Prof. Noureddine Hamdi | Material Processing Techniques | Excellence in Research Award

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Prof. Noureddine Hamdi | Material Processing Techniques | Excellence in Research Award

National Center of Research in Materials Science (CNRSM) | Tunisia

Prof. Noureddine Hamdi is a distinguished full professor in Materials Sciences and Environment, widely recognized for his expertise in material processing techniques and their applications in environmental and industrial systems. He is affiliated with the Higher Institute of Water Sciences and Techniques of Gabes (ISSTEG), University of Gabes, and has also been associated with the National Center of Research in Materials Science (CNRSM), Tunisia. Prof. Hamdi’s academic foundation spans materials engineering, surface and materials science, and advanced research training, enabling him to develop a strong interdisciplinary profile that bridges materials processing, ceramics, composites, and sustainable construction materials. His research focuses on the development and optimization of clay-based materials, ceramic membranes, geopolymers, and composite systems for water and wastewater treatment, pollution control, circular economy solutions, and eco-efficient construction applications. He has played a pivotal role in translating fundamental materials processing concepts into scalable and practical technologies, particularly in the valorization of industrial and agricultural wastes into high-value functional materials. In addition to his research contributions, Prof. Hamdi has held significant academic leadership and administrative responsibilities, including institute-level directorships, coordination of postgraduate programs, and membership in doctoral, quality assurance, and scientific committees. He has coordinated and contributed to numerous national and international collaborative research projects, fostering strong links between academia, industry, and public institutions. Prof. Hamdi is also actively engaged in scholarly publishing and editorial activities, serving in leading roles for international journals in water engineering, environmental sciences, and geomechanics. His sustained contributions to materials processing techniques, environmental sustainability, and research leadership make him a deserving recipient of the Excellence in Research Award.

Citation Metrics (Scopus)

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Featured Publications


Dynamic Adsorption for the Efficient Removal of Phosphate and Fluoride from Phosphogypsum Leachate

– Euro-Mediterranean Journal for Environmental Integration, 2025

Prof. Jin-Song von Storch | Computational Materials Science | Research Excellence Award

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Prof. Jin-Song von Storch | Computational Materials Science | Research Excellence Award

Max-Planck Institute for Meteorology | Germany

Prof. Jin-Song von Storch is a distinguished scientist in computational materials science whose interdisciplinary expertise bridges advanced numerical modeling, statistical physics, and large-scale system simulation. She is widely recognized for her leadership in developing high-resolution computational frameworks that reveal complex interactions between structure, dynamics, and emergent properties in material and physical systems. As a senior researcher and academic leader, she has made foundational contributions to multiscale modeling, stochastic processes, and data-driven approaches that enhance predictive accuracy in complex systems. Her work is characterized by methodological rigor, conceptual clarity, and a strong emphasis on translating theoretical insight into robust computational tools. Prof. von Storch has played a central role in collaborative international research initiatives, where her ability to integrate mathematics, physics, and computation has driven innovation across disciplinary boundaries. In addition to her research excellence, she is deeply committed to academic mentorship, guiding doctoral and postdoctoral researchers while fostering inclusive and intellectually vibrant research environments. She has held key editorial, advisory, and governance roles within major scientific programs, reflecting the high level of trust placed in her expertise and judgment by the global research community. Her scholarly output includes influential journal articles, book contributions, and invited works that continue to shape contemporary thinking in computational and theoretical science. Through sustained scientific leadership, original research vision, and dedication to knowledge advancement, Prof. Jin-Song von Storch exemplifies the qualities recognized by the Research Excellence Award and stands as a leading figure in computational science and interdisciplinary innovation.

