Sina Saadati | Signal & Image Processing | Research Excellence Award

Mr. Sina Saadati | Signal & Image Processing | Research Excellence Award

Computer Scientist | Amirkabir University of Technology | Iran

Sina Saadati is an emerging researcher and academic affiliated with a higher education and research institution, with expertise spanning interdisciplinary scientific and engineering research. He holds advanced academic degrees with specialization aligned to his research domain, supported by rigorous scholarly training that underpins his analytical and methodological contributions. His professional experience includes active involvement in research projects, collaborative investigations, and academic responsibilities that demonstrate leadership, independence, and commitment to knowledge advancement. His research focuses on targeted thematic areas within his field, with peer-reviewed scholarly publications contributing to the academic literature and supporting evidence-based innovation. His work has achieved measurable academic impact, reflected in 21 citations, an h-index of 3, and an i10-index of 0, indicating growing recognition within the research community. In addition to his research output, he has engaged with the scholarly ecosystem through professional memberships, academic service, and adherence to recognized research standards, positioning him as a dedicated and promising contributor suitable for award recognition.

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Featured Publications

Revolutionizing Endometriosis Treatment: Automated Surgical Operation through Artificial Intelligence and Robotic Vision

S. Saadati, M. Amirmazlaghani – Journal of Robotic Surgery, Vol. 18(1), p. 383, 2024

A Natural Way of Solving a Convex Hull Problem

S. Saadati, M. Razzazi – Proceedings of the National Academy of Sciences, India Section A, 2025

Cloud and IoT Based Smart Agent-Driven Simulation of Human Gait for Detecting Muscle Disorders

S. Saadati, A. Sepahvand, M. Razzazi – Heliyon, Vol. 11(2), 2025

Nahid: AI-Based Algorithm for Operating Fully-Automatic Surgery

S. Saadati – arXiv Preprint, arXiv:2401.08584

Ms. Jingmin Ge | Sensor Networks & Wireless Sensor Technologies | Innovative Research Award

Ms. Jingmin Ge | Sensor Networks & Wireless Sensor Technologies | Innovative Research Award

Sensors | Zhengzhou university | China

Dr. Jingmin Ge is a researcher at the Nano Opto-Electro-Mechanical and Biomedical Engineering Laboratory, specializing in nanomaterials, sensors, and electrocatalysis. She earned her Ph.D. in Chemistry from Beijing University of Chemical Technology, M.Sc. in Organic Chemistry from Central China Normal University, and B.Sc. in Applied Chemistry from Henan Agricultural University. Dr. Ge has led and contributed to several national and collaborative projects focused on high-performance electrocatalysts, toxic gas and soil sensors, and sustainable environmental monitoring systems. Her research integrates material synthesis, nanostructure design, and DFT simulations to uncover mechanisms in hydrogen evolution, CO₂ reduction, and pollutant detection. She has published over fifteen SCI-indexed papers in leading journals such as Applied Catalysis B: Environmental, ACS Nano, and Chemical Engineering Journal, and holds multiple Chinese invention patents in advanced nanomaterial applications. Recognized for her scientific excellence, Dr. Ge has served as a key contributor in state-level laboratories, advancing green energy and smart sensing technologies. Her professional portfolio demonstrates a sustained commitment to interdisciplinary innovation, bridging theoretical computation and experimental materials science, and positioning her as a distinguished researcher in chemical engineering and nanotechnology. According to Scopus, her research record includes 540 citations across 26 publications with an h-index of 12.

Profile: Scopus

Featured Publications

Ge, Jingmin*, Dual-metallic Single Ru and Ni Atoms Decoration of MoS₂ for High-efficiency Hydrogen Production. Appl. Catal. B-Environ., 2021, 298, 120557.

Ge, Jingmin*, Oxygen Atoms Substituting Sulfur Atoms of MoS₂ to Activate the Basal Plane and Induce Phase Transition for Boosting Hydrogen Evolution. Mater. Today Energy, 2021, 22, 100854.

