energy research. His academic activities encompass synthesis, characterization, electrochemical evaluation, photocatalytic testing, and scientific data interpretation. The supplied record further identifies an emerging computational dimension through research on artificial intelligence, machine learning, and materials discovery for renewable-energy applications. [4]

Research Profile

Jeslin Jebish G P pursued doctoral research at Manonmaniam Sundaranar University, Tirunelveli, with Scott Christian College, Nagercoil, on the development and analysis of titania enhanced with reactive non-metals and transition metals for energy applications. His research experience covers TiO₂ nanotube synthesis by electrochemical anodisation, dopant engineering, hydrogen evolution, photocatalytic and photoelectrochemical evaluation, and advanced material characterization. [1]

Research Contributions

The research profile focuses on engineering TiO₂ nanotube architectures for solar-driven hydrogen production, examining structure, composition, photocatalytic activity, and electrochemical performance. Related research explores nitrogen and transition-metal doping, heterostructures, seawater splitting, and computational materials discovery, demonstrating an interdisciplinary trajectory connecting experimental nanomaterials research with emerging data-driven methodologies for sustainable energy applications. [2]

Publications

The supplied publication record contains eight scholarly works addressing titania nanotubes, anodisation, solar-driven water splitting, green hydrogen generation, photocatalytic systems, seawater splitting, and machine-learning-assisted materials discovery. The publications indicate a progression from experimental nanostructure fabrication and characterization toward broader computational and interdisciplinary approaches in energy-materials research. [2] [3] [4]

Research Impact

The available scholarly record documents publications, presentations, peer-review activity, professional development, and research training across materials physics and renewable-energy themes. His work on TiO₂ nanotube architectures and hydrogen generation contributes to the broader research landscape of sustainable energy materials, while machine-learning-oriented materials discovery represents an emerging interdisciplinary direction. [1] [2]

Award Suitability

For the Innovative Research Award, the record demonstrates interdisciplinary research across nanostructured materials, hydrogen energy, photocatalysis, electrochemical methods, scientific data analysis, and emerging machine-learning approaches to materials discovery. The documented publications and research activities support consideration for recognition, while alignment with database-focused research should be assessed according to the award’s formal criteria. [4]

Conclusion

Jeslin Jebish G P presents an early-career research profile centered on functional nanomaterials, photocatalysis, photoelectrochemical water splitting, and green hydrogen generation. His publication record additionally identifies machine learning and computational materials discovery as an emerging interdisciplinary direction. Collectively, these activities demonstrate sustained engagement with experimental and data-informed research in advanced energy materials. [1]

References

  1. Google Scholar. (n.d.). Jeslin Jebish G P — Google Scholar profile. https://scholar.google.com/citations?hl=en&user=IUCgka8AAAAJ
  2. GP, J. J., & Jebin, R. P. (2026). Designing titania nanotube architectures through anodization for high-performance green hydrogen generation. Next Materials, 11, Article 101840. https://doi.org/10.1016/j.nxmate.2026.101840
  3. Jebish, J. J. G. P. (2026). Junction-engineered TiO₂ nanotube photocatalysts for efficient solar hydrogen generation: Doping strategies, structure–activity relationships, and future perspectives. Journal of Materials Science: Materials in Energy, 2(1), Article 14. https://link.springer.com/article/10.1007/s44308-026-00024-3
  4. Jeslin Jebish, G. P., & Jebin, R. P. (n.d.). Advances in titania nanotubes development by anodization and their role in solar-driven water splitting. Journal of Advanced Manufacturing and Processing, 7(3), Article e70005. https://doi.org/10.1002/amp2.70005

Muneer Khan | Edge Computing and Databases | Innovative Research Award

Published on by International Database Scientist Awards

Innovative Research Award

Muneer Khan — Harvard University, United States

Muneer Khan
Affiliation Harvard University
Country United States
Google Scholar 3JiYbkEAAAAJ&hl
Documents 15
Citations 110
h-index 3
Subject Area Edge Computing and Databases
Event International Database Scientist Awards
Scopus 60246354500

Muneer Khan is an electrical engineering researcher whose documented work connects embedded artificial intelligence, soft robotics, sensing systems, Internet of Things technologies, intelligent devices, and applied machine learning. His academic and research experience spans Harvard University, Columbia University, and earlier research institutions, with projects addressing biomedical, assistive, environmental, and connected-device applications.

Abstract

Muneer Khan’s supplied academic record documents interdisciplinary research across electrical engineering, embedded AI, soft robotics, Internet of Things systems, sensing, and intelligent devices. His experience includes research and development at Harvard University and Columbia University, environmental monitoring systems, biomedical and assistive technologies, patents, scholarly publications, technical projects, and professional reviewing activities.[1]

Keywords

Edge Computing and Databases; embedded artificial intelligence; soft robotics; Internet of Things; intelligent devices; sensor systems; machine learning; electrical engineering; edge computing; biomedical technology; assistive technology; environmental monitoring; connected systems.

Introduction

Muneer Khan’s research reflects an interdisciplinary approach integrating electronic hardware, embedded computing, sensing, machine learning, robotics, and connected devices. His documented work includes AI-powered engineering systems, environmental monitoring, and healthcare applications. Publications on automated IoT-based water-quality assessment and digital patient-eye technology demonstrate practical applications of intelligent sensing, data processing, and connected systems. [3] [4]

Research Profile

Muneer Khan’s research profile integrates electrical engineering, embedded artificial intelligence, soft robotics, IoT systems, sensing, and intelligent devices. His experience includes applied research at Harvard University and Columbia University, environmental monitoring using machine learning and sensors, and academic training in electrical engineering, electronics, embedded AI, connected devices, and related technologies. [1]

Research Contributions

Muneer Khan’s contributions encompass AI-integrated soft robotics, embedded sensing, adaptive actuation, intelligent control, and electronic hardware. His work also includes IoT-based soil and water monitoring using machine learning and data analytics, alongside research in millimeter-wave circuits and communication systems. Patents further document innovations in insect monitoring, assistive vision, and smart agriculture.