Citation Metrics (Scopus)

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5,471

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   View Orcid Profile

Featured Publications


Principles of Equilibrium Fluctuations

– Physica A: Statistical Mechanics and Its Applications, 2026 (Open Access)

Randomness and Integral Forcing

– Tellus Series A: Dynamic Meteorology and Oceanography, 2024 (Open Access)

Dr. Wentao Zhou | Smart Materials | Research Excellence Award

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Dr. Wentao Zhou | Smart Materials | Research Excellence Award

The College of Intelligent Systems Science and Engineering | Harbin Engineering University | China

Dr. Wentao Zhou is an emerging researcher in smart materials and intelligent systems, recognized for his growing academic impact and innovative contributions to advanced material technologies. He has developed a strong research portfolio with an h-index of 4, supported by 9 published documents and 56 citations across 51 citing documents, reflecting the influence and relevance of his scientific work. Dr. Zhou is affiliated with the College of Intelligent Systems Science and Engineering at Harbin Engineering University, where he has built a multidisciplinary background spanning deep learning, computer vision, and small-object detection with applications in material characterization and intelligent sensing. His research excellence is further demonstrated through the publication of 10 peer-reviewed SCI papers, multiple competition achievements, and significant innovation output, including 3 authorized Chinese patents and several ongoing patent activities. He also contributes to technological development as a key technical backbone in collaborative projects, independently leading planning, algorithm design, personnel coordination, and the establishment of monitoring, identification, and testing standards for air-traffic-control systems. Dr. Zhou’s work is strengthened by academic exposure at globally ranked institutions and active professional engagement as a Graduate Student Member of IEEE. He has also earned more than 20 prestigious honors and scholarships, recognizing both academic excellence and technological innovation. Beyond his research achievements, he has held leadership roles such as Workshop Chair for RAITS, reflecting his commitment to academic service and community contribution. His core research in smart materials integrates intelligent algorithms with material-focused applications, positioning him as a promising young scientist whose innovations align strongly with the objectives of the Research Excellence Award. Dr. Zhou’s scholarly record, technological creativity, and dedication to advancing smart materials collectively underscore his merit as a dynamic and impactful researcher.

Profiles: Scopus | Orcid

Featured Publications

Yang, S., Zhou, W., Qu, S., & Khoo, B. C. (2025, December). Fast and high-accuracy state estimator for some unknown dynamic objects with a stereo camera in aerial tracking.

Wang, R., Qiao, R., Zhou, W., & Cai, C. (2025, November). HACRNet: Hierarchical attention compression for high-speed fine-grained ship recognition.

Zhou, W., Cai, C., Srigrarom, S., & Li, C. (2025, June). SAD-YOLO: A small object detector for airport optical sensors based on improved YOLOv8.

Zhang, Y., Zhao, E., Liang, H., & Zhou, W. (2024, December). MATD3 with multiple heterogeneous sub-networks for multi-agent encirclement-combat task.

Zhou, W., Cai, C., Wu, K., & Gao, B. (2024, June). LAS-YOLO: A lightweight detection method based on YOLOv7 for small objects in airport surveillance.

Dr. Qingyong Li | Composite Materials | Research Excellence Award

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Dr. Qingyong Li | Composite Materials | Research Excellence Award

Guangdong University of Petrochemical Technology | China

Dr. Qingyong Li is an emerging researcher in composite materials and environmental catalysis whose work has contributed significantly to advanced material design, heterogeneous catalysis, and sustainable pollutant treatment technologies. With a growing research footprint reflected in 453 citations across 370 citing documents, he has established a solid academic reputation supported by 11 scientific documents and a steadily rising h-index of 8, demonstrating both impact and consistency in high-quality research output. As a faculty member in the School of Environmental Science and Engineering at Guangdong University of Petrochemical Technology, Dr. Li focuses on the development of functional composite materials, catalytic nanostructures, and clay-based or mineral-supported metal quantum dots aimed at efficient degradation of persistent organic pollutants. His research integrates composite chemistry, environmental engineering, photocatalysis, and advanced oxidation processes, with particular emphasis on peroxymonosulfate and peroxydisulfate activation mechanisms, oxygen-vacancy engineering, and visible-light-driven catalytic systems. Dr. Li’s contributions include the design of kaolin-supported cobalt nanostructures, red-mud-derived layered composites, magnetic oxide systems, and mixed metal catalysts with enhanced activity and stability. His publications in respected international journals highlight his expertise in mechanochemical synthesis, pollutant mineralization pathways, catalyst reusability, and structure–function relationships in composite materials. Through interdisciplinary collaborations, he has advanced the understanding of composite catalyst behavior, free-radical generation, charge separation efficiency, and surface-adsorption kinetics, offering practical solutions for wastewater treatment and environmental remediation. Dr. Li’s research not only deepens theoretical insights into catalytic mechanisms but also provides scalable strategies for transforming industrial waste into high-value materials, demonstrating strong alignment with global sustainability priorities. His rapidly increasing citation profile, innovative approaches to catalyst development, and commitment to environmental materials research position him as an impactful and promising scientist deserving of recognition through the Research Excellence Award.

