Akiko Morimoto l Data Analysis | Research Excellence Award

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Prof. Akiko Morimoto l Data Analysis | Research Excellence Award

Osaka Metropolitan University | Japan

Professor. Akiko Morimoto is a professor in nursing informatics with leadership experience in university administration and national research governance. Education includes a Ph.D. in Health Sciences. Professional experience spans lecturer to professor roles across major universities, along with service as a research fellow and senior advisor. Research interests center on nursing informatics, nursing data science, evidence-based practice, cardiovascular epidemiology, health communication, and social determinants of health. Research skills include health data analysis, epidemiological methods, educational program evaluation, evidence synthesis, and interdisciplinary health research. Awards and honors recognize excellence in research, education, and leadership. Overall, Professor. Akiko Morimoto advances data-driven nursing science and evidence-based healthcare practice.

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Featured Publications

Morimoto A, Watanabe S, Ohno Y, et al.

Associations among work-related stress and mental fatigue, and regular exercise in Japanese employees with or without diabetes


– Diabetology International, 11, 105–113, 2020. [IF(2022): 2.2]
Tatsumi Y, Asayama K, Morimoto A, et al.

Hyperuricemia predicts the risk for developing hypertension independent of alcohol drinking status in men and women: the Saku study


– Hypertension Research, 43, 442–449, 2020. [IF(2022): 5.4]
Sonoda N, Watanabe S, Ohno Y, et al.

Work-related, personal, and diabetes-related factors relevant to dropout from outpatient diabetes treatment visits among Japanese male employees with diabetes


– Diabetology International, 11, 261–268, 2020. [IF(2022): 2.2]
Shikama M, Sonoda N, Morimoto A, et al.

Association of crossing capillaries in the finger nailfold with diabetic retinopathy in type 2 diabetes mellitus


– Journal of Diabetes Investigation, 12(6), 1007–1014, 2021.
Shikama M, Sonoda N, Morimoto A, et al.

Association of abdominal obesity with crossing capillaries in the finger nailfold in type 2 diabetes mellitus


– Diabetology International, 12(3), 260–267, 2021.

Bin Zhang l Semantic Segmentation for Autonomous Driving | Research Excellence Award

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Assoc. Prof. Dr. Bin Zhang l Semantic Segmentation for Autonomous Driving | Research Excellence Award

Kanagawa University | Japan

Assoc. Prof. Dr. Bin Zhang is an associate professor and principal investigator specializing in intelligent machines, robotics, and AI-driven systems. Education includes a Ph.D. and M.S. in Mechanical and Intelligent Systems Engineering from The University of Electro-Communications and a B.S. in Automation. Professional experience spans academia and industry, with roles in mechanical engineering education and robotics research. Research interests focus on service robots, autonomous navigation, human–robot interaction, perception, deep learning, and assistive systems. Research skills include robot design, control, SLAM, computer vision, sensor fusion, and machine learning. Awards and honors recognize excellence in robotics, AI applications, and assistive technologies. Overall, Assoc. Prof. Dr. Bin Zhang contributes impactful research and education advancing intelligent robotic systems for society.

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Featured Publications

A Study on Improving the Accuracy of Semantic Segmentation for Autonomous Driving
Computers, Materials & Continua, 2026 · Open Access
A Quantitative Diagnosis Method for Assessing the Severity of Intake Filter Blockage of Heavy-Duty Gas Turbines Based on the Fusion of Physical Mechanisms and Deep Learning
Applied Thermal Engineering, 2025

A Performance Diagnosis Method for Full Gas-Path Components of Heavy-Duty Gas Turbines Based on Model- and Data-Hybrid Drive

Proceedings of the Chinese Society of Electrical Engineering, 2025

An End-to-End Pillar Feature-Based Neural Network Improved by Attention Modules for Object Detection of Autonomous Vehicles

IEEJ Transactions on Electrical and Electronic Engineering, 2025

2D Mapping Considering Potential Occupancy Space of Mobile Objects for a Guide Dog Robot

IEEJ Transactions on Electronics, Information and Systems, 2025

Sibel Cevik Bektas l Energy Management/Optimization | Research Excellence Award

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Dr. Sibel Cevik Bektas l Energy Management/Optimization | Research Excellence Award

Karadeniz Technical University | Turkey

Dr. Sibel Cevik Bektas is an electrical and electronics engineering researcher specializing in energy systems and renewable integration. Education includes undergraduate, postgraduate, and doctoral degrees in electrical engineering from Karadeniz Technical University. Professional experience centers on academic research, peer-reviewed publications, conference contributions, and leadership of a TUBITAK-funded project. Research interests cover optimal energy management, power systems, load forecasting, solar irradiance prediction, and data-driven optimization. Research skills include machine learning, deep learning, time-series forecasting, optimization, MATLAB modeling, and power system analysis. Awards and honors include competitive national research funding. Overall, Dr. Sibel Cevik Bektas contributes impactful, applied solutions advancing energy systems.

