Hung-Chang Liao | Data processing | Best Researcher Award

Published on by Research Data Analysis

Best Researcher Award

Hung-Chang Liao — Chung Shan Medical University, Taiwan

Hung-Chang Liao
Affiliation Chung Shan Medical University
Country Taiwan
Scopus ID 7201507629
Documents 82
Citations 1,563
h-index 19
Subject Area Data processing
Event International Research Data Analysis Excellence & Awards
ORCID 0000-0002-4538-9334

Hung-Chang Liao is a professor affiliated with Chung Shan Medical University in Taiwan whose research has included data-oriented methods, healthcare management, education, quality engineering, occupational safety and health, inventory control, data mining, and related analytical approaches. His documented scholarly work spans methodological and applied studies connecting quantitative analysis with healthcare, education, and management contexts. [1]

Abstract

Hung-Chang Liao’s academic profile reflects multidisciplinary research integrating quantitative analysis, data processing, healthcare management, education, quality engineering, and related applied methodologies. His publication record includes studies involving optimization, statistical methods, computational approaches, medical education, and healthcare applications, providing a broad basis for academic recognition in research and data-oriented scholarship. [2]

Keywords

Hung-Chang Liao; data processing; data analysis; healthcare management; quality engineering; data mining; statistical methods; optimization; medical education; research analytics.

Introduction

Hung-Chang Liao is a professor at Chung Shan Medical University whose documented research encompasses data-oriented methodologies and interdisciplinary applications. His work connects analytical techniques with healthcare management, education, engineering, and organizational problems. Published studies demonstrate sustained engagement with quantitative research and computational approaches across several academic and applied domains. [3]

Research Profile

Liao’s research profile includes data processing, experimental design, medical education, quality engineering, healthcare management, inventory control, data mining, and related analytical disciplines. His institutional profile identifies expertise in healthcare supply-chain management, healthcare enterprise resource planning, biostatistics, management science, occupational safety, and medical humanities education, illustrating a multidisciplinary research orientation. [2]

Research Contributions

Liao’s documented contributions include applications of optimization, statistical quality methods, computational intelligence, and data-driven modelling to practical problems. His publications address inventory systems, healthcare supply chains, predictive modelling, educational interventions, and quantitative research design. These studies demonstrate the application of analytical methods to complex interdisciplinary questions involving healthcare, management, engineering, and education. [2]

Publications

Liao has contributed to peer-reviewed publications covering engineering, management, healthcare, education, and computational research. Representative works include studies of cooperative learning, medical humanities, healthcare optimization, inventory control, machine-learning-based diagnosis, and robust healthcare management. Recent publications continue to apply quantitative and methodological frameworks to healthcare and educational research.[3]

Research Impact

The available bibliographic record indicates that Liao’s research has been cited across scholarly literature, while his publications address practical questions in healthcare, education, engineering, and management. His work on computational and quantitative methods provides examples of how data-driven techniques can support decision-making, evaluation, optimization, and evidence-based practice in interdisciplinary settings. [2]

Award Suitability

For an award focused on research excellence, Liao’s documented record provides several relevant indicators, including a substantial publication portfolio, interdisciplinary research activity, and work involving data processing and analytical methodologies. His research spans healthcare, education, engineering, and management, allowing his profile to be considered in relation to multidisciplinary data-analysis research and applied scholarly contributions. [1]

Conclusion

Hung-Chang Liao’s academic record presents a multidisciplinary body of research incorporating data processing, quantitative analysis, healthcare management, education, and engineering methods. His peer-reviewed publications and documented research activities provide a scholarly foundation for consideration within research recognition programs emphasizing analytical methods, interdisciplinary investigation, and applied research contributions.[2]

References

  1. Multi-response optimization using weighted principal component.
    https://link.springer.com/article/10.1007/s00170-004-2248-7
  2. A Robust ORMS Framework for Taiwanese Healthcare: Taguchi’s Dynamic Method in Action
    https://www.mdpi.com/2227-9032/13/9/1024
  3. Developing a scale measuring need for cognition among medical and healthcare students and professionals
    https://www.researchgate.net/publication/410725370

Prosper Oghenemaro Ugbehe | Emerging Research Trends | Innovative Research Award

Published on by Research Data Analysis

Innovative Research Award

Prosper Oghenemaro Ugbehe
Federal Polytechnic Orogun, Nigeria
Prosper Oghenemaro Ugbehe
Affiliation Federal Polytechnic Orogun
Country Nigeria
Scopus ID 59967740700
Documents 1
Citations 4
h-index 1
Subject Area Emerging Research Trends
Event International Research Data Analysis Excellence & Awards
ORCID 0000-0003-3686-9552

Prosper Oghenemaro Ugbehe is an engineering and technical education researcher affiliated with Federal Polytechnic Orogun, Nigeria. His documented scholarly work includes research addressing engineering design, technical skills, employability, and applied technological development, providing a relevant foundation for recognition within an emerging research context. [1]

Abstract

This recognition profile presents Prosper Oghenemaro Ugbehe in relation to the Innovative Research Award and the subject area of Emerging Research Trends. His documented research includes applied engineering design, technical skills development, employability, and technology-oriented studies, reflecting an emphasis on practical research questions and emerging applications.[2]

Keywords

Innovative Research Award, Emerging Research Trends, Prosper Oghenemaro Ugbehe, engineering research, technical education, mechanical engineering technology, research innovation, applied research, technology development, Nigeria.