Publications

Muneer Khan’s publication record includes work on visual assistance, machine learning, IoT-enabled water-quality assessment, wideband baluns, cognitive radio, and digital health technologies. Among the supplied publications, the IEEE SMART paper addresses automated water-quality assessment, while the IoT-SIU paper concerns a digital patient-eye system, illustrating applications across sensing, connectivity, and healthcare research. [2] [3] [4]

Research Impact

Muneer Khan’s academic record reports 15 documents, 110 citations, and an h-index of 3 on the stated Google Scholar profile. His portfolio includes patents, environmental monitoring research, technical awards, and peer-review activities. Application-oriented projects address assistive vision, diabetes-related sensing, water and soil monitoring, robotics, connected devices, and embedded intelligence.[1]

Award Suitability

Muneer Khan’s record supports consideration for an Innovative Research Award through interdisciplinary engineering research, applied AI, robotics, IoT development, patents, publications, technical projects, and reported recognition. His expertise in embedded AI, sensing, data acquisition, and intelligent connected systems aligns with AI-powered technologies and edge computing, subject to formal award eligibility and evaluation criteria.[1]

Conclusion

Muneer Khan’s supplied profile describes an interdisciplinary research trajectory combining electrical engineering with artificial intelligence, embedded systems, soft robotics, sensing, IoT, and intelligent-device applications. His documented publications, patents, research appointments, projects, and reported recognitions provide a substantive basis for academic consideration under an innovation-oriented research award, subject to independent verification and the applicable award criteria.

References

  1. Google Scholar. (n.d.). Google Scholar profile:: Muneer Khan. https://scholar.google.com/citations?user=3JiYbkEAAAAJ&hl=en&oi=sra
  2. Akhtar, M. N., Khan, M., Khan, S. A., Afzal, A., Subbiah, R., Ahmad, S. N., et al. (2021). Determination of non-recrystallization temperature for niobium microalloyed steel. Materials, 14(10), Article 2639. https://www.mdpi.com/1996-1944/14/10/2639
  3. Pant, D., Bhatt, A., Khan, M., Nautiyal, O. P., & Adhikari, P. (2019). Automated IoT based smart water quality assessment system. In 2019 8th International Conference on System Modeling and Advancement in Research Trends (SMART). https://ieeexplore.ieee.org/abstract/document/9117271
  4. Khan, M., & Tripathi, A. (2018). Digital patient eye. In 2018 3rd International Conference on Internet of Things: Smart Innovation and Usages (IoT-SIU). https://ieeexplore.ieee.org/abstract/document/8519841

Vinay Rishiwal | AI-Powered Database Systems | Innovative Research Award

Published on by International Database Scientist Awards

Innovative Research Award

Vinay Rishiwal
Mahatma Jyotiba Phule Rohilkhand University, Bareilly, India

Vinay Rishiwal
Affiliation Mahatma Jyotiba Phule Rohilkhand University
Country India
Google Scholar BwAAAAJ&hl
Documents 165
Citations 2250
h-index 25
Subject Area AI-Powered Database Systems
Event International Database Scientist Awards
Scopus 18134724800

Vinay Rishiwal is a computer science academic and researcher affiliated with Mahatma Jyotiba Phule Rohilkhand University, India. His reported research activities encompass artificial intelligence, machine learning, wireless sensor networks, blockchain, Internet of Things technologies, and technology applications addressing societal challenges. His academic record includes teaching, research, projects, patents, publications, and professional service.

Abstract

Vinay Rishiwal’s academic profile reflects sustained work in computer science research, teaching, technological development, and institutional service. His reported scholarly activities cover artificial intelligence, machine learning, IoT, blockchain, wireless sensor networks, cybersecurity, smart systems, and technology-enabled societal applications. His record also includes patents, research projects, books, doctoral supervision, and peer-reviewed publications. [1]

Keywords

Artificial Intelligence; Machine Learning; AI-Powered Database Systems; Blockchain; Internet of Things; Wireless Sensor Networks; Cybersecurity; Smart Agriculture; Quantum Computing; Data Security; Supply Chain Management; Intelligent Systems.

Introduction

Vinay Rishiwal is a professor in the Department of Computer Science and Information Technology at Mahatma Jyotiba Phule Rohilkhand University. His reported career spans more than two decades across teaching, research, academic administration, technology projects, and professional activities. His research portfolio connects computational intelligence with networked, secure, and data-oriented applications.

Research Profile

The reported research profile includes artificial intelligence, machine learning, wireless sensor networks, blockchain, IoT, and applications of technology to societal problems. His career record identifies teaching and research appointments at MJP Rohilkhand University, along with adjunct or visiting academic roles at institutions in Malaysia and Indonesia. The profile reports 165 documents, 2250 citations, and an h-index of 25. [1]

Research Contributions

Reported contributions span secure supply chains, blockchain and IoT security, wireless sensor-network routing, smart agriculture, cybersecurity, quantum machine learning, and intelligent technological systems. Research examples address blockchain-based supply-chain protection, crypto-forensics, IoT security, and the interaction of machine learning with quantum computing, demonstrating a broad systems-oriented research portfolio. [2] [3] [4] [5]

Publications

The supplied publication record reports 126 publications, including journal and conference contributions, alongside books and edited works. Recent research addresses blockchain-enabled supply chains, crypto-forensics, IoT network security, and machine learning with quantum computing. Selected publications appear in IEEE Access, IEEE Internet of Things Journal, International Journal of Network Management, and Peer-to-Peer Networking and Applications. [2] [3] [4] [5]

Research Impact

The career record reports 72 granted patents, including 68 national and 4 international patents, alongside government, institutional, and externally supported research projects. It records 10 awarded and 8 ongoing PhD scholars. Research applications span AI, IoT, smart agriculture, blockchain, healthcare, disaster management, and intelligent sensing, emphasizing practical technological innovation.

Award Suitability

The profile demonstrates suitability for an Innovative Research Award through sustained interdisciplinary research spanning AI, IoT, blockchain, machine learning, and networked systems, supported by patents, publications, funded projects, and academic leadership. Reported Best Researcher, Outstanding Teacher, and best-paper recognitions further provide contextual evidence of research, professional, and academic contributions.

Conclusion

Vinay Rishiwal’s supplied academic record presents a broad research portfolio centered on artificial intelligence, machine learning, IoT, blockchain, wireless sensor networks, cybersecurity, and emerging computational technologies. His reported publications, patents, projects, doctoral supervision, and academic responsibilities collectively establish a substantial record for scholarly consideration under an Innovative Research Award category.

