Takeshi Tsuda | Data Science | International Soil Scientist Awards

Published on by Soil Scientist

International Soil Scientist Awards

Takeshi Tsuda
Nemours Children’s Hospital Delaware

Takeshi Tsuda
Affiliation Nemours Children’s Hospital Delaware
Country United States
Scopus ID 7402162750
Documents 70
Citations 1,810
h-index 22
Subject Area Data Science and Analytics
Event International Soil Scientist Awards

Takeshi Tsuda is a physician-researcher affiliated with Nemours Children’s Hospital Delaware whose indexed scholarly record includes work in pediatric cardiology, cardiovascular physiology, exercise performance, cardiomyopathy, and related clinical research. Institutional and bibliographic sources identify his affiliation with Nemours Children’s Health Delaware and associate his publications with the ORCID identifier 0000-0001-8110-1388. [1] [2]

Abstract

This academic recognition profile presents the research record of Takeshi Tsuda in the context of the International Soil Scientist Awards. The supplied bibliometric profile records 70 documents, 1,810 citations, and an h-index of 22. Independent institutional and bibliographic sources document a broader research program centered on pediatric cardiovascular medicine and quantitative clinical investigation. [1] [3]

Keywords

  • Takeshi Tsuda
  • Research Analytics
  • Data Science and Analytics
  • Pediatric Cardiology
  • Cardiovascular Research
  • International Soil Scientist Awards

Introduction

Takeshi Tsuda’s documented academic activity spans clinical research and quantitative investigation in pediatric cardiovascular medicine. Nemours identifies him as a physician at its Delaware children’s hospital, while its researcher profile records research interests involving exercise physiology, cardiac rehabilitation, cardio-oncology, and dystrophic heart research. [2] These areas provide a substantial evidence base for examining how structured research data, clinical measurements, and analytical methods can contribute to biomedical investigation.

Research Profile

The supplied Scopus-based profile lists 70 documents, 1,810 citations, and an h-index of 22. Scopus-associated bibliographic information independently identifies author ID 7402162750 and reports publications across multiple biomedical journals. [1] The profile should therefore be interpreted as a bibliometric snapshot rather than a complete measure of research quality or significance.

Research Contributions

Published studies associated with Tsuda include investigations of cardiovascular abnormalities, exercise performance, cardiomyopathy, and pediatric cardiac conditions. For example, research has examined subclinical coronary abnormalities following arterial switch procedures, while later work addressed cardiovascular changes associated with Duchenne muscular dystrophy. [4] [5] Such publications illustrate the use of clinical observations, physiological measurements, and quantitative analysis in pediatric cardiovascular research.

Publications

  • Tsuda T. Transient Ischemic Attack and Ischemic Stroke in Danon Disease with Formation of Left Ventricular Apical Thrombus despite Normal Systolic Function. doi:10.1155/2017/6576382. [3]
  • Tsuda T. et al. Identifying Subclinical Coronary Abnormalities and Silent Myocardial Ischemia After Arterial Switch Operation. doi:10.1007/s00246-019-02085-4. [4]
  • Tsuda T. et al. Sinus Tachycardia and Unrelieved Wall Stress Precede Left Ventricular Systolic Dysfunction During Preclinical Cardiomyopathic Changes in Duchenne Muscular Dystrophy. doi:10.3390/jcdd12080280. [5]

Research Impact

The supplied citation metrics indicate a substantial indexed publication and citation record. Bibliographic sources also show continuing publications through 2026, including research on exercise performance and cardiovascular conditions. [1] [6] Citation counts and h-index values are useful indicators of scholarly visibility, but they do not independently establish the methodological quality, clinical importance, or originality of individual studies.

Award Suitability

For an award consideration process, the documented publication record, citation indicators, institutional affiliation, and identifiable research outputs provide material that can be evaluated against the award’s published criteria. The available evidence is primarily biomedical rather than soil-science-specific; consequently, any assessment should distinguish general research achievements from contributions directly relevant to soil science, environmental systems, or agricultural applications. The award committee may use the complete submitted record for its formal evaluation.

