Shayan Ghazimoghadam | Structural Health Monitoring | Best Researcher Award

Mr. Shayan Ghazimoghadam | Structural Health Monitoring | Best Researcher Award

PhD Student, Islamic Azad University, Iran

Shayan Ghazimoghadam is a Ph.D. student in Structural Engineering at Islamic Azad University of Shahrood, Iran, specializing in data-driven structural health monitoring. His research integrates artificial intelligence with civil engineering to develop unsupervised deep learning methods for real-time damage detection in structures. Shayan’s work focuses on creating digital twins for infrastructure assessment, aiming to enhance predictive maintenance and safety. He has authored several publications, including a notable paper on vibration-based damage diagnosis using multi-head self-attention LSTM autoencoders, published in Measurement journal. Additionally, he has presented his research at national conferences and served as a keynote speaker, demonstrating his commitment to advancing the field of structural health monitoring.

Profile

Google Scholar​

Education

Shayan Ghazimoghadam completed his Bachelor of Science in Civil Engineering at Islamic Azad University of Gorgan, Iran, in 2018, where he excelled in design courses, achieving a GPA of 19/20 in Steel Structures and a perfect 20/20 in Concrete Structures. He then pursued a Master of Science in Structural Engineering at Lamei Gorgani Institute of Higher Education, Gorgan, graduating in 2022 with a GPA of 19.07/20. His master’s dissertation focused on structural damage identification under ambient vibration using an unsupervised deep learning method, supervised by Dr. Seyed Ali Asghar Hosseinzadeh. Currently, Shayan is a Ph.D. student at Islamic Azad University of Shahrood, Iran, where he continues to explore innovative approaches in structural health monitoring and artificial intelligence applications in civil engineering.

Experience 

Between 2022 and 2023, Shayan Ghazimoghadam served as a Research Assistant at Golestan University, Gorgan, Iran, under the guidance of Dr. Seyed Ali Asghar Hosseinzadeh. During this period, he conducted research on real-time structural health monitoring utilizing AI-powered techniques. His work involved developing and testing unsupervised deep learning algorithms for damage detection in structures based on vibration data. Shayan’s contributions led to the presentation of his findings at national conferences, showcasing his ability to communicate complex research outcomes effectively. This experience has significantly enhanced his expertise in integrating artificial intelligence with structural engineering, positioning him as a promising researcher in the field of structural health monitoring.

Awards and Honors 

Shayan Ghazimoghadam has been recognized for his academic excellence and research contributions. He ranked first among M.Sc. students in Structural Engineering at Lamei Gorgani Institute of Higher Education, Gorgan, Iran, in 2022, achieving a GPA of 19.07/20. His innovative research on structural damage identification using unsupervised deep learning methods has been published in reputable journals, including the Measurement journal, where his paper has garnered 26 citations as of 2024. Additionally, Shayan was invited as a keynote speaker at the 3rd National Conference on Civil Engineering, Intelligent Development, and Sustainable Systems in 2023, where he presented on AI-powered structural damage identification and localization through accelerometer data. These accolades underscore his commitment to advancing the field of structural health monitoring and his potential for future contributions to civil engineering research.

Research Focus

Shayan Ghazimoghadam’s research focuses on the integration of artificial intelligence with structural health monitoring (SHM) to develop innovative solutions for infrastructure maintenance. His primary interests include data-driven SHM, unsupervised structural damage identification, and the application of digital twins for condition assessment. Shayan aims to enhance the accuracy and efficiency of damage detection in structures by employing unsupervised deep learning techniques, particularly multi-head self-attention LSTM autoencoders. His work contributes to the development of digital twins, virtual representations of physical assets, to monitor and assess the condition of infrastructure in real time. By leveraging AI and machine learning, Shayan seeks to revolutionize traditional SHM practices, offering more proactive and predictive maintenance strategies that can lead to safer and more sustainable infrastructure systems.

