Shiyu Liu | Feature extraction | Best Researcher Award

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Shiyu Liu | Feature extraction | Best Researcher Award

Lecturer | Hebei University | China

Dr. Shiyu Liu is a Lecturer and Postdoctoral Researcher at the College of Quality and Technical Supervision, Hebei University, specializing in spectral detection, artificial intelligence, and battery health monitoring. He earned his Ph.D. in Instrument Science and Technology from Yanshan University and served as a visiting Ph.D. student at the University of Huddersfield, where he worked on battery health monitoring using AI methods. His professional experience includes leading and contributing to multiple national and provincial research projects on spectral detection, lithium-ion battery state-of-health estimation, and environmental monitoring technologies. His research integrates chemometrics, machine learning, and deep learning to advance near-infrared spectroscopy for accurate detection of complex organic compounds and diesel quality management. Dr. Liu has authored numerous peer-reviewed articles in high-impact journals such as Chemometrics and Intelligent Laboratory Systems, Journal of Energy Storage, and Spectrochimica Acta Part A, and holds several patents related to NIR-based classification systems. He is skilled in big data analysis, ensemble learning, and deep learning algorithm development, with applications across energy storage systems, environmental monitoring, and process optimization. His academic contributions include presenting at international conferences, providing technical support, and publishing innovative methodologies. He has received multiple honors, including the Excellence Award at the Hebei Provincial Competition of the National Postdoctoral Innovation and Entrepreneurship Competition, a CSC Scholarship, and national and provincial “excellent graduate” titles, reflecting his significant impact on advancing intelligent measurement and monitoring technologies.

Profile: Scopus

Featured Publications

Liu, S., Fang, L., & Wang, S. (2025). Accurate determination of alcohol-based diesels using optimal chemical factors. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 326, 125140.

Liu, S., Fang, L., & Zhao, X. (2024). State-of-health estimation of lithium-ion batteries using a kernel support vector machine tuned by a new nonlinear gray wolf algorithm. Journal of Energy Storage, 102, 114052.

Xu Zhang | Motor control | Best Researcher Award

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Dr. Xu Zhang | Motor control | Best Researcher Award

Lecturer | North China Electric Power University | China

Xu Zhang is a productive researcher and lecturer specializing in advanced electrical drive systems, with a research record focused on flux-weakening control, sensorless control, model predictive control, and high-speed AC motor performance. He has authored over forty academic papers, with many appearing in top-tier journals such as IEEE Transactions on Power Electronics, IEEE Transactions on Industrial Electronics, and IEEE Transactions on Transportation Electrification; he is frequently the first or corresponding author. Xu has led competitive research projects funded by national and provincial agencies and served as a guest editor and reviewer for major journals. His work blends theoretical control design, algorithm development, and experimental validation, resulting in recognized contributions to torque quality improvement, overmodulation suppression, and rotor-parameter estimation techniques. Colleagues and collaborators note his consistent research leadership, strong grant-winning record, and commitment to translating advanced control methods toward practical motor-drive implementations.

Professional Profile

ORCID

Education 

Xu Zhang completed a progressive program of formal training in electrical engineering. He earned his Bachelor of Engineering in Electrical Engineering, followed by a Master’s degree and then a Ph.D. in Electrical Engineering. His postgraduate training emphasized electric drives, power electronics, and control theory, and his doctoral research concentrated on high-speed flux-weakening control and dynamic current control for AC and induction motors. His graduate studies included coursework and research in control systems, power converters, electrical machines, and signal processing for sensorless drives. The academic training was complemented by research placements and collaborations at institutions active in power-electronics research, enabling him to couple rigorous theoretical foundations with experimental and simulation-based validation. His formal education prepared him to design advanced control architectures and to publish in high-impact venues in power electronics and industrial electronics.

Experience 

Xu Zhang’s professional experience combines academic appointment, doctoral research, and project leadership. He currently serves as a Lecturer in Electrical Engineering, where he teaches courses and supervises student research while leading laboratory experiments on motor-drive control. Previously he completed his Ph.D., during which he executed multiple research projects on flux-weakening, sensorless control, and predictive control. He has been principal investigator or project lead on grants from national postdoctoral programs and from provincial and university funding agencies. His duties include designing control algorithms, directing prototype testing, preparing grant proposals, and coordinating collaborators across institutions. He has chaired conference sessions, served as guest editor for journals, and regularly reviews submissions for leading IEEE journals. This combination of teaching, laboratory leadership, grant management, and scholarly service underpins his capacity to deliver research that is both rigorous and application-ready.

Research focus 

Xu Zhang’s research centers on control strategies and practical techniques that improve the performance of electric motor drives under constrained-voltage and high-speed conditions. Key focus areas include flux-weakening control for PMSMs and induction motors, sensorless position estimation and back-EMF methods, overmodulation and torque ripple mitigation, and model predictive control tailored to finite-control-set converters. He also investigates online parameter estimation (e.g., rotor time-constant estimation) to maintain performance across operating conditions and develops methods for improved current dynamic response and torque quality under voltage saturation and hexagon voltage extension. His work integrates analytical modeling, adaptive/robust control design, and experimental validation on drive testbeds, aiming to increase torque output, reduce harmonic distortion, and enhance position estimation accuracy without relying on mechanical sensors. Research directions increasingly include bridging advanced control methods to real-time implementation and industrial testbed validation.

Publication Top Notes

Title: Accuracy Improvement of Back-EMF-Based Position Sensorless Control of PMSM Drives Based on Virtual Current Sampling
Year: 2025

Title: A Model Reference Adaptive System-Based Online Rotor Time Constant Estimation Method for Induction Motor Field-Weakening Control Utilizing Dot Product of Stator Voltage and Stator Current
Year: 2024

Title: Analysis and Optimization of Current Coupling Control in Flux-Weakening Region of PMSM
Year: 2024

Title: Optimization of Current Dynamic Performance and Torque Harmonic for Induction Motor Field-Weakening Control Under Hexagon Voltage Extension
Year: 2024

Title: Overmodulation Harmonic Modeling and Suppression for Induction Motor Field-Weakening Control With Extended Voltage Tracking Method
Year: 2023

Title: Torque Adaptive Hexagon Voltage Extension Method for PMSM Flux-Weakening Control Based on Dual PI Cascade Structure
Year: 2023

Title: Minimum-Nonlinear-Voltage Method for Torque Ripple Suppression in Induction Motor Overmodulation and Field-Weakening Control
Year: 2022

Title: Overmodulation Index Optimization Method for Torque Quality Improvement in Induction Motor Field-Weakening Control
Year: 2021

Title: Unified Complex Vector Field-Weakening Control for Induction Motor High-Speed Drives
Year: 2021

Conclusion

Xu Zhang is a highly promising and impactful researcher whose work bridges theoretical innovation with practical applications in electric motor drive systems. His strong publication record, consistent funding acquisition, leadership roles in top conferences, and recognition through prestigious awards position him as a deserving candidate for the Best Researcher Award. With continued efforts toward expanding interdisciplinary collaboration and exploring next-generation control strategies, Xu Zhang is poised to become a leading figure in the field of advanced motor control systems.

Lei Tian | Embedded Systems | Best Paper Award

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Assoc Prof. Dr. Lei Tian | Embedded Systems | Best Paper Award

Laboratory Director at Xi’an University of Posts and Telecommunications | China

Lei Tian is a laboratory director at Xi’an University of Posts & Telecommunications whose work spans embedded systems, new semiconductor materials, and optoelectronic interconnection. He has focused on the analysis, modeling, and design of photoelectric coupling systems, including conversion‑efficiency optimization and noise‑reduction modeling. He has led and completed provincial and municipal R&D projects, contributed to State Grid initiatives, and authored both a monograph and a ministry‑planned textbook. His publication record includes more than sixty papers across SCI, EI, and core journals, with recent articles in the International Journal of Hydrogen Energy, Diamond & Related Materials, Physica Status Solidi B, and on power‑management circuits. Tian’s recent research advances 2D/Janus heterostructures for water splitting and gas sensing, and investigates device‑level co‑design strategies where materials inform embedded hardware architectures. His work targets sustainable energy, intelligent sensing, and robust, low‑noise, high‑efficiency systems suitable for real‑world deployment.

