Prof. Dr .Gianluigi Bacchetta | Botanica | Lifetime achievement Award

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Prof. Dr .Gianluigi Bacchetta | Botanica | Lifetime achievement Award

Director , University of Cagliari , Italy  

🌿 Professor Gianluigi Bacchetta is an internationally renowned botanist and conservation biologist 🌍. Currently a full professor at the University of Cagliari 🇮🇹, he also serves as an adjunct professor at the University of Tehran 🇮🇷. He directs the Conservation Centre of Biodiversity and the Germplasm Bank of Sardinia 🌱. With over 700 publications 📚 and global collaborations, Bacchetta has made remarkable contributions to Mediterranean biodiversity research 🌸. He is an editor, reviewer, and project leader in several international conservation initiatives 🌐, making a lasting impact on plant science and ecosystem protection 🏞️.

Professional Profile

GOOGLE SCHOLAR

SCOPUS

Education & Experience 

🎓 Gianluigi Bacchetta earned his degree in Life Sciences in 1996 🧬, followed by multiple master’s degrees in Landscape Planning (1997) 🗺️ and Vegetation Analysis (2000) 🌳. He completed his PhD in Geomorphology and Geobotany in 2000 🪨, and later achieved a European PhD in Plant Biology from the University of Valencia 🇪🇸. His academic career includes roles from Lecturer to Full Professor at the University of Cagliari 🏛️. He served as director of Hortus Botanicus Karalitanus 🌺 and now leads major biodiversity and germplasm conservation centers in Sardinia 🌾.

Professional Development 

🔬 Prof. Bacchetta has evolved through diverse academic and leadership roles in botany, ecology, and conservation 🌲. As editor of Plant Sociology and associate editor for several journals 📖, he actively shapes scientific communication 🌐. His role in doctoral education as deputy director of a PhD program 🎓, along with memberships in international scientific councils 🌍, showcases his dedication to professional excellence. He has authored 24 scientific books 📘 and mentors upcoming researchers. As president of GENMEDA and leader of multiple EU-funded projects 💡, Bacchetta exemplifies continuous professional growth and collaborative leadership 🌱.

Research Focus 

🧪 Prof. Bacchetta’s research specializes in plant diversity, conservation biology, geobotany, and Mediterranean ecosystem studies 🌿. His work encompasses phytoclimatology, phytogeography, and island plant biodiversity 🏝️. He leads germplasm conservation efforts 🌾, with a focus on endemic and endangered Mediterranean species 🏵️. His integrative studies connect vegetation science with climate, soil, and land use patterns 🌍. The outcomes support environmental planning and ecological restoration 🔄. With emphasis on genetic resource preservation and sustainable development 🌱, Bacchetta’s research bridges field biology, conservation policy, and ecosystem services 🏞️.

Awards & Honors 

🏅 Prof. Bacchetta has received national and international recognition for his work in plant biodiversity and conservation biology 🌍. He has led prestigious European projects such as LIFE+, Interreg, and Erasmus+ 🌐. As president of GENMEDA and deputy president of the CBNC scientific council 🇫🇷, his leadership is widely respected. He is regularly invited to contribute to international conservation strategies 🌿. His contributions as a reviewer, editor, and academic mentor have earned him high esteem in the global scientific community 📚. His prolific publication record and active collaboration networks are testaments to his exceptional achievements 🥇.

Publication Top Notes

1. Assessing Eco-Physiological Patterns of Ailanthus altissima (Mill.) Swingle and Differences with Native Vegetation Using Copernicus Satellite Data on a Mediterranean Island

Authors: F. Marzialetti, V. Lozano, A. Große-Stoltenberg, L. Podda, G. Brundu
Journal: Ecological Informatics, 2025 (Open Access)
DOI/Link: Link not available
Citations: 0
Summary:
This study uses high-resolution Copernicus satellite data to evaluate the eco-physiological behavior of the invasive tree Ailanthus altissima compared to native vegetation on a Mediterranean island. The research highlights significant differences in vegetation indices, phenological traits, and water-use patterns. The findings provide critical insight into the invasive potential of A. altissima and suggest targeted remote-sensing approaches for early detection and management in Mediterranean ecosystems.