References

  1. Google Scholar. (n.d.). Google Scholar profile: Vinay Rishiwal. https://scholar.google.com/citations?user=Yph__BwAAAAJ&hl=en&oi=sra/scholar
  2. Agarwal, U., Rishiwal, V., Tanwar, S., Chaudhary, R., Sharma, G., Bokoro, P. N., et al. (2022). Blockchain technology for secure supply chain management: A comprehensive review. IEEE Access, 10, 85493–85517. https://ieeexplore.ieee.org/abstract/document/9841565
  3. Agarwal, U., Rishiwal, V., Tanwar, S., & Yadav, M. (2024). Blockchain and crypto forensics: Investigating crypto frauds. International Journal of Network Management, 34(2), e2255. DOI: https://doi.org/10.1002/nem.2255
  4. Maurya, V., Rishiwal, V., Yadav, M., Shiblee, M., Yadav, P., Agarwal, U., et al. (2025). Blockchain-driven security for IoT networks: State-of-the-art, challenges and future directions. Peer-to-Peer Networking and Applications, 18(1), Article 53. https://link.springer.com/article/10.1007/s12083-024-01812-w
  5. Rishiwal, V., Agarwal, U., Yadav, M., Tanwar, S., Garg, D., & Guizani, M. (2025). A new alliance of machine learning and quantum computing: Concepts, attacks, and challenges in IoT networks. IEEE Internet of Things Journal, 12(12), 18865–18886. https://ieeexplore.ieee.org/abstract/document/10856200

Miriam Martínez Muñoz | Data Modeling and Database Design | Innovative Research Award

Published on by International Database Scientist Awards

Innovative Research Award

Miriam Martínez Muñoz
Madrid Open University (UDIMA)Spain,

Miriam Martínez Muñoz
Affiliation Madrid Open University (UDIMA)
Country Spain
Google Scholar FjmoqCkAAAAJ
Documents 36
Citations 77
h-index 6
Subject Area Data Modeling and Database Design
Event International Database Scientist Awards
Scopus 55941441500

Miriam Martínez Muñoz is an academic and researcher whose interdisciplinary work connects engineering, data analytics, educational technology, cybersecurity, artificial intelligence, and unmanned aerial vehicle applications. Her documented research and academic activities provide a multidisciplinary basis for consideration under the Innovative Research Award, particularly through technology-enabled approaches to education, engineering, and environmental assessment. [1]

Abstract

Miriam Martínez Muñoz is a Spanish academic whose professional profile combines physical sciences, communications, engineering, computing, data science, educational technology, cybersecurity, and UAV applications. Her research trajectory includes magnetic microdevices, wireless-powered microrobotics, educational innovation, environmental monitoring, and data-driven decision-making, demonstrating sustained interdisciplinary engagement across technological domains. [1]

Keywords

Federated Databases; Business Intelligence; Text-to-SQL; Large Language Models; Internet of Things Security; Device Authentication; Data Privacy; Smart Cities; Cybersecurity; Business Continuity.

Introduction

Martínez Muñoz’s academic development spans physics, signal theory and communications, software-related education, engineering research, and university management. Her doctoral studies at the University of Alcalá and subsequent academic activities provide a foundation for research addressing technological systems, digital learning, environmental assessment, and interdisciplinary applications of computational methods. [1]

Research Profile

Her research profile encompasses magnetic systems, microrobotics, UAV-based environmental assessment, cybersecurity, data science, artificial intelligence, digital assessment, and educational innovation. This breadth reflects an interdisciplinary research approach in which engineering, computation, data analysis, and technology-enhanced learning are applied to practical scientific and organisational problems. [1]

Research Contributions

Documented contributions include research on multipole magnetisation patterning, magnetic gears, wireless-powered microrobotic systems, space-related technologies, mobile learning, digital assessment, and UAV environmental auditing. These activities illustrate a research orientation toward developing and evaluating technological methods with applications in engineering, healthcare, education, and environmental monitoring. [2] [3]

Publications

Selected publications address magnetic microdevices and magnetic gears, while more recent work extends toward UAV magnetometry, environmental assessment, and educational innovation. The documented publication record therefore connects physical engineering research with emerging digital and technology-enabled applications. Bibliographic details should be confirmed against the original publisher records where indicated. [2] [3] [4] [5]

Research Impact

The supplied academic record reports 36 documents, 77 citations, and an h-index of 6. Her research activities additionally connect engineering research with education, cybersecurity, UAV technology, and environmental applications. These indicators and documented projects provide measurable and contextual evidence of research activity, while broader impact should be evaluated from verified database records. [1]

Award Suitability

The Innovative Research Award recognizes research profiles demonstrating meaningful technological or methodological development. Martínez Muñoz’s documented combination of engineering research, data analytics, educational technology, UAV applications, and cybersecurity provides a relevant interdisciplinary basis for consideration. Final award assessment should rely on independently verified publications, research outputs, and eligibility criteria. [1]

Conclusion

Miriam Martínez Muñoz presents an interdisciplinary academic profile spanning engineering, communications, data science, education, cybersecurity, and UAV technologies. Her documented publications, projects, teaching activities, and research metrics establish a substantive foundation for scholarly recognition, with the Innovative Research Award providing an appropriate context for evaluating this breadth of technological research. [1] [3]

References

  1. Elsevier. (n.d.). Scopus author details: Miriam Martínez Muñoz, Author ID 57194528924. Scopus. https://www.scopus.com/authid/detail.uri?authorId=55941441500
  2. Díez-Jiménez, E., Sánchez-Montero, R., & Martínez-Muñoz, M. (2018). Analysis of the influence of geometric parameters in PCB fixtures for 2D multipole magnetisation patterning of thin-layer micromagnets. Journal of Applied Electromagnetism. https://doi.org/10.3233/JAE-80121
  3. Díez-Jiménez, E., Sánchez-Montero, R., & Martínez-Muñoz, M. (2018). Towards miniaturization of magnetic gears: Torque performance assessment. Micromachines, 9(1), 16. https://doi.org/10.3390/mi9010016
  4. Martínez-Muñoz, M., et al. (2026). An integrated UAV magnetometry workflow for magnetic anomaly detection and environmental assessment in Cascais (Portugal). Results in Engineering. Publication metadata should be verified against the original publisher record. https://doi.org/10.1016/j.rineng.2026.113542
  5. Martínez-Muñoz, M., et al. (2025). Dronics in higher education and inclusive education: Proposal for educational innovation from above. Comunicar, issue 80, volume XXXIII. Publication metadata should be verified against the original journal record. https://doi.org/10.58262/C80-2025-07

Wael Alnahari | Federated Databases | Innovative Research Award

Published on by International Database Scientist Awards

Innovative Research Award

Wael Alnahari — King AbdulAziz University, Saudi Arabia

Wael Alnahari
Affiliation King AbdulAziz University
Country Saudi Arabia
Scopus 57223959943
Documents 4
Citations 34
h-index 3
Subject Area Federated Databases
Event International Database Scientist Awards
ORCID 0000-0001-5247-3713

Wael Alnahari is a Saudi computer and information systems researcher and PhD candidate at King AbdulAziz University whose scholarly work addresses federated databases, Internet of Things (IoT) security, and data management in smart-city environments. His research is indexed in Scopus [1]. This page summarizes his research profile in the context of the Innovative Research Award presented at the International Database Scientist Awards [5].