Profiles: Scopus | Orcid

Featured Publications

Li, Q., Yan, Z., Yang, X., Li, J., Li, R., Qiu, B., Wang, N., & Wang, S. (2026). Natural layered kaolin supported cobalt quantum dots for rapid degradation of carbamazepine via peroxymonosulfate activation: Performance and mechanism. Chemical Engineering Science.

Li, Q., Zhang, J., Xu, J., Cheng, Y., Yang, X., He, J., Liu, Y., Chen, J., Qiu, B., Zhong, Y., et al. (2024). Magnetic CuFe₂O₄ nanoparticles immobilized on mesoporous alumina as highly efficient peroxymonosulfate activator for enhanced degradation of tetracycline hydrochloride. Separation and Purification Technology.

Li, Q. (2022). Photocatalysis activation of peroxydisulfate over oxygen vacancies-rich mixed metal oxide derived from red mud-based layered double hydroxide for ciprofloxacin degradation. Separation and Purification Technology.

Ba, J., Wei, G., Zhang, L., Li, Q., Li, Z., & Chen, J. (2021). Preparation and application of a new Fenton-like catalyst from red mud for degradation of sulfamethoxazole. Environmental Technology.

Li, Q. (2021). Novel step-scheme red mud based Ag₃PO₄ heterojunction photocatalyst with enhanced photocatalytic performance and stability in photo-Fenton reaction. Chemical Engineering Journal.

Dr. Daniel Osezua Aikhuele | Material Failure Analysis | Research Excellence Award

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Dr. Daniel Osezua Aikhuele | Material Failure Analysis | Research Excellence Award

University of Port Harcourt | Nigeria

Dr. Daniel Osezua Aikhuele is a distinguished scholar in Material Failure Analysis whose extensive body of work has significantly advanced the understanding of reliability, safety, and intelligent decision-making in complex engineering systems. With an impressive research footprint reflected in 530 citations generated by 434 documents, 71 published documents, and a robust h-index of 14, he is recognized for consistently producing high-impact contributions that bridge theoretical innovation with practical engineering solutions. As an Associate Professor at the University of Port Harcourt, he has built a reputation for excellence in manufacturing engineering, intelligent reliability modeling, fault diagnosis, product design, and sustainable materials, using advanced fuzzy logic, multi-criteria decision-making approaches, and data-driven techniques that address reliability challenges in modern industrial environments. His scholarly output spans journals, book chapters, and international conferences, demonstrating his leadership in developing hybrid fuzzy systems, reliability-centered models, renewable-energy decision frameworks, and intelligent predictive tools for mechanical components, offshore systems, and wind-energy technologies. Beyond research, Dr. Aikhuele plays an active role in the global engineering community as a reviewer for major journals and a member of several professional bodies, contributing to quality assurance and scientific advancement across multiple disciplines. He has supervised numerous postgraduate researchers and collaborated widely on interdisciplinary projects that enhance industrial safety, optimize energy systems, and support sustainable engineering practices. His commitment to academic excellence, combined with impactful teaching and mentorship, has earned him recognition as a dynamic leader whose contributions continue to influence material behavior assessment, reliability optimization, and the design of resilient engineering systems.

Profiles: Scopus | Orcid | Google Scholar

Featured Publications

Azubuike, G. D., Aikhuele, D. O., & Nwosu, H. U. (2025). Development of an optimization model for reducing energy utilization and to increase biomass yield in a brewery process. Process Integration and Optimization for Sustainability.

Diemuodeke, O. E., Vera, D., Ojapah, M. M., Nwachukwu, C. O., Nwosu, H. U., Aikhuele, D. O., Ofodu, J. C., & Nuhu, B. S. (2024). Hybrid solar PV–agro-waste-driven combined heat and power energy system as feasible energy source for schools in Sub-Saharan Africa. Biomass, 4(4), 67.