2. Functional and Habitat Characteristics Associated with Nativeness, Rarity, and Invasiveness in the Aquatic Vascular Flora of Sardinia

Authors: M. Fois, A. Cuena-Lombraña, J.N. Boyd, L. Podda, G. Bacchetta
Journal: Global Ecology and Conservation, 2025 (Open Access)
DOI/Link: Link not available
Citations: 0
Summary:
This research investigates the ecological traits and habitat preferences of aquatic vascular plants in Sardinia. By analyzing traits linked with nativeness, rarity, and invasiveness, the authors aim to support conservation and management efforts. The study reveals that invasive species tend to exhibit broader ecological tolerances and reproductive strategies, posing threats to native aquatic ecosystems. This functional trait analysis offers a framework for assessing plant behavior in freshwater habitats under changing environmental pressures.

3. Tracing the Emergence of Domesticated Grapevine in Italy

Authors: M. Ucchesu, S. Ivorra, V. Bonhomme, A. Usai, L. Bouby
Journal: PLoS ONE, 2025
DOI/Link: Link not available
Citations: 0
Summary:
This archaeobotanical study employs molecular and morphometric evidence to trace the early domestication of grapevine (Vitis vinifera) in Italy. Through analysis of ancient seeds and plant remains, the authors provide evidence for independent domestication events and early viticulture practices. The interdisciplinary approach connects cultural practices with evolutionary plant biology, offering a new timeline and geographic perspective on grape domestication in the western Mediterranean.

4. The First Inventory of Sardinian Mining Vascular Flora

Authors: M.E. Boi, M. Sarigu, M. Fois, M. Casti, G. Bacchetta
Journal: Plants, 2025 (Open Access)
DOI/Link: Link not available
Citations: 0
Summary:
This paper presents the first systematic inventory of vascular plants in mining areas across Sardinia. Mining landscapes are often biodiversity hotspots due to their unique soil chemistry and disturbance regimes. The study identifies species adapted to metal-rich and degraded soils, including several endemic and threatened taxa. This inventory contributes to ecological restoration planning and highlights the conservation value of post-industrial habitats.

Conclusion

Prof. Gianluigi Bacchetta embodies the values of scientific excellence, global collaboration, and lifelong commitment to biodiversity conservation. His exceptional academic record, mentorship legacy, and leadership in both national and international conservation efforts make him a prime candidate for a Lifetime Achievement Award.

 

 

Prof. Dr Mokhtar Hjiri | Metal oxide gas sensors | Best Researcher Award

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Prof. Dr . Mokhtar Hjiri | Metal Oxide Gas Sensors | Best Researcher Award

Associate Professor ,  Imam Mohammad Ibn Saud Islamic University , best researcher award

Mokhtar Hjiri is an associate professor at Imam Mohammed Ibn Saud Islamic University, Riyadh, specializing in nanomaterials synthesis for gas sensors and wastewater treatment. 🎓 He earned his PhD in 2016 from the University of Monastir in collaboration with the University of Messina. 🇹🇳🇮🇹 With teaching and research experience in Tunisia, Saudi Arabia, and Italy, he is skilled in spin coating, hydrothermal synthesis, and gas sensing techniques. 🔬 His work advances environmental safety and sensor technology. 🌿⚙️ He speaks Arabic, English, French, and Italian, bridging international research communities. 🌍

Professional Profile

GOOGLE SCHOLAR

Education and Experience

Mokhtar Hjiri completed his Master’s degree in Materials and Nanomaterials at University of Monastir in 2010 🎓 and earned his PhD in 2016 jointly with University of Monastir and University of Messina. 🇹🇳🇮🇹 He worked as assistant professor at King Abdulaziz University (2016-2020) and advanced to associate professor there until 2022. Currently, he holds an associate professor role at Imam Mohammed Ibn Saud Islamic University. 🏫 His expertise spans from lecturing physics to supervising nanomaterial synthesis projects, contributing to international research collaborations. 🌐