Abstract

This article presents an academic overview of Wael Alnahari, a researcher affiliated with King AbdulAziz University, Saudi Arabia. His work spans federated database systems, sovereignty-aware business intelligence, large language model evaluation for text-to-SQL tasks, and security and privacy in IoT and smart-city infrastructures. Bibliometric data from Scopus indicate four indexed documents, 34 citations, and an h-index of 3 [1]. The article reviews his research profile, principal contributions, selected publications, and relevance to the Innovative Research Award.

Keywords

Federated databases; business intelligence; text-to-SQL; large language models; Internet of Things security; device authentication; privacy; smart cities; cybersecurity; business continuity.

Introduction

Federated databases enable autonomous data sources to be queried without full centralization, which suits settings with strict governance and security constraints. Wael Alnahari, a PhD candidate at King AbdulAziz University with a cybersecurity master’s degree, combines database research with extensive professional cybersecurity leadership experience. His work is indexed in Scopus.[1].

Research Profile

Wael Alnahari’s research has progressed from IoT security, covering authentication, sensor-level requirements, and privacy risks [2][3][4], toward federated data architectures and language-model database querying. His background in business continuity and security risk management shapes the applied focus of his scholarship.

Research Contributions

Wael Alnahari’s contributions span IoT authentication using security keys [2], sensor security requirements [3], and smart-city privacy risks [4]. His recent work evaluates large language models for text-to-SQL generation [5] and proposes sovereignty-aware risk assessment infrastructure for federated business intelligence, known as SARAI [1].

Publications

Wael Alnahari’s Scopus-listed publications include conference papers on IoT device and server authentication using security keys [2], IoT sensor security requirements [3], and privacy concerns in smart cities [4], alongside an IEEE Access study on complexity-tiered text-to-SQL performance of large language models in smart-city decision support [5].

Research Impact

According to Scopus, Alnahari has four indexed documents, 34 citations, and an h-index of 3 [1]. For a researcher at an early stage of doctoral study, these indicators suggest that his earlier IoT security publications have been used by other scholars, while his recent database-focused work is still developing its readership. Bibliometric figures vary over time and across databases, and should be interpreted alongside the substance of the work.

Award Suitability

The International Database Scientist Awards recognize contributions to database science and related fields [5]. Alnahari’s nomination for the Innovative Research Award reflects his focus on federated databases, the link between data governance and cybersecurity, language-model-based query generation, and peer-reviewed publications. His practical experience in risk management and business continuity strengthens the applied relevance of his work.

Conclusion

Wael Alnahari is an emerging researcher whose work connects federated database systems, IoT security, and language-model-based data access. His Scopus-indexed publications, citation record, and interdisciplinary background provide a reasonable basis for consideration under the Innovative Research Award [1]. Continued doctoral research is expected to extend his contributions in sovereignty-aware and secure data infrastructures.

References

  1. Elsevier. (n.d.). Scopus author details: Wael Alnahari, Author ID 57223959943. Scopus. https://www.scopus.com/authid/detail.uri?authorId=57223959943
  2. Alnahari, W., & Quasim, M. T. (2021). Authentication of IoT device and IoT server using security key. In 2021 International Congress of Advanced Technology and Engineering (ICOTEN) (pp. 1–9). https://doi.org/10.1109/ICOTEN52080.2021.9493492
  3. Alqarni, H., Alnahari, W., & Quasim, M. T. (2021). Internet of things (IoT) security requirements: Issues related to sensors. In 2021 National Computing Colleges Conference (NCCC) (pp. 1–6). https://doi.org/10.1109/NCCC49330.2021.9428857
  4. Alnahari, W., & Quasim, M. T. (2021). Privacy concerns, IoT devices and attacks in smart cities. In 2021 International Congress of Advanced Technology and Engineering (ICOTEN) (pp. 1–5). https://doi.org/10.1109/ICOTEN52080.2021.9493559
  5. Alnahari, W., Hasanin, T., & Jambi, S. (n.d.). Complexity-tiered text-to-SQL performance of large language models in smart-city decision support. IEEE Access. https://doi.org/10.1109/ACCESS.2026.3728618

Matthew Alexander | Data Modeling and Database Design | Innovative Research Award

Published on by International Database Scientist Awards

Innovative Research Award

Matthew Alexander
Universitas Kristen Duta Wacana Yogyakarta, Indonesia

Matthew Alexander
Affiliation Universitas Kristen Duta Wacana Yogyakarta
Country Indonesia
Scopus 60687830000
Document 1
Subject Area Data Modeling and Database Design
Event International Database Scientist Awards

Matthew Alexander is a computer science professional and software engineer associated with Universitas Kristen Duta Wacana Yogyakarta. His professional and academic work spans full-stack software development, machine learning, case-based reasoning, database-oriented system development, and blockchain applications. His documented research activity includes work on knowledge-based systems and case-based datasets, providing a basis for consideration in the Innovative Research Award category.

Abstract

Matthew Alexander is a computer science graduate and software engineer specializing in full-stack development, machine learning, case-based reasoning, decision-support systems, and Web3 technologies. His academic work includes Hybrid Fuzzy TOPSIS for multi-criteria decision-making and expert-system development. His research profile includes a 2026 knowledge-based systems publication in Array, published by Elsevier. [2]

Keywords

Data Modeling, Database Design, Case-Based Reasoning, Knowledge-Based Systems, Machine Learning, Decision Support Systems, Hybrid Similarity, Fuzzy TOPSIS, Full-Stack Development, Software Engineering, Blockchain Applications.

Introduction

The development of intelligent information systems integrates structured data, computational reasoning, software engineering, and decision-support methodologies. Alexander’s profile reflects this interdisciplinary approach through case-based reasoning, machine learning, and structured information processing. His work is particularly relevant to data modeling and database design, while his publication on a knowledge-based system generator demonstrates this research direction. [2]

Research Profile

Matthew Alexander holds a Bachelor of Computer Science (S.Kom.) in Informatics from Duta Wacana Christian University, Yogyakarta. His academic and technical profile encompasses software engineering, machine learning, case-based reasoning, computer vision, database technologies, and blockchain development. His experience combines intelligent systems research, full-stack implementation, and knowledge-based system development supported by academic publication. [2]

Research Contributions

Alexander’s research contributions focus on computational reasoning and intelligent information systems. His work includes Case-Based Reasoning using Jaccard and Cosine similarity, Hybrid Fuzzy TOPSIS for multi-criteria retrieval, and knowledge-based system development. These efforts demonstrate applied expertise in computational retrieval, decision support, structured data processing, and intelligent system integration, supported by a 2026 publication. [2]