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Scenario-Based Data-Driven Approaches for Short-Term Load Forecasting
Energy and Buildings, 2026

Rosa Maria Roman-Cuesta l Climate Change Mitigation-Paris Agreement | Research Excellence Award

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Dr. Rosa Maria Roman-Cuesta l Climate Change Mitigation-Paris Agreement | Research Excellence Award

European Commission. Joint Research Centre | Italy

Dr. Rosa Maria Roman-Cuesta is a forest ecologist with over 20 years of experience in land-use mitigation and adaptation under UNFCCC and the Paris Agreement. Her education includes a PhD in Forest Sciences from the Autonomous University of Barcelona and postdoctoral research at Oxford University and the Technical University of Munich. Her professional experience spans the European Commission JRC, FAO, CIFOR, and multiple international consultancies. Her research interests focus on AFOLU, Nature-Based Solutions, carbon dynamics, REDD+, and climate policy. Her research skills include remote sensing, GIS, carbon accounting, and land-use modeling. Her awards include the Alexander von Humboldt Fellowship. Overall, Dr. Rosa Maria Roman-Cuesta bridges science and policy to support global climate action.

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Featured Publications

Land Remains a Blind Spot in Tracking Progress under the Paris Agreement Due to Lack of Data Comparability

Communications Earth & Environment, 2025 · Open Access

A Global Assemblage of Regional Prescribed Burn Records — GlobalRx

Scientific Data, 2025 · Open Access

Considerations for Determining Warm-Water Coral Reef Tipping Points

Earth System Dynamics, 2025 · Open Access

Relationship of Litterfall Anomalies with Climatic Anomalies in a Mangrove Swamp of the Yucatán Peninsula, Mexico

PLOS ONE, 2024 · Open Access

Correction to: Current National Proposals Are Off Track to Meet Carbon Dioxide Removal Needs

Nature Climate Change, 2024 · Erratum · Open Access

Mmasechaba Lebogang Moropane l Environmental Sciences | Research Excellence Award

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Ms. Mmasechaba Lebogang Moropane l Environmental Sciences | Research Excellence Award

 University of the Western Cape | South Africa

Ms. Mmasechaba Lebogang Moropane is a Doctoral Researcher in Environmental and Water Sciences at the University of the Western Cape with an MSc (Summa Cum Laude) and BSc Honours in the same field. Her education integrates remote sensing, hydrology, geospatial analysis, machine learning, and big data analytics. Professionally, she serves as an Earth Science Departmental Research Assistant, supporting teaching and contributing to peer-reviewed journals, policy-oriented reports, and international conferences. Her research interests focus on groundwater-dependent ecosystems, non-perennial rivers, land degradation, and food–water security in semi-arid regions, employing advanced satellite analytics, AI, and XAI. She has received recognition through high-impact publications, peer-review roles, and national and international research collaborations, positioning her as a promising early-career researcher with strong interdisciplinary impact.

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Featured Publications

International Journal of Applied Earth Observation and Geoinformation, 2026
Mmasechaba L. Moropane; Cletah Shoko; Timothy Dube; Dominic Mazvimavi

Apoorva Gupta l Economics of Education | Best Researcher Award

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Dr. Apoorva Gupta l Economics of Education | Best Researcher Award

Hansraj College | India

Dr. Apoorva Gupta is an economist with a Ph.D. from Delhi School of Economics, specializing in development economics with a focus on education, health, gender, caste, and social mobility. With extensive teaching experience at Ramjas College and Hansraj College, Dr. Apoorva Gupta has instructed courses in microeconomics, macroeconomics, econometrics, development economics, and data analysis. Her research integrates social networks and intergenerational mobility, supported by experience as a senior research assistant and multiple fellowships. Proficient in Stata, R, and MS Office, Dr. Apoorva Gupta has received awards including Best Paper at the G20 Conference and excels in academic leadership, research, and policy-oriented economic analysis.

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Featured Publications

Exploring the Linkage between Education and Financial Autonomy of Women Working in the Urban Informal Sector
– Economic & Political Weekly, 2025, 60(31), 29
Article · Co-author
Do school facilities increase school enrolment? Evidence from primary school enrolment in India
– Indian Economic Review, 2025, 1-29
Article · Co-author
Probing the linkages between educational level and occupational choices of women working in urban informal sector of India
– Work, 2024, 79(4), 1637-1652
Article · Co-author
Does domestic monetary policy affect foreign direct investment to India?
– Economics Bulletin, 2024, 44(1), 173-181
Article · Co-author
Determinants of income earned by women working in the urban informal sector in India: An empirical study
– Anwesak, 2024, 53(2), 72-86
Article · Co-author