Professional Development

Mokhtar continuously develops expertise in nanomaterials and gas sensor technologies. 🔬 He has trained extensively in Italy, learning advanced hydrothermal and green chemistry methods. 🇮🇹 His research proficiency includes spin coating, X-ray diffraction, and gas sensing systems. 🧪 He mentors Master’s students in innovative projects on spinel ferrite and doped ZnO nanoparticles. 🎓 Proficient in Matlab, LaTeX, and Microsoft Office, he balances research with teaching general physics and semiconductors. 💻 Multilingual skills (Arabic, English, French, Italian) enable global collaboration. 🌍

Research Focus

Mokhtar’s research centers on the synthesis of metal oxide nanomaterials for gas sensor applications and wastewater treatment. 🧫 He specializes in hydrothermal synthesis, green chemistry, and spin coating techniques to create functional thin films and nanopowders. 🌱 His work targets environmental monitoring and pollution control via advanced chemoresistive sensors and heavy metal adsorption. ⚗️ Combining materials science with applied physics, his research contributes to safer industrial processes and sustainable technologies. 🌿🔧

Awards and Honors

Mokhtar Hjiri has earned recognition for his pioneering research in nanomaterials and sensor technology. 🏅 His papers published in top journals and presentations at IEEE workshops highlight his contributions. 📚 His commitment to innovative methods for environmental safety has gained academic respect and collaborative opportunities. 🌐 He is known for successfully supervising graduate theses and promoting cross-disciplinary knowledge exchange. 🎓 His growing impact in materials science and engineering reflects his leadership and dedication to advancing nanotechnology applications. 🔝✨

Publication Top Notes

1. Al-doped ZnO for highly sensitive CO gas sensors

Authors: M. Hjiri, L. El Mir, S.G. Leonardi, A. Pistone, L. Mavilia, G. Neri
Journal: Sensors and Actuators B: Chemical, Volume 196, Pages 413-420, 2014
Citations: 441
Summary:
This study reports on the development of aluminum-doped zinc oxide (Al-ZnO) nanomaterials tailored for detecting carbon monoxide (CO) gas with high sensitivity. Using advanced synthesis methods, the authors optimized the doping concentration to enhance sensor performance, improving response time and selectivity. The Al doping effectively modulates the electrical properties of ZnO, leading to superior detection capabilities suitable for environmental monitoring and industrial safety applications.

2. Harnessing bacterial endophytes for promotion of plant growth and biotechnological applications: an overview

Authors: A.M. Eid, A. Fouda, M.A. Abdel-Rahman, S.S. Salem, A. Elsaied, R. Oelmüller, et al.
Journal: Plants, Volume 10, Issue 5, Article 935, 2021
Citations: 198
Summary:
This comprehensive review highlights the role of bacterial endophytes—microorganisms living within plants—in enhancing plant growth and their diverse biotechnological applications. While not authored solely by Hjiri, this work involves him as a co-author contributing expertise on the microbial interactions and applications in agriculture and environmental science. The article emphasizes sustainable agricultural practices and future potential for biofertilizers and biocontrol agents.

3. Enhanced performance of novel calcium/aluminum co-doped zinc oxide for CO2 sensors

Authors: R. Dhahri, S.G. Leonardi, M. Hjiri, L. El Mir, A. Bonavita, N. Donato, et al.
Journal: Sensors and Actuators B: Chemical, Volume 239, Pages 36-44, 2017
Citations: 120
Summary:
This research presents the synthesis and testing of zinc oxide sensors co-doped with calcium and aluminum for improved detection of carbon dioxide (CO2). The co-doping strategy enhances sensitivity and selectivity by modifying the surface properties and electrical conductivity of ZnO nanostructures. The sensors demonstrate fast response and recovery times, making them promising for environmental monitoring and industrial gas detection systems.