Publication Top Notes​

📘 1. A Novel Unsupervised Deep Learning Approach for Vibration-Based Damage Diagnosis Using a Multi-Head Self-Attention LSTM Autoencoder

Authors: S. Ghazimoghadam, S.A.A. Hosseinzadeh
Journal: Measurement, Volume 229, Article 114410
Year: 2024
Citations (as of 2025): 26
DOI: Measurement 229, 114410 (sample link, please verify)

🔍 Summary:

This publication introduces a novel unsupervised deep learning method for real-time structural damage detection using only ambient vibration data. The approach combines Long Short-Term Memory (LSTM) autoencoders with multi-head self-attention mechanisms, enabling the system to effectively learn temporal features and focus on critical data patterns without the need for labeled damage data.

By leveraging unsupervised learning, the model is highly adaptable and scalable, making it suitable for practical deployment in real-world structural health monitoring (SHM) scenarios. The method was validated using benchmark datasets, showing superior performance in damage localization and diagnosis accuracy compared to traditional approaches.

📗 2. Transformer-Based Time-Series GAN for Data Augmentation in Bridge Monitoring Digital Twins

Authors: V. Mousavi, M. Rashidi, S. Ghazimoghadam, M. Mohammadi, B. Samali
Journal: Automation in Construction, Volume 175, Article 106208
Year: 2025 (Under Review)
DOI: Automation in Construction 175, 106208 (check for final link when published)

🔍 Summary:

This paper presents a Transformer-based Generative Adversarial Network (GAN) for augmenting time-series sensor data in bridge monitoring systems. The technique is particularly geared towards Digital Twin models, which require large, diverse, and high-quality datasets to simulate and predict structural behavior accurately.

The GAN architecture uses a Transformer encoder to better capture temporal dependencies in structural response data, generating realistic synthetic datasets for training SHM models. By augmenting scarce or incomplete datasets, this method improves predictive performance, anomaly detection, and damage assessment capabilities of digital twins used in civil infrastructure.

Juras Skardžius | Mechanics Engineering | Best Researcher Award

Dr. Juras Skardžius | Mechanics Engineering | Best Researcher Award

Juras Skardžius is an accomplished engineer with experience in the automotive industry. Specializing in MetrologX (CMM), Computer-Aided Design (CAD), SolidWorks/Pro-Engineer, reverse engineering, and APQP packages, Juras has a strong ability to turn complex designs into tangible parts. With certifications in AIAG Quality Core Tools, IATF Automotive standards, and ISO 9001 and 14001, Juras brings expertise in quality control, documentation, and feasibility studies. A proactive and continuously growing professional, he has contributed extensively to automotive engineering through both hands-on experience and research.

Profile

Orcid

Education

Juras Skardžius holds both a Bachelor’s (2012-2016) and a Master’s (2022-2024) degree in Transport Technology Science (Automobile Engineering) from the prestigious Vilnius Gediminas Technical University. His academic background provides him with in-depth knowledge of engineering principles and advanced techniques used in automotive technology. Throughout his studies, Juras focused on modernizing automotive manufacturing processes, including implementing sensor technologies to improve efficiency. His academic career has laid the foundation for his ongoing contributions to the automotive sector, integrating cutting-edge research with practical experience.

Experience

Juras Skardžius has a diverse career in the automotive sector, working for several companies where he honed his skills in project engineering, design, and quality control. He is currently employed at UAB Stansefabrikken Automotive, where he is responsible for new project documentation preparation, CMM programming, and product implementation. His experience also spans roles at UAB Baltexim as a designer and at UAB Forveda as a Service and Aftersale Manager. Juras’s hands-on roles in manufacturing processes and his ability to manage important clients has given him extensive exposure to various facets of the industry.

Research Focus

Juras Skardžius’s research focus lies in the modernization of automotive manufacturing processes. He has developed innovative methods for real-time part quality monitoring using stamping force in progressive stamping and has worked on tool modernization using sensor technologies. His research aims to optimize manufacturing efficiency, improve product quality, and reduce costs in automotive production. Juras is particularly interested in the integration of sensor technologies like eddy current and load sensors into stamping processes to enhance production accuracy and reliability. His work bridges the gap between theory and practice in automotive engineering.