Introduction

Emerging research trends often develop where technological change, practical engineering problems, and interdisciplinary knowledge intersect. Ugbehe’s documented work reflects this applied orientation through studies involving technical skills, employability, engineering design, and technological development. Such research provides an appropriate context for examining innovation through practical problem solving and technology-focused scholarly activity. [3]

Research Profile

Prosper Oghenemaro Ugbehe is associated with Federal Polytechnic Orogun and has contributed to engineering and technology-oriented scholarly work. Available publication records identify his participation in research concerning portable agricultural machinery and automotive technical and entrepreneurship skills. These areas connect engineering practice with education, workforce preparation, and applied technological development. [1]

Research Contributions

The documented contributions associated with Ugbehe include collaborative work on the design and fabrication of a portable palm kernel extractor and research examining employability, technical, and entrepreneurship skills in the automotive sector. These studies demonstrate an applied research approach directed toward equipment development, technical competence, and practical workforce requirements.[3]

Publications

A documented publication involving Ugbehe is Design and Fabrication of a Portable Palm Kernel Extractor, published in the Journal of Emerging Technologies and Innovative Research in 2023. Another indexed publication record identifies his participation in research on employability, technical, and entrepreneurship skills for the automotive industry.[2]

Research Impact

The potential practical relevance of Ugbehe’s research is reflected in its focus on equipment mechanization, technical competencies, employability, and entrepreneurship. The portable palm kernel extractor study addresses mechanization of an agricultural processing activity, while automotive skills research considers capabilities relevant to technical employment and economic recovery. [1]

Award Suitability

The documented research record provides evidence of applied engineering and technology research that can be considered in an Innovative Research Award assessment. Relevant considerations include originality of the research problem, methodological quality, documented outcomes, practical applicability, publication record, collaboration, and contribution to emerging technical or educational needs.[3]

Conclusion

Prosper Oghenemaro Ugbehe’s documented scholarly activities demonstrate engagement with applied engineering, technical education, equipment development, and workforce-oriented research. His research themes intersect with practical innovation and emerging technological needs. The available publications and institutional records provide a factual basis for presenting his profile in connection with an Innovative Research Award.[2]

References

  1. Estimation and Forecasting of Nigeria’s Residential, Commercial, and Industrial Electricity Demands.
    https://www.researchgate.net/publication/393113153
  2. RELIABILITY-CENTERED MAINTENANCE FOR
    https://www.researchgate.net/publication/414436895
  3. Ugbehe, P. O. (n.d.). Scopus author profile, Author ID 59967740700. Elsevier Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=59967740700

Peter Obami | Artificial Intelligence | Best Researcher Award

Published on by Research Data Analysis

Best Researcher Award

Peter Obami
National Health Service, United Kingdom

Peter Obami
Affiliation National Health Service
Country United Kingdom
Documents 8
Citations 3
h-index 1
Subject Area Artificial Intelligence
Event International Research Data Analysis Excellence & Awards
ORCID 0009-0007-3738-0683

Peter Obami is affiliated with the National Health Service in the United Kingdom and is associated with research activity in Artificial Intelligence. The profile records eight documents, three citations, and an h-index of one. These bibliometric indicators provide a concise representation of the research information supplied for this recognition profile. [1]

Abstract

This academic recognition profile presents Peter Obami, affiliated with the National Health Service, United Kingdom, with Artificial Intelligence identified as the principal subject area. The supplied profile records eight documents, three citations, and an h-index of one. The article summarizes the documented research profile and its relevance to academic recognition. [2]

Keywords

Artificial Intelligence; Machine Learning; Data Analysis; Intelligent Systems; Research Profile; Artificial Intelligence Research; National Health Service; United Kingdom; Research Impact; Academic Recognition.

Introduction

Artificial Intelligence encompasses computational methods designed to perform tasks associated with perception, reasoning, learning, and decision-making. Contemporary AI research includes machine learning, neural networks, natural language processing, and intelligent systems. The field increasingly intersects with healthcare and data-intensive disciplines, creating opportunities for research-driven technological development and evidence-based applications. [3]

Research Profile

Peter Obami is identified in the supplied information as a researcher affiliated with the National Health Service in the United Kingdom, with Artificial Intelligence listed as the subject area. The profile contains eight documents, three citations, and an h-index of one. These indicators describe the documented publication and citation record provided for this article. [1]

Research Contributions

Research contributions in Artificial Intelligence may involve developing computational models, analyzing data, improving prediction, or applying intelligent methods to practical problems. For Peter Obami, the supplied subject classification places the profile within Artificial Intelligence. Specific contributions should be assessed through individual publications, methodologies, datasets, implementations, and documented research outputs rather than bibliometric indicators alone. [2]