Professional Profile

Scopus

Education 

Lei Tian earned a Ph.D. in Circuits and Systems from Xidian University, emphasizing the intersection of signal integrity, noise modeling, and device‑level architectures for mixed‑signal and optoelectronic systems. Postdoctoral training at the Institute of Modern Physics, Northwest University, strengthened his first‑principles and multi‑physics modeling toolkit, including density‑functional workflows that bridge material properties to circuit‑level specifications. This background shaped a research style that connects quantum‑scale material parameters with embedded‑system requirements such as power budgets, spectral response, and noise floors. Coursework and mentoring activities have centered on semiconductor devices, optoelectronic interfaces, embedded firmware for instrumentation, and algorithm‑hardware co‑optimization. Tian’s graduate and postdoctoral path fostered collaborations across materials science, device physics, and systems engineering, informing a translational approach from theory to prototypes. The resulting expertise supports end‑to‑end pipelines—from ab initio predictions and sensor stack design to embedded control, calibration routines, and system‑level validation for power, reliability, and real‑time performance.

Experience 

As Laboratory Director at Xi’an University of Posts & Telecommunications, Lei Tian leads a group focused on optoelectronic interconnection and embedded hardware–software co‑design. The team develops modeling frameworks for photoelectric conversion efficiency, designs low‑noise coupling schemes, and validates concepts through simulations and targeted prototypes. He has steered key provincial R&D programs and municipal science projects, as well as multiple State Grid engagements, delivering deployable insights for power and sensing infrastructure. Tian’s portfolio extends from novel 2D/Janus heterostructures and graphene‑based stacks to practical power‑management ICs such as high‑voltage, low‑quiescent‑current LDOs with stability‑oriented impedance buffers. He regularly collaborates with materials scientists and circuit designers to translate computed properties into embedded constraints, addressing latency, energy, thermal limits, and field robustness. Alongside publications and books, his experience includes curriculum and lab development, fostering hands‑on training that connects material innovation with firmware, drivers, diagnostics, and system bring‑up.

Research Focus

Tian’s research targets the convergence of embedded systems with novel semiconductor and 2D materials. The thrusts include first‑principles discovery of van der Waals and Janus heterojunctions optimized for hydrogen evolution and gas sensing  photoelectric conversion analysis and noise‑reduction modeling for optoelectronic coupling embedded co‑design, where device physics informs circuit topologies, firmware routines, and on‑board diagnostics; and power‑management solutions such as high‑voltage LDOs with ultra‑low quiescent current for edge instrumentation. A defining feature is the “materials‑to‑metrics” pipeline—mapping band alignments, excitonic effects, and defect physics to embedded KPIs like SNR, dynamic range, and power efficiency. This enables predictive selection of sensor stacks and control algorithms prior to fabrication, accelerating time‑to‑prototype. Recent studies on MoSSe‑based heterostructures for water splitting exemplify this approach, linking catalytic descriptors to embedded monitoring strategies and stability management for scalable, field‑ready hydrogen‑generation systems.

Publication Top Notes

Title: Z-scheme WSTe/MoSSe van der Waals heterojunction as a hydrogen evolution photocatalyst: First-principles predictions
Year: 2025

Title: First-principles exploration of hydrogen evolution ability in MoS₂/hBNC/MoSSe vdW trilayer heterojunction for water splitting
Year: 2025

Title: Research of Power Inspection Based on Intelligent Algorithm
Year: 2025.

Conclusion

Lei Tian’s research exhibits high originality, technical depth, and relevance to global energy challenges, making the candidate a strong contender for the Best Paper Award. The contributions to hydrogen evolution photocatalysts using novel van der Waals heterojunctions represent valuable advancements in computational materials science. With further emphasis on experimental validation and broader impact demonstration, the works could achieve even greater recognition. Overall, the candidate’s publications align well with the award’s objectives, and the research output shows significant promise for long-term influence in sustainable energy technologies.

Xiao Liang | Multi-Agent Control | Outstanding Scientist Award

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Prof. Xiao Liang | Multi-Agent Control | Outstanding Scientist Award

Associate Dean | Shenyang Aerospace University | China

Xiao Liang is a Professor at Shenyang Aerospace University and Associate Director of the Key Laboratory of Liaoning Province. He earned his B.S. in Automation from Northeastern University, followed by an M.S. in Control Theory and Control Engineering. He received his Ph.D. in Navigation, Guidance, and Control from Beihang University. With over 30 academic papers published in leading journals and five authorized patents, Liang has significantly advanced unmanned aerial and ground vehicle (UAV/UGV) research. His international engagement includes academic exchanges at Swansea University, UK. He has led numerous prestigious projects funded by the National Natural Science Foundation of China, the Aeronautical Science Foundation, and provincial foundations. His research integrates intelligent decision-making, mission planning, and heterogeneous multi-agent collaboration. Liang’s work bridges theory and practice, addressing challenges in robotics, aerospace, and autonomous systems.

Professional Profiles

ORCID | Scopus

Education 

Xiao Liang’s academic journey demonstrates consistent excellence and commitment to automation and control engineering. He completed his undergraduate studies in Automation at Northeastern University, where he distinguished himself by being recommended for postgraduate study without entrance examinations. He pursued a Master’s degree in Control Theory and Control Engineering, which he earned. His doctoral studies at Beihang University culminated in a Ph.D. in Navigation, Guidance, and Control, specializing in autonomous systems and flight control technologies. This educational trajectory provided Liang with a robust foundation in aircraft design, control theory, and computational modeling. His exposure to both theoretical and applied dimensions of control engineering has enabled him to integrate multidisciplinary perspectives into his research. An academic exchange visit to Swansea University further enriched his international outlook. His education laid the groundwork for his leadership in UAV/UGV cooperative systems and intelligent autonomous decision-making.

Experience 

Xiao Liang is a Professor at Shenyang Aerospace University’s School of Automation, where he supervises master’s students and directs cutting-edge research. He also serves as Associate Director of the Key Laboratory of Liaoning Province. His research leadership includes hosting major projects funded by the National Natural Science Foundation of China, the Aeronautical Science Foundation of China, and multiple provincial agencies. Liang’s projects span UAV/UGV path planning, dynamic tracking, collaborative sensing, and obstacle avoidance, with applications in rescue missions and aerospace systems. He has published widely in journals such as Intelligent Service Robotics, Robotics and Autonomous Systems, and Aerospace Science and Technology. Beyond academia, his work includes five authorized patents on UAV control systems and path planning methods, underscoring his role in translating research into technological innovations. Liang’s international experience, including research exchange at Swansea University, has further shaped his multidisciplinary approach to intelligent decision-making and multi-agent collaboration.

Research Focus 

Xiao Liang’s research lies at the intersection of control theory, artificial intelligence, and aerospace systems. His primary focus is the design and optimization of heterogeneous multi-agent systems, particularly UAV/UGV collaboration in dynamic and complex environments. His contributions span navigation, guidance, and control (GNC), pursuit-evasion strategies, multi-agent decision-making, and robust target-tracking algorithms. Liang has introduced innovative approaches such as visual SLAM in dynamic environments, semantic octree mapping, and adaptive path planning methods under uncertain conditions. His work also explores mission planning under adversarial and rescue scenarios, emphasizing robustness and fault tolerance. Liang integrates hardware-software design into UAV/UGV systems, ensuring practical applicability. His pioneering contributions extend beyond aerospace into areas such as smart shopping systems for unmanned supermarkets, reflecting the adaptability of his methods. With a balance of theoretical advancement and applied innovation, his research continues to push the boundaries of intelligent multi-agent control and autonomous robotics.