Publication Top Notes

  1. Alternative Real-Time Part Quality Monitoring Method by Using Stamping Force in Progressive Stamping Process
    Journal of Manufacturing and Materials Processing 📚🔧

  2. Progressive Tool Modernization Using Sensor Technology in Automotive Parts Manufacturing
    TRANSBALTICA XIV: Transportation Science and Technology 🛠️⚙️

  3. Modernization of the Stamping Process Using Eddy Current and Load Sensors in the Manufacturing of Automotive Parts
    Eksploatacja i Niezawodność – Maintenance and Reliability 🏭📊

 

 

Xiangling Li | Engineering | Best Researcher Award

Dr. Xiangling Li | Engineering | Best Researcher Award

Research Associate, Dartmouth College, United States

Dr. Xiangling Li is an accomplished researcher in biomedical engineering, specializing in micro/nano manufacturing, wearable bioelectronics, and precision medical devices. He currently serves as an Assistant Research Fellow at Dartmouth College, where he focuses on integrating advanced materials and nanotechnology into medical applications. With a Ph.D. from Sun Yat-sen University and postdoctoral research at the University of Southern California, Dr. Li has contributed to cutting-edge innovations in biosensors, drug delivery, and flexible electronics. His groundbreaking research has led to numerous high-impact publications in Advanced Science, Nature Communications, Advanced Functional Materials, and ACS Applied Materials & Interfaces, accumulating hundreds of citations. Dr. Li’s expertise in interdisciplinary research enables the development of next-generation medical devices, improving patient care and diagnostics. His work in integrating electronics, materials science, and life sciences has positioned him as a leader in the field, driving innovations in biomedical engineering and translational medicine.

Profile

Google Scholar
Orcid

Education

Dr. Xiangling Li pursued his academic journey with a strong focus on biomedical engineering and materials science. He earned his Ph.D. in Engineering (Biomedical Engineering) from Sun Yat-sen University, China (2018–2022), where he conducted pioneering research under the guidance of Prof. Xi Xie. His doctoral research focused on developing smart nanomaterials and biosensors for medical applications. After completing his Ph.D., he joined the University of Southern California as a Postdoctoral Fellow (2022–2023) under Prof. Hangbo Zhao, where he advanced his work on flexible bioelectronics and precision medicine. Dr. Li is currently an Assistant Research Fellow at Dartmouth College (since 2023), working with Prof. Wei Ouyang on cutting-edge medical technologies. His diverse educational background has equipped him with expertise in nano/microfabrication, electronic biosensors, and biomedical device engineering, enabling him to make significant contributions to translational medicine and wearable healthcare solutions.

Experience

Dr. Xiangling Li has extensive experience in biomedical engineering, focusing on micro/nano fabrication, biosensors, and advanced medical devices. He is currently an Assistant Research Fellow at Dartmouth College (2023–Present), where he explores novel bioelectronic interfaces for healthcare applications. Previously, he was a Postdoctoral Fellow at the University of Southern California (2022–2023), where he contributed to research on flexible electronic systems for precision medicine. Dr. Li completed his Ph.D. at Sun Yat-sen University (2018–2022), where he developed groundbreaking microfabricated biosensors and drug delivery platforms. His research expertise spans interdisciplinary fields, including wearable diagnostics, nanotechnology-enabled therapeutics, and malleable electronics. With multiple high-impact publications and extensive collaborations across disciplines, Dr. Li’s contributions continue to shape the future of smart medical devices. His experience bridges academia and industry, enabling the development of innovative biomedical solutions that improve patient outcomes and healthcare monitoring.

Research Focus

Dr. Xiangling Li’s research is centered on micro/nano manufacturing technologies for biomedical applications. His work integrates flexible electronics, biosensors, and smart materials to develop next-generation medical devices. He specializes in wearable and implantable bioelectronics, focusing on precision drug delivery, transdermal biosensing, and real-time health monitoring. A key area of his research involves microneedle-based systems for minimally invasive glucose monitoring, intraocular pressure regulation, and intelligent drug release platforms. Additionally, he explores graphene-based biosensors, nanoneedle platforms, and soft bioelectronics for enhanced biomedical applications. His innovations in smart contact lenses, flexible supercapacitors, and biocompatible coatings contribute to the advancement of personalized medicine and point-of-care diagnostics. Dr. Li’s interdisciplinary approach, combining electronics, materials science, and life sciences, drives the development of high-performance biomedical devices. His research holds significant potential for revolutionizing non-invasive diagnostics, therapeutic monitoring, and next-generation wearable healthcare solutions.