Publications

The supplied profile records eight documents associated with the researcher. A complete publication assessment would consider article titles, journals or conferences, publication dates, research methods, co-authorship, and persistent identifiers such as DOIs. Because individual publication metadata were not supplied, this article does not attribute specific papers or findings beyond the documented publication count. [3]

Research Impact

Research impact can be considered through scholarly citations, adoption of methods, technological implementation, collaborations, and contributions to professional or public practice. The supplied record reports three citations and an h-index of one. These measures provide limited quantitative evidence and should be interpreted alongside publication quality, research context, and documented practical outcomes. [2]

Award Suitability

The profile is presented in connection with the International Research Data Analysis Excellence & Awards and identifies Artificial Intelligence as its subject area. Award suitability may be evaluated using documented research outputs, originality, methodological rigor, relevance, dissemination, and measurable impact. Final recognition should be determined according to the applicable award criteria and independently verifiable evidence. [1]

Conclusion

Peter Obami’s supplied academic profile identifies Artificial Intelligence as a research subject and records eight documents, three citations, and an h-index of one. The profile provides a concise basis for academic recognition while emphasizing the importance of evaluating detailed publications, research methods, and verifiable outcomes when assessing contributions to Artificial Intelligence. [2]

References

  1. Liquid Biopsy for Early Detection of Breast Cancer: Evidence and Public Health Implications from a Systematic Review.
    https://www.researchgate.net/publication/411920713
  2. Multiomic immune microenvironment signatures associated with response and resistance to immune checkpoint blockade across solid tumours
    https://link.springer.com/article/10.1007/s12672-026-05652-3
  3. Applications of Immunohistochemistry in Disease Diagnosis: Principles, Challenges, and Recent Advances
    https://www.researchgate.net/publication/407458875

Fulufhelo Tshikhudo | Corrosion Science | Best Researcher Award

Published on by Research Data Analysis

Best Researcher Award

Fulufhelo Tshikhudo
University of South Africa, South Africa

Fulufhelo Tshikhudo
Affiliation University of South Africa
Country South Africa
Scopus ID 59952578300
Documents 2
Citations 10
h-index 2
Subject Area Corrosion science
Event International Research Data Analysis Excellence & Awards
ORCID 0000-0003-0315-2627

Fulufhelo Tshikhudo is a South African chemistry researcher whose documented research focuses on corrosion inhibition, electrochemical analysis, computational chemistry, and the development of organic compounds for corrosion-control applications. Her research record includes experimental and computational investigations of corrosion inhibitors for metallic surfaces in acidic environments.[2]

Abstract

Fulufhelo Tshikhudo’s research profile is centered on corrosion science and corrosion inhibition, combining laboratory experimentation with computational approaches. Documented studies address substituted organic compounds, electrochemical characterization, density functional theory, and molecular adsorption on metallic surfaces. These activities contribute to understanding corrosion-control mechanisms and the evaluation of potential inhibitor systems.[3]

Keywords

  • Corrosion science
  • Corrosion inhibitors
  • Electrochemical analysis
  • Density functional theory
  • Computational chemistry
  • Materials protection

Introduction

Corrosion science examines the chemical and electrochemical degradation of metals and methods for reducing material deterioration. Tshikhudo’s documented work addresses corrosion inhibition using organic molecules and computational methods. Her research includes investigations of metallic surfaces exposed to acidic media, linking experimental measurements with theoretical calculations to examine inhibitor performance and molecular interactions.[1]

Research Profile

Tshikhudo’s research profile is associated with chemistry and corrosion-inhibition studies involving experimental characterization and computational modelling. Her academic work includes substituted organic compounds investigated as corrosion inhibitors, with techniques such as electrochemical impedance spectroscopy, potentiodynamic polarization, weight-loss measurements, and density functional theory used to evaluate corrosion-control behavior and molecular interactions.[2]

Research Contributions

Her documented contributions include the investigation of substituted triazines and quinoline-based Schiff bases as potential corrosion inhibitors. These studies combine experimental and computational evidence to examine adsorption, inhibitor effectiveness, molecular interactions, and surface protection. The work demonstrates an interdisciplinary approach connecting synthetic chemistry, electrochemical analysis, quantum calculations, and corrosion science.[3]

Publications

A documented 2025 publication co-authored by Tshikhudo investigated substituted triazines for corrosion inhibition of mild steel in hydrochloric acid using electrochemical, weight-loss, and density functional theory methods.A 2026 publication examined quinoline-based Schiff-base corrosion inhibitors on an Al surface using density functional theory and Monte Carlo simulations.[1]

Research Impact

The potential research impact of Tshikhudo’s work lies in its contribution to corrosion-inhibition research through combined experimental and computational approaches. Studies of organic inhibitor adsorption can provide molecular-level information relevant to materials protection. Her publications also place corrosion research within broader interdisciplinary workflows involving electrochemistry, surface science, computational chemistry, and materials engineering.[2]