Imran Rehan l Data Classification | Best Researcher Award

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Assist. Prof. Dr. Imran Rehan l Data Classification | Best Researcher Award

Gomal University | Pakistan

Assist. Prof. Dr. Imran Rehan is a physicist specializing in biophotonics with a Ph.D. in Physics and a postdoctoral focus on AI in healthcare. With over a decade of teaching and research experience, Dr. Imran Rehan integrates Raman spectroscopy, LIBS, fluorescence spectroscopy, and phase-lock-in polarimetry with machine learning to develop noninvasive, label-free diagnostic tools for diseases such as diabetes, cancer, and kidney disorders. His research emphasizes clinical spectroscopy, biomarker discovery, and optical instrumentation development. Dr. Imran Rehan has published extensively, contributed to book chapters, designed advanced spectroscopic systems, and received honors including the International Outstanding Scientist Award and Best Researcher of the Year, demonstrating impactful interdisciplinary innovation.

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Featured Publications

Machine learning assisted laser spectroscopy for simultaneous multiclass diabetes detection from blood plasma
– Microchemical Journal, 2026
Cardiovascular risk prediction in diabetes: a hybrid machine learning approach
– Biomedical Physics Engineering Express, 2025
A CatBoost and ExtraTrees-based softvoting ensemble approach for non-invasive diabetes detection using hair LIBS spectral data
– Microchemical Journal, 2025
Synthesis and characterization of CNTs doped polymeric composites: comparative studies on exploring impact of CNT concentration on morphological, structural, thermokinetic and mechanical attributes
– Zeitschrift Fur Physikalische Chemie, 2025
Application of Laser Spectroscopy and Machine Learning for Diagnostics of Uncontrolled Type 2 Diabetes
– Applied Spectroscopy, 2025

Shaokai Wang l Engineering Geology | Research Excellence Award

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Dr. Shaokai Wang l Engineering Geology | Research Excellence Award

North China University of Water Resources and Electric Power | China

Dr. Shaokai Wang is a geological engineering scholar with a Ph.D. in Geological Engineering and academic affiliation at North China University of Water Resources and Electric Power. With five years of teaching and research experience, Dr. Shaokai Wang has contributed to undergraduate and postgraduate instruction while advancing scientific research. His research interests focus on geological engineering, geotechnical engineering, loess geohazards, soil improvement technologies, and disaster mechanisms. He has led and participated in National Natural Science Foundation of China projects, published SCI-indexed journal articles, and developed strong research skills in field investigation, laboratory testing, numerical analysis, and academic writing. His work demonstrates sustained innovation and growing academic impact in geological engineering.

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Static liquefaction capacity of saturated undisturbed loess in South Jingyang platform
– Water (Switzerland), 2020

Article · Open Access · Co-authored · Citations: 11

Mingming Wu l Quantitative Magnetresonanztomographie | Research Excellence Award

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Dr. Mingming Wu l Quantitative Magnetresonanztomographie | Research Excellence Award

Technical University of Munich | Germany

Dr. Mingming Wu is a physician–scientist and radiology resident with a strong background in physics and biomedical engineering. She completed a PhD in physics focused on MR thermometry and later obtained a medical degree, combining clinical training with advanced quantitative MRI research. Her experience spans academic hospitals, international research centers, and industry collaborations. Her research interests include quantitative MRI, adipose tissue imaging, MR thermometry, and AI-based image analysis. She has authored numerous high-impact publications and book chapters. Her work has been recognized with prestigious fellowships, merit awards, and competitive research grants, reflecting sustained excellence in translational imaging research.

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Ammar Khaleel l Reinforcement Learning | Research Excellence Award

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Mr. Ammar Khaleel l Reinforcement Learning | Research Excellence Award

Széchenyi István Egyetem | Hungary

Mr. Ammar Khaleel PhD-level researcher in computer science with ongoing doctoral training, focusing on reinforcement learning–based decision making for autonomous vehicles. Experience includes designing, training, and evaluating deep reinforcement learning and control algorithms for autonomous driving, particularly lane-changing, within large-scale traffic simulations using SUMO and the TraCI Python API. Research interests span reinforcement learning, deep learning, model predictive control, intelligent transportation systems, and traffic modeling. Technical expertise covers Python, C/C++, simulation frameworks, and reproducible research workflows. Academic contributions emphasize simulation-driven experimentation and algorithmic innovation; no formal awards are listed. Overall, the work aims to advance safe, efficient, and intelligent mobility systems.

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Featured Publications


A Multi-Levels RNG Permutation

– Indonesian Journal of Electrical Engineering and Computer Science, 2019

A New Permutation Method for Sequence of Order 28

– Journal of Theoretical and Applied Information Technology, 2019

N/A and Signature Analysis for Malwares Detection and Removal

– Indian Journal of Science and Technology, 2019