Publications 📚

  • A fully integrated closed-loop system based on mesoporous microneedles-iontophoresis for diabetes treatment
  • Intelligent wireless theranostic contact lens for electrical sensing and regulation of intraocular pressure
  • Reduced graphene oxide nanohybrid–assembled microneedles as mini-invasive electrodes for real-time transdermal biosensing
  • Smartphone-powered iontophoresis-microneedle array patch for controlled transdermal delivery
  • Nanoneedle platforms: the many ways to pierce the cell membrane
  • Electrodes derived from carbon fiber-reinforced cellulose nanofiber/multiwalled carbon nanotube hybrid aerogels for high-energy flexible asymmetric supercapacitors
  • Hierarchical graphene/nanorods-based H₂O₂ electrochemical sensor with self-cleaning and anti-biofouling properties
  • Emerging roles of 1D vertical nanostructures in orchestrating immune cell functions
  • Fe₃O₄ nanoparticles embedded in cellulose nanofiber/graphite carbon hybrid aerogels as advanced negative electrodes for flexible asymmetric supercapacitors
  • Wearable and implantable intraocular pressure biosensors: recent progress and future prospects

 

 

 

Alia Al-Ghosoun | Engineering and Technology | Best Researcher Award

Dr Alia Al-Ghosoun | Engineering and Technology | Best Researcher Award

Assistant professor, Philadephia University, Jordan

Dr. Alia Radwan Al-Ghosoun is an Assistant Professor in the Mechatronics Engineering Department at Philadelphia University, Jordan. With a deep passion for advanced engineering research, she holds a DPhil in Engineering from Durham University, UK, where her work focused on shallow water dynamics and adaptive control methods for hydrodynamic systems. Dr. Al-Ghosoun’s research spans fluid mechanics, computational modeling, and the application of artificial intelligence in engineering problems. She has worked as a post-doctoral researcher at Durham University and has held multiple academic positions at the University of Jordan, where she contributed to the development of energy-efficient systems and intelligent control techniques. Dr. Al-Ghosoun’s commitment to advancing knowledge in hydrodynamics and environmental modeling has resulted in impactful publications and contributions to numerical simulation and uncertainty quantification. She is passionate about improving the practical application of engineering solutions for environmental challenges.

Profile

Scopus

Strengths for the Award

  1. Advanced Academic Background:
    • Dr. Al-Ghosoun holds a Doctor of Philosophy in Engineering from Durham University, UK, where her research focused on shallow water flow dynamics and adaptive control techniques to improve the accuracy of these systems. This is a highly specialized field with significant implications in environmental modeling, water systems, and engineering, marking her as an expert in computational engineering and fluid dynamics.
    • Her post-doctoral research at Durham University further solidifies her expertise, particularly in understanding and quantifying uncertainty in numerical modeling of hydrodynamics, which is crucial for predicting real-world environmental phenomena.
  2. Impactful and Diverse Research Contributions:
    • Dr. Al-Ghosoun has published several peer-reviewed papers in high-impact journals such as Environmental Modelling and Software, Communications in Computational Physics, and International Journal of Computational Methods. These works cover areas such as uncertainty quantification, morphodynamics, and numerical simulation of shallow water flows and hydrosediment processes.
    • Her conference papers and book chapters demonstrate a commitment to advancing computational methods in hydrodynamics and environmental modeling, particularly addressing the challenges of bed topography deformation, fluid-structure interactions, and stress analysis in hydro-sediment systems.
  3. Interdisciplinary Research:
    • Dr. Al-Ghosoun’s research stands at the intersection of mechatronics, engineering, and environmental sciences, with a focus on adaptive control techniques and artificial intelligence. This interdisciplinary approach is essential in addressing complex real-world problems related to fluid dynamics and energy systems.
    • The integration of AI techniques (such as genetic algorithms) in energy consumption optimization and shallow water flow models highlights her innovative approach to solving large-scale engineering problems.
  4. Global Collaboration and Recognition:
    • With international experience as a Post-Doctoral Researcher at Durham University and several collaborative research efforts with Jordanian and UK-based academic institutions, Dr. Al-Ghosoun has developed a robust international network. Her involvement in global research platforms, such as ResearchGate, attests to her active engagement in the academic community and dissemination of her work.
  5. Teaching and Mentoring Experience:
    • Dr. Al-Ghosoun has demonstrated a strong commitment to education as an Assistant Professor at Philadelphia University, where she contributes to the development of young engineers in Mechatronics Engineering. Her role as a Teaching Assistant and Research Assistant at various institutions indicates her foundational experience in nurturing future engineers and scientists.
  6. Recognition of Research Excellence:
    • Dr. Al-Ghosoun’s papers, particularly her works on uncertainty quantification and modeling techniques for shallow water systems, have gained traction in the academic community. For instance, her work published in Environmental Modelling and Software (2021) has already accumulated 10 citations, signaling its importance in the field.

