Mahmoud Ahmad l Water Management Resources | Excellence in Research

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Mr. Mahmoud Ahmad l Water Management Resources | Excellence in Research

Széchenyi István University | Hungary

Mr. Mahmoud Ahmad is a PhD student in Civil Engineering at Széchenyi István University with academic and professional expertise in water management resources and civil engineering practice. His education includes a PhD in progress, an MSc in Water and Irrigation Engineering, and a BSc in Civil Engineering, supported by strong academic distinction. His professional experience covers lecturing in water and irrigation engineering at Tishreen University and managing post-earthquake construction projects as a site civil engineer. Mr. Mahmoud Ahmad’s research interests focus on groundwater–surface water interaction, water resources management, and climate-change adaptation, supported by advanced modeling, GIS-based analysis, and recognized academic awards, reflecting strong potential for impactful research and practice.

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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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Featured Publications

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

Ali Ali | Artificial Intelligence and water Resources Management | Research Excellence Award

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Dr. Ali Ali | Artificial Intelligence and water Resources Management | Research Excellence Award

Brunel University London | United Kingdom

Dr. Ali Ali is an emerging researcher and PhD student in Civil Engineering at Brunel University London, specializing in Artificial Intelligence applications for water resources management. He holds a BEng (Hons) in Civil Engineering from Brunel University and a Diploma with Distinction from Kaplan International College, demonstrating strong academic performance. Dr. Ali has extensive experience with civil engineering and computational software, including AutoCAD, Groundwater Modelling System (GMS), ABAQUS, MATLAB, and programming languages Python and R, enabling advanced modeling, simulation, and data analysis. He has applied his expertise in designing steel and concrete structures while addressing economic, social, and environmental challenges. His professional experience includes multiple Graduate Teaching Assistant roles in Civil Engineering and Computer Science, supporting both undergraduate and master’s students with lab sessions, course material, tutoring, and programming instruction. Additionally, he gained practical industry experience through internships in project management and turbomachinery design, enhancing his skills in project coordination and operational efficiency. Dr. Ali’s research interests focus on optimizing water resources management using AI, sustainable infrastructure design, and hydrological modeling. He has contributed to academic documents, reports, and simulations, with ongoing work aimed at publication in peer-reviewed journals. His work demonstrates a commitment to bridging theoretical research and practical engineering solutions, advancing innovation in civil and environmental engineering.

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Featured Publication

Aquifer-specific flood forecasting using machine learning: A comparative analysis for three distinct sedimentary aquifers – Science of the Total Environment, 2025 | Open Access

Ali Raza Shaikh | Mechanical Engineering | Research Excellence Award

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Mr. Ali Raza Shaikh | Mechanical Engineering | Research Excellence Award

Technical University of Darmstadt | Germany

Ali Raza Shaikh is a dedicated mechanical engineer and researcher specializing in fluid mechanics, heat transfer, ice adhesion, aerodynamics, and surface-interface science. He holds a B.Eng. in Mechanical Engineering, an M.Eng. in Power Engineering and Engineering Thermophysics, and is currently pursuing a Dr.-Ing. in Mechanical Engineering. With extensive research and practical experience, he has developed experimental setups, conducted thermal-fluid and droplet impact studies, and contributed to advancements in superhydrophobic and anti-icing surfaces. He has served as a research assistant in the MSCA-ITN SURFICE project, conducted industrial research internships, and held graduate research positions, collaborating on international projects and presenting findings at leading conferences. His technical expertise spans experimental design, surface characterization, high-speed imaging, and numerical simulations, complemented by proficiency in MATLAB, Python, LabView, CAD, and LaTeX. His research interests focus on ice adhesion dynamics, surface wetting, thermal-fluid interactions, and functional surface design. Ali has actively participated in workshops and training schools worldwide, contributing to knowledge exchange and innovation in engineering applications. His dedication to advancing research and practical solutions in fluid mechanics and surface science underscores his commitment to scientific excellence and technological development.

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Featured Publications


Transient testing of tensile ice adhesion

– Cold Regions Science and Technology, 2025

Ice adhesion dynamics in the tensile mode

– Smart Surface Design for Efficient Ice Protection and Control, 2025

The physics of icing

– Smart Surface Design for Efficient Ice Protection and Control, 2025

Understanding the physics of ice adhesion on complex substrates

– International Conference on Icing of Aircraft, Engines, and Structure, 2023

Xiaochen Xie | Quantitative Research | Research Excellence Award

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Dr. Xiaochen Xie | Quantitative Research | Research Excellence Award