Publications

The publication by Delima et al. (2026), co-authored by Matthew Alexander, presents a knowledge-based system generator built around a case-based dataset. Published in Array, the study reflects Alexander’s research involvement in knowledge-based systems, case-based reasoning, and structured data processing.[2]

Research Impact

Matthew Alexander’s research profile demonstrates applied expertise in computational systems, including case-based reasoning, similarity computation, fuzzy decision methods, and knowledge-based systems. His software engineering experience extends these concepts into full-stack, machine-learning, and blockchain applications.[1][2]

Award Suitability

The Innovative Research Award assessment is supported by Matthew Alexander’s work in Case-Based Reasoning, Hybrid Similarity, Hybrid Fuzzy TOPSIS, knowledge-based systems, and software engineering. His research connects computational methods with practical system development, supported by a peer-reviewed publication on knowledge-based system generation using case-based datasets and documented academic contributions. [2]

Conclusion

Matthew Alexander presents a multidisciplinary computer science profile combining software engineering with applied research in knowledge-based systems, case-based reasoning, machine learning, and data-oriented application development. His academic work and documented publication provide relevant evidence for an Innovative Research Award assessment, particularly in areas involving structured data, intelligent retrieval, and computational decision support. [1] [2]

References

  1. Elsevier. (n.d.). Scopus author details: Matthew Alexander, Author ID 60687830000. Scopus. https://www.scopus.com/authid/detail.uri?authorId=60687830000
  2. Delima, R., Purwadi, J., Wibowo, A., Sinaga, J. Y., Imanuel, V., & Alexander, M. (2026). Knowledge-based system generator based on case-based dataset. Array. https://doi.org/10.1016/j.array.2026.101145

Mawloud Titah | Knowledge Graphs | Innovative Research Award

Published on by International Database Scientist Awards

Innovative Research Award

Mawloud Titah — Associate Professor of Industrial Engineering, University of Oran 2 Mohamed Ben Ahmed, Algeria

Mawloud Titah
Affiliation University of Oran 2 Mohamed Ben Ahmed
Country Algeria
Scopus 57194528924
Documents 10
Citations 30
h-index 3
Subject Area Knowledge Graphs
Event International Database Scientist Awards
ORCID 0000-0002-0632-5450

Mawloud Titah is an Associate Professor at the Institute of Maintenance and Industrial Safety, University of Oran 2 Mohamed Ben Ahmed, and a researcher at the LISIDD laboratory in Algeria. His research focuses on knowledge engineering, ontologies, knowledge graphs and semantic reasoning for explainable decision support, particularly in healthcare equipment maintenance and pharmaceutical logistics. [1]

Abstract

Mawloud Titah’s research addresses knowledge-driven decision support through ontology engineering, semantic technologies and knowledge graphs. His work applies OWL, SWRL, SQWRL and SPARQL to healthcare problems including medical-device maintenance, equipment criticality and pharmaceutical logistics. The research emphasizes transparent knowledge representation, semantic interoperability and explainable reasoning for operational healthcare decision-support systems. [1]

Keywords

Knowledge graphs; knowledge engineering; ontology engineering; OWL; SWRL; SQWRL; SPARQL; semantic reasoning; healthcare decision support; medical-device maintenance; pharmaceutical logistics; knowledge representation; explainable decision support; CommonKADS; semantic interoperability.

Introduction

Mawloud Titah’s academic work connects industrial engineering with knowledge-based information systems. His research trajectory includes tacit-knowledge externalization and CommonKADS before extending toward semantic models for healthcare. This progression places formal knowledge representation at the centre of decision-support development, using structured ontologies and rule-based inference to make domain knowledge explicit and computationally queryable. [2]

Research Profile

Mawloud Titah is an Associate Professor and researcher at the Institute of Maintenance and Industrial Safety, University of Oran 2 Mohamed Ben Ahmed, and is associated with the LISIDD laboratory. His stated research interests include ontology-driven knowledge modelling, semantic reasoning, knowledge graphs and explainable decision support for healthcare systems, particularly maintenance, medical-device risk and pharmaceutical logistics. [1] His technical research uses OWL and OWL 2 ontologies, SWRL rules, SQWRL and SPARQL queries, with tools including Protégé Editor and GraphDB. Earlier work addressed knowledge acquisition and the formalization of tacit manufacturing knowledge through the CommonKADS methodology, providing a foundation for later knowledge-based applications. [2]

Research Contributions

Mawloud Titah’s research develops explainable knowledge-graph and ontology-driven frameworks for healthcare decision support. His contributions address risk-aware medical-device maintenance, equipment criticality assessment, and pharmaceutical logistics. By integrating OWL ontologies, semantic rules, SPARQL and knowledge graphs, his work supports transparent prioritisation, knowledge sharing, traceability and interoperability, providing structured approaches for practical and explainable healthcare management and decision-making.[3][4][5]

Publications

Mawloud Titah’s publications span knowledge engineering, healthcare maintenance, semantic technologies and tacit-knowledge management. His recent studies examine explainable knowledge graphs for medical-device maintenance and ontology-driven hospital equipment management, while earlier work addresses tacit-knowledge reuse. Together, these publications demonstrate a progression from knowledge externalization and management toward semantic, ontology-based approaches for explainable healthcare decision support and knowledge-driven systems.[3] [4][5][6]

Research Impact

The reported research impact is concentrated in the application of semantic knowledge technologies to operational healthcare problems. The combination of ontology engineering, knowledge graphs and rule-based inference provides a framework for representing maintenance and logistics knowledge in a form that can be queried and inspected. Scopus records supplied for this profile list 10 documents, 30 citations and an h-index of 3. [1]

Award Suitability

Mawloud Titah’s research profile is closely aligned with database and knowledge-graph research through its emphasis on formal knowledge representation, semantic querying, interoperability and structured healthcare information. His work connects knowledge graphs and ontologies with practical decision-support applications, making the profile relevant to recognition focused on database science, semantic data technologies and knowledge-driven systems. [1]

Conclusion

Mawloud Titah’s research represents an interdisciplinary pathway from industrial knowledge management to ontology-based and knowledge-graph-driven healthcare decision support. His publications address semantic modelling, explainable reasoning, equipment maintenance and pharmaceutical logistics, while his technical work employs established semantic-web standards and tools. The profile therefore demonstrates sustained engagement with structured knowledge and database-related technologies. [3] [4]