Award Suitability

The documented research record provides evidence relevant to recognition in corrosion science, particularly through published work on corrosion inhibitors and computationally supported materials studies. Her research combines established experimental techniques with theoretical analysis, while her publication record demonstrates participation in collaborative investigations of corrosion-control systems. These documented activities provide a factual basis for award consideration.[1]

Conclusion

Fulufhelo Tshikhudo’s documented scholarly work focuses on corrosion science, corrosion inhibition, electrochemical characterization, and computational investigation of molecular interactions. Her research includes studies of triazine and Schiff-base inhibitor systems and demonstrates collaboration across experimental and theoretical chemistry. The available publications establish a focused research profile in corrosion-related materials protection [3]

References

  1. MOLECULAR INSIGHTS INTO THE ADSORPTION BEHAVIOUR OF QUINOLINE-BASED SCHIFF BASE CORROSION INHIBITORS ON AL (111) SURFACE: A DFT AND MONTE CARLO STUDY.
    https://www.researchgate.net/publication/406477045
  2. Investigating the Inhibitory Potential of Halogenated Quinoline Derivatives against MAO‑A and MAO-B: Synthesis, Crystal Structure, Density Functional Theory, and Molecular Dynamics Simulations
    https://pubs.acs.org/acsodf/article/10/25/26500/3654732/Investigating-the-Inhibitory-Potential-of
  3. Synthesis of substituted triazines and evaluation of their corrosion inhibition performance on Fe(100) in 1 M HCl: a combined experimental and DFT study.
    https://rgu-repository.worktribe.com/output/2979801

Gang Li | Agricultural Data Analysis | Best Researcher Award

Published on by Research Data Analysis

Best Researcher Award

Gang Li – Nanjing Agricultural University, China

Gang Li
Affiliation Nanjing Agricultural University
Country China
Scopus ID 56520660900
Documents 109
Citations 395
h-index 11
Subject Area Agricultural Data Analysis
Event International Research Data Analysis Excellence & Awards

Gang Li is a researcher affiliated with Nanjing Agricultural University, China, whose academic profile is associated with agricultural data analysis. The supplied Scopus record reports 109 documents, 395 citations, and an h-index of 11. This article summarizes the available profile information and its relevance to research recognition. [1]

Abstract

This article presents the available academic profile of Gang Li of Nanjing Agricultural University, China, in the context of agricultural data analysis. The supplied Scopus information lists 109 documents, 395 citations, and an h-index of 11. These indicators provide a bibliometric overview but do not independently establish research quality or award eligibility. [2]

Keywords

Gang Li; Best Researcher Award; Agricultural Data Analysis; Nanjing Agricultural University; Bibliometrics; Research Publications; Citation Analysis; Research Impact.

Introduction

Agricultural data analysis supports evidence-based research by examining agricultural observations, identifying patterns, and informing scientific interpretation. Gang Li, affiliated with Nanjing Agricultural University, is presented here in this research context. The available profile provides bibliometric indicators for reviewing scholarly activity, while detailed publications and research contributions require independent verification. [3]

Research Profile

Gang Li is affiliated with Nanjing Agricultural University in China. The supplied Scopus author record identifies the researcher through author ID 56520660900 and reports 109 documents, 395 citations, and an h-index of 11. These figures summarize the provided profile snapshot; publication-level details and current metrics should be checked against the indexed record. [2]

Research Contributions

Agricultural data analysis can contribute to research through data interpretation, statistical evaluation, and evidence-based assessment of agricultural systems. The supplied subject area associates Gang Li’s profile with this field. Specific methods, datasets, findings, and applications cannot be established from bibliometric indicators alone and should be described using verified publications and institutional research information. [1]

Publications

The supplied Scopus profile reports 109 documents associated with Gang Li. This document count offers a broad indication of indexed scholarly output but does not identify individual titles, publication dates, journals, or author roles. A complete publication overview should therefore be prepared from the verified author record and checked against each publication’s bibliographic details. [3]

Research Impact

The supplied bibliometric snapshot records 395 citations and an h-index of 11 for Gang Li. Citation counts reflect indexed citation activity, while the h-index combines publication and citation information. Both indicators depend on database coverage and timing; they should be interpreted alongside research quality, contribution details, disciplinary context, and the relevance of individual studies. [1]

Award Suitability

Gang Li’s supplied academic profile provides information relevant to consideration for the Best Researcher Award associated with the International Research Data Analysis Excellence & Awards. The reported publication and citation indicators may support an application review. Final suitability depends on the award’s published eligibility criteria, documented research contributions, supporting evidence, and the organizers’ assessment. [2]

Conclusion

The available profile identifies Gang Li as a researcher affiliated with Nanjing Agricultural University, China, and provides a bibliometric snapshot of scholarly activity. These details offer a starting point for academic recognition. A comprehensive evaluation should include verified publications, research contributions, methodological significance, and documented alignment with the award’s eligibility requirements. [3]