Renmin University of China | China

Dr. Xiaochen Xie is an Assistant Professor at the School of Finance, Renmin University of China, with concurrent research associate positions at the China Financial Policy Research Center, the Institute of Public Finance and Taxation, and the Institute of Digital Economics and Taxation. He holds a Ph.D. in Economics from Pennsylvania State University, an M.S. in Economics from the University of Wisconsin–Madison, and dual B.E. in Finance and B.S. in Statistics from Peking University. His research spans international trade, industrial organization, public economics, and urban economics, with a focus on firm entry, pricing strategies, and the economic impacts of policy interventions. Dr. Xie has published in leading journals, including the Journal of Economic Geography, and has multiple working papers under review in top-tier journals. He has received awards such as Outstanding Young Scholar at Renmin University and recognition for outstanding papers at the NCER-CCER Chinese Economy Conference. Dr. Xie has extensive teaching experience in undergraduate and graduate courses on public finance, quantitative empirical economics, and structural methods. His research integrates empirical and structural approaches to inform trade, fiscal, and industrial policies. Dr. Xie’s work contributes to understanding the intersection of market structure, policy design, and economic welfare, influencing both academic research and practical policy-making.

Profiles : Orcid | Google Scholar

Featured Publications

Chen, Y., Huang, T., & Xie, X. (2025). “Place-based policies: First-mover advantage and persistence.” Journal of Economic Geography, lbaf001.

Lu, W., & Xie, X. (2024). “Trade, markups, and consumer welfare: Evidence from the global smartphone industry.” Available at SSRN 4804811.

Xie, X. (2021). Two essays on firm entry and pricing. The Pennsylvania State University.

Arun Kumar | Mathematical Modeling | Best Researcher Award

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Dr. Arun Kumar | Mathematical Modeling | Best Researcher Award

IIT Mandi | India

Dr. Arun Kumar is a Research Associate (Postdoctoral) at the Indian Institute of Technology Mandi, specializing in mathematical and computational modeling of complex biological, ecological, and epidemiological systems. He earned his Ph.D. in Mathematics from Banaras Hindu University in 2023, following an M.Sc. in Mathematics from the same institution and a B.Sc. from CCS University, Meerut. His research focuses on nonlinear dynamics, bifurcation theory, delay differential equations, reaction–diffusion systems, Turing patterns, and the integration of deep learning and physics-informed neural networks (PINNs) for solving partial differential equations. Dr. Kumar has published extensively in high-impact journals on topics including SIR/SIRS epidemic models, cross-diffusion models, predator–prey dynamics, and pattern formation in spatial ecological systems. His work bridges theoretical mathematics and practical applications in disease modeling and ecology, offering insights into complex population interactions, control strategies, and spatio-temporal dynamics. Currently, he is developing deep learning algorithms to solve PDEs with applications in ecological and epidemiological systems. His ongoing research explores predator-prey interactions, learning in ecological models, and the forecasting of infectious diseases such as monkeypox. Dr. Kumar’s contributions have advanced the understanding of nonlinear systems, providing both analytical and computational tools for studying complex biological and ecological phenomena.

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Featured Publications

Gupta, R. P., & Kumar, A. (2022). “Endemic bubble and multiple cusps generated by saturated treatment of an SIR model through Hopf and Bogdanov–Takens bifurcations.” Mathematics and Computers in Simulation, 197, 1–21.

Gupta, R. P., Kumar, A., & Yadav, D. K. (2024). “The complex dynamical study of a UAI epidemic model in non-spatial and spatial environments.” The European Physical Journal Plus, 139(2), 117.

Yadav, D. K., Gupta, R. P., & Kumar, A. (2022). “Nonlinear dynamics of a three species prey-predator system incorporating fear effect and harvesting.” Journal of Mathematical Control Science and Applications.

Kumar, A., Gupta, R. P., & Tiwari, S. (2022). “Influences of nonlinear cross-diffusion on a reduced SI epidemic model with saturated treatment.”

Kumar, A., Kumari, N., Mandal, S., & Tiwari, P. K. (2025). “Autonomous and non-autonomous dynamics of an SIRS model with convex incidence rate.” Journal of the Franklin Institute, 108236.

Xiaoxia Yu | Data Analysis Innovation |  Best Scholar Award  

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Mr. Xiaoxia Yu | Data Analysis Innovation |  Best Scholar Award  

Chongqing University of Technology | China

Dr. Xiaoxia Yu is a scholar in mechanical engineering whose work advances intelligent diagnostics and predictive maintenance for large-scale rotating machinery, particularly wind turbines. With a Ph.D. in Mechanical Engineering and earlier degrees in Vehicle Engineering and Armored Vehicle Engineering, she has built a strong interdisciplinary foundation that integrates mechanical systems knowledge with advanced computational modeling. Her research spans fault diagnosis, health assessment, digital twin systems, graph neural networks, reinforcement learning, and signal processing, supported by a growing publication record that includes 29 documents, 477 citations by 448 documents, and an h-index of 7. As a Lecturer, she leads research projects funded by regional scientific agencies and has contributed to national-level R&D initiatives related to machinery health management. Her work appears in high-impact journals, and she has secured patents focused on structural health monitoring, image recognition, and intelligent fault detection. Recognized with competitive grants and academic honors, she continues to influence the fields of renewable energy reliability and smart manufacturing. Through her commitment to innovation, research leadership, and engineering application, she is emerging as a key contributor to the development of intelligent, data-driven mechanical health monitoring systems.

