Mr. Zhigang Wu | Microstructure and Properties | Best Researcher Award

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Mr. Zhigang Wu | Microstructure and Properties | Best Researcher Award

Jiangxi University of Science and Technology | China

Mr. Zhigang Wu is an accomplished researcher in microstructure and material properties, recognized for his growing contributions to advanced electromechanical systems, smart materials, and precision engineering. With 150 citations generated from 141 documents, he has established a strong scholarly footprint supported by 20 published documents and an h-index of 8. Currently serving as an Associate Professor at the School of Energy and Mechanical Engineering at Jiangxi University of Science and Technology, he brings extensive expertise in flexure-based mechanisms, micro-grippers, compliant systems, nanopositioning platforms, and intelligent control strategies. His academic path spans electromechanical engineering, mechanical engineering, and doctoral specialization in electromechanical systems, fueling a multidisciplinary approach that integrates mechanical design, precision control, micro-robotics, and advanced actuation technologies. Mr. Wu has made notable contributions to the development of piezo-driven micro-manipulation systems, nonlinear control methods, and micro-robotic devices, with several of his works appearing in reputable international journals and conferences. His research addresses practical challenges in precision motion control, micro-scale manipulation, image-based tracking, and actuator hysteresis modeling, advancing next-generation micro-robotic applications. He has been actively involved in major research projects related to high-precision parallel manipulators, hybrid actuator-based micro-positioning platforms, and robust optimization frameworks for intelligent systems, demonstrating his capacity to contribute to both theoretical development and technological innovation. Beyond research, he serves as a reviewer for multiple high-impact journals and conferences, reflecting his standing in the scientific community and his commitment to maintaining academic quality. Mr. Wu’s combination of technical depth, interdisciplinary outlook, and sustained productivity highlights his continuing impact in the field of smart materials, micro-systems, and precision engineering, positioning him as a promising leader driving advancements in micro-scale actuation and intelligent material-based device design.

Profile: Scopus

Featured Publications

Liu, R., Zhang, Y., Chen, J., Wu, Z.*, Zhu, Y., Liu, J., & Chen, M. (2025). BiAttentionNet: A dual-branch automatic driving image segmentation network integrating spatial and channel attention mechanisms. Scientific Reports, 15, 13193.

Wu, Z.*, Chen, M., He, P., et al. (2020). Tracking control of PZT-driven compliant precision positioning micromanipulator. IEEE Access, 8, 126477–126487.

Wu, Z.*, & Zhu, Y. (2024). BiConvNet: Integrating spatial details and deep semantic features in a bilateral-branch image segmentation network. IEICE Transactions on Information and Systems. https://doi.org/10.1587/transinf.2024EDP7025

Wu, Z.*, & Zhu, Y. (2024). SWformer-VO: A monocular visual odometry model based on Swin Transformer. IEEE Robotics and Automation Letters, 9(5), 4766–4773.

Wu, Z.*, & Chen, M. (2019, August 20–25). Model and study of clamping force for micro-gripper with PZT-driven [Conference presentation]. 2019 IEEE World Robot Conference, Beijing, China.

Wu, Z., Li, Y.*, & Hu, M. (2018). Design and optimization of full decoupled micro/nano-positioning stage based on mathematical calculation. Mechanical Sciences, 9(2), 417–429.

Dr. Ayantika Pal | Nanomaterials | Women Researcher Award

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Dr. Ayantika Pal | Nanomaterials | Women Researcher Award

Shri Rawatpura Sarkar University | India

Dr. Ayantika Pal is an accomplished biochemist and interdisciplinary nanomaterials researcher whose work spans molecular toxicology, nanomaterial–cell interactions, neurobiology, and biomedical applications of nanotechnology. She has built an impressive scientific profile with 325 citations referenced across 262 citing documents, supported by a growing portfolio of impactful publications, an h-index of 7, and an i10-index of 6, reflecting her strong and steadily rising academic influence. Dr. Pal’s research journey integrates expertise in neurodegenerative disease mechanisms, nanoparticle-mediated toxicity, natural-compound therapeutics, nanoconjugate-based anti-cancer platforms, and environmental nanotoxicology. Her studies have shed light on dendritic spine remodeling, addiction-related molecular pathways, oxidative and nitrative stress mechanisms, nanomaterial toxicity in microbial systems, and the apoptotic effects of bioactive compounds such as bromelain in cancer models. She has authored peer-reviewed publications in respected journals spanning neurochemistry, toxicology, environmental nanotechnology, pharmacology, and biomedical science, and has contributed to multiple international book chapters addressing nanoscience-driven applications in medicine, dentistry, food safety, and environmental remediation. Dr. Pal has successfully led an independent research project on nanoconjugate-based therapeutics and has developed strong laboratory expertise across proteomics, genomics, molecular biology, genetic epidemiology, and animal-model experimentation. She has extensive teaching experience at both undergraduate and postgraduate levels, covering physiology, molecular biology, biochemistry, immunology, zoology, developmental biology, and nanotechnology, and has played vital roles in academic coordination, accreditation processes, and student mentoring. Her active involvement in major conferences, research training programs, and scientific workshops reflects her commitment to continuous learning and international collaboration. Dr. Pal’s scientific contributions, leadership in biomedical and nanomaterials research, and dedication to advancing women’s representation in science position her as a highly deserving nominee for the Women Researcher Award.

Profiles: Google Scholar | Scopus

Featured Publications

Moktan, N., Panigrahi, S., Pal, A., Banerjee, A., & Roy, D. N. (2026). Zirconia nanoparticle in dentistry: An update report and further prospect. In Applications of Nanomaterials in Dentistry (pp. 297–317).

Roy, D. N., Tandi, A., & Pal, A. (2025). Moringa oleifera leaf extract functions as a potent inhibitor of snake venom. Journal of Herbs, Spices & Medicinal Plants, 31(1), 96–112.

Pal, A., & Das, S. (2019). Terpenoids in treatment of neurodegenerative disease. In Terpenoids Against Human Diseases (pp. 95–117).

Pal, A. (2013). Studies on molecular mechanisms associated with narcotic addiction (Master’s/Doctoral thesis, CU).

Pal, A., & Das, S. (2013). Potential role of Shank1 in the alteration of dendritic spine morphology during long-term morphine exposure. Journal of Neurochemistry, 125, 263.

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. Sumit Gahletia | Additive Manufacturing (3D Printing) | Best Scholar Award

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Mr. Sumit Gahletia | Additive Manufacturing (3D Printing) | Best Scholar Award

Deenbandhu Chhotu Ram University of Science and Technology DCRUST Murthal Haryana | India

Mr. Sumit Gahletia is an ambitious early-career researcher specializing in additive manufacturing, 3D scanning, and advanced materials, recognized for his rapidly growing academic influence and practical contributions to biomedical and orthodontic engineering. His research portfolio reflects 9 scientific documents, an h-index of 6, and 88 citations recorded across 70 citing documents, demonstrating the strong scholarly reception of his emerging work in material optimization, 3D printing mechanics, scanning metrology, and patient-specific medical device fabrication. Mr. Gahletia’s ongoing PhD research focuses on the design and performance evaluation of orthodontic retainers fabricated through precision 3D scanning and high-resolution resin printing, where he examines scanning parameters, printing conditions, mechanical behavior, and dimensional accuracy to develop clinically reliable and personalized dental solutions. He has published impactful journal articles in areas such as fused filament fabrication, resin-based printing systems, metrological assessment of dental models, and optimization of biocompatible materials, along with multiple conference contributions showcasing novel approaches to sustainable manufacturing, polymer-matrix composites, and digital dentistry. His earlier work includes the mechanical evaluation of fiber-reinforced Onyx composites using FDM, further highlighting his versatility across additive-manufacturing platforms. In addition to his research excellence, Mr. Gahletia has established a strong presence in academic and professional communities, serving in various leadership and organizational roles across technical societies, innovation platforms, scouting organizations, and national committees. He has also participated in numerous international conferences, workshops, and scientific training programs, strengthening his exposure to global advancements in engineering and materials science. With practical industrial experience, proficiency in advanced design software, and a strong commitment to interdisciplinary innovation, Mr. Gahletia continues to contribute meaningfully to the evolving landscape of 3D printing and biomedical manufacturing, positioning himself as a promising scholar making impactful strides in research, technology integration, and next-generation material applications.

Profiles: Scopus | Orcid | Google Scholar

Featured Publications

Gahletia, S., & Garg, R. K. (2025). Dismantling barriers in integrating patient-centred care with additive manufacturing to assess the fit of orthodontic retainers for futuristic preventative healthcare. Progress in Additive Manufacturing.

Gahletia, S., Kaushik, A., & Garg, R. K. (2024). Analysis of the surface roughness of 3D-printed occlusal splints fabricated using biocompatible resins. Journal of Emerging Science and Engineering.

Sharma, P., Gahletia, S., & Bhardwaj, K. (2023). Ameliorating surface roughness and tensile strength of ASA fabricated parts by analyzing significant FDM printing parameters using response surface methodology. Journal of Polymer & Composites.

Kaushik, A., Kumar, P., Gahletia, S., Garg, R. K., Kumar, A., Yadav, M., Giri, J., & Chhabra, D. (2023). Optimization of dual extrusion fused filament fabrication process parameters for 3D-printed nylon-reinforced composites: Pathway to mobile and transportation revolution. SAE International Journal of Materials and Manufacturing.

Kaushik, A., Punia, U., Gahletia, S., Garg, R. K., & Chhabra, D. (2023). Identification and overcoming key challenges in the 3D printing revolution. In Advances in Additive Manufacturing (Chapter 5). CRC Press.

Ms. Amna H. M. Mahmoud | Nanomaterials | Research Excellence Award

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Ms. Amna H. M. Mahmoud | Nanomaterials | Research Excellence Award

Minia University | Egypt

Ms. Amna H. M. Mahmoud is an emerging researcher in nanomaterials and computational chemistry, recognized for her rapidly growing contributions to density functional theory (DFT), nanosheet adsorption mechanisms, and computational materials design. She serves as a Research Assistant at the CompChem Research Laboratory, Faculty of Science, Minia University, where she plays a key role in advancing theoretical modeling for environmental applications, drug delivery, biosensing, and corrosion inhibition. Her scientific impact is reflected through 13 publications, an h-index of 7, and 131 citations indexed across 107 citing documents, demonstrating the strong scholarly attention her work has earned within a short span. With expertise spanning quantum mechanical calculations, molecular dynamics simulations, and advanced computational tools such as Gaussian, Quantum Espresso, and VESTA, she has contributed to highly cited collaborative studies exploring adsorption phenomena on graphene, borophene, and RuC nanosheets. Her research addresses critical challenges, including toxic molecule detection, metal corrosion protection, pharmaceutical molecule interactions, and nanosheet-based sensing strategies. As a Research Assistant, she has also contributed to a funded national project focused on developing corrosion inhibitors for active metals in space environments using cutting-edge computational techniques, showcasing her ability to work at the interface of materials science and aerospace-oriented applications. In addition to her research accomplishments, she actively manages the Computational Chemistry Laboratory, supports quality assurance and accreditation processes, and participates in scientific conferences with multiple oral presentations on σ-hole interactions, surface adsorption, and nanoscale material behavior. Her growing academic influence in Egypt and internationally is further supported by her membership in the Egyptian Society of Theoretical and Computational Chemistry. Through a strong portfolio of impactful publications, interdisciplinary collaborations, and specialized computational expertise, Ms. Amna H. M. Mahmoud continues to establish herself as a promising scientist contributing meaningful advancements to the fields of nanomaterials and computational chemistry.

Profiles: Scopus | Orcid

Featured Publications

Ibrahim, M. A. A., Mahmoud, A. H. M., Mekhemer, G. A. H., El‐Tayeb, M. A., Khan, S., & Shoeib, T. (2025). Adsorption features of toxic pnictogen hydrides over pristine and C/Be‐doped borophene nanosheets as potential sensors: A DFT investigation.

Mahmoud, A. H. M., Aziz, M. E. S., Rabee, A. I. M., El‐Tayeb, M. A., Mekhemer, G. A. H., Shoeib, T., & Ibrahim, M. A. A. (2025). Exploring the adsorption features of furan and 1,n-dioxane as environmental toxins on two-dimensional RuC nanosheet: A DFT study.

Mahmoud, A. H. M., Al-saied, T. M. T., Rabee, A. I. M., El-Tayeb, M. A., Mekhemer, G. A. H., Shoeib, T., & Ibrahim, M. A. A. (2025). Two-dimensional RuC nanosheet as potential sensor for toxic cyanogen halides (NCX; X = H, F, Cl, Br, and I): A DFT study.

Ibrahim, M. A. A., Ahmed, N. K. M., Mahmoud, A. H. M., El-Tayeb, M. A., Abdelbacki, A. M. M., Khan, S., Soliman, M. E. S., & Shoeib, T. (2024). RuC nanosheet as a promising biosensing material for detecting the aromatic amino acids: A DFT study.

Mohamed, L. A., Mahmoud, A. H. M., Rady, A. S. M., El‐Tayeb, M. A., Rabee, A. I. M., Shoeib, T., & Ibrahim, M. A. A. (2024). Allopurinol, oxypurinol, and thiopurinol expired drugs as corrosion inhibitors toward Al (111) surface: A DFT and FPMD simulation study.

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.

Ms. Houda Hassini | Materials Characterization Techniques | Interdisciplinary Research Award

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Ms. Houda Hassini | Materials Characterization Techniques | Interdisciplinary Research Award

Telecom SudParis | France

Ms. Houda Hassini is an interdisciplinary data scientist whose research bridges deep learning, computer vision, biomedical imaging, and remote-sensing technologies, making her a powerful emerging voice at the intersection of artificial intelligence and advanced materials characterization. With a strong academic foundation spanning mathematics, computer science, and artificial-intelligence engineering, she has developed specialized expertise in multimodal imaging, physics-informed machine learning, optical flow modeling, generative adversarial networks, and high-resolution computational imaging. As a Postdoctoral Researcher at ONERA – The French Aerospace Lab, she focuses on designing advanced deep-learning pipelines for sea-ice drift estimation using multimodal satellite imagery, combining synthetic aperture radar, multispectral optical data, and physics-guided simulation strategies to achieve more accurate environmental monitoring under complex real-world conditions. Her doctoral research at Télécom SudParis introduced innovative physics-inspired generative models for biomedical imaging, particularly within Fourier Ptychographic Microscopy, where she developed end-to-end pipelines capable of significantly improving rare blood-cell classification and enhancing phase-amplitude coupled imaging for automated diagnostics. Ms. Hassini has contributed to several high-visibility scientific projects in collaboration with clinical partners and industry teams, and her work has been published in peer-reviewed journals and major conferences in optics, photonics, and computational imaging. She has expertise in statistical modeling, clustering, attention-based architectures, unsupervised learning, and large-scale data processing using Python, R, modern deep-learning frameworks, and GPU-accelerated computing environments. In addition to her research achievements, she has taught and supervised students in applied mathematics, signal processing, biomedical data science, and AI-based image analysis across leading French academic institutions. Multilingual and globally engaged, Ms. Hassini also maintains active involvement in community leadership and scientific outreach. Her innovative contributions, interdisciplinary impact, and commitment to advancing imaging intelligence technologies make her an exceptional candidate for the Interdisciplinary Research Award.

Profile: Orcid

Featured Publications

Hassini, H., Dorizzi, B., Leymarie, V., Klossa, J., & Gottesman, Y. (2025). Enhancing classification of rare white blood cells in FPM with a physics-inspired GAN. Scientific Reports, 15(1), 42310.

Hassini, H., Dorizzi, B., Thellier, M., Klossa, J., & Gottesman, Y. (2023). Investigating the joint amplitude and phase imaging of stained samples in automatic diagnosis. Sensors, 23(18), 7932.

Hassini, H., Dorizzi, B., Klossa, J., & Gottesman, Y. (2024). Optimization of FPM using phase images for malaria detection. In Proceedings of SPIE Photonics Europe (Paper 12996-10). SPIE.

Hassini, H., Bouchama, L., Sextius, M., Klossa, J., Fleury, C., Baran-Marszak, F., Delhommeau, F., Dorizzi, B., Gottesman, Y., & Leymarie, V. (2024). The TAMIS project: Hematology cytology and artificial intelligence: Revolution is in the light bulb. ISLH Proceedings. (Accepted).

Hassini, H., Bouchama, L., Sextius, M., Klossa, J., Fleury, C., Baran-Marszak, F., Delhommeau, F., Dorizzi, B., Gottesman, Y., Leymarie, V., & others. (2024). The TAMIS project: Hematology cytology and artificial intelligence: Revolution is in the light bulb. Presented at the SFH — 43rd Annual Congress, Paris, France.

Hassini, H., Niang, N., & Audigier, V. (2021). SOM-based clusterwise regression. In DSSV 2021: Data Science, Statistics & Visualization Conference, Rotterdam.

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.