4. CO and NO2 Selective Monitoring by ZnO-Based Sensors

Authors: M. Hjiri, L. El Mir, S.G. Leonardi, N. Donato, G. Neri
Journal: Nanomaterials, Volume 3, Issue 3, Pages 357-369, 2013
Citations: 116
Summary:
This paper investigates zinc oxide-based sensors engineered for selective detection of carbon monoxide (CO) and nitrogen dioxide (NO2). By tailoring the material properties and sensor architecture, the authors achieve selective sensing capabilities critical for air quality control. The study also examines sensor response under varying environmental conditions, confirming the robustness and potential of ZnO nanomaterials for real-world applications.

5. Effect of indium doping on ZnO based-gas sensor for CO

Authors: M. Hjiri, R. Dhahri, K. Omri, L. El Mir, S.G. Leonardi, N. Donato, G. Neri
Journal: Materials Science in Semiconductor Processing, Volume 27, Pages 319-325, 2014
Citations: 110
Summary:
This article explores how indium doping influences the gas sensing performance of zinc oxide sensors targeting carbon monoxide. Indium incorporation enhances ZnO’s electrical conductivity and surface reactivity, leading to improved sensor sensitivity and selectivity. The research includes detailed characterization of material morphology and electronic properties, contributing to optimized gas sensor design.

Conclusion

Mokhtar Hjiri’s focused contributions on enhancing gas sensor technology using innovative nanomaterials and doping methods position him as a leading researcher in the field of materials science and sensor engineering. His impactful research directly supports environmental safety and sustainability, key priorities in modern science and technology. Given his high citation record, continuous scientific output, and mentorship roles, he is an excellent candidate for a Best Researcher Award, recognizing both his scientific excellence and societal relevance.

Dr. Vahideh Bafandegan Emroozi | Maintenance | Women Researcher Award

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Dr. Vahideh Bafandegan Emroozi | Maintenance | Women Researcher Award

Author , Ferdowsi university of Mashhad , Iran

Vahideh Bafandegan Emroozi is a passionate Iranian researcher specializing in industrial management and optimization. 🎓 With a Ph.D. from Ferdowsi University of Mashhad, her work bridges technology and human-centric approaches. 📊 Her research spans supply chain innovation, IoT applications, and human error analysis. 🤖🧠 She has published in esteemed journals and held research fellowships at Ferdowsi and Sanabad Universities. 📚✍️ Known for her analytical skills and academic dedication, Vahideh continues to contribute significantly to industrial systems and decision sciences. 🔍📈 Her collaborative spirit and teaching experience further highlight her dynamic role in academia. 👩‍🏫🌐

Professional Profile:

SCOPUS

Education & Experience:

Vahideh earned her Ph.D. in Industrial Management (2019–2024) 🎓 from Ferdowsi University, where her thesis focused on IoT-based maintenance and human error modeling. 📡🛠️ She also completed an M.Sc. in Industrial Management (2014–2017) with a high GPA of 18.96/20 📚 and a B.Sc. in Industrial Engineering (2008–2012). 🏗️ Her academic journey led to research fellow roles at Ferdowsi University (2021–2023) and Sanabad University (2023–2024). 🔬🏛️ In addition to research, she has taught Operations Research, Strategic Management, and Multi-Criteria Decision Making. 👩‍🏫 Her experience reflects a strong foundation in both theory and application. 💼🧮

Professional Development:

Vahideh continually builds her academic and technical skills through professional development. 📈💡 She has mastered analytical and modeling tools such as Python, MATLAB, GAMS, LINGO, LaTeX, and Vensim. 💻📐 Her commitment to research excellence is evident in her publications in Scopus-indexed journals 📄🔍 and her work on complex topics like green supply chain management and pandemic response strategies. 🌍📦 She actively contributes to knowledge dissemination through teaching, collaborative research, and methodological innovation. 📊🧠 Her engagement with multidisciplinary topics ensures she remains at the forefront of industrial and systems engineering. 🚀📘