Awards and Honors 

Xiao Liang has been recognized with multiple prestigious awards for his scientific and technological contributions. He received the Science and Technology Award from the Chinese Society of Astronautics, highlighting his impact in aerospace innovation. His mentorship and leadership have also led student teams to achieve top recognition: Second Prize in the 16th “Challenge Cup” National Undergraduate Academic Science and Technology Works, and First Prize in both the 13th and 14th China Graduate Electronic Design Competitions. He was selected for the Liaoning BaiQianWan Talents Program and recognized among the High-level Talents of Shenyang in the same year. His earlier contributions earned the Progress in Science and Technology Award from both Liaoning Province and Shenyang City. Collectively, these honors reflect Liang’s sustained excellence, leadership, and innovation in aerospace engineering, intelligent systems, and multi-agent control research, positioning him as a leading scientist of his generation.

Publication Top Notes

Title: Design and Development of Full Self-Service Smart Shopping System for Unmanned Supermarket
Year: 2025

Title: STSLAM: Robust Visual SLAM in Dynamic Scenes via Image Segmentation and Instance Tracking
Year: 2025

Title: Real-Time Semantic Octree Mapping under Aerial-Ground Cooperative System
Year: 2025

Title: Collaborative Pursuit-Evasion Game of Multi-UAVs Based on Apollonius Circle in the Environment with Obstacle
Year: 2023

Title: Design and Development of Ground Station for UAV/UGV Heterogeneous Collaborative System
Year: 2021

Title: Fault-Tolerant Control for the Multi-Quadrotors Cooperative Transportation under Suspension Failures
Year: 2021

Title: Target Tactical Intention Recognition in Multiaircraft Cooperative Air Combat
Year: 2021

Title: Collaborative Pursuit-Evasion Strategy of UAV/UGV Heterogeneous System in Complex Three-Dimensional Polygonal Environment
Year: 2020

Title: Moving Target Tracking Method for Unmanned Aerial Vehicle/Unmanned Ground Vehicle Heterogeneous System Based on AprilTags
Year: 2020

Title: Moving Target Tracking of UAV/UGV Heterogeneous System Based on Quick Response Code
Year: 2019

Title: Real-Time Moving Target Tracking Algorithm of UAV/UGV Heterogeneous Collaborative System in Complex Background
Year: 2019

Title: A Geometrical Path Planning Method for Unmanned Aerial Vehicle in 2D/3D Complex Environment
Year: 2018

Conclusion

Xiao Liang demonstrates a strong combination of scholarly excellence, technological innovation, and leadership in the field of unmanned systems and intelligent robotics. His contributions to UAV/UGV heterogeneous collaboration, robust control, and path planning strategies position him as a highly competitive candidate for the Research for Outstanding Scientist Award. With further efforts toward global collaboration and broader application of his research outcomes, his profile will continue to strengthen and align well with the award’s standards of excellence.

Hongzhen Wang | HealthTech and Wearables | Women Researcher Award

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Dr. Hongzhen Wang | HealthTech and Wearables | Women Researcher Award

Associate Professor at Zhejiang A&F University, China

Wang Hongzhen is an accomplished Associate Professor and Master’s Supervisor specializing in plant biochemistry and medicinal plant research. With over two decades of academic and research experience, she has focused on advancing the authenticity, classification, and cultivation of Anoectochilus roxburghii, a highly valued medicinal orchid. Her academic journey spans Shanxi University, Guizhou University, and a Ph.D. from Linnaeus University in Sweden, equipping her with a global research perspective. Currently serving at Zhejiang A&F University, she integrates traditional plant sciences with modern biotechnological tools, including hyperspectral imaging and machine learning, to address challenges in medicinal plant authenticity and health applications. Having authored more than 30 high-impact papers, led numerous provincial and national projects, and earned awards for her contributions, Wang’s research significantly contributes to the advancement of health-related technologies and the sustainable development of medicinal plant resources.

Professional Profile

Scopus

Education

Wang Hongzhen’s education reflects a solid foundation in biological sciences and plant biochemistry. She began her academic training at the College of Life Sciences, Shanxi University, where she acquired essential knowledge of genetics and plant physiology. She then pursued postgraduate studies at the Institute of Genetic Engineering and Molecular Biology, Guizhou University, focusing on genetic regulation and biochemical pathways in plants. To advance her expertise, she completed her doctoral studies at Linnaeus University, Sweden, where she conducted extensive research in plant biochemistry, molecular biology, and the physiological mechanisms underlying medicinal plants. Her education uniquely combines traditional Chinese medicine plant studies with modern molecular tools and international scientific methodologies. This broad educational background prepared her to address critical questions in plant-based healthcare and medicinal resource development. Through this journey, she gained the capacity to integrate advanced research technologies, including hyperspectral imaging and bioinformatics, into her research on medicinal plant authentication.

Experience

Wang Hongzhen has built a rich academic and research career that bridges plant biochemistry, medicinal plant cultivation, and health-related applications. She began her professional journey as a teacher at Zhejiang Forestry College, where she contributed to developing courses in biotechnology and plant sciences. After completing her Ph.D. in Sweden, she joined Zhejiang A&F University in, where she continues to serve in the Discipline of Chinese Medicine. Over her career, she has presided over or contributed to more than 14 national and provincial projects, including studies funded by the National Natural Science Foundation of China. Her project leadership includes topics such as germplasm quality evaluation, resistance mechanisms, and cultivation innovations for Anoectochilus roxburghii. Beyond academic teaching, she has actively collaborated in advancing agricultural biotechnology and integrating medicinal plant research with modern imaging and computational analysis. Her career illustrates a continuous progression toward interdisciplinary, impactful scientific contributions in HealthTech and plant sciences.

Research Focus

Wang Hongzhen’s research focuses on the intersection of plant biochemistry, computational imaging, and medicinal resource sustainability. Her primary work centers on Anoectochilus roxburghii, a rare and valuable medicinal orchid widely used in traditional medicine. She investigates quality evaluation of germplasm resources, development of high-yield and disease-resistant varieties, and protocorm-like body formation mechanisms for scalable cultivation. Recently, she has integrated hyperspectral imaging and machine learning to achieve small-sample authenticity identification and variety classification, bridging biotechnology with cutting-edge computational methods. This research ensures authenticity, prevents adulteration, and enhances traceability of medicinal plants in healthcare applications. Additionally, she has explored molecular mechanisms such as polyamine regulation, enzyme gene function, and stress resistance in medicinal species. Her work is not only fundamental for improving the pharmacological reliability of herbal resources but also future-oriented in connecting plant sciences with HealthTech innovations, including wearable biosensing and AI-based diagnostic tools.

Publication Top Note

Title: Small-Sample Authenticity Identification and Variety Classification of Anoectochilus roxburghii (Wall.) Lindl. Using Hyperspectral Imaging and Machine Learning
Authors: Wang Hongzhen.
Summary: The study combines hyperspectral imaging with machine learning to authenticate and classify A. roxburghii from small samples, offering a fast and reliable method to prevent adulteration in medicinal plants.

Conclusion

Wang Hongzhen’s research demonstrates a rare combination of depth in plant biochemistry and breadth in applying advanced computational tools such as hyperspectral imaging and machine learning to address real-world problems in medicinal plant science. Her contributions in germplasm evaluation, cultivation, and molecular regulation of Anoectochilus roxburghii are significant, impactful, and forward-looking. With further emphasis on interdisciplinary international collaboration and AI-driven translational outputs, she is highly suitable for the Women Researcher Award.