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Featured Publications

Yu, X., Tang, B., & Zhang, K. (2021). Fault Diagnosis of Wind Turbine Gearbox Using a Novel Method of Fast Deep Graph Convolutional Networks. IEEE Transactions on Instrumentation and Measurement, 70, 1–14.

Yu, X., Tang, B., & Deng, L. (2023). Fault Diagnosis of Rotating Machinery Based on Graph Weighted Reinforcement Networks Under Small Samples and Strong Noise. Mechanical Systems and Signal Processing, 186, 109848.

Zhang, K., Tang, B., Deng, L., & Yu, X. (2021). Fault Detection of Wind Turbines by Subspace Reconstruction‑Based Robust Kernel Principal Component Analysis. IEEE Transactions on Instrumentation and Measurement, 70, 1–11.

Li, B., Tang, B., Deng, L., & Yu, X. (2020). Multiscale Dynamic Fusion Prototypical Cluster Network for Fault Diagnosis of Planetary Gearbox Under Few Labeled Samples. Computers in Industry, 123, 103331.

Xiong, P., Tang, B., Deng, L., Zhao, M., & Yu, X. (2021). Multi‑block Domain Adaptation with Central Moment Discrepancy for Fault Diagnosis. Measurement, 169, 108516.

Samaneh Saeedinia | Engineering | Best Researcher Award

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Prof.Dr. Samaneh Saeedinia | Engineering | Best Researcher Award

Iran University of Science and Technology | Iran

Dr. Samaneh Alsadat Saeedinia is an accomplished researcher and engineer specializing in Control Electrical Engineering, Computational Neuroscience, Robotics, and Artificial Intelligence. She earned her Ph.D. from Iran University of Science and Technology (IUST), where her thesis focused on designing and stabilizing intelligent decision-making systems to support the treatment of adult focal epilepsy, receiving an excellent grade. She also holds a Master’s degree in Control Electrical Engineering from IUST and a Bachelor’s degree from Imam Khomeini International University. Dr. Saeedinia has extensive experience in signal processing, machine learning, spiking neural networks, EEG and MRI data analysis, and adaptive control systems. She has contributed to numerous high-impact publications in IEEE Transactions, Scientific Reports, and other international journals. Her research interests include computational modeling, intelligent control, data fusion, robotics path planning, and neuroinformatics. Beyond academia, she has led industrial projects in electrical power systems, automation, and instrumentation. She has received multiple awards, including the Khwarizmi International Award and top ranks in academic performance. Dr. Saeedinia is also an experienced educator and mentor, guiding students in advanced control systems, neural networks, and data analysis. Her work bridges engineering, neuroscience, and artificial intelligence, aiming to develop innovative solutions for healthcare and robotic applications.

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Featured Publications

Saeedinia, S.A., Jahed-Motlagh, M.R., Tafakhori, A., & Kasabov, N.K. (2024). “Diagnostic biomarker discovery from brain EEG data using LSTM, reservoir-SNN, and NeuCube methods in a pilot study comparing epilepsy and migraine” in Scientific Reports, 14(1), 10667.

Mohaghegh, M., Saeedinia, S.A., & Roozbehi, Z. (2023). “Optimal predictive neuro-navigator design for mobile robot navigation with moving obstacles” in Frontiers in Robotics and AI, 10, 1226028.

Roozbehi, Z., Narayanan, A., Mohaghegh, M., & Saeedinia, S.A. (2024). “Dynamic-structured reservoir spiking neural network in sound localization” in IEEE Access, 12, 24596–24608.

Saeedinia, S.A., & Tale Masouleh, M. (2022). “The synergy of the multi-modal MPC and Q-learning approach for the navigation of a three-wheeled omnidirectional robot based on the dynamic model with obstacle collision” in Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science.

Roozbehi, Z., Narayanan, A., Mohaghegh, M., & Saeedinia, S.A. (2025). “Enhanced Multiple Sound Event Detection and Classification Using Physical Signal Properties in Recurrent Spiking Neural Networks” in IEEE Access.