References

  1. Elsevier. (n.d.). Scopus author details: Mawloud Titah, Author ID 57194528924. Scopus. https://www.scopus.com/authid/detail.uri?authorId=57194528924
  2. Titah, M., Aitouche, S., Mouss, M. D., & Soussa, A. (2017). Externalising and reusing tacit knowledge in manufacturing tasks. International Journal of Knowledge Management Studies, 8(3–4), 351–374. https://doi.org/10.1504/IJKMS.2017.087078
  3. Titah, M., & Bouchaala, M. A. (2026). An explainable knowledge graph framework for risk-aware maintenance of medical devices. Network Modeling Analysis in Health Informatics and Bioinformatics, 15, 179. https://doi.org/10.1007/s13721-026-00827-y
  4. Titah, M., & Bouchaala, M. A. (2024). An ontology-driven model for hospital equipment maintenance management: A case study. Journal of Quality in Maintenance Engineering, 30(2), 409–433. https://doi.org/10.1108/JQME-10-2023-0097
  5. Bouzidi, K. R., Titah, M., & Mekki, I. E. K. (2026). ODTIC-KG: A knowledge graph framework for proactive oncology drug inventory management in hospitals. International Journal of Medical Informatics, 106677. https://doi.org/10.1016/j.ijmedinf.2026.106677
  6. Titah, M., & Hachemi, K. (2024). Improvement of human performance in healthcare systems by taking into account physician behavior. Journal of Health Organization and Management, 38(6), 888–904. https://doi.org/10.1108/JHOM-01-2024-0029

Saeid Rezaei | Query Optimization | Innovative Research Award

Published on by International Database Scientist Awards

Innovative Research Award

Saeid Rezaei — Bu-Ali Sina University, Iran
Saeid Rezaei
Affiliation Bu-Ali Sina University
Country Iran
Google Scholar rnUbxhAAAAAJ
Documents 32
Citations 275
h-index 8
Subject Area Query Optimization
Event International Database Scientist Awards
Scopus 56982653700

Saeid Rezaei is a researcher and lecturer affiliated with Bu-Ali Sina University in Iran. His academic profile combines industrial engineering, operations research, supply chain and logistics, sustainable development, optimization, decision-making, and data-oriented analytical methods. His documented publications include studies of sustainable closed-loop supply chains, cross-docking, reverse logistics, competitive networks, and virtual alliances.[1]

Abstract

Saeid Rezaei’s academic work is situated primarily within industrial engineering and operations research, with research interests spanning supply chain and logistics, sustainable development, multi-objective decision-making, game theory, production planning, transportation planning, and data analysis. His publication record includes research on sustainable closed-loop supply chains, cross-docking operations, reverse logistics, competitive supply networks, and virtual alliances.[1][2]

Keywords

Industrial Engineering; Query Optimization; Operations Research; Supply Chain Management; Sustainable Supply Chains; Closed-Loop Supply Chains; Cross-Docking; Reverse Logistics; Multi-Objective Decision Making; Game Theory; Data Analysis; Production Planning; Logistics.

Introduction

Saeid Rezaei’s academic profile reflects an interdisciplinary approach to industrial engineering and decision sciences. His stated interests include supply chain and logistics, competitive planning, sustainable development, operations research, production and service strategy, transportation planning, and engineering economy. These areas provide a methodological context for his published studies of network design and optimization.[1][2]

Research Profile

Saeid Rezaei holds a Ph.D. in Industrial Engineering from the Department of Industrial Engineering, Faculty of Engineering, Bu-Ali Sina University, Hamedan, Iran. His professional background includes university lecturing and industry consulting, with research interests spanning supply chain and logistics, operations research and optimization, multi-objective decision-making, game theory, sustainable development, production planning, data analysis, and computational methods. His technical skills include GAMS, MATLAB, Python, RapidMiner, SPSS, Microsoft Project, and engineering and statistical methods.

Research Contributions

Saeid Rezaei’s documented research contributions address sustainable closed-loop supply chain network design, cross-docking, reverse logistics, competitive supply networks, and partnership structures. One study develops a comprehensive approach combining sustainable closed-loop network design with cross-docking operations, while later work examines competitive and sustainable decisions across multi-echelon supply chains.[1][2]

Publications

Saeid Rezaei’s publications focus on sustainable supply chain networks, closed-loop logistics, cross-docking operations, reverse logistics, competitive supply networks, and virtual alliances. His research examines optimization-based approaches to sustainable production, multi-echelon supply chains, strategic decision-making, and partnership structures. His scholarly work has appeared in international journals, including Computational and Mathematical Organization Theory, Environment, Development and Sustainability, Production Engineering, and Journal of Cleaner Production, demonstrating continued contributions to quantitative modeling, supply chain optimization, sustainability, and operations research. [2][3][4][5][6]

Research Impact

The supplied Google Scholar profile records 32 documents, 275 citations, and an h-index of 8. These figures are profile-level bibliometric indicators and should be interpreted according to the indexing coverage, date of retrieval, and citation practices of the underlying database.[1]

Award Suitability

For the International Database Scientist Awards, the supplied profile provides documented evidence of research activity in optimization, quantitative decision-making, data analysis, and computational approaches. His publication record also demonstrates sustained work on network optimization and analytical supply-chain models, providing research areas that can be considered in the context of an Innovative Research Award nomination.[1][6]

Conclusion

Saeid Rezaei’s documented academic profile combines industrial engineering, operations research, optimization, supply chain management, sustainability, and data analysis. His selected publications provide evidence of research on closed-loop networks, cross-docking, reverse logistics, competitive supply networks, and virtual alliances, while the supplied scholarly metrics provide additional bibliometric context.[1][2][6]

References

  1. Google Scholar. (n.d.). Saeid Rezaei, Google Scholar profile, user ID rnUbxhAAAAAJ. https://scholar.google.com/citations?user=rnUbxhAAAAAJ&hl=en
  2. Rezaei, S., & Kheirkhah, A. (2018). A comprehensive approach in designing a sustainable closed-loop supply chain network using cross-docking operations. Computational and Mathematical Organization Theory, 24(1), 51–98. https://doi.org/10.1007/s10588-017-9247-3
  3. Rezaei, S., & Maihami, R. (2020). Optimizing the sustainable decisions in a multi-echelon closed-loop supply chain of the manufacturing/remanufacturing products with a competitive environment. Environment, Development and Sustainability, 22(7), 6445–6471. https://doi.org/10.1007/s10668-019-00491-5
  4. Kheirkhah, A., & Rezaei, S. (2016). Using cross-docking operations in a reverse logistics network design: A new approach. Production Engineering, 10(2), 175–184. https://doi.org/10.1007/s11740-015-0646-3
  5. Rezaei, S., & Kheirkhah, A. (2017). Applying forward and reverse cross-docking in a multi-product integrated supply chain network. Production Engineering, 11(4), 495–509. https://doi.org/10.1007/s11740-017-0743-6
  6. Rezaei, S., & Behnamian, J. (2021). A survey on competitive supply networks focusing on partnership structures and virtual alliance: New trends. Journal of Cleaner Production, 287, 125031. https://doi.org/10.1016/j.jclepro.2020.125031