Research Focus:

Vahideh’s research spans across multiple domains in industrial management. 🏭🔍 Her core interests include supply chain management, optimization, and maintenance planning. 🧾🛠️ She also explores the effects of human error, reliability analysis, and inventory control systems. ⚙️🧠📦 A significant part of her work integrates the Internet of Things (IoT) 🌐 with system dynamics and mathematical modeling 📊📉 to improve industrial decision-making. Her goal is to create smarter, more resilient, and sustainable industrial systems. 🌱💡 Her innovative contributions are driving progress in operational efficiency and risk reduction. 🚚📈

Awards & Honors:

While specific awards were not listed, Vahideh’s academic record speaks to her excellence. 🌟 She achieved outstanding GPAs in both her Ph.D. (19.49/20) and M.Sc. (18.96/20) programs. 🥇📘 Her research has been recognized with publications in high-impact international journals like Process Integration and Optimization for Sustainability and Journal of Industrial and Management Optimization. 📚✨ She has contributed novel methodologies in green supplier selection, VIKOR optimization, and system dynamics during COVID-19. 🧪🌐 Her roles as research fellow at top Iranian universities also reflect her academic merit and potential. 🏛️🔬

Publication Top Notes

1. Markov Chain-Based Model for IoT-Driven Maintenance Planning with Human Error and Spare Part Considerations

Authors: Bafandegan Emroozi, Vahideh; Doostparast, Mahdi
Journal: Reliability Engineering and System Safety
Year: 2025
Access: Open Access
Citations: 0 (as of now)

🔍 Summary:
This article introduces a novel Markov chain-based framework that integrates the Internet of Things (IoT) into industrial maintenance planning. The model accounts for human error probabilities and spare part availability, creating a dynamic and realistic approach to predictive maintenance. 📈 The use of Markov chains enables the system to model stochastic transitions between equipment states, improving decision-making accuracy. 🤖📦 The study enhances reliability and safety in industrial systems by aligning IoT data with probabilistic risk and resource planning, offering a scalable tool for real-time maintenance strategy optimization. 🛠️📊

2. Enhancing Industrial Maintenance Planning: Optimization of Human Error Reduction and Spare Parts Management

Authors: Bafandegan Emroozi, Vahideh; Kazemi, Mostafa; Doostparast, Mahdi
Journal: Operations Research Perspectives
Year: 2025
Access: Open Access
Citations: 0 (as of now)

🔍 Summary:
This paper proposes an optimization model aimed at improving maintenance planning by focusing on human error mitigation and efficient spare parts management. 👷⚙️ It applies advanced operations research techniques to identify cost-effective strategies for minimizing failures and delays due to incorrect human actions or resource shortages. The model bridges the gap between human factors engineering and logistical planning, integrating real-time data and decision analysis. 🧠📦 It offers a comprehensive framework suitable for modern industries aiming to balance cost, reliability, and safety. 🧾📉

Conclusion

Vahideh Bafandegan Emroozi exemplifies the qualities celebrated by Women in Research Awards: innovation, impact, leadership, and academic excellence. 🌟 Her work addresses critical industrial challenges through smart technologies and rigorous modeling, while her dedication to teaching and mentoring amplifies her influence. As a pioneering female researcher in a highly technical and traditionally male-dominated field, she is not only technically accomplished but also a role model for aspiring women in STEM. 🧠🔬👩‍🏫 She is highly deserving of recognition through a Women Researcher Award.