Conclusion

Takeshi Tsuda’s documented research profile combines an established bibliometric record with continuing scholarly activity in pediatric cardiovascular and quantitative clinical research. Institutional records and indexed publications provide verifiable evidence of his affiliation and research outputs, while the supplied Scopus metrics offer a concise representation of citation-based visibility. [1] [2]

References

  1. Elsevier. (n.d.). Scopus author details: Takeshi Tsuda, Author ID 7402162750. Scopus/ScienceDirect author record.
    https://www.sciencedirect.com/author/7402162750/takeshi-tsuda
  2. Nemours Children’s Health. (n.d.). Takeshi Tsuda, MD — Researcher Profile.
    https://www.nemours.org/find-a-researcher/4660-takeshi-tsuda-wilmington.html
  3. Tsuda, T. et al. (2017). Transient Ischemic Attack and Ischemic Stroke in Danon Disease with Formation of Left Ventricular Apical Thrombus despite Normal Systolic Function. Case Reports in Pediatrics. DOI: https://doi.org/10.1155/2017/6576382
  4. Tsuda, T. et al. (2019). Identifying Subclinical Coronary Abnormalities and Silent Myocardial Ischemia After Arterial Switch Operation. Pediatric Cardiology. DOI: https://doi.org/10.1007/s00246-019-02085-4
  5. Tsuda, T., Walczak, A., & O’Neil, K. (2025). Sinus Tachycardia and Unrelieved Wall Stress Precede Left Ventricular Systolic Dysfunction During Preclinical Cardiomyopathic Changes in Duchenne Muscular Dystrophy. Journal of Cardiovascular Development and Disease. DOI: https://doi.org/10.3390/jcdd12080280

DongHee Park | Data Science and Analytics | Innovative Research Award

Published on by Soil Scientist

Innovative Research Award

DongHee Park
DAVISS, South Korea

DongHee Park
Affiliation DAVISS
Country South Korea
Documents 8
Subject Area Data Science and Analytics
Event International Soil Scientist Awards
ORCID 0009-0008-0010-1756

DongHee Park is a researcher affiliated with DAVISS in South Korea whose documented scholarly work applies data science and machine-learning methods to industrial fault diagnosis. The available record contains eight publications addressing condition-index representations, diagnostic automation, convolutional neural networks and feature classification for machinery faults. [1]

Abstract

Park’s research focuses on computational approaches for identifying and classifying faults in industrial machinery. Recent work compares conventional feature-based representations with physics-guided condition-index representations for motor fault diagnosis, while related studies investigate automated elevator diagnosis, rotor-system classification and machine-learning methods for blade-rubbing defects. [1] [2] The research demonstrates a consistent interest in translating operational data into diagnostic information.

Keywords

Data science; machine learning; fault diagnosis; condition monitoring; industrial motors; diagnostic automation; convolutional neural networks; rotor systems; blade rubbing; feature classification; predictive maintenance.

Introduction

Modern industrial systems generate large quantities of operational data that can be analysed to detect abnormal behaviour. Machine-learning algorithms and engineered diagnostic features provide methods for distinguishing normal operation from specific fault conditions. Park’s publication record addresses these challenges across several machinery categories, linking data analytics with practical condition-monitoring problems. [3]

Research Profile

The supplied profile records eight documents in the subject area of Data Science and Analytics. Citation and h-index values were not provided and therefore are not inferred. The publication sequence from 2024 to 2026 indicates sustained investigation of machine-learning feature development, classification methods, automated diagnosis and physics-guided representations.

Research Contributions

  • Evaluation of conventional and physics-guided representations for industrial motor fault diagnosis. [1]
  • Development of rule-based automation technology for elevator fault diagnosis. [2]
  • Use of combination images of feature vectors with convolutional neural networks for rotor fault classification. [3]
  • Development and classification of machine-learning features for early blade-rubbing diagnosis. [4] [5]

Publications

Comparative Evaluation of Conventional Feature-Based and Physics-Guided Condition-Index Representations for Industrial Motor Fault Diagnosis (2026), Machines. [1]

Development of Rule-Based Diagnostic Automation Technology for Elevator Fault Diagnosis (2025), Sensors. [2]

CNN-based fault classification using combination image of feature vectors in rotor systems (2024), Journal of Mechanical Science and Technology. [3]

A Study on Machine Learning-Based Feature Classification for the Early Diagnosis of Blade Rubbing (2024), Sensors.[4]

Development of features for blade rubbing defect classification in machine learning (2024), Journal of Mechanical Science and Technology.[5]

Research Impact

Because citation and h-index values were not supplied, quantitative scholarly impact cannot be assessed from the available profile alone. The publication record nevertheless demonstrates application of data-driven techniques to multiple industrial systems, including motors, elevators, rotor systems and machinery affected by blade rubbing. Publication across Machines, Sensors and the Journal of Mechanical Science and Technology provides a documented scholarly context for these contributions. [1] [3]

Award Suitability

The Innovative Research Award profile is supported by a coherent body of research applying machine learning and analytical representations to industrial fault diagnosis. The progression from feature development and classification toward automated diagnostic systems and physics-guided condition indices indicates methodological continuity. [2] [5] Final recognition should be determined using the complete scholarly record, originality, methodological quality, practical relevance, reproducibility and broader research contributions.