Mahsa Fadavighaffari | Neurorehabilitation | Best Researcher Award

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Ms. Mahsa Fadavighaffari | Neurorehabilitation | Best Researcher Award

Faculty member occupational therapy Instructor at Ahvaz Jundishapur University of Medical Sciences, Iran

Mahsa Fadavighaffari is an accomplished researcher and lecturer in occupational therapy, specializing in neurorehabilitation and geriatric rehabilitation. She serves as a faculty member at the Musculoskeletal Research Center, School of Rehabilitation Sciences, Ahvaz Jundishapur University of Medical Sciences. Her career has been marked by a strong integration of teaching, clinical practice, and research, focusing on rehabilitation strategies for individuals with neurological disorders and older adults with cognitive or motor impairments. Mahsa’s academic journey began with a Bachelor of Occupational Therapy at Shiraz University of Medical Sciences, followed by a Master’s in Occupational Therapy at Iran University of Medical Sciences. Her contributions include randomized clinical trials, psychometric validation of rehabilitation tools, and cross-cultural adaptation of assessment instruments, ensuring evidence-based practice for Iranian populations. With numerous peer-reviewed publications and presentations at national and international conferences, she has established herself as a promising voice in the global rehabilitation research community.

Professional Profile

Google Scholar | ORCID

Education

Mahsa Fadavighaffari pursued her Bachelor of Science in Occupational Therapy at Shiraz University of Medical Sciences, where she graduated with distinction. Her bachelor thesis explored the effects of Cawthorne and Cooksey exercises on balance and quality of life in elderly individuals, highlighting her early commitment to geriatric care. Building on this foundation, she completed her Master of Science in Occupational Therapy at Iran University of Medical Sciences. Her master thesis focused on the translation, reliability, and validation of the Persian version of the Patient-Rated Wrist Evaluation (PRWE) for patients with distal radius and scaphoid fractures. This work demonstrated her strong expertise in psychometric evaluation and evidence-based practice. Through her education, Mahsa combined clinical research with academic rigor, producing impactful findings that remain highly relevant in rehabilitation sciences. Her educational background reflects both scientific depth and a strong focus on practical applications of occupational therapy within diverse patient populations.

Experience 

Mahsa has accumulated extensive academic and clinical experience in occupational therapy and rehabilitation sciences. She has served as a faculty member and lecturer at Ahvaz Jundishapur University of Medical Sciences, teaching courses on neurorehabilitation, occupational therapy for neurological disorders, and geriatric care. She has supervised student internships in stroke rehabilitation centers and hospital-based clinical settings. Previously, she held a lecturer position at Shiraz University of Medical Sciences, where she guided internships in multiple sclerosis and spinal cord injury rehabilitation. Her clinical experience includes working at Tavanafza Private Clinic, Shahid Rajaei Hospital, and Noorafshar Hospital, where she treated patients with neurological disorders. She also contributed to geriatric rehabilitation at Shiraz School of Rehabilitation Sciences. Across academic and clinical settings, Mahsa has consistently integrated teaching, research, and therapy, fostering evidence-based interventions while mentoring students. Her dual role as educator and practitioner highlights her dedication to advancing occupational therapy practice.

Research Focus 

Mahsa Fadavighaffari’s research is centered on neurorehabilitation, psychometrics, and geriatric care. She focuses on developing and validating culturally adapted rehabilitation assessment tools, ensuring reliability and applicability in clinical practice. Her notable works include the Persian adaptation of the Patient-Rated Wrist Evaluation (PRWE) and the psychometric validation of the Falls Efficacy Scale for elderly populations in nursing homes. She has also contributed to clinical trials, such as evaluating Cawthorne and Cooksey exercises for balance and quality of life among older adults. More recently, her research extends to innovative therapies such as action observation training for multiple sclerosis patients, reflecting her forward-looking approach to neurological rehabilitation. Mahsa’s interdisciplinary scope includes musculoskeletal rehabilitation, behavioral assessment, and geriatric fall prevention, aligning clinical practice with research-driven solutions. Her focus on bridging evidence-based research with direct patient outcomes positions her as a promising leader in neurorehabilitation research and clinical translation.

Awards and Honors 

Mahsa has received several honors that underscore her academic excellence and professional contributions. At Shiraz University of Medical Sciences, she won multiple first-place awards at the “Celebration of Knowledge and Research of Distinguished Students” and the Shiraz University Alumni Association Festival. During her master’s program at Iran University of Medical Sciences, she ranked among the top students in occupational therapy. She earned recognition at the Shahid Motahari Festival for her innovative teamwork project in clinical education. She was also awarded a provincial prize for successfully implementing life skills education in schools. Beyond individual accolades, she has actively contributed to academic committees, including the executive and scientific committees of the National and International Clinical Movement Sciences Congresses. These recognitions reflect her blend of research impact, teaching excellence, and community engagement. Her awards highlight her growing influence in rehabilitation sciences and her capacity to translate research into meaningful clinical and educational practices.

Publication Top Notes

Title: Effect of Cawthorne and Cooksey Exercises on Balance and Quality of Life of 60 to 80 Year-Old Individuals in Shiraz: A Randomized Clinical Trial
Authors: AF Abarghuei, M Fadavi-Ghaffari, S Tousi, M Amini, AR Salehi
Journal: Medical Journal of the Islamic Republic of Iran.
Summary: This trial showed that Cawthorne and Cooksey exercises significantly improved balance and quality of life in elderly participants, offering a cost-effective fall-prevention method.

Title: Falls Efficacy Scale and Single Item Question: Screening Accuracy for Older Adults Residing in Nursing Homes
Authors: M Meimandi, M Fadavi-Ghaffari, G Taghizadeh, A Azad, L Lajevardi
Journal: Clinical Gerontologist.
Summary: The study validated simple tools for assessing fall risk in nursing home residents, proving effective for large-scale geriatric screening.

Title: Translation, Cultural Adaptation, Face and Content Validity of the Persian Version “Patient-Rated Wrist Evaluation” (PRWE-Persian) Questionnaire
Authors: M Fadavi-Ghaffari, A Azad, H Shariatzadeh, G Taghizadeh, S Aminizadeh
Journal: Journal of Modern Rehabilitation.
Summary: This research adapted and validated the PRWE into Persian, ensuring reliability for evaluating pain and disability in wrist injuries.

Title: The Psychometric Properties of Falls Efficacy Scale in the Elderly Iranian Residents of Nursing Homes
Authors: M Fadavi-Ghaffari, A Azad, M Meimandi, Z Arani-Kashani, H Ghorbanpoor
Journal: Iranian Rehabilitation Journal.
Summary: Confirmed the validity and reliability of the Falls Efficacy Scale in elderly Iranian nursing home residents, enhancing clinical use.

Title: The Psychometric Features of the Patient-Rated Wrist Evaluation in Iranians With Scaphoid or Distal Radius Fracture
Authors: M Fadavi-Ghaffari, A Azad, G Taghizadeh, N Nakhostin-Ansari, H Shariatzadeh, S Aminizadeh
Journal: Iranian Rehabilitation Journal.
Summary: Evaluated PRWE’s psychometric properties in fracture patients, confirming its reliability across musculoskeletal injuries.

Title: Comparison of Students’ Performance and Attitude Between Collaboration Testing and Written Examination of Ethics Course
Authors: R Mofateh, N Orakifar, N NooriMombeini, M Fadavighaffari
Journal: Future of Medical Education Journal.
Summary: Compared collaborative testing with traditional exams, finding that teamwork assessments improved engagement and learning.

Title: Effect of Zoledronic Acid and Vitamin E on Surgical–Induced Femoral Head Osteonecrosis in Rabbit
Authors: K Gharanizadeh, S Aminizadeh, AX Amir Darbandi, S Nadjafi, M Fadavighaffari, et al.
Journal: Archives of Bone and Joint Surgery.
Summary: Animal model study showing that combined therapy reduced osteonecrosis risk, suggesting potential for clinical application.

Conclusion

Mahsa Fadavighaffari’s research contributions, particularly in psychometric validation, geriatric rehabilitation, and evidence-based occupational therapy interventions, establish her as a promising and impactful researcher in rehabilitation sciences. Her blend of clinical, academic, and research expertise supports her suitability for recognition under the Best Researcher Award. With continued expansion of her international research visibility and adoption of emerging rehabilitation technologies, she is well-positioned to make even greater contributions to the field.