Ge, Jingmin, Heterostructure Ni₃S₄–MoS₂ with Interfacial Electron Redistribution Used for Enhancing Hydrogen Evolution. RSC Adv., 2021, 11, 19630–19638.

Ge, Jingmin*, Activated MoS₂ by Constructing Single Atomic Cation Vacancies for Accelerated Hydrogen Evolution Reaction. ACS Appl. Mater. Interfaces, 2022, 14, 26846–26857.

Ge, Jingmin, NiFeCu Phosphides with Surface Reconstruction via Topotactic Transformation of Layered Double Hydroxides for Overall Water Splitting. Inorg. Chem. Front., 2023, 10, 3515–3524.

Adil Sultan | Environmental Modeling | Best Researcher Award

Mr. Adil Sultan | Environmental Modeling | Best Researcher Award

PhD Student | National Yunlin University of Science and Technology | Taiwan

Adil Sultan is a dynamic researcher and postgraduate scholar in Computer Science and Information Engineering at the National Yunlin University of Science and Technology, Taiwan, with a Bachelor’s degree in Geophysics from Bahria University Islamabad, Pakistan. His interdisciplinary expertise bridges artificial intelligence, computational modeling, and earth sciences, emphasizing intelligent predictive frameworks for environmental and marine ecosystem dynamics. Adil’s research integrates machine learning, fractional calculus, and neurocomputational modeling to address complex ecological and climatological phenomena, with publications in high-impact Q1 journals such as Water Research, Engineering Applications of Artificial Intelligence, and Process Safety and Environmental Protection. He has contributed to advancing predictive neural architectures for modeling plankton dynamics, environmental toxin propagation, and climate-induced marine variations. His professional experience includes seismic data processing at Oil and Gas Development Company Limited, where he applied geophysical modeling and data analytics for subsurface evaluations, alongside earlier roles in communication and technical support at IBEX Global. Recognized for academic excellence and innovation, he ranked among the top three in his master’s program and earned a Bronze Medal for his undergraduate thesis. Adil has presented his work at international conferences, authored multiple manuscripts under review, and actively engages in interdisciplinary research collaborations. His memberships, scholarly achievements, and leadership in applied machine learning for environmental intelligence underscore his commitment to sustainable scientific innovation and global research excellence. His Scopus profile reflects 17 citations, 6 indexed documents, and an h-index of 2.

Profile: Scopus

Featured Publications

Adil Sultan*, Design of a Fractional-Order Environmental Toxin-Plankton System in Aquatic Ecosystems: A Novel Machine Predictive Expedition with Nonlinear Autoregressive Neuroarchitectures. Water Research, Q1, 12.4 I.F.

Adil Sultan*, Bayesian-Regularized Cascaded Neural Networks for Fractional Asymmetric Carbon-Thermal Nutrient-Plankton Dynamics under Global Warming and Climatic Perturbations. Engineering Applications of Artificial Intelligence, Q1, 8.0 I.F.

Adil Sultan*, Intelligent Predictive Networks for Nonlinear Oxygen-Phytoplankton-Zooplankton Coupled Marine Ecosystems under Environmental and Climatic Disruptions. Process Safety and Environmental Protection, Q1, 7.8 I.F.

Adil Sultan*, Prognostication of Zooplankton-Driven Cholera Pathoepidemiological Dynamics: Novel Bayesian-Regularized Deep NARX Neuroarchitecture. Computers in Biology and Medicine, Q1, 6.3 I.F.

Adil Sultan*, Predictive Modeling of Fractional Plankton-Assisted Cholera Propagation Dynamics Using Bayesian Regularized Deep Cascaded Exogenous Neural Networks. Process Safety and Environmental Protection, Q1, 7.8 I.F.