Yahya Dorostkar Navaei | Graph Databases | Innovative Research Award

Published on by International Database Scientist Awards

Innovative Research Award

Yahya Dorostkar Navaei — University of Mohaghegh Ardabili, Iran

Yahya Dorostkar Navaei
Affiliation University of Mohaghegh Ardabili
Country Iran
Google Scholar ZjcawoYAAAAJ
Documents 20
Citations 507
h-index 12
Subject Area Graph Databases
Event International Database Scientist Awards
Scopus 57222517231

Yahya Dorostkar Navaei is a computer engineering researcher and AI/ML practitioner affiliated with the University of Mohaghegh Ardabili in Iran. His research profile encompasses graph-based recommendation systems, social-network analysis, machine learning, deep learning, reinforcement learning, optimization, cybersecurity, healthcare analytics, Internet of Things, and data-driven computing. His doctoral research addressed hierarchical friend recommendation using deep reinforcement learning, while his broader publication record includes graph-based recommendation, influence maximization, anomaly detection, network security, routing optimization, and recommender systems. His current professional work also includes agentic artificial intelligence, retrieval-augmented generation, vector databases, financial forecasting, and real-time data processing.

Abstract

Yahya Dorostkar Navaei is a computer engineering researcher specializing in artificial intelligence, machine learning, deep learning, reinforcement learning, recommendation systems, and graph-based computing. His research covers social networks, cybersecurity, healthcare, wireless networks, and edge computing, with recent work in agentic AI, RAG, vector databases, and financial forecasting. The supplied academic profile reports 20 documents, 507 citations, and an h-index of 12 on Google Scholar.

Keywords

Graph Databases, Artificial Intelligence, Machine Learning, Deep Learning, Reinforcement Learning, Recommendation Systems, Social Network Analysis, Data Mining, Optimization, Agentic AI, Retrieval-Augmented Generation

Introduction

Yahya Dorostkar Navaei’s research spans artificial intelligence, machine learning, recommender systems, data mining, graph-based social-network analysis, and optimization. His doctoral research focused on friend recommendation using deep reinforcement learning, while his publications address graph recommendation, influence maximization, cybersecurity, healthcare analytics, wireless networks, and edge computing. His professional expertise also includes agentic AI, RAG, LangGraph, vector databases, and AI-driven financial forecasting.

Research Profile

Yahya Dorostkar Navaei holds a Ph.D. and M.Sc. in Computer Engineering and a B.Sc. in Computer Science. His academic and professional experience covers artificial intelligence, data mining, machine learning, recommendation systems, optimization, and financial-data analysis. His current technical interests include agentic AI, retrieval-augmented generation, semantic search, and vector databases.

Research Contributions

Yahya Dorostkar Navaei’s research contributions span graph and social-network computing, recommendation systems, artificial intelligence, cybersecurity, and IoT-based distributed computing. His work includes graph-based friend recommendation, social-network influence analysis, e-commerce and music recommendation, healthcare recommender systems, machine learning, deep learning, reinforcement learning, intrusion detection, anomaly detection, wireless sensor networks, and edge computing. His recent professional activities further extend into agentic AI, LangGraph workflows, retrieval-augmented generation, semantic search, vector databases, and financial-market forecasting.

Publications

Yahya Dorostkar Navaei’s publications span graph-based recommendation, social-network analysis, deep learning, cybersecurity, healthcare, wireless sensor networks, and edge computing. His research applies machine learning, optimization, and data mining to recommendation systems, anomaly detection, network security, IoT healthcare, and data-intensive computational problems.[2][3][4][5][6]

Research Impact

Yahya Dorostkar Navaei’s Google Scholar profile reports 20 documents, 507 citations, and an h-index of 12. His publications span social networks, recommendation systems, cybersecurity, healthcare, wireless communications, and edge computing, with research applying machine learning and optimization to data-driven computational problems.[5] [6] The supplied profile also describes ongoing work involving agentic AI, retrieval-augmented generation, and financial forecasting, extending the application of data-driven methods toward contemporary AI engineering workflows.

Award Suitability

Yahya Dorostkar Navaei’s research aligns with database-oriented and data-intensive computing through graph-based recommendation, social-network analysis, data mining, recommender systems, IoT, edge computing, and machine learning. His work demonstrates applications in graph data, network analytics, intelligent data processing, and AI-enabled information retrieval, making these areas relevant to consideration for the International Database Scientist Awards.

Conclusion

Yahya Dorostkar Navaei’s profile spans artificial intelligence, machine learning, graph-based analysis, recommendation systems, cybersecurity, and data-intensive computing. His research includes social networks, healthcare, wireless communications, and edge computing, while his current work focuses on agentic AI, RAG, vector databases, and financial-data forecasting.

References

  1. Elsevier. (n.d.). Scopus author details: Yahya Dorostkar Navaei, Author ID 57222517231. Scopus. https://www.scopus.com/authid/detail.uri?authorId=57222517231
  2. Navaei, Y. D., et al. (2026). A graph-based friend recommendation system in social networks using Q-Deep Learning Network. Information Sciences. https://doi.org/10.1016/j.ins.2026.124129
  3. Navaei, Y. D., et al. (2025). Identifying Key Influencers in Social Networks: A New Neighborhood-Based Method. Journal of Soft Computing and Information Technology. https://doi.org/10.22034/jscit.2025.219059
  4. Navaei, Y. D., et al. (2025). Ordered Clustering-Based Semantic Music Recommender System Using Deep Learning Selection. Computers, Materials & Continua. https://doi.org/10.32604/cmc.2025.061343
  5. Einy, S., Oz, C., & Navaei, Y. D. (2021). The anomaly- and signature-based IDS for network security using hybrid inference systems. Mathematical Problems in Engineering, 2021, 6639714. https://doi.org/10.1155/2021/6639714
  6. Nanehkaran, Y. A., Licai, Z., Chen, J., Zhongpan, Q., Xiaofeng, Y., Navaei, Y. D., et al. (2022). Diagnosis of chronic diseases based on patients’ health records in IoT healthcare using the recommender system. Wireless Communications and Mobile Computing, 2022, 5663001. https://doi.org/10.1155/2022/5663001

Goran Gagula | Indexing Techniques | Innovation in Data Migration Techniques

Published on by International Database Scientist Awards

Innovation in Data Migration Techniques

Goran Gagula — Josip Juraj Strossmayer University of Osijek, Croatia

Goran Gagula
Affiliation Josip Juraj Strossmayer University of Osijek
Country Croatia
Scopus 55939984100
Documents 8
Citations 53
h-index 3
Subject Area Indexing Techniques
Event international Database Scientist Awards
ORCID 0009-0007-8388-3484

Goran Gagula is a Croatian food technology and biotechnology professional whose career combines industrial production, quality management, environmental health and safety, higher education, consultancy, and applied research. His publications examine beer quality, packaging materials, storage, volatile compounds, predictive modelling, and food biotechnology, supporting interdisciplinary research relevant to data-informed quality and process evaluation.