Ashutosh K Giri | Engineering | Best Researcher Award

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Dr. Ashutosh K Giri | Engineering | Best Researcher Award

Government Engineering College Bharuch | India

Dr. Ashutosh K. Giri is an Assistant Professor in the Department of Electrical Engineering at Government Engineering College, Bharuch, Gujarat, India, with over 20 years of teaching and research experience. He earned his Ph.D. from Sardar Vallabhbhai National Institute of Technology (SVNIT), Surat, focusing on control algorithms for power quality issues in wind-based distributed power generation systems. He also holds an M.E. in Electrical Engineering from L.D. College of Engineering, Gujarat University, and a B.Tech in Electrical Engineering from G.B. Pant University of Agriculture and Technology, Pantnagar. Dr. Giri’s research interests include renewable energy power converters, microgrid topology, operation and control, power quality enhancement, electric vehicle technologies, and adaptive control algorithms. He has published extensively in leading journals including IEEE, IET, Springer, Wiley, Elsevier, and Taylor & Francis, authored and edited multiple international books, and contributed to state- and national-level funded research projects. His work has been cited 478 times across 234 documents, with an h-index of 14, reflecting his significant impact in the field. Recognized among the top 2% of scientists globally, Dr. Giri continues to advance research and innovation in distributed generation systems and renewable energy, mentoring students and contributing to the scientific community through high-quality research and academic leadership.

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Featured Publications

Joshi, D., Deb, D., & Giri, A. K. (2025). “Metaheuristic adaptive input output feedback linearization control for BLDC motor drive” in IEEE Transactions on Consumer Electronics.

Kanojia, S. S., Suthar, B. N., & Giri, A. K. (2025). “Hybrid optimization approach for optimal location of electric vehicle charging station (EVCS) in distribution network considering voltage stability” in Smart Grids and Sustainable Energy.

Kundu, S., Giri, A. K., & Kadiyan, S. (2025). “An adaptive mixed-step size normalized least means fourth control approach for stand-alone power generation system considering dynamic conditions” in IEEE Journal of Emerging and Selected Topics in Power Electronics.

Bhatt, K. A., Bhalja, B. R., & Giri, A. K. (2024). “Controlled single-phase auto-reclosing technique for shunt reactor compensated lines in electrical energy systems transmission lines” in International Journal of Ambient Energy.

Parmar, D. B., & Giri, A. K. (2024). “Integration of field-oriented and steady-state linear Kalman filter control in PMSG-based grid-connected system for improving voltage control and power balance operation” in Electrical Engineering.

Abdelouahad Achmamad | Engineering | Best Researcher Award

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Dr. Abdelouahad Achmamad | Engineering | Best Researcher Award

Universite De Mans | France

Dr. Abde Louahad Achmamad is an embedded software engineer and researcher specializing in intelligent systems, AI algorithms, biomedical signal processing, and advanced embedded technologies for electrical, biomedical, and IoT applications. He holds a PhD in Electrical Engineering, complemented by master’s degrees in biomedical engineering and electrical/electronic engineering, supported by earlier qualifications in electromechanics and electronics. His professional experience spans biomedical R&D, embedded system design, AI-based medical diagnostics, MRI segmentation, IoT health monitoring, wearable sensor development, and security applications for STM32 microcontrollers. He has worked with leading institutions and companies across France and Morocco, including STMicroelectronics, Akkodis, Diabtech, Université de Rouen, ENSIAS, and multiple research laboratories. His scientific output includes 102 citations by 95 documents, 16 publications, and an h-index of 6, reflecting contributions in EMG-based diagnostics, phonoangiography, neuromuscular disorder detection, sensor fusion, and few-shot learning for medical imaging. He has also reviewed more than 200 scientific manuscripts for major journals from Springer, Elsevier, and MDPI, demonstrating recognized expertise in signal processing and embedded systems. His research interests include embedded AI, biomedical instrumentation, wearable sensors, IoT-based healthcare, security-focused embedded design, and intelligent diagnostic systems. Dr. Achmamad remains dedicated to advancing high-impact engineering solutions that enhance healthcare, sensing technologies, and intelligent embedded platforms.

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Featured Publications

Achmamad, A., & Jbari, A. (2020). A comparative study of wavelet families for electromyography signal classification based on discrete wavelet transform. Bulletin of Electrical Engineering and Informatics.

Achmamad, A., Belkhou, A., & Jbari, A. (2020). Fast automatic detection of amyotrophic lateral sclerosis disease based on Euclidean distance metric. In 2020 International Conference on Electrical and Information Technologies .

Belkhou, A., Achmamad, A., & Jbari, A. (2019). Classification and diagnosis of myopathy EMG signals using the continuous wavelet transform. In 2019 Scientific Meeting on Electrical-Electronics and Biomedical Engineering and Computer Science .

Belkhou, A., Achmamad, A., & Jbari, A. (2019). Myopathy detection and classification based on the continuous wavelet transform. Journal of Communications Software and Systems.

Abdelouahad, A., Belkhou, A., Jbari, A., & Bellarbi, L. (2018). Time and frequency parameters of sEMG signal–force relationship. In 2018 International Conference on Optimization and Applications .