 

Dr. Fei Huang | electronic textiles | Best Researcher Award

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Dr. Fei Huang | electronic textiles | Best Researcher Award

lecturer at Jiangsu College of Engineering and Technology , China

Fei Huang 👩‍🔬 is a dynamic researcher and lecturer in textile engineering, specializing in flexible and stretchable strain sensors 🧵🔋. She earned her PhD from Donghua University under the guidance of Prof. Jiyong Hu and Xiong Yan 🎓. Her cutting-edge work on wearable sensor technologies has led to several high-impact journal publications and innovative patents 📄💡. Currently teaching at Jiangsu College of Engineering and Technology 👩‍🏫, she blends scientific rigor with practical application. Fei is passionate about smart textiles, precision agriculture 🌿, and human-motion tracking 👟. Her skills in research, technology, and collaboration make her a rising star 🌟 in smart material science.

Professional Profile

SCOPUS

ORCID

Education & Experience 

Fei Huang began her academic career at Jiangnan University 🏫, where she earned a B.S. in Textile Science and Engineering 🎓 (2015–2019). She pursued a PhD at Donghua University in Shanghai 🧪, researching flexible and stretchable strain sensors under Professors Jiyong Hu and Xiong Yan (2019–2025) 📘. Following her doctorate, she joined Jiangsu College of Engineering and Technology in Nantong as a lecturer 👩‍🏫 in March 2025. Her academic journey reflects a strong foundation in textile science 🧵 and a commitment to advancing wearable sensor technology 🤖. Fei has evolved into an experienced researcher and educator in smart materials.

Professional Development 

Fei Huang has developed a diverse skill set combining textile engineering 🧵, materials science 🧬, and sensor technology 📊. She is proficient in software like MATLAB, SPSS, ABAQUS, CAD, and Photoshop 💻, supporting her deep technical analysis and design capabilities. Fluent in both Mandarin and English 🌐, she collaborates effectively on global research projects. She demonstrates strength in laboratory techniques, literature review, and data interpretation 🔍. With hobbies including running, hiking, and reading 🏃‍♀️📚, Fei maintains balance in her academic life. Her commitment to continuous learning and innovation 🔄 positions her as a forward-thinking researcher in wearable technology.

Research Focus 

Fei Huang’s research focuses on flexible, stretchable, and wearable strain sensors 🧵🔋. Her innovations target real-time motion monitoring 🦵, gait analysis 🚶‍♀️, and precision agriculture 🌾 through sensor integration into textiles. She designs yarn-based capacitive and resistive sensors with ultra-low detection limits and high responsiveness ⚙️. Her work explores encapsulation, structural design, and braiding technologies to improve sensor performance and durability 🔄. Fei also investigates graphene-based devices for environmental sensing 🌿. Her contributions lie at the intersection of smart textiles, wearable electronics, and functional materials, aiming to make textile-integrated electronics practical for health, sports, and agricultural use 🤖🌍.

Awards & Honors

Fei Huang has received notable awards for her academic and research achievements 🏆. She earned the National Scholarship (2017–2018) for outstanding performance 🌟 and was honored with First-Class (2015–2016) and Third-Class (2016–2017) Academic Scholarships 📘. In 2022, she received the Graduate Student Innovation Fund and Fundamental Research Funds for the Central Universities at Donghua University 💡—a testament to her innovative sensor work. These honors reflect her dedication to academic excellence and research impact 📖. With her track record of recognition and productivity, Fei stands out as a promising contributor to the future of smart material technologies 🧪.

Publication Top Notes

1. A Wide-linear-range and Low-hysteresis Resistive Strain Sensor Made of Double-threaded Conductive Yarn for Human Movement Detection

Journal: Journal of Materials Science & Technology
Publication Date: February 2024
DOI: 10.1016/j.jmst.2023.06.047
Authors: Fei Huang, Jiyong Hu, Xiong Yan

🔍 Summary:
This study introduces a novel resistive strain sensor composed of double-threaded conductive yarn engineered for wide linear range and minimal hysteresis. The sensor demonstrates high sensitivity and durability, making it ideal for human movement detection applications such as wearable health monitors and motion tracking suits. The work emphasizes material optimization and structural innovation to enhance repeatability and responsiveness, paving the way for smart textile integration in biomechanical systems.