References

  1. Elsevier. (n.d.). Scopus author details: Gang Li, Author ID 56520660900. Scopus. Retrieved September 28, 2026, from
    https://www.scopus.com/authid/detail.uri?authorId=56520660900
  2. International Research Data Analysis Excellence & Awards. (n.d.). Research Data Analysis. Retrieved September 28, 2026, from
    https://researchdataanalysis.com/
  3. Laser Radar and Micro-Light Polarization Image Matching and Fusion Research.
    https://www.mdpi.com/2079-9292/14/15/3136

Mandisa Zameko | Temporal Data Patterns | Best Researcher Award

Published on by Research Data Analysis

Best Researcher Award

Mandisa Zameko   – University of Fort Hare, South Africa

Mandisa Zameko
Affiliation University of Fort Hare
Country South Africa
Scopus ID 60888056600
Documents 1
Subject Area Temporal Data Patterns
Event International Research Data Analysis Excellence & Awards
ORCID 0000-0003-4401-2540

Mandisa Zameko is a researcher affiliated with the University of Fort Hare in South Africa. Her listed subject area is Temporal Data Patterns, which concerns the analysis of observations and changes over time. Her academic profile is associated with the International Research Data Analysis Excellence & Awards. The supplied information identifies one Scopus-indexed document. [1]

Abstract

This article presents the available academic profile of Mandisa Zameko, affiliated with the University of Fort Hare, South Africa. Her listed subject area is Temporal Data Patterns. The article summarizes her researcher identifiers, publication information, and recognition context while distinguishing supplied information from details requiring further verification. [2]

Keywords

Mandisa Zameko; University of Fort Hare; South Africa; Temporal Data Patterns; temporal data analysis; research recognition; academic research; Scopus author profile.

Introduction

Temporal data analysis examines observations recorded across time to identify changes, recurring patterns, and relationships. Mandisa Zameko is associated with this subject area through her supplied research profile. Her institutional affiliation is the University of Fort Hare in South Africa. This article summarizes her available academic information and recognition context. [3]

Research Profile

Mandisa Zameko is identified as a researcher affiliated with the University of Fort Hare, South Africa. Her supplied profile lists Temporal Data Patterns as her subject area and provides Scopus author identifier 60888056600. The available record indicates one document. Citation count, h-index, and detailed research history have not been independently established here.[2]

Research Contributions

Research involving temporal data can support the identification of trends, sequential relationships, and variations across observation periods. Zameko’s listed subject area provides a basis for describing her academic profile in this research context. Specific methods, datasets, findings, and applications cannot be attributed without examining her publication. Further bibliographic verification is necessary before detailing individual contributions. [1]

Publications

The supplied Scopus profile information lists one document associated with Mandisa Zameko’s author identifier. The publication title, journal or conference, publication year, co-authors, and DOI have not been provided for confirmation. Accordingly, this article does not assign an unverified title or bibliographic record. The linked author profile can be consulted for publication-level details. [3]

Research Impact

Research impact may be examined through scholarly citations, methodological contributions, practical applications, and subsequent research activity. The available information identifies one document but does not establish a citation count or h-index. Therefore, the scale of Zameko’s scholarly influence cannot be quantified from the supplied details alone. Verified publication and citation data would support a fuller assessment. [2]

Award Suitability

The Best Researcher Award recognizes research activity and scholarly contributions. Zameko’s listed affiliation, subject area, and publication record provide information relevant to an academic recognition profile. Determining eligibility requires the organizer’s criteria and supporting evidence, including publication details and documented contributions. No independent selection decision or award outcome is asserted in this article.[3]

Conclusion

Mandisa Zameko’s available academic profile connects her with the University of Fort Hare and the subject area of Temporal Data Patterns. The supplied information records one Scopus document and identifies her researcher profiles. Additional verified publication, citation, and contribution details would provide a more comprehensive account of her research. This article summarizes available information without unsupported claims [2]

References

  1. Elsevier. (n.d.). Scopus author details: Mandisa Zameko, Author ID 60888056600. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=60888056600
  2. ORCID. (n.d.). Mandisa Zameko: ORCID record. ORCID.
    https://orcid.org/0000-0003-4401-2540
  3. Research Data Analysis. (n.d.). International Research Data Analysis Excellence & Awards.
    https://researchdataanalysis.com/

Aysegul Kilicli | Anova | Best Researcher Award

Published on by Research Data Analysis

Best Researcher Award

Aysegul Kilicli – Gaziantep University Faculty of Health Sciences, Turkey

Aysegul Kilicli
Affiliation Gaziantep University Faculty of Health Sciences
Country Turkey
Scopus ID 57221392337
Documents 18
Citations 32
h-index 4
Subject Area ANOVA
Event International Research Data Analysis Excellence & Awards
ORCID 0000-0003-1105-9991

Aysegul Kilicli is a researcher affiliated with Gaziantep University Faculty of Health Sciences, Turkey. Her academic profile includes research outputs indexed in Scopus, with 18 documents, 32 citations, and an h-index of 4, as reported in the supplied profile information. This article presents her research background, scholarly contributions, and recognition context. [1]

Abstract

This article presents the academic profile of Aysegul Kilicli, affiliated with Gaziantep University Faculty of Health Sciences in Turkey. It summarizes supplied bibliometric information, identifies her stated subject area as analysis of variance (ANOVA), and outlines research contributions and award relevance. Publication-level details require verification through authoritative scholarly records. [2]

Keywords

Aysegul Kilicli; Best Researcher Award; research data analysis; analysis of variance; ANOVA; health sciences; academic research; bibliometrics; scholarly publications; research impact; Gaziantep University; Turkey.