Conclusion

DongHee Park’s documented research is centred on data science and analytics for industrial condition monitoring and fault diagnosis. Publications from 2024 through 2026 cover machine-learning features, convolutional neural networks, rule-based automation and physics-guided diagnostic representations. This focused research trajectory provides a substantive basis for consideration for the Innovative Research Award within the International Soil Scientist Awards framework.

References

  1. Park, DongHee. (2026). Comparative Evaluation of Conventional Feature-Based and Physics-Guided Condition-Index Representations for Industrial Motor Fault Diagnosis. Machines.
    https://doi.org/10.3390/machines14091024
  2. Park, DongHee. (2025). Development of Rule-Based Diagnostic Automation Technology for Elevator Fault Diagnosis. Sensors.
    https://doi.org/10.3390/s26010223
  3. Park, DongHee. (2024). CNN-based fault classification using combination image of feature vectors in rotor systems. Journal of Mechanical Science and Technology.
    https://doi.org/10.1007/s12206-024-1006-z
  4. Park, DongHee. (2024). A Study on Machine Learning-Based Feature Classification for the Early Diagnosis of Blade Rubbing. Sensors.
    https://doi.org/10.3390/s24186013
  5. Park, DongHee. (2024). Development of features for blade rubbing defect classification in machine learning. Journal of Mechanical Science and Technology.
    https://doi.org/10.1007/s12206-023-1201-3

Axelle Hego | Data Science | Best Researcher Award

Published on by Soil Scientist

Dr. Axelle Hego | Data Science | Best Researcher Award

Dr. Axelle Hego, University of Lorraine, France

Dr. Axelle Hego is a research engineer at the University of Lorraine, France. She specializes in model analysis, sensitivity analysis, and environmental system modeling. Her doctoral research focused on hydric models for green roof structures, exploring the influence of soil and meteorological parameters using advanced control and analysis techniques. With a strong background in Control and Complex Systems Engineering, Dr. Hego has collaborated with institutions like Cerema and contributed to international conferences and journals in the field of environmental modeling and risk assessment.

Profile

Scopus

Orcid

Summary:

Dr. Axelle Hego’s research stands at the intersection of control systems and environmental modeling, with significant implications for urban ecological design. Her innovative work on dynamic hydric models for green roofs addresses key questions in soil science, water retention, and climate resilience. Though still early in her career, her technical strengths, research relevance, and international engagement make her a compelling candidate.

🎓 Education

Axelle HEGO holds an engineering degree complemented by a specialized master’s degree in Control and Complex Systems Engineering. Her academic training laid the foundation for her later doctoral research focused on hydric modeling, specifically applied to green roof structures. Her educational journey provided her with a deep understanding of control systems, dynamic models, and analytical methods suited to complex environmental systems.

💼Experience

Currently serving as a Research Engineer at CEA-Liten, Axelle brings both academic and applied research experience to her role. During her PhD, she worked closely with Cerema—a public institution under France’s Ministry for Ecological Transition and Territorial Cohesion—which provided technical expertise and experimental data crucial to her research. She is also affiliated with the CRAN (Research Center on Automatic Control of Nancy), within the department of Control, Identification, and Diagnostic. Her role has encompassed model analysis and sensitivity testing, contributing to advancements in hydric modeling for ecological infrastructure.

🔬Research Focus

Her primary research focus revolves around the analysis of hydric models adapted for green roof systems, particularly in understanding how soil properties and meteorological variables influence model behavior. Her work uniquely adapts sensitivity analysis methodologies to handle dynamic outputs, addressing uncertainties inherent in time-dependent environmental parameters. This research is of high relevance to urban sustainability and ecological engineering.

🛠️Skills

Axelle HEGO possesses specialized skills in model analysis, dynamic system modeling, and sensitivity analysis. She is proficient in adapting analytical methodologies to account for uncertainties in environmental parameters. Her expertise lies in integrating engineering control systems with environmental modeling, focusing on soil and meteorological interactions in urban green infrastructure.

🏆Awards

Axelle HEGO has been actively involved in presenting her research at international conferences. Her PhD work has resulted in two proceedings presented at conferences on Automatic Control and Modeling, as well as a dedicated presentation at a Sensitivity Analysis conference. Additionally, she has published a journal paper in the reputable Stochastic Environmental Research and Risk Assessment. She has applied for the prestigious Best Researcher Award under the International Soil Scientist Awards 2025.