2. High-linearity, Ultralow-detection-limit, and Rapid-response Strain Sensing Yarn for Data Gloves

Journal: Journal of Industrial Textiles
Publication Date: June 2022
DOI: 10.1177/15280837221084369
Authors: Fei Huang, Jiyong Hu, Xiong Yan, Fenye Meng

🔍 Summary:
This paper presents a strain sensing yarn with exceptional linearity, low detection threshold, and fast response time. Designed specifically for data gloves, this sensor enables accurate hand gesture recognition and real-time motion monitoring. The research blends material engineering and textile design to create a sensor with strong durability, making it suitable for immersive human–machine interface technologies, virtual reality, and robotic control applications.

3. Review of Fiber- or Yarn-Based Wearable Resistive Strain Sensors: Structural Design, Fabrication Technologies and Applications

Journal: Textiles
Publication Date: February 2022
DOI: 10.3390/textiles2010005
Authors: Fei Huang, Jiyong Hu, Xiong Yan

🔍 Summary:
This comprehensive review covers recent advancements in fiber- and yarn-based resistive strain sensors for wearable electronics. The authors analyze structural designs, material compositions, and fabrication techniques, along with their applications in health monitoring, sports, and robotics. The review serves as a valuable guide for researchers and engineers developing next-generation smart textiles, offering insight into performance optimization and integration strategies for flexible electronics.

Conclusion

Fei Huang’s originality, impact, and interdisciplinary contributions make her an ideal recipient for awards such as:
Best Researcher Award, AI and Smart Technology Innovation Awards, or Young Scientist Award.
Her commitment to creating intelligent wearable systems that address real-world needs places her at the forefront of next-generation sensor research.

 

 

 

Prof . Len Gelman | Artificial Intelligence | Best Researcher Award

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Prof . Len Gelman | Artificial Intelligence | Best Researcher Award

Prof. Len Gelman , University of Huddersfield , United Kingdom

Professor Len Gelman 🇬🇧 is a globally recognized expert in signal processing and condition monitoring 🔍. He currently serves as Chair Professor and Director at the University of Huddersfield 🏫. With over two decades of academic leadership, he has significantly contributed to vibro-acoustics and non-destructive testing 🔧. A Fellow of multiple prestigious organizations 🌐, Prof. Gelman’s international collaborations span across Europe, Asia, and the USA 🌏. His innovations have advanced aerospace and medical diagnostics ✈️🧬. He continues to lead global initiatives and research committees, shaping the future of engineering diagnostics and reliability technologies 🔬🛠️.

Professional Profile

SCOPUS

Education and Experience 

Prof. Len Gelman holds a PhD and Doctor of Science (Habilitation) 🎓, with BSc (Hons) and MSc (Hons) degrees in engineering 📘. He is a British citizen 🇬🇧. Since 2017, he has been a Professor and Chair at the University of Huddersfield 🏛️. Prior to that, he served at Cranfield University (2002–2017) as Chair in Vibro-Acoustical Monitoring 🔊. His distinguished academic journey includes visiting professorships in China 🇨🇳, Denmark 🇩🇰, Poland 🇵🇱, Spain 🇪🇸, Italy 🇮🇹, and the USA 🇺🇸. Prof. Gelman combines deep technical expertise with global educational outreach 🌍👨‍🏫.

Professional Development 

Prof. Gelman has held key international leadership roles including Chair of the International Scientific Committee of the Condition Monitoring Society 🌐. He is a Fellow of BINDT, IAENG, IDE, and HEA 🎖️, and an Academician of the Academy of Sciences of Applied Radio Electronics 🧠. He chairs award and honors committees for top acoustics and vibration institutions 🏅. As Visiting Professor at Tsinghua, Jiao Tong, and Aalborg Universities, among others 🎓, he mentors emerging researchers globally 🌎. Prof. Gelman’s commitment to professional excellence shapes the advancement of diagnostic technologies and engineering education 📈🔧.