Peter Passias | Adult Spinal Deformity | Best Researcher Award

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Prof. Peter Passias | Adult Spinal Deformity | Best Researcher Award

Professor at Duke University, United States

Dr. Peter G. Passias is a leading academic spine surgeon and clinical researcher specializing in adult spinal deformity and cervical spine disorders. Based at NYU Langone Orthopedic Hospital, he has developed an international reputation for advancing surgical strategies that improve patient outcomes while reducing risks and healthcare costs. His career is marked by over 400 peer-reviewed publications, extensive participation in multicenter collaborative studies, and pioneering efforts to incorporate artificial intelligence and algorithmic frameworks into surgical planning. He serves on the editorial boards of top-tier journals including The Spine Journal and Journal of Neurosurgery: Spine, shaping the direction of global spine research. Beyond his clinical and research impact, Dr. Passias has contributed significantly to training the next generation of spine surgeons and researchers worldwide. His combination of leadership, innovation, and translational research places him at the forefront of adult spinal deformity care and scientific advancement.

Professional Profile

ORCID | Scopus

Education

Dr. Passias’ educational background reflects a rigorous combination of clinical training and academic development that underpins his research excellence. He completed his medical education and advanced residency in orthopedic surgery at Tufts University School of Medicine, followed by fellowship training in complex spinal surgery at the Massachusetts General Hospital/Harvard Medical School program. His education was augmented by continued scholarly training in biomechanics, surgical innovation, and translational science, equipping him with the expertise to approach spinal deformity from multiple perspectives. Over time, he has further enhanced his academic profile with advanced research engagements, including NIH-supported projects that integrate computational models, biomechanics, and biologics into spinal reconstruction. This combination of strong surgical foundation, research acumen, and exposure to multidisciplinary collaboration during his formative years laid the groundwork for his leadership role at NYU Langone and his international recognition as one of the foremost researchers in adult spinal deformity surgery.

Experience 

Dr. Passias currently serves as Professor of Orthopedic Surgery at NYU Langone Orthopedic Hospital. His professional experience includes leading complex adult spinal deformity surgeries, overseeing NIH- and industry-funded clinical trials, and mentoring research fellows and residents. Previously, he trained and practiced at premier institutions including Tufts Medical Center and Harvard-affiliated hospitals, gaining expertise in complex spine care. He has also held visiting professorships and delivered invited lectures internationally, reinforcing his global reputation. Beyond direct clinical practice, he has spearheaded multicenter research collaborations across North America and Europe, tackling challenges such as revision risk, perioperative complications, alignment optimization, and cost-effectiveness. Dr. Passias has combined academic leadership with clinical innovation, driving advancements in surgical planning, biologic therapies, and AI integration. His career reflects a balanced contribution across patient care, translational science, academic publishing, and mentorship, making him a leading authority in adult spinal deformity research.

Research Focus 

Dr. Passias’ research focuses on advancing surgical strategies and improving patient outcomes in adult spinal deformity and cervical deformity surgery. His work integrates biomechanics, artificial intelligence, predictive analytics, and biologics to address key challenges such as surgical alignment, cost-effectiveness, and revision risk. A core aspect of his research involves quantifying spinal parameters to develop patient-specific surgical planning models, including AI-driven decision support systems. He has pioneered multicenter studies evaluating outcomes, complications, and realignment goals across diverse populations, providing evidence that directly informs surgical guidelines. Recently, he has explored the role of biologics and regenerative strategies in enhancing fusion and recovery. His work also emphasizes healthcare value, using cost-utility frameworks to ensure that surgical innovations translate to sustainable clinical practice. Overall, his research trajectory is both innovative and translational, bridging the gap between advanced science and practical solutions that improve long-term outcomes for patients with complex spinal deformities.

Awards and Honors 

Dr. Passias’ distinguished career has been recognized through numerous national and international awards. He has received multiple nominations for the prestigious Whitecloud Award, honoring excellence in spinal deformity research, and the Hibbs Award from the Scoliosis Research Society for his innovative contributions. The North American Spine Society (NASS) has acknowledged his work with Value Awards for studies linking surgical strategies with improved cost-effectiveness. His role as principal investigator on NIH-funded research further underscores his standing as a leading academic surgeon-scientist. Beyond research accolades, he has been invited as a keynote speaker and visiting professor at international conferences, reflecting global recognition of his expertise. His editorial leadership on journals such as The Spine Journal and Artificial Intelligence Surgery highlights his influence in shaping the future of spine research. Collectively, these awards and honors demonstrate not only his personal achievement but also his enduring impact on advancing the field of adult spinal deformity.

Publication Top Notes

Title: Implementation of Artificial Intelligence (AI) in ASD Treatment
Journal: North American Spine Society Journal (NASSJ).
Contributors: Kyriakos D. Chatzis; Peter Tretiakov; Peter G. Passias
Summary: Showcases AI-driven predictive tools in adult spinal deformity treatment, supporting better alignment planning and lowering risks of complications.

Title: Proximal Junctional Kyphosis and Failure Prophylaxis Improves Cost Efficacy, While Maintaining Optimal Alignment, in Adult Spinal Deformity Surgery
Journal: Neurosurgery.
Contributors: Peter G. Passias; Oscar Krol; Tyler K. Williamson; Claudia Bennett-Caso; Justin S. Smith; Bassel Diebo; Virginie Lafage; Renaud Lafage; Breton Line; Alan H. Daniels et al.
Summary: Demonstrates that targeted prophylaxis reduces failure rates in adult spinal deformity surgery, achieving cost savings while preserving spinal alignment.

Title: Are we Getting Better at Achieving Optimal Lumbar Segmental Sagittal Alignment in Complex Adult Spine Deformity Surgery?
Journal: Spine.
Contributors: Peter G. Passias; Oluwatobi O. Onafowokan; Renaud Lafage; Justin Smith; Kojo D. Hamilton; Andrew J. Schoenfeld; Anthony Yung; Max R. Fisher; Bassel Diebo; Alan H. Daniels et al.
Summary: Evaluates progress in lumbar segmental alignment, highlighting improved surgical precision but ongoing challenges in complex cases.

Title: Cause and Effect of Revisions in Adult Spinal Deformity Surgery: A Multicenter Study on Outcomes Based on Etiology
Journal: The Spine Journal.
Contributors: Peter G. Passias; Pooja Dave; Justin S. Smith; Renaud Lafage; Oluwatobi O. Onafowokan; Peter Tretiakov; Jamshaid Mir; Breton Line; Bassel Diebo; Alan H. Daniels et al.
Summary: Multicenter study identifies underlying causes of revision surgeries and links them to outcome variability, guiding prevention strategies.

Title: Spinal Idiopathic Hypertrophic Pachymeningitis: A Systematic Review of Diagnostic Features, Clinical Management, and Surgical Outcomes
Journal: World Neurosurgery.
Contributors: Kishore Balasubramanian; Abdurrahman F. Kharbat; Francisco Call-Orellana; Sruthi Ranganathan; Romulo A. A. de Almeida; Kiran Sankarappan; Nitin Agrawal; Steven Hwang; Peter Passias; Angela Downes et al.
Summary: Reviews clinical and surgical management of this rare spinal condition, standardizing diagnostic markers and outcomes.

Title: Optimizing TLIF Approach Selection: An Algorithmic Framework with Illustrative Cases
Journal: Journal of Clinical Medicine.
Contributors: Alyssa M. Bartlett; Summer Shabana; Caroline C. Folz; Mounica Paturu; Christopher I. Shaffrey; Parastou Quist; Olumide Danisa; Khoi D. Than; Peter Passias; Muhammad M. Abd-El-Barr
Summary: Proposes an algorithmic framework for TLIF approach selection, improving decision-making in patient-specific spine surgery.