Introduction

Aysegul Kilicli is affiliated with Gaziantep University Faculty of Health Sciences in Turkey. Her academic profile is presented in connection with the Best Researcher Award and the International Research Data Analysis Excellence & Awards. This overview introduces her research background using the supplied profile details and bibliometric indicators. [3]

Research Profile

Kilicli’s supplied academic profile identifies Gaziantep University Faculty of Health Sciences as her institutional affiliation and Turkey as her country. Her listed subject area is ANOVA, a statistical method used to examine differences among group means. Her Scopus profile provides a reference point for reviewing indexed scholarly output. [1]

Research Contributions

Research contributions are assessed through the questions addressed, methods applied, findings reported, and relevance to the scholarly field. The supplied profile associates Kilicli with ANOVA and health sciences. Evaluating individual contributions requires examining her publications, research designs, results, and documented applications rather than relying solely on bibliometric indicators. [2]

Publications

The supplied Scopus information lists 18 documents associated with Aysegul Kilicli. These records may include different scholarly publication types, and individual titles, dates, coauthors, and journals should be checked directly against the indexed author profile. A verified publication list is necessary for describing specific research topics and findings accurately. [3]

Research Impact

The supplied bibliometric indicators report 32 citations and an h-index of 4. Citation counts can indicate scholarly attention, while the h-index combines publication productivity and citation distribution. These measures vary over time and across disciplines, so they should be interpreted alongside research quality, methodological rigor, collaboration, and broader academic contributions. [1]

Award Suitability

The Best Researcher Award recognizes research activity and scholarly contributions. Kilicli’s supplied affiliation and bibliometric indicators provide background information for consideration. A complete assessment would also examine publication quality, originality, research significance, ethical standards, and documented contributions. Eligibility and final selection remain subject to the award organizer’s published criteria. [2]

Conclusion

Aysegul Kilicli’s supplied profile identifies her institutional affiliation, research area, and Scopus indicators. These details provide an introductory overview of her academic record in health sciences and research data analysis. Further evaluation should use verified publications, documented research outcomes, and the official award criteria to establish the scope and significance of her work. [3]

References

  1. Effect of Reflexology on Pain, Fatigue, Sleep Quality, and Lactation in Postpartum Primiparous Women After Cesarean Delivery: A Randomized Controlled Trial.
    https://pubmed.ncbi.nlm.nih.gov/38426483/
  2. Stress, Anxiety, and Postpartum Depression in Parents with Premature Infants in Neonatal Intensive Care Unit.
    https://pubmed.ncbi.nlm.nih.gov/37404210/
  3. Comparison of sexual self-consciousness, self-confidence, self-efficacy, satisfaction, and dyadic adjustment between people living with HIV and HIV-negative individuals: Case–control study.
    https://www.researchgate.net/publication/403338666_

Der Liang Young | Machine Learning | Best Researcher Award

Published on by Research Data Analysis

Best Researcher Award

Der Liang Young
National Taiwan University, Taiwan

Der Liang Young
Affiliation National Taiwan University
Country Taiwan
Scopus ID 24340146800
Documents 200
Citations 4,538
h-index 38
Subject Area Machine Learning
Event International Research Data Analysis Excellence & Awards
ORCID 0000-0002-3611-2982

Der Liang Young is a researcher affiliated with National Taiwan University whose documented scholarly work connects computational methods, numerical analysis, meshless techniques, and machine-learning approaches for mathematical and engineering problems. Bibliographic records identify research involving radial basis functions, partial differential equations, physics-informed learning, and residual neural-network architectures. [1]

Abstract

Der Liang Young’s academic profile reflects sustained research activity at the intersection of numerical computation, engineering analysis, and machine learning. His documented publications include work on radial basis function methods, partial differential equations, meshless computation, and neural-network approaches for interpolation and inverse problems. Bibliographic sources associate him with National Taiwan University and ORCID identifier 0000-0002-3611-2982. [2]

Keywords

  • Machine Learning
  • Numerical Analysis
  • Radial Basis Functions
  • Physics-Informed Neural Networks
  • Partial Differential Equations
  • Meshless Methods

Introduction

Der Liang Young’s research profile is situated within computational engineering and mathematical modeling, with published work addressing numerical methods and machine-learning techniques for complex problems. His research includes collaborations involving National Taiwan University and applications of advanced computational approaches to interpolation, inverse problems, and partial differential equations.[3]