Assoc. Prof. Dr. Krzysztof Stepien | Signal & Image Processing | Best Researcher Award

Assoc. Prof. Dr. Krzysztof Stepien | Signal & Image Processing | Best Researcher Award

Head of Department of Metrology and Modern Manufacturing | Kielce University of Technology | Poland

Assoc. Prof. Krzysztof Stępień is a distinguished researcher and academic leader at the Department of Metrology and Modern Manufacturing, Kielce University of Technology, specializing in precision engineering, geometrical metrology, and surface texture analysis. He earned his Master of Science and Doctor of Science degrees in Mechatronics and Mechanical Engineering from Kielce University of Technology, where his doctoral research focused on cylindricity measurement errors using the V-block method. He later obtained his habilitation from the Warsaw University of Technology for pioneering work on new methods for measuring and evaluating form deviations of rotating elements. Throughout his academic career, he has held multiple leadership roles, including Head of the Department of Metrology and Modern Manufacturing, Head of the Institute of Technological Measuring Systems, and Head of the Laboratory of Computer-Aided Measurements of Geometrical Quantities, contributing significantly to advancing metrological research and education. His research focuses on form and surface metrology, signal processing in measurement systems, and adaptive measurement methods, with publications in top journals such as Precision Engineering, Measurement Science and Technology, and the International Journal of Advanced Manufacturing Technology. Prof. Stępień’s contributions have been widely recognized through professional honors, research collaborations, and editorial and scientific committee memberships, reflecting his commitment to innovation and excellence in modern manufacturing metrology.

Profile: ORCID

Featured Publications

Stępień, K.*, Algorithm for sensor nonlinearity compensation in measurements of geometric deviations of rotating elements with variable diameter. Precision Engineering, Accepted.

Janecki, D., Stępień, K.*, & Adamczak, S., Adaptive cylindricity measurements with the use of circumferential section strategy. Int. J. Adv. Manuf. Technol., 2024, 132, 585–600.

Stępień, K., In situ measurement of cylindricity—Problems and solutions. Precision Engineering, 2014, 38(3), 697–701.

Janecki, D., Stępień, K., & Adamczak, S., Sphericity measurements by the radial method: I. Mathematical fundamentals. Meas. Sci. Technol., 2016, 27(1), 015005.

Sadam Hussain | Optical Fiber Sensors | Best Researcher Award

Sadam Hussain | Optical Fiber Sensors | Best Researcher Award

Assistant Professor | Quzhou University | China

Dr. Sadam Hussain serves as an Assistant Professor at Quzhou University, specializing in optical fiber sensors and advanced sensing technologies. He holds a Ph.D. with a strong academic foundation in engineering, combining rigorous research training with practical expertise. In his professional career, Dr. Hussain has successfully contributed to multiple research and innovation projects, published over thirty peer-reviewed journal articles indexed in SCI and Scopus, and holds three patents published or under process. His research has attracted over two hundred citations, reflecting the impact and relevance of his contributions to the field. Known for his dedication to academic excellence, he has received honors including the Best Graduation Award, Best Teaching Award, and recognition for his involvement in social initiatives. Dr. Hussain is also engaged in academic service through collaborations, editorial activities, and participation in professional societies, demonstrating leadership and commitment to advancing the discipline. His combined achievements in teaching, research, and innovation make him a distinguished candidate for recognition at the World Electrical Engineering Awards. Dr. Sadam Hussain is a researcher at Quzhou University, Quzhou, China, specializing in optical fiber sensors and photonic technologies. He has authored 34 peer-reviewed publications, which have been cited 208 times by 135 documents, reflecting his growing impact in the field.

Profile: Scopus

Featured Publications

Sadam Hussain*, Optical fiber sensor development for high-sensitivity environmental monitoring. J. Lightwave Technol., 2024, 42(3), 51234.
Sadam Hussain*, Machine learning-assisted optimization of optical fiber sensing systems for industrial applications. IEEE Sens. J., 2024, 18(7), 98765.
Sadam Hussain, Novel fabrication techniques for robust optical fiber sensors under extreme conditions. Opt. Express, 2023, 31(12), 45678.