Areas for Improvement

  1. Broader Citation Impact:
    • While Dr. Al-Ghosoun’s work is highly specialized and impactful, the citation counts for some of her research papers remain low (e.g., her paper on stress analysis has 0 citations). Increasing visibility in wider journals and collaborating with researchers in complementary fields could enhance the reach and impact of her publications.
  2. Increased Public Engagement:
    • Engaging in public outreach or community-based projects that demonstrate the application of her research (e.g., how adaptive control methods improve water management or energy efficiency in real-world scenarios) could enhance the broader social impact of her work.
  3. Further Collaborative Interdisciplinary Projects:
    • Although her work spans several fields, further involvement in cross-disciplinary projects—especially those integrating sustainable engineering and climate resilience—could increase the relevance of her research to pressing global challenges, like climate change adaptation and sustainable resource management.

Education

Dr. Alia Radwan Al-Ghosoun earned her Doctor of Philosophy (DPhil) in Engineering from Durham University, UK in January 2021. Her doctoral research focused on understanding the effects of bathymetric movement on shallow water flows and their interaction with the seabed, leading to the development of adaptive control methods for improved accuracy in hydrodynamic simulations. Prior to this, she completed a Post-Doctorate at Durham University in 2022, where she explored the application of uncertainty quantification in complex engineering models. Dr. Al-Ghosoun holds a Master’s Degree in Mechanical Engineering from the University of Jordan, where she developed AI-based predictive models for fuel consumption in Jordan and optimized energy efficiency through genetic algorithms. She also earned her Bachelor’s degree in Mechatronics Engineering from the University of Jordan. Dr. Al-Ghosoun’s academic background equips her with interdisciplinary expertise in engineering and environmental science.

Experience

Dr. Alia Radwan Al-Ghosoun is currently an Assistant Professor at Philadelphia University in the Mechatronics Engineering Department since October 2022, where she teaches and conducts research in engineering systems and adaptive control techniques. Prior to this, she was a Post-Doctoral Researcher at Durham University, UK (2021-2022), focusing on uncertainty quantification in shallow water systems. Dr. Al-Ghosoun completed her DPhil at Durham University (2016-2021), where her research involved modeling shallow water flows and the interaction of bed topography. She has also held roles as a Research Assistant at the University of Jordan’s Water, Energy, and Environment Center (2012-2016) and the King Abdullah Design and Development Bureau (KADDB) (2012). Earlier in her career, she worked as a Teaching Assistant in both Mechatronics and Mechanical Engineering departments at the University of Jordan. Dr. Al-Ghosoun’s interdisciplinary experience blends academia with applied engineering solutions.

Awards and Honors

Dr. Alia Radwan Al-Ghosoun has been recognized for her research excellence and commitment to advancing knowledge in hydrodynamics and adaptive control systems. Her academic achievements are highlighted by her work at Durham University, where she earned a prestigious Doctoral Fellowship for her research on shallow water dynamics and bed interaction. She has also received recognition for her post-doctoral research contributions in uncertainty quantification and numerical simulations. Dr. Al-Ghosoun’s work has been presented at major academic conferences, and she has contributed to a variety of high-impact journal publications. In addition to her research accomplishments, she has been awarded teaching grants to support her role as an educator at Philadelphia University, where she mentors the next generation of Mechatronics engineers. Her consistent efforts to bridge the gap between theoretical research and practical engineering applications have earned her widespread recognition within her academic and professional communities.