Abstract

Goran Gagula is affiliated with Josip Juraj Strossmayer University of Osijek and has developed a multidisciplinary professional profile spanning food technology, biotechnology, brewing technology, quality assurance, environmental management, health and safety, and academic teaching. His documented scholarly work includes studies of beer physicochemical properties, packaging effects, volatile compounds, predictive modelling, and malt quality. His Scopus record is identified by Author ID 55939984100 and reports 8 documents, 53 citations, and an h-index of 3. [1]

Keywords

Goran Gagula; Innovation in Data Migration Techniques; Indexing Techniques; Food Technology; Biotechnology; Brewing Science; Beer Quality; Packaging Materials; Volatile Compounds; Predictive Modelling; Quality Assurance; Environmental Health and Safety; Process Engineering; Food Biotechnology.

Introduction

Goran Gagula’s professional and academic activities connect laboratory science, industrial process engineering, quality systems, environmental management, and higher education. His research publications demonstrate particular engagement with brewing and food technology, including analytical assessment of beer during storage, packaging-related changes, aroma compounds, malt quality, and predictive modelling. These areas provide an applied scientific context for evaluating process variables and product quality.

Research Profile

Goran Gagula is a food technology and biotechnology professional with doctoral study in biotechnical sciences and postgraduate, master’s, and bachelor’s qualifications from Croatian universities. His expertise spans food safety, HACCP, ISO 9001/22000, environmental and occupational health and safety, auditing, leadership, project management, and finance. His career includes Croatian Ministry advisory roles, brewery consultancy and management, university teaching, process engineering, quality control, production, packaging, maintenance, EHS, ESG, budgeting, CAPEX, investments, and organizational development.

Research Contributions

The publication record indicates a sustained applied-research interest in food and beverage technology. One research stream investigates how packaging materials affect the physicochemical characteristics and volatile composition of pale lager beer during storage. [2] Another examines predictive modelling of microbial growth in different milk matrices, illustrating the application of quantitative modelling to food biotechnology. [3] Additional work addresses multivariate modelling of quality changes in lager and malt beer during storage, while later research examines the effects of pasteurisation and storage on aroma compounds in lager. [4] [5] Collectively, these studies demonstrate an applied research orientation involving analytical measurement, process variables, product quality, storage conditions, and statistical or predictive approaches. His practical work includes brewery start-ups, wastewater and water systems, beer stabilization, centrifugation optimization, PET packaging, quality assurance, product development, production savings, and technology transfer, connecting scientific and engineering principles with industrial operations.

Publications

Goran Gagula has contributed to research in food technology and brewing science through publications addressing beer quality, packaging materials, storage stability, volatile compounds, predictive modelling, and malt quality. His studies published in journals including Food Packaging and Shelf Life, Journal of the Institute of Brewing, Journal of the American Society of Brewing Chemists, and Mljekarstvo demonstrate an applied research focus on physicochemical analysis, quality assessment, and process-related changes in food and beverage systems. His work on packaging effects and beer storage provides evidence-based insights into product quality and technological optimization. [2][3][4][5] [6]

Research Impact

The documented Scopus profile reports 8 indexed documents, 53 citations, and an h-index of 3 for the identified author record. [1] Within the supplied publication record, research on packaging materials, beer storage, aroma compounds, predictive modelling, and malt quality represents an applied body of work connecting food science with quantitative evaluation and industrial process considerations. The relevance of this research is particularly evident in studies where product characteristics are evaluated across processing, packaging, pasteurisation, or storage conditions. Such investigations can contribute to evidence-based quality management by identifying measurable changes in product composition and quality attributes.

Award Suitability

Goran Gagula’s profile presents several documented characteristics relevant to consideration for recognition under the Innovation in Data Migration Techniques: multidisciplinary academic training, extensive industrial experience, university teaching, professional certification, applied research publications, and an indexed scholarly record. His research demonstrates the use of analytical and modelling approaches in food and beverage systems, while his professional work provides an additional applied-technology context. For an award assessment, the available evidence is best considered through the combination of publication activity, indexed research, citations, subject relevance, professional contribution, and documented technical projects rather than through a single bibliometric indicator. The Scopus record and associated publications provide identifiable sources for evaluating his scholarly activity. [1]

Conclusion

Goran Gagula’s academic and professional profile combines food technology, biotechnology, brewing science, quality assurance, environmental management, EHS, process engineering, consultancy, and higher education. His publications provide evidence of applied research into beer quality, packaging, storage, volatile compounds, predictive modelling, and malt quality. The documented Scopus record further provides a quantitative basis for scholarly profile assessment. [1]

References

  1. Elsevier. (n.d.). Scopus author details: Goran Gagula, Author ID 55939984100. Scopus. https://www.scopus.com/authid/detail.uri?authorId=55939984100
  2. Gagula, G., Mastanjević, K., Mastanjević, K., Krstanović, V., Horvat, D., & Magdić, D. (2020). The influence of packaging material on volatile compounds of pale lager beer. Food Packaging and Shelf Life, 24, 100496. https://doi.org/10.1016/j.fpsl.2020.100496
  3. Slačanac, V., Lučan, M., Hardi, J., Habschied, K., Krstanović, V., & Gagula, G. (2013). Predictive modeling of Bifidobacterium animalis subsp. lactis Bb-12 growth in cow’s, goat’s and soy milk. Mljekarstvo, 63(4), 220–227. https://hrcak.srce.hr/110610
  4. Gagula, G., Magdić, D., & Horvat, D. (2016). PLSR modelling of quality changes of lager and malt beer during storage. Journal of the Institute of Brewing, 122(1), 116–125. https://doi.org/10.1002/jib.312
  5. Gagula, G., Đurđević-Milošević, D., Ncube, T., & Magdić, D. (2024). The effect of pasteurisation and storage on aroma compounds in lager. Journal of the Institute of Brewing, 130(2), 83–92. https://doi.org/10.58430/jib.v130i2.52
  6. Gagula, G., Šarić, G., Rezić, T., Horvat, D., & Magdić, D. (2023). Changes in the physicochemical properties of pale lager beer during storage in different packaging materials. Journal of the American Society of Brewing Chemists, 81(2), 351–356. https://doi.org/10.1080/03610470.2022.2134110