Research Profile

Young’s documented research spans numerical computation, computational mechanics, meshless methods, radial basis functions, and machine learning. Bibliographic records identify publications addressing partial differential equations, piezoelectric problems, quasicrystal plates, and neural-network methods. His ORCID record provides a persistent identifier supporting the organization and attribution of his scholarly research outputs. [1]

Research Contributions

His published contributions include numerical formulations for differential equations, local radial basis function collocation, and machine-learning architectures for interpolation and inverse problems. A 2024 study with collaborators examined a power-enhanced residual network for function approximation and physics-informed inverse problems, illustrating the connection between neural-network design and computational mathematics. [2]

Publications

Available bibliographic records document publications by Young and collaborators in journals and conference proceedings covering computational mathematics and engineering. Examples include work on two-step MPS-MFS ghost point methods, local radial basis function collocation for piezoelectric problems, and power-enhanced residual networks. These publications demonstrate continuity between numerical methods and contemporary computational learning approaches. [3]

Research Impact

The supplied Scopus metrics record 200 documents, 4,538 citations, and an h-index of 38 for the identified author profile. These bibliometric indicators provide quantitative measures of publication activity and citation visibility, while individual publications demonstrate application of the research across numerical analysis, engineering computation, and machine-learning methodology. [1]

Award Suitability

The documented publication record, research themes, institutional affiliation, and supplied bibliometric indicators provide relevant evidence for consideration under a Best Researcher Award framework. The profile combines established computational research with machine-learning applications, while indexed scholarly outputs and citation measures offer quantitative information that can support an independent award-review process.[3]

Conclusion

Der Liang Young’s documented scholarly profile combines numerical analysis, computational engineering, meshless methods, and machine-learning research. His publication record includes studies of advanced numerical algorithms and neural-network methodologies, while the supplied bibliometric data indicate substantial indexed research activity and citation visibility. [2]

References

  1. Biochemical and anatomical characterization of forepaw adjusting steps in rat models of Parkinson’s disease: studies on medial forebrain bundle and striatal lesions.
    https://pubmed.ncbi.nlm.nih.gov/10197780/
  2. Implicit Branch Selection in Physics-Informed Neural Networks for an Underdetermined Exterior Laplace Problem: Potential Flow Around a Circular Cylinder with Weak Far-Field Regularization.
    https://www.researchgate.net/publication/408048483_
  3. A BC–GE-embedded strong-form meshless method for three-dimensional incompressible Navier–Stokes flows
    https://link.springer.com/article/10.1007/s00707-026-04874-4

Adedoyin Bello | Descriptive Analytics | Excellence in Research

Published on by Research Data Analysis

Excellence in Research

Adedoyin Bello
University of Cape Town, Nigeria

Adedoyin Bello
Affiliation University of Cape Town
Country Nigeria
Scopus ID 57208341897
Documents 2
Citations 28
h-index 1
Subject Area Descriptive Analytics
Event International Research Data Analysis Excellence & Awards
ORCID 0009-0006-7492-4718

Adedoyin Bello is associated with the University of Cape Town and is identified in the supplied researcher records with the subject area of descriptive analytics. The available bibliographic information records two documents, 28 citations, and an h-index of 1. These indicators provide a concise basis for documenting the research profile. [1]

Abstract

Adedoyin Bello is documented as a researcher associated with the University of Cape Town, with descriptive analytics identified as the principal subject area in the supplied profile. Bibliographic records indicate two documents and 28 citations, while the reported h-index is 1. This page summarizes the available research information and recognition context.[2]

Keywords

  • Adedoyin Bello
  • Descriptive Analytics
  • Research Data Analysis
  • Bibliometric Research
  • University of Cape Town

Introduction

Descriptive analytics provides methods for organizing, summarizing, and interpreting observed data to support evidence-based understanding of research and practical questions. Adedoyin Bello is documented in the supplied records within this broad analytical context, with an affiliation to the University of Cape Town and a stated subject area of descriptive analytics. [3]

Research Profile

The available research profile identifies Adedoyin Bello with the University of Cape Town and the subject area of descriptive analytics. The supplied Scopus information reports Scopus Author ID 57208341897, two documents, 28 citations, and an h-index of 1. ORCID provides an additional persistent identifier for researcher identification and record linkage.[2]

Research Contributions

The documented contribution profile can be described through its connection with descriptive analytics and the production of scholarly documents indexed in Scopus. Two recorded documents provide the available publication base, while their reported citation count indicates subsequent scholarly referencing. Further assessment of individual contributions requires examination of the underlying publications and research outputs. [1]

Publications

The supplied Scopus profile records two documents associated with Adedoyin Bello. The available information does not provide publication titles, journals, publication years, or DOI identifiers for these documents. Accordingly, the publication record is presented at aggregate level rather than assigning titles or bibliographic details that cannot be independently established from the supplied profile information. [3]

Research Impact

The supplied bibliometric record reports 28 citations for two documents and an h-index of 1. These measures describe citation activity recorded in the referenced profile at the time represented by the supplied data. Citation counts can change as databases are updated and should therefore be interpreted as time-sensitive bibliometric indicators rather than permanent measures. [2]