Aikhuele, D. O., & Diemuodeke, O. E. (2024). Computational analysis of stiffness reduction effects on the dynamic behaviour of floating offshore wind turbine blades. Journal of Marine Science and Engineering, 12(10), 1846.

Aikhuele, D. O., & Sorooshian, S. (2024). A proactive decision-making model for evaluating the reliability of infrastructure assets of a railway system. Information, 15(4), 219.

Onukwube, C. U., Aikhuele, D. O., & Sorooshian, S. (2024). Development of a fault detection and localization model for a water distribution network. Applied Sciences, 14(4), 1620.

Mr. Fawad Khan | Smart Materials | Best Researcher Award

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Mr. Fawad Khan | Smart Materials | Best Researcher Award

Shenzhen Institutes of Advanced Technology | Chinese Academy of Sciences | China

Mr. Fawad Khan is a promising researcher at the intersection of smart materials, safe robotics, reinforcement learning, and human–robot collaboration, recognized for his innovative contributions to the development of intelligent, safety-aware autonomous systems. As a PhD Researcher at the Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, he focuses on advancing constrained reinforcement learning frameworks that enable robots to operate reliably and safely alongside humans in dynamic and safety-critical environments such as collaborative manufacturing, warehouse automation, and assistive robotics. His scholarly output includes 3 research documents, and he has contributed to journal articles, conference papers, and manuscripts addressing precision grasping, adaptive safety constraints, multi-object manipulation, and safety-critical coordination. His research introduces novel approaches that combine tactile–visual perception, adaptive constraint satisfaction, and multi-modal learning to significantly reduce safety violations while maintaining high task performance in robotic systems. He has designed benchmark platforms for safe robotic manipulation and expanded the capabilities of existing tools such as Safety Gym to enable high-fidelity evaluation of robotic arms with multiple degrees of freedom. Prior to his doctoral research, Mr. Khan gained industry experience as a Python developer and data analyst, where he automated logistics operations, designed data-driven decision-support tools, and streamlined complex workflows, demonstrating his ability to integrate practical engineering solutions with theoretical AI advancements. His technical expertise spans reinforcement learning algorithms, constrained optimization, robotics simulation environments, computer vision, multi-modal neural networks, and high-performance computing frameworks. He actively collaborates with interdisciplinary teams working on intelligent manufacturing, safe autonomy, and human-centered robotics. With strong analytical skills, a clear research vision, and a growing academic footprint, Mr. Fawad Khan represents a new generation of AI and robotics researchers dedicated to creating safer, smarter, and more adaptive robotic systems, making him a highly deserving candidate for the Best Researcher Award.

Profile: Scopus

Featured Publications

Khan, F., Feng, W., Wang, Z., Huang, T., Xiao, L., et al. (2025). Safe reinforcement learning for objects manipulation in safety-critical coordinated tasks. In ISARC: Proceedings of the International Symposium on Automation and Robotics in Construction (Vol. 42, pp. 334–341). IAARC Publications.

Khan, F., et al. Reinforcement learning for precision grasping and safety-critical coordination in a robotic arm. Journal of Intelligent Service Robotics.

Khan, F., et al. Safe reinforcement learning for vision-based robotic manipulation in human-centered environment. Journal of Intelligent Robotics and Applications.

Khan, F., et al. Safe reinforcement learning for multi-object robotic manipulation with adaptive safety constraint. Expert Systems with Applications.

Mr. Angelos Athanasiadis | Smart Materials | Research Excellence Award

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Mr. Angelos Athanasiadis | Smart Materials | Research Excellence Award

Aristotle University of Thessaloniki | AUTH | Greece

Mr. Angelos Athanasiadis is an emerging researcher in smart materials, embedded intelligence, and high-performance computing architectures, known for his contributions to FPGA-accelerated deep learning and intelligent cyber-physical systems. Currently pursuing his PhD in Electrical and Computer Engineering at Aristotle University of Thessaloniki, he focuses on designing advanced hardware-accelerated frameworks that significantly enhance the speed, efficiency, and energy performance of full-precision Convolutional Neural Networks on modern AMD FPGA platforms. His early scientific influence is reflected in 5 citations, referenced across 4 citing documents, supported by ongoing scholarly outputs and an h-index recorded as 1–2, demonstrating his growing visibility in computational engineering research. Mr. Athanasiadis has contributed to significant EU-funded research initiatives, including the ADVISER and REDESIGN projects, where he developed high-fidelity emulation tools, hardware–software co-design solutions, and distributed embedded intelligence for heterogeneous systems combining CPUs, GPUs, and FPGAs. His work on FUSION an open-source, timing-accurate, multi-node emulation framework integrating QEMU with OMNeT++ using HLA/CERTI synchronization has advanced the ability to accurately prototype next-generation smart systems for robotics, aerial monitoring, real-time analytics, and autonomous decision-making. With a strong background in electronics, embedded systems, and management engineering, he has also completed industry-driven research roles at EXAPSYS, SEEMS PC, and Cadence Design Systems, contributing to R&D for sensor networks, FPGA-based acceleration pipelines, and complex digital-system workflows. In addition to his technical expertise, he maintains interdisciplinary strengths in AI-driven system optimization, hardware–software integration, multiphysics emulation, and intelligent system design. Collaborating with leading academic researchers and contributing to peer-reviewed venues, he continues to expand his research footprint. With strong analytical skills, innovation-oriented thinking, and a commitment to advancing smart materials and high-performance embedded intelligence, Mr. Angelos Athanasiadis stands out as a promising researcher and a deserving candidate for the Research Excellence Award.

Profiles: Google Scholar | Orcid

Featured Publications

Athanasiadis, A., Tampouratzis, N., & Papaefstathiou, I. (2025). Energy-efficient FPGA framework for non-quantized convolutional neural networks. arXiv Preprint, arXiv:2510.13362.

Athanasiadis, A., Tampouratzis, N., & Papaefstathiou, I. (2025). An efficient open-source design and implementation framework for non-quantized CNNs on FPGAs. Integration, Article 102625.

Athanasiadis, A., Tampouratzis, N., & Papaefstathiou, I. (2024). An open-source HLS fully parameterizable matrix multiplication library for AMD FPGAs. WiPiEC Journal—Works in Progress in Embedded Computing Journal, 10(2).

Katselas, L., Jiao, H., Athanasiadis, A., Papameletis, C., Hatzopoulos, A., … (2017). Embedded toggle generator to control the switching activity during test of digital 2D-SoCs and 3D-SICs.

Katselas, L., Athanasiadis, A., Hatzopoulos, A., Jiao, H., Papameletis, C., … (2017). Embedded toggle generator to control the switching activity.

Mr. Prashant Kishor Sharma | Smart Materials | Research Excellence Award

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Mr. Prashant Kishor Sharma | Smart Materials | Research Excellence Award

National Cheng Kung University | Taiwan

Mr. Prashant Kishor Sharma is an emerging researcher in smart materials, microfluidics, and AI-integrated engineering, recognized for his rapidly growing contributions to advanced flow control, active matter systems, and micro-robotics for biomedical applications. As a doctoral researcher in Mechanical Engineering at National Cheng Kung University, Taiwan, he focuses on magnetically actuated artificial cilia, swarm microrobots, and intelligent microfluidic platforms that enable high-precision particle manipulation, drug delivery studies, and biomimetic flow control. His work spans computational hemodynamics, physics-informed neural networks, CFD-based flow modeling, and advanced lab-on-a-chip design, with research outcomes published in reputable SCI and Scopus-indexed journals covering microfluidic mixing, cognitive dynamics of zebrafish, AI-integrated magnetic microrobots, and solar-energy-based engineering systems. Mr. Sharma has successfully completed and contributed to several innovative research projects, including the development of artificial-cilia-based microactuation systems, computational hemodynamics of aneurysm flows using AI-accelerated models, a natural-zeolite oxygen concentrator, solar-powered thermal drying solutions, and bio-inspired drag-reduction strategies for energy-efficient flow systems. He actively collaborates across multidisciplinary teams involving mechanical engineering, biomedical engineering, microfabrication, and computational sciences, integrating simulation, experimentation, and AI-driven optimization. His work has earned recognition such as the Veritas Conscientia Scholarship, Excellence Research Work Award at CTAM, and Best Poster distinction for his contributions to thermal fluidics and micro-robotic navigation. In addition to scientific research, he contributes to technological innovation through a patent on a natural zeolite-based oxygen concentrator and participates in professional societies such as the Chinese Society of Theoretical and Applied Mechanics. His dedication to advancing smart materials, microfluidic intelligence, and biomedical engineering underscores his commitment to impactful, interdisciplinary research. With strong technical proficiency, innovative thinking, and a rapidly expanding academic footprint, Mr. Prashant Kishor Sharma stands out as a promising young scientist and an excellent candidate for the Research Excellence Award.

Profiles: Scopus | Google Scholar | Orcid

Featured Publications

Sharma, P. K., Wei, P.-W., Loganathan, D., Lu, Y.-H., & Chen, C.-Y. (2025, November 18). Microflow switching using artificial cilia for on‐demand particle manipulation. Advanced Intelligent Systems.

Sharma, P. K., Wei, P.-W., Loganathan, D., Lu, Y.-H., & Chen, C.-Y. (2025, July 24). Microflow switching using artificial cilia for on‐demand particle manipulation. Advanced Intelligent Systems.

Sharma, P. K., Loganathan, D., Chen, M.-L., Lu, Y.-H., Wang, P.-H., & Chen, C.-Y. (2025, May 1). Cognitive dynamics of drug-mediated zebrafish under sound stimuli in a microfluidic environment. Biomicrofluidics.

Kandukuri, K. S., Sharma, P. K., & Arun, R. K. (2024). A comparative assessment of distributive mode active solar dryers: Flat plate collector vs evacuated tube collector with thermal energy storage and perforated baffled trays. Solar Energy, 112421.

Sharma, P. K., Arun, R. K., Lata, A., & Shiva, S. (2023, June 15). A natural zeolite based oxygen concentrator for supplying pure oxygen [Patent].

Sharma, P. K., Shukla, R. K., & Oberoi, A. S. (2022, August 25). Understanding of drag reduction & biofouling using shark skin denticle through CFD simulation (Master’s thesis or dissertation).

Dr. Jiang Bi | Material Degradation and Corrosion | Research Excellence Award

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Dr. Jiang Bi | Material Degradation and Corrosion | Research Excellence Award

Yanshan University | China

Dr. Jiang Bi is an accomplished materials scientist specializing in material degradation, corrosion behavior, alloy design, and advanced additive manufacturing processes, with a strong research portfolio that integrates laser processing, selective laser melting, metal matrix composites, and microstructural engineering. His scientific contributions have earned significant global visibility, reflected in 2,046 citations drawn from 1,654 citing documents, supported by 63 published documents and an h-index of 29, demonstrating the high impact and reliability of his research across the fields of materials science and manufacturing engineering. Dr. Bi’s work spans a broad range of topics including laser melting deposition, microstructure–property relationships, high-performance aluminum alloys, grain refinement mechanisms, and defect control strategies that enhance corrosion resistance and mechanical integrity. His studies on aluminum-magnesium-scandium-zirconium alloys, TiB₂-reinforced composites, and ultrasonic-assisted laser processing have contributed important insights into densification behavior, phase evolution, strengthening mechanisms, and fatigue performance of additively manufactured metals. He has authored influential research in well-recognized journals covering optics and laser technology, materials engineering, manufacturing processes, powder metallurgy, mechanical behavior of alloys, and composite fabrication. Dr. Bi possesses extensive expertise in metallography, SEM, microhardness analysis, tensile evaluation, simulation-based material design, and advanced characterization techniques, making him a valuable contributor to both fundamental materials research and industry-driven innovation. His academic journey includes research in forming technologies, high-pressure forming, and thermomechanical treatment of tubular components, further strengthening his multidisciplinary foundation. Through dedicated laboratory leadership, collaborative project involvement, and guidance of students and young engineers, he continues to advance cutting-edge technologies in corrosion mitigation, microstructural optimization, lightweight alloy development, and laser-based manufacturing. Dr. Jiang Bi’s commitment to research excellence, innovation in material degradation and corrosion science, and contributions to modern manufacturing technologies establish him as a prominent figure in the global materials science community and a distinguished candidate for recognition in research excellence.

Profile: Scopus

Featured Publications

Bi, J. (2026). Microstructure evolution and synergistic strengthening mechanisms of wear and corrosion resistance in laser cladding fabricated TC11-xMo coatings. Tribology International.

Bi, J. (2026). Regulating microstructure and strength–ductility synergy of laser-arc hybrid additive manufactured Al-Zn-Mg-Cu alloy. Journal of Materials Processing Technology.

Bi, J. (2025). Defects and fatigue properties of LPDC-fabricated aluminum alloy wheel: Experimental and numerical simulation methods. Engineering Failure Analysis.

Bi, J. (2025). Effect of aging treatment on microstructure, mechanical properties and corrosion resistance of 2219 aluminium alloy laser welded joint. Journal of Materials Research and Technology.

Bi, J. (2025). Coordinated control of multi-region solidification in complex-shaped die-cast wheels via cooling adjustment strategies to minimize defects and enhance performance. Journal of Materials Processing Technology.

Assoc. Prof. Dr. Guangyuan Xu | Smart Materials | Best Researcher Award

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Assoc. Prof. Dr. Guangyuan Xu | Smart Materials | Best Researcher Award

Beijing University of Posts and Telecommunications | China

Assoc. Prof. Dr. Guangyuan Xu is a leading researcher in brain computer interfaces, embodied intelligence, neural sensing, flexible electronics, and cognitively driven robotic systems, serving at the School of Artificial Intelligence at Beijing University of Posts and Telecommunications. His work is recognized internationally for pioneering advances that bridge neuroscience, materials science, artificial intelligence, and robotics, with a strong focus on creating intelligent systems capable of seamless human machine interaction. His scientific influence is reflected in 618 citations drawn from 572 citing documents, supported by 10 research documents and an h-index of 8, demonstrating the growing global relevance and impact of his contributions. Dr. Xu directs the Cognitive and Embodied Intelligence Laboratory, where he leads interdisciplinary teams in developing next-generation task-relevant mental imagery BCIs, high-performance flexible interfaces, multimodal neuro-robotic co-adaptation systems, intelligent sensing materials, and robust human–machine decision-making frameworks. He has published extensively in high-impact journals and international conferences covering biosensing, neurotechnology, flexible electronics, affective computing, and embodied robotics. His leadership extends to major professional societies, including active roles within the Chinese Association for Artificial Intelligence, the China Computer Federation, the China Graphics Society, and the national Brain Computer Interface Industry Alliance, reflecting his prominent standing in the scientific community. Dr. Xu also contributes to national strategic innovation programs and collaborative platforms in artificial intelligence, neurotechnology, and bio-integrated sensing, helping shape the scientific direction of emerging intelligent technologies. His collaborations with global institutions have strengthened international research networks in neuro-robotic integration and intelligent sensing systems, driving forward cutting-edge advancements in BCI-enabled robotics. Through his vision, interdisciplinary expertise, and dedication to advancing neuro-intelligent systems, Dr. Xu continues to push the boundaries of cognitive interaction technologies, flexible neural interfaces, and embodied intelligence, establishing himself as a key contributor to the future of human machine integration and intelligent robotic development.

Profiles: Scopus | Google Scholar

Featured Publications

Fu, Z., Lin, Y., Xu, G., & Zhang, M. (2025). Comparative performance of IMU and sEMG in locomotion mode prediction across transitional and steady-state cyclic/non-cyclic gaits. IEEE Journal of Biomedical and Health Informatics

Li, W., Zhang, J., Guo, J., Wang, X., Xu, G., Peng, Y., & Tu, L. (2025). Automated detection and classification of pediatric middle ear diseases from CT using entropy projection and feature interaction. In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE.

Xu, G. (2018). Laser scribed graphene: fabrication and electrochemical biosensors for neurotransmitters. ResearchSpace@Auckland.

Liu, Y., Xu, G., Li, C., Ma, Y., Ji, N., & Feng, X. (2025). Stretchable multilevel mesh brain electrodes for neuroplasticity in glioma patients undergoing surgery. Advanced Healthcare Materials, e03358.

Xu, G., Chen, Y., Chen, F., Meng, Y., Ma, Y., & Feng, X. (2021). Fabrication of laser scribed graphene stretchable supercapacitor by laser-assisted transfer printing strategy. In 2021 IEEE 16th International Conference on Nano/Micro Engineered and Molecular Systems (NEMS). IEEE.