📚 Publications

Title: Sensitivity analysis of a green roof model with uncertain time-varying parameter
Year: 2025
Author: Axelle Hego
Journal: Stochastic Environmental Research and Risk Assessment

Conclusion:

Dr. Axelle Hego is a suitable and promising candidate for the Best Researcher Award in the soil science domain. While her academic portfolio is still growing, her contributions demonstrate innovation, interdisciplinary insight, and a clear commitment to sustainability. With continued support and recognition, she is well-positioned to become a leading researcher in environmentally driven systems modeling.

Fatimah Hameed Naser | Data Science | Best Researcher Award

Published on by Soil Scientist

Prof. Dr. Fatimah Hameed Naser | Data Science | Best Researcher Award

Prof. Dr. Fatimah Hameed Naser, Al-Qasim Green University, Iraq

Prof. Dr. Fatimah Hameed Naser is a distinguished professor of structural engineering at Al-Qasim Green University, Iraq. With a Ph.D. in Structural Engineering from Babylon University, her research specializes in advanced concrete technologies, fiber-reinforced polymers (FRP), finite element modeling, and sustainable construction materials. She has authored numerous peer-reviewed publications and contributed significantly to civil engineering education and innovation in Iraq.

Profile

Scopus

Orcid

Google Scholar

Summary:

Prof. Dr. Fatimah Hameed Naser is an accomplished structural engineering academic whose research is both technically rigorous and socially relevant. Her contributions to sustainable concrete materials, structural behavior modeling, and civil engineering education highlight her as a strong candidate for the Research for Best Researcher Award. She combines domain-specific knowledge with a commitment to innovation and education in Iraq’s engineering sector.

🎓 Education

Dr. Fatimah Hameed Naser Al-Mamoori holds a distinguished academic background in civil and structural engineering. She earned her Bachelor of Science in Civil Engineering in 2008 from Babylon University, Iraq. She continued her graduate studies at the same institution, completing a Master of Science in Structural Engineering in 2011. Her academic journey culminated in a Ph.D. in Structural Engineering from Babylon University in 2015, further solidifying her expertise in the field.

💼Experience

Dr. Al-Mamoori has served as a researcher and Assistant Professor in Structural Engineering at the College of Engineering, Al-Qasim Green University, Babylon, Iraq, since October 2015. Over the years, she has contributed significantly to academia through both teaching and research. Her academic role involves mentoring students, supervising research, and advancing structural engineering knowledge with a focus on innovative materials and modeling techniques.

🔬Research Focus

Her research primarily focuses on the structural behavior of concrete systems and the use of advanced materials to enhance performance. Key themes in her work include lightweight concrete reinforced with carbon fiber-reinforced polymer (CFRP) bars, the flexural and shear behavior of concrete elements, the incorporation of local materials in structural concrete, and the application of steel and carbon fibers to improve structural integrity. She also explores innovative solutions to issues like concrete behavior in hot climates, the role of industrial and waste fibers in construction, and finite element modeling for structural optimization.

🛠️Skills

Dr. Al-Mamoori possesses a strong portfolio of technical and analytical skills in structural engineering. Her expertise includes fiber-reinforced polymers (FRP), finite element analysis, structural analysis, construction techniques, and the use of finite element modeling. She is highly skilled in civil engineering materials, concrete technologies, reinforced concrete structures, and the evaluation of flexural and shear strength. Her applied knowledge spans both experimental and analytical research methodologies.

🏆Awards

Dr. Al-Mamoori is recognized for her prolific contributions to structural engineering through numerous research publications in peer-reviewed journals and international platforms. Her work has been indexed in Scopus, Publons, Google Scholar, and ResearchGate, reflecting both impact and global visibility. Although specific awards are not listed, her sustained academic and research excellence positions her as a leading researcher in structural engineering in Iraq.

📚 Publications

Title: Torsional Behavior of Concrete-Filled Steel Tubular Members: State of the Mini Art Review
Authors: Mohammed Jassim Soudi Al-Mangoshi (M.J.S.), Ali Hameed Naser Almamoori (A.H.N.), Fatimah H. Naser (F.H.)

Title: Load Capacity of RC Members Subjected to Marine Environment and Rehabilitated with FRP: State of the Art Review
Authors: Mohammed Karar Hadi (M.K.), Ali Hameed Naser Almamoori (A.H.N.), Fatimah H. Naser (F.H.)

Conclusion:

Prof. Dr. Fatimah Hameed Naser is suitable for the Research for Best Researcher Award. Her blend of academic leadership, applied research excellence, and technical innovation aligns well with the award’s objectives. With strategic enhancements in research impact visibility and international recognition, she can further solidify her standing as one of the leading researchers in her field.