Award Suitability

For recognition under an excellence-in-research framework, the documented profile provides identifiable affiliation, persistent researcher identification, indexed publications, and citation information. These elements can support an evidence-based review of research activity. Final award consideration would ordinarily require assessment against the event’s applicable criteria and verification of current scholarly records and supporting documentation. [1]

Conclusion

Adedoyin Bello’s available academic profile documents an association with the University of Cape Town and a research focus identified as descriptive analytics. The supplied bibliometric indicators comprise two documents, 28 citations, and an h-index of 1. Together, these records provide a concise foundation for documenting research activity while recognizing the need for continued verification.[2]

References

  1. Incentives for collaborative governance of natural resources: A case study of forest management in southwest Nigeria.
    https://www.researchgate.net/publication/332469511
  2. Drivers of Deforestation and Land-Use Change in Southwest Nigeria
    https://link.springer.com/rwe/10.1007/978-3-319-71025-9_139-1
  3. Protected Areas and Management Practices: Evidence in Southwest Nigeria.
    https://www.researchgate.net/publication/362524538

Gulshan Sharma | Power System Operation | Best Researcher Award

Published on by Research Data Analysis

Best Researcher Award

Gulshan Sharma
University of Johannesburg, South Africa

Gulshan Sharma
Affiliation University of Johannesburg
Country South Africa
Scopus ID 57216326306
Documents 241
Citations 4,606
h-index 35
Subject Area Power System Operation
Event International Research Data Analysis Excellence & Awards
ORCID 0000-0002-4726-0956

Gulshan Sharma is a researcher affiliated with the University of Johannesburg whose listed research subject area is Power System Operation. The available bibliographic profile records 241 documents, 4,606 citations, and an h-index of 35. These indicators provide a bibliometric context for considering the researcher for academic recognition. [1]

Abstract

This academic recognition profile presents Gulshan Sharma, University of Johannesburg, in the context of research in Power System Operation. Bibliographic information identifies 241 documents, 4,606 citations, and an h-index of 35. The profile summarizes research activity, scholarly contributions, publication indicators, research impact, and relevance to the Best Researcher Award. [2]

Keywords

Gulshan Sharma; Power System Operation; electrical power systems; research publications; bibliometrics; scholarly impact; University of Johannesburg; energy systems; academic recognition; Best Researcher Award.

Introduction

Power system operation encompasses the monitoring, analysis, control, and coordination of electrical networks to maintain reliable and efficient system performance. Research in this field increasingly incorporates computational methods, optimization, renewable integration, and data-driven analysis. Gulshan Sharma’s documented research profile is associated with Power System Operation and provides a basis for scholarly recognition. [3]

Research Profile

Gulshan Sharma is affiliated with the University of Johannesburg, South Africa, with Power System Operation identified as the principal subject area in the supplied bibliographic information. The recorded Scopus author profile contains 241 documents, 4,606 citations, and an h-index of 35, providing measurable indicators of sustained scholarly publication and citation activity. [1]

Research Contributions

Research contributions associated with Power System Operation can encompass system analysis, operational planning, optimization, control strategies, reliability, and integration of emerging energy technologies. Sharma’s bibliographic record indicates an established body of scholarly output. The available indicators support examination of contributions through published research, citation activity, and documented subject-area engagement. [2]

Publications

The supplied Scopus information records 241 documents for Gulshan Sharma, indicating substantial publication activity across the researcher’s indexed scholarly record. Individual publications should be assessed using their titles, venues, abstracts, citation information, and persistent identifiers such as DOI records. Bibliographic databases provide structured evidence for examining publication volume and scholarly dissemination. [3]

Research Impact

The supplied bibliometric profile reports 4,606 citations and an h-index of 35. These indicators can be used to describe citation-based visibility within indexed scholarly literature, although they do not independently measure research quality or broader societal impact. Interpretation should therefore consider publication context, field differences, collaboration patterns, and individual research contributions. [1]

Award Suitability

The Best Researcher Award profile can be considered in relation to documented scholarly activity, research subject area, publication record, citation indicators, and institutional affiliation. Sharma’s listed Power System Operation focus, 241 documents, 4,606 citations, and h-index of 35 provide objective bibliometric information for an award review, subject to the event’s stated evaluation criteria. [2]

Conclusion

Gulshan Sharma’s profile reflects sustained scholarly activity associated with Power System Operation at the University of Johannesburg. The supplied bibliometric indicators document 241 publications, 4,606 citations, and an h-index of 35. Together with the researcher’s subject-area focus, these data provide a structured basis for documenting academic activity and evaluating award eligibility. [1]

References

  1. A novel Hankel norm approximation-based AGC for a hydro-dominated power system.
    https://www.nature.com/articles/s41598-026-35235-9
  2. Blockchain for Industry 5.0: Vision, Opportunities, Key Enablers, and Future Directions.
    https://www.researchgate.net/publication/361522371
  3. Improved load frequency control of a hybrid Thermal–PV–Wind power system via SO-TPIDnAn technique.
    https://www.researchgate.net/publication/413807421