Research Focus 

Prof. Gelman’s research focuses on signal processing, vibro-acoustics, and condition monitoring of engineering systems 🔍🔊. His work spans non-destructive testing (NDT), fault diagnostics, and performance optimization in sectors such as aerospace, healthcare, and manufacturing ✈️🏥🏭. He develops advanced algorithms for fault detection and predictive maintenance using machine learning and big data 🧠📊. His interdisciplinary approach benefits both industry and academia 🌐🔬. Prof. Gelman also pioneers applications in medical diagnostics and intelligent systems for real-time monitoring 🧬⚙️. His innovations contribute to safer, more efficient engineering systems across global platforms 🌍🚀.

Awards and Honors 

Prof. Gelman has received numerous prestigious awards for innovation and research excellence 🏅. These include the Rolls-Royce Innovation Award (2012, 2019) ✈️, William Sweet Smith Prize by IMechE 🛠️, and COMADIT Prize by BINDT for impactful contributions to condition monitoring 🧲. He also received Best Paper Awards at CM/MFPT conferences 📄 and recognition from the USA Navy and Acoustical Society of America 🇺🇸🔊. His European and UK fellowships support cutting-edge human capital projects 🧠🇪🇺. He has chaired international committees in NDT and acoustics, continuing to shape future technologies through global leadership and innovation 🌐👨‍🔬.

Publication Top Notes

1. Vibration Analysis of Rotating Porous Functionally Graded Material Beams Using Exact Formulation

  • Citation: Amoozgar, M.R., & Gelman, L.M. (2022). Vibration analysis of rotating porous functionally graded material beams using exact formulation. Journal of Vibration and Control, 28(21–22), 3195–3206. https://doi.org/10.1177/10775463211027883Nottingham Repository+1SAGE Journals+1

  • Summary: This study investigates the free vibration behavior of rotating functionally graded material (FGM) beams with porosity, employing geometrically exact fully intrinsic beam equations. The research considers both even and uneven porosity distributions to simulate manufacturing imperfections. Findings reveal that material gradation and porosity significantly influence natural frequencies and mode shapes, emphasizing the necessity of accounting for these factors in the design and analysis of rotating FGM structures. Huddersfield Research Portal+2SAGE Journals+2Nottingham Repository+2

2. Vibration Health Monitoring of Rolling Bearings Under Variable Speed Conditions by Novel Demodulation Technique

  • Citation: Zhao, D., Gelman, L.M., Chu, F., & Ball, A.D. (2021). Vibration health monitoring of rolling bearings under variable speed conditions by novel demodulation technique. Structural Control and Health Monitoring, 28(2), e2672. https://doi.org/10.1002/stc.2672Wiley Online Library

  • Summary: Addressing the challenges of diagnosing rolling bearing faults under variable speed conditions, this paper introduces an optimization-based demodulation transform method. The technique effectively estimates fault characteristic frequencies with weak amplitudes and adapts to time-varying rotational speeds. Validation through simulations and experimental data demonstrates the method’s superior diagnostic capabilities compared to existing approaches. Huddersfield Research Portal+1Wiley Online Library+1

3. Novel Method for Vibration Sensor-Based Instantaneous Defect Frequency Estimation for Rolling Bearings Under Non-Stationary Conditions

  • Citation: Zhao, D., Gelman, L.M., Chu, F., & Ball, A.D. (2020). Novel method for vibration sensor-based instantaneous defect frequency estimation for rolling bearings under non-stationary conditions. Sensors, 20(18), 5201. https://doi.org/10.3390/s20185201MDPI

  • Summary: This research presents a novel approach for estimating instantaneous defect frequencies in rolling bearings operating under non-stationary conditions. Utilizing vibration sensor data, the method enhances the accuracy of defect frequency estimation, facilitating improved fault diagnosis in dynamic operational environments. MDPI

4. Novel Fault Identification for Electromechanical Systems via Spectral Technique and Electrical Data