Title: Quantifying the Importance of Upper Cervical Extension Reserve in Adult Cervical Deformity Surgery and Its Impact on Baseline Presentation and Outcomes
Journal: Neurosurgery.
Contributors: Peter G. Passias; Jamshaid M. Mir; Andrew J. Schoenfeld; Anthony Yung; Justin S. Smith; Virginie Lafage; Renaud Lafage; Bassel Diebo; Alan H. Daniels; Breton G. Line et al.
Summary: Highlights the predictive value of cervical extension reserve for surgical outcomes, informing preoperative assessment.

Title: Have We Made Advancements in Optimizing Surgical Outcomes and Enhancing Recovery for Patients With High-Risk Adult Spinal Deformity Over Time?
Journal: Operative Neurosurgery.
Contributors: Peter G. Passias; Lara Passfall; Peter S. Tretiakov; Ankita Das; Oluwatobi O. Onafowokan; Justin S. Smith; Virginie Lafage; Renaud Lafage; Breton Line; Jeffrey Gum et al.
Summary: Longitudinal study showing improved recovery trends in high-risk deformity patients through refined surgical protocols.

Conclusion

Dr. Peter G. Passias is a highly suitable candidate for the Best Researcher Award. His exceptional research record, leadership in both clinical and academic settings, and pioneering integration of biomechanics, biologics, and AI into spinal deformity surgery underscore his position at the forefront of the field. Continued emphasis on investigator-driven trials, interdisciplinary applications, and policy engagement could further amplify his impact. Nonetheless, his sustained excellence and global recognition make him a strong and deserving nominee for this award.

Ruth Cristina Martín Sanz | Technology Scientists Innovations | Best Researcher Award

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Assist. Prof. Dr. Ruth Cristina Martín Sanz | Technology Scientists Innovations | Best Researcher Award

Assistant Professor at Universidad de Valladolid, Spain.

Ruth Cristina Martín Sanz is an Assistant Professor at the University of Valladolid, Spain, specializing in forest ecology, soil science, and climate resilience. She earned her Ph.D. in Conservation and Sustainable Use of Forest Systems (2018, Cum Laude, International Mention), advancing research on soil fertility and sustainable forest management. Over the past decade, she has built a reputation as a dynamic scholar, combining rigorous research with strong outreach activities. Her work focuses on adaptive traits in Mediterranean pines, forest-soil interactions, and fire ecology, positioning her at the intersection of climate change adaptation and ecosystem resilience. She has published extensively in Q1/D1 international journals, contributed to European and national projects, and received recognition for notable papers such as her award-winning publication in Forests. Beyond academia, she is deeply engaged in public science communication, mentoring, and editorial roles, making her a versatile and influential figure in her field.

Professional Profile

ORCID | Google Scholar

Education 

Ruth Cristina Martín Sanz obtained her Ph.D. in Conservation and Sustainable Use of Forest Systems from the University of Valladolid, graduating Cum Laude with International Mention. Her doctoral research integrated soil-forest interactions, adaptive forest genetics, and sustainable resource management, bridging ecology and applied forestry. Prior to her doctorate, she completed a master’s program recognized for academic excellence, focusing on forest productivity and ecological sustainability. During her studies, she undertook multiple international research stays, gaining experience at leading global institutions such as Charles Darwin University in Australia, the University of Georgia (USA), and the UK Centre for Ecology and Hydrology. These experiences enriched her methodological approaches, ranging from field ecology to advanced spectroscopy. She has also undertaken postdoctoral training through European and Spanish-funded research programs, ensuring continuity between theoretical ecology, applied soil sciences, and adaptive management of Mediterranean forest ecosystems.

Experience 

Ruth Cristina Martín Sanz currently serves as an Assistant Professor at the University of Valladolid. She has participated in national and European research projects addressing forest genetics, soil fertility, and the resilience of Mediterranean ecosystems to climate change. Notably, she has contributed to projects funded by the Spanish Ministry of Science, European Union, and regional excellence programs. She has worked in roles ranging from project researcher to project manager, contributing both scientific expertise and organizational leadership. Beyond her research, she has coordinated outreach initiatives such as Science in Action and Ciencia en el 109, merging academic science with community engagement. She has also served as Chief Editor of the Cuadernos de la Sociedad Española de Ciencias Forestales. Her experience blends academic rigor, applied project development, and science dissemination, ensuring wide-reaching impact across research, education, and public engagement.

Research Focus

Ruth Cristina Martín Sanz’s research focuses on forest science, evolutionary ecology, and soil-forest interactions in Mediterranean ecosystems. Her core work explores adaptive traits in pines, including serotiny, bark allocation, and fire-adaptive strategies, contributing to the evolutionary ecology of resilience under climate stress. She also investigates soil phosphorus dynamics, ecosystem services, and nutrient cycles, employing advanced analytical tools like ATR-FTIR spectroscopy and 31P-NMR. Her integrated approach connects above-ground tree traits with below-ground soil processes, offering holistic insights into forest productivity and sustainability. She emphasizes the trade-offs and trait integration in forest phenotypes, contributing to international discussions on sustainable use of genetic resources. Her work aligns with global challenges in climate change adaptation, biodiversity preservation, and sustainable forestry. By bridging genetics, ecology, and soil science, her research provides practical frameworks for forest management, conservation, and restoration, ensuring both scientific advancement and applied solutions.

Publication Top Notes

Title: Early dynamics of natural revegetation on roadcuts of the Salamanca province 
Authors: R.C. Martín-Sanz, B. Fernández-Santos, C. Martínez-Ruiz
Journal: Ecological Engineering.
Citations: 22
Summary: Analyzes vegetation recovery on roadcuts, showing soil–plant interactions drive early succession and providing restoration guidelines.

Title: Disentangling plasticity of serotiny, a key adaptive trait in a Mediterranean conifer
Authors: R.C. Martín-Sanz, L. Santos-del-Blanco, E. Notivol, M.R. Chambel, J. Climent
Journal: American Journal of Botany.
Citations: 33
Summary: Explores how plasticity shapes serotiny in Mediterranean pines, linking fire adaptation to environmental variability.

Title: Maintenance costs of serotiny in a variably serotinous pine: the role of water supply
Authors: R.C. Martín-Sanz, M. Callejas-Díaz, J. Tonnabel, J.M. Climent
Journal: PLoS ONE.
Citations: 23
Summary: Shows serotiny incurs water-related maintenance costs, highlighting adaptive trade-offs under drought conditions.

Title: How Does Environment Affect the Allocation to Bark in a Mediterranean Conifer?
Authors: R.C. Martín-Sanz, R. San-Martín, H. Poorter, A. Vázquez de la Cueva, J. Climent
Journal: Frontiers in Plant Science.
Citations: 19
Summary: Examines how environmental factors shape bark allocation, emphasizing its role in fire resistance and growth balance.

Title: Trade-offs and trait integration in tree phenotypes: consequences for the sustainable use of genetic resources
Authors: J. Climent, R. Alía, K. Karkkainen, C. Bastien, M. Benito-Garzon, L. Bouffier, R.C. Martín-Sanz, et al.
Journal: Current Forestry Reports.
Citations: 17
Summary: Discusses trait trade-offs and integration in trees, offering insights into sustainable forestry and genetic resource management.

Title: Influence of soil properties on P pools and its effect on forest productivity in Mediterranean calcareous soils
Authors: R.C. Martín-Sanz, V. Pando, T. Bueis, M.B. Turrión
Journal: Forests.
Citations: 8
Summary: Investigates phosphorus pools in Mediterranean soils, linking soil fertility with forest productivity and sustainability.

Title: Evolutionary ecology of fire-adaptive traits in a Mediterranean pine species
Authors: R.C. Martín-Sanz
Journal: Conference Contribution.
Citations: 2
Summary: Explores fire-adaptive traits in Mediterranean pines, emphasizing evolutionary drivers of serotiny and resilience.

Title: Characterization of soil phosphorus in different land use over calcareous soils by chemical extraction methods and 31P-NMR spectroscopy
Authors: R.C. Martín-Sanz, F. Lafuente, M.B. Turrión
Journal: Revista de Ciências Agrárias.
Citations: 1
Summary: Provides soil phosphorus characterization across land uses, advancing analytical methods for nutrient management.

Conclusion

Ruth Cristina Martín Sanz is a highly promising and impactful researcher whose work advances both scientific understanding and practical solutions in forest ecology, adaptive traits, and soil-forest interactions. Her balance of high-quality publications, research innovation, and commitment to science communication makes her a strong candidate for the Best Researcher Award. With further growth in citation impact, broader project leadership, and international recognition, she is poised to become a leading figure in sustainable forestry research and climate resilience.

Sima Rezvantalab | Neuroscience | Best Researcher Award

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Dr. Sima Rezvantalab | Neuroscience | Best Researcher Award

Assistant professor at Urmia University of Technology, Iran.

Dr. Sima Rezvantalab is a faculty member at Urmia University of Technology, Iran, with a strong interdisciplinary background in chemical engineering, nanomedicine, and computational neuroscience. Her academic journey began with a Master’s degree in Polymer Engineering at Sharif University of Technology, followed by a Ph.D. at Amirkabir University of Technology in collaboration with RWTH Aachen, Germany, where she focused on riboflavin-functionalized nanocarriers for drug delivery. Over the years, she has advanced research on PLGA nanoparticles, microfluidics, AI-driven nanomedicine, and novel biomaterials. She has authored influential publications, including the widely cited study on PLGA nanoparticles in cancer therapy, and has expanded her research to areas such as neurodegenerative disease modeling, β-amyloid targeting, and nanoscaffold design for tissue regeneration. Her work is notable for blending molecular simulations, machine learning, and experimental validation. As an emerging leader in neuroscience-related nanotechnology, she continues to pioneer innovations that bridge chemistry, biology, and computational science.

Professional Profile

Google Scholar | Scopus

Education 

Dr. Rezvantalab’s educational journey reflects depth in both engineering fundamentals and biomedical applications. She earned her Ph.D. in Chemical Engineering from Amirkabir University of Technology, Tehran, with a joint research collaboration at RWTH Aachen University, Germany, under the supervision of Prof. Fabian Kiessling and Prof. Mostafa Keshavarz Moraveji. Her dissertation explored microfluidic synthesis of riboflavin-functionalized nanocarriers, integrating targeted drug delivery and advanced characterization. Prior to her doctorate, she completed her Master of Science in Polymer Engineering at Sharif University of Technology, focusing on polypropylene production with broad molecular weight distribution, supervised by Prof. Ahmad Ramezani. Her strong foundation began with undergraduate studies in Chemical Engineering at the University of Mohaghegh Ardabili, where she consistently ranked among the top students. This rigorous educational background equipped her with a strong understanding of polymer chemistry, nanotechnology, and bioengineering, setting the stage for her impactful contributions to neuroscience, nanomedicine, and translational research.

Experience 

Dr. Rezvantalab has served as a faculty member at Urmia University of Technology, where she teaches advanced courses including Physical Chemistry of Polymers, Transport Phenomena in Polymer Systems, Advanced Thermodynamics, and Computational Methods in Chemical Engineering. Her academic career is marked by active involvement in both research and mentoring, contributing to the growth of young scientists in nanomedicine and computational neuroscience. Beyond teaching, she has collaborated internationally, publishing extensively with researchers from Germany, Finland, and other global institutions. Her expertise spans experimental nanocarrier design, AI-based predictive modeling, and molecular dynamics simulations. She has also presented her work at numerous international conferences, ranging from membrane science to environmental engineering, reflecting her interdisciplinary scope. Through her leadership and cross-disciplinary collaborations, Dr. Rezvantalab has advanced research at the intersection of polymer engineering, nanotechnology, and brain health applications, positioning herself as a driving force in the neuroscience research community.

Research Focus

Dr. Rezvantalab’s research is centered on nanomedicine for neurological and systemic diseases, with a strong emphasis on polymeric nanoparticles, targeted drug delivery, and AI-driven biomaterials design. Her work integrates computational molecular dynamics, machine learning, and experimental nanotechnology to develop intelligent drug delivery systems. A significant part of her contributions includes engineering PLGA-based nanoparticles for cancer and neurodegenerative disorders, investigating β-amyloid targeting frameworks for Alzheimer’s disease, and creating 3D-printed nanoscaffolds for tissue regeneration. She also explores microfluidics-based nanoparticle synthesis for precision medicine and artificial intelligence for predicting nanoparticle behaviors. Another research line includes environmental and biomedical membranes, applying nanomaterials to water purification and ion-selective separations. This multidisciplinary approach allows her to bridge neuroscience, computational biology, and material sciences, making her work highly relevant for translational medicine. Her ability to combine drug delivery platforms with neurobiological applications positions her as a leader in next-generation therapeutics at the nano-bio interface.

Awards and Honors 

Dr. Rezvantalab’s academic excellence has been consistently recognized through multiple honors. She was ranked first among Ph.D. students in Chemical Engineering at Amirkabir University of Technology and second in her B.Sc. class at the University of Mohaghegh Ardabili. She also achieved national distinction by ranking within the top 30 and 35 among more than 1000 participants in competitive entrance exams for graduate studies in Chemical Engineering and Biotechnology. Beyond academic rankings, her publications have been highlighted in top journals, including cover features in ACS Biomaterials Science & Engineering and ACS Omega, reflecting international recognition of her innovative contributions. She has delivered invited talks and presentations at prestigious conferences, advancing discussions on nanomedicine, environmental engineering, and neurotherapeutics. These honors underscore her strong academic foundation, research leadership, and growing influence in the global scientific community. Her achievements position her as a deserving recipient of awards that celebrate excellence in neuroscience-related innovation.

Publication Top Notes

Title: PLGA-based nanoparticles in cancer treatment
Authors: S. Rezvantalab, N.I. Drude, M.K. Moraveji, N. Güvener, E.K. Koons, Y. Shi, T. Lammers, F. Kiessling
Journal: Frontiers in Pharmacology.
Citations: 599
Summary: This highly cited review discusses PLGA nanoparticles as versatile, biocompatible carriers for cancer therapy. It highlights synthesis strategies, ligand targeting, and clinical translation, making it a cornerstone in nanomedicine.

Title: Microfluidic assisted synthesis of PLGA drug delivery systems
Authors: S. Rezvantalab, M.K. Moraveji
Journal: RSC Advances.
Citations: 151
Summary: This paper introduces microfluidics for controlled PLGA nanoparticle synthesis. It shows how micro-scale flow systems improve uniformity, reproducibility, and scalability in drug delivery.

Title: A molecular investigation of urea and creatinine removal in the wearable dialysis device using Two-Dimensional materials
Authors: R. Maleki, A.M. Jahromi, S. Mohaghegh, S. Rezvantalab, M. Khedri, L. Tayebi
Journal: Applied Surface Science.
Citations: 42
Summary: Using molecular dynamics, this work shows how 2D nanomaterials can adsorb toxins like urea and creatinine, paving the way for wearable dialysis technologies.

Title: 3D printing of complicated GelMA-coated Alginate/Tri-calcium silicate scaffold for accelerated bone regeneration
Authors: N. Beheshtizadeh, A. Farzin, S. Rezvantalab, Z. Pazhouhnia, et al.
Journal: International Journal of Biological Macromolecules.
Citations: 39
Summary: This study develops a hybrid GelMA-alginate/Tri-calcium silicate scaffold via 3D printing. The construct improves osteogenic activity and bone repair, with strong applications in regenerative medicine.

Title: Artificial intelligence deep exploration of influential parameters on physicochemical properties of curcumin‐loaded electrospun nanofibers
Authors: M. Khedri, N. Beheshtizadeh, M. Rostami, A. Sufali, S. Rezvantalab, M. Dahri, et al.
Journal: Advanced NanoBiomed Research.
Citations: 30
Summary: This work applies machine learning to optimize electrospun curcumin-loaded nanofibers. The approach identifies key parameters for drug release and mechanical performance.

Title: Engineering of 2D nanomaterials to trap and kill SARS-CoV-2: a new insight from multi-microsecond atomistic simulations
Authors: M. Khedri, R. Maleki, M. Dahri, M.M. Sadeghi, S. Rezvantalab, H.A. Santos, et al.
Journal: Drug Delivery and Translational Research.
Citations: 29
Summary: The paper uses simulations to show how 2D nanomaterials destabilize viral proteins, offering strategies for antiviral coatings and filters against COVID-19.

Title: Investigation of recent changes in Urmia salt lake
Authors: S. Rezvantalab, M.H. Amrollahi
Journal: International Journal of Chemical and Environmental Engineering.
Citations: 27
Summary: This environmental study analyzes the chemical and hydrological decline of Urmia Salt Lake, stressing sustainable water management.

Title: An insight into the role of riboflavin ligand in the self-assembly of PLGA nanoparticles – a molecular simulation and experimental approach
Authors: S. Rezvantalab, M.K. Moraveji, M. Khedri, R. Maleki
Journal: Soft Matter.
Citations: 24
Summary: This paper shows how riboflavin ligands influence PLGA nanoparticle assembly, enhancing stability and drug targeting. It combines simulations with experimental validation.

Conclusion

The candidate demonstrates outstanding scholarly impact, innovation, and interdisciplinary strength, making them highly suitable for the Best Researcher Award. Their contributions in polymeric nanoparticles, computational modeling, and AI-driven nanomedicine are of global relevance, addressing critical challenges in cancer therapy, regenerative medicine, and advanced materials. With further focus and translational emphasis, their research trajectory is poised to achieve even greater influence in both academic and applied domains.

Amin Najafi | Robotics and Automation | Best Researcher Award

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Mr. Amin Najafi | Robotics and Automation | Best Researcher Award

PhD candidate at University of Zanjan, Iran.

Amin Najafi is a researcher specializing in advanced fault-tolerant control, robotics, and intelligent transportation systems. His expertise lies in designing resilient control algorithms for UAVs, MAGLEV trains, and autonomous guidance systems. Through a strong portfolio of high-quality publications, Najafi has contributed significantly to enhancing the stability, safety, and performance of robotic systems operating under uncertain and fault-prone conditions. His work in adaptive barrier sliding mode control and finite-time stabilization has been widely recognized for bridging theoretical advancements with practical applications. Najafi’s research has appeared in leading journals, including IEEE Transactions on Transportation Electrification, Mathematics, ISA Transactions, and the Journal of Vibration and Control. Beyond research, he actively contributes to the scientific community through peer-review engagements across prestigious journals. His growing influence demonstrates his commitment to advancing robust, intelligent, and reliable autonomous systems, making him a promising candidate for recognition in robotics and automation research.

Professional Profile

Google Scholar | Scopus | ORCID

Education

Amin Najafi’s academic training has been grounded in control engineering, robotics, and automation. His education equipped him with advanced knowledge in nonlinear control, adaptive systems, and fault-tolerant design, laying a strong foundation for tackling complex challenges in autonomous platforms. Building on this foundation, Najafi engaged deeply with theories of stability, guidance, and fault diagnosis while also exploring practical aspects of UAVs and intelligent transportation. His progression through academic programs allowed him to develop both analytical rigor and applied research capabilities. The interdisciplinary nature of his training helped him connect mathematics, control theory, and engineering applications, which is reflected in his publications that combine theoretical robustness with engineering relevance. Najafi’s educational journey reflects a balance of theory and practice, giving him the ability to produce impactful work that speaks to both the academic community and the broader engineering industry in robotics and automation.

Experience

Amin Najafi has developed his career around solving critical problems in robotics, automation, and transportation electrification. His research experience includes designing innovative fault-tolerant controllers for quadrotor UAVs, advancing resilient strategies for MAGLEV train systems, and contributing to aerospace and defense-related guidance systems. His international collaborations with researchers such as S. Mobayen, A. Fekih, and L. Fridman demonstrate his ability to work within diverse, high-caliber teams. Najafi has also built strong credentials as a peer reviewer, having reviewed more than 60 manuscripts for prestigious journals including IEEE Transactions on Transportation Electrification, IEEE Access, and the Asian Journal of Control. This dual role as an author and reviewer highlights both his subject matter expertise and his standing in the global robotics and control community. Through his experience, he has consistently contributed to advancing autonomous and fault-resilient systems, ensuring his research holds both academic and applied significance.

Research Focus

Najafi’s research is anchored in fault-tolerant control, nonlinear dynamics, and resilient robotics. His primary focus lies in developing adaptive barrier sliding mode controllers, finite-time stabilization strategies, and robust diagnosis methods for actuator faults. UAVs represent a central application in his portfolio, where he has addressed actuator reliability, real-time guidance, and performance optimization under uncertain conditions. Beyond UAVs, he has extended his contributions to MAGLEV trains and interceptor-target systems, demonstrating the versatility of his control strategies. His work is characterized by integrating theoretical rigor, such as linear matrix inequality approaches, with real-world engineering challenges, making his contributions impactful across multiple domains. The broader vision of his research is to enable safe, intelligent, and adaptive robotic systems capable of operating in dynamic and fault-prone environments. By combining control theory with automation and robotics, Najafi continues to advance the frontiers of resilient and intelligent autonomous technologies.

Publication Top Notes

Title: Adaptive Barrier Fast Terminal Sliding Mode Actuator Fault-Tolerant Control Approach for Quadrotor UAVs
Authors: A. Najafi, M.T. Vu, S. Mobayen, J.H. Asad, A. Fekih
Journal: Mathematics.
Citations: 51
Summary: Proposes an adaptive barrier fast terminal sliding mode controller for quadrotor UAVs. Ensures finite-time stability, fault tolerance, and resilience against actuator faults with validated simulations.

Title: Design of Linear Matrix Inequality-Based Adaptive Barrier Global Sliding Mode Fault-Tolerant Control for Uncertain Systems with Faulty Actuators
Authors: K. Naseri, M.T. Vu, S. Mobayen, A. Najafi, A. Fekih
Journal: Mathematics.
Citations: 22
Summary: Introduces an LMI-based adaptive barrier global sliding mode controller. Provides robust stability and effective fault management in uncertain nonlinear systems.

Title: Robust Adaptive Fault-Tolerant Control for MAGLEV Train Systems: A Non-Singular Finite-Time Approach
Authors: A. Najafi, S. Mobayen, S.H. Rouhani, Z. Mokhtare, A. Jalilvand, L. Fridman, et al.
Journal: IEEE Transactions on Transportation Electrification.
Citations: 3
Summary: Develops a finite-time robust adaptive controller for MAGLEV trains. Enhances fault tolerance, passenger safety, and system robustness under disturbances.

Title: Multiple Actuator Fault Diagnosis Based on Parity Space for Quadrotor System
Authors: A. Najafi, D. Bustan
Journal: Journal of Aeronautical Engineering (JOAE).
Citations: 2
Summary: Presents a parity-space-based approach to detect and isolate multiple actuator faults in quadrotors, ensuring reliable UAV performance.

Title: Design of Adaptive Barrier Function-Based Backstepping Finite-Time Guidance Control for Interceptor-Target Systems
Authors: Z. Mokhtare, M.A. Sepestanki, S. Mobayen, A. Najafi, W. Assawinchaichote, et al.
Journal: Journal of Vibration and Control.
Citations: –
Summary: Proposes a backstepping control method with adaptive barrier functions for interceptor-target systems. Guarantees finite-time convergence and robust guidance under uncertainties.

Conclusion

Amin Najafi demonstrates strong potential and achievement in fault-tolerant control systems for UAVs and transportation applications, with impactful publications, innovative methodologies, and active engagement in peer review. While there is scope for growth in terms of citation impact and broader collaborations, his research contributions are highly relevant to the advancement of resilient and intelligent autonomous systems. He can be considered a suitable and promising candidate for the Best Researcher Award, particularly within the subject category of Control Systems, UAVs, and Intelligent Transportation.