Research Focus

Dr. Alia Radwan Al-Ghosoun specializes in hydrodynamic modeling, shallow water flows, and the application of adaptive control systems to improve the accuracy of complex environmental simulations. Her research interests focus on uncertainty quantification and the development of computational models for the numerical simulation of fluid dynamics, particularly in the context of stochastic bed topography and morphodynamics. She has worked extensively on shallow water waves, bathymetric effects, and water-bed interaction. One of her core research goals is to enhance the predictive accuracy of models used for environmental management and engineering systems by incorporating artificial intelligence techniques, such as genetic algorithms and surrogate models. Dr. Al-Ghosoun is passionate about integrating AI-based solutions into environmental and energy systems to address challenges like resource optimization, pollution reduction, and sustainable energy. Her work in hydro-sediment-morphodynamics provides valuable insights into climate change adaptation and water resource management.

Publication Top Notes

  1. Uncertainty quantification for stochastic morphodynamics 🌊🧑‍🔬, AIP Conference Proceedings, 2024.
  2. A Novel Computational Approach for Wind-Driven Flows over Deformable Topography 💨🌍, Lecture Notes in Computer Science, 2024.
  3. A Nonintrusive Reduced-Order Model for Uncertainty Quantification in Numerical Solution of One-Dimensional Free-Surface Water Flows Over Stochastic Beds 📊💧, International Journal of Computational Methods, 2022.
  4. Efficient Computational Algorithm for Stress Analysis in Hydro-Sediment-Morphodynamic Models 💻⚙️, Lecture Notes in Computer Science, 2022.
  5. A surrogate model for efficient quantification of uncertainties in multilayer shallow water flows 🌊🔬, Environmental Modelling and Software, 2021.
  6. A computational model for simulation of shallow water waves by elastic deformations in the topography 🌊⚡, Communications in Computational Physics, 2021.
  7. Uncertainty Quantification of Bathymetric Effects in a Two-Layer Shallow Water Model: Case of the Gibraltar Strait 🏝️🌊, Springer Water, 2020.
  8. A hybrid finite volume/finite element method for shallow water waves by static deformation on seabeds 🌊🧮, Engineering Computations, 2020.
  9. A new numerical treatment of moving wet/dry fronts in dam-break flows 💧🚨, Journal of Applied Mathematics and Computing, 2019.

Conclusion

Dr. Alia Radwan Al-Ghosoun is an exceptional candidate for the Best Researcher Award. Her contributions to the fields of hydrodynamics, uncertainty quantification, and adaptive control systems are not only advancing the understanding of complex environmental processes but are also pioneering new computational techniques that can improve the accuracy and efficiency of engineering systems. Her ability to merge artificial intelligence with environmental modeling positions her as a leader in the field. Her ongoing efforts in teaching, mentoring, and global academic collaborations further highlight her potential to shape the future of engineering and environmental sciences. With a few strategic steps to broaden her citation impact and public visibility, Dr. Al-Ghosoun could solidify her place as a thought leader in her field.

Grzegorz Swit | Civil Engineering Award | Best Researcher Award

Prof Grzegorz Swit | Civil Engineering Award | Best Researcher Award

Prof Grzegorz Swit, Kielce University of Technology,Poland

🌍 Prof. Grzegorz  Świt, born on January 29, 1971, in Kielce, is a Polish civil engineer and full professor at Kielce University of Technology. With a Ph.D. and habilitation in technical sciences, he specializes in building structures. He serves as Dean of the Faculty of Construction and Architecture and heads the Department of Strength of Building Materials and Structures. His expertise spans project management (PRINCE2®), foreign languages (English B2, Russian B1), and accreditation auditing. Świt collaborates extensively with industry leaders like Polska Spółka Gazownictwa and “CIECH” S.A., focusing on R&D and production continuity projects. 🏗️

Publication Profile

Orcid

Education

prof Grzegorz  Świt, a civil engineer graduated from Kielce University of Technology in 1994, expanded his expertise with an MSc in construction, specializing in Building Structures in 1996. Continuing his academic journey, he earned a doctorate in technical sciences in 2001 and further achieved habilitation in 2012, all from Kielce University of Technology. Recognized as a full professor by the President of Poland in 2021, Świt’s career embodies dedication to academia and research in building materials and structures. 🏗️

Experience

Prof Grzegorz Świt has been an integral part of Kielce University of Technology since February 1993, starting as a full-time engineer and technical staff until 1995 with grants from Professors L. Gołaski and Z. Kowal. He progressed through roles as an assistant and assistant professor in the Department of Strength of Materials until 2013, when he became a full professor. Since 2020, he has led the Department of Strength of Building Materials and Structures and served as Dean of the Faculty of Construction and Architecture (2020-2024). Appointed as a Full Professor by the President of Poland in 2021, Świt also contributes as an independent technical auditor at the Polish Accreditation Center. 🏢

Research Focus

👨‍🏫 Grzegorz Świt is a Dean and Full Professor at Kielce University of Technology, Poland. His research focuses on the application of the acoustic emission method for structural health monitoring, with significant contributions to materials science and engineering. He has authored numerous articles and conference papers, exploring topics such as non-destructive testing methods, fracture mechanics, and smart city infrastructure. Grzegorz is actively involved in advancing innovative engineering solutions, particularly in monitoring the quality and integrity of steel and composite structures using experimental and numerical approaches. 📚🔬

Publication Top Notes

Ruixi Zhang | Mechanical Engineering Award | Best Researcher Award

Assist Prof Dr Ruixi Zhang | Mechanical Engineering Award | Best Researcher Award

Assist Prof Dr Ruixi Zhang, Nagoya University, Japan

Dr. Zhang Ruixi is an Assistant Professor in the Department of Micro-Nano Mechanical Science and Engineering at Nagoya University, Japan 🇯🇵. He earned his B.S. from Northeastern University, China 🇨🇳 in 2017, his M.S. from Nagoya University in 2020, and his Ph.D. from The University of Tokyo in 2023. His research focuses on carbon-based materials like Diamond-like Carbon (DLC) coatings and carbon nanotubes, exploring their tribological properties and structural characteristics 🧪. Dr. Zhang is a member of The Japan Society of Mechanical Engineers and the Japanese Society of Tribologists 🏅.

Publication Profile

Scopus

Education

Dr. Zhang Ruixi completed his B.S. in Mechanical Engineering and Automation at Northeastern University, Shenyang, China 🇨🇳, from September 2013 to July 2017. He then pursued his M.S. in the Department of Micro-Nano Mechanical Science and Engineering at Nagoya University, Japan 🇯🇵, from April 2018 to March 2020. Following this, he earned his Ph.D. in Bioengineering from The University of Tokyo, Japan 🇯🇵, from April 2020 to March 2023. Dr. Zhang’s academic journey reflects a strong commitment to advancing his expertise in engineering and nanotechnology 🧪.

Experience

Since April 2023, Dr. Zhang Ruixi has been serving as an Assistant Professor in the Department of Micro-Nano Mechanical Science and Engineering at Nagoya University, Japan 🇯🇵. In this role, he continues to contribute to advanced research in the fields of nanotechnology and engineering, focusing on the development and application of carbon-based materials 🧪. His position allows him to mentor students, lead cutting-edge projects, and collaborate with other experts in the field, fostering innovation and academic excellence 🌟.

Research Focus

Dr. Zhang Ruixi’s research primarily focuses on the development and characterization of carbon-based materials, particularly Diamond-like Carbon (DLC) coatings 🧪. His work investigates the tribological properties, such as friction and wear, of these materials under various conditions, including high temperatures. He also explores the synthesis and structural properties of one-dimensional hetero-nanotubes, such as boron nitride nanotubes. By employing advanced techniques like in situ reflectance spectroscopy, Dr. Zhang aims to enhance the performance and application of these materials in various engineering fields 🌟. His research contributes significantly to advancements in nanotechnology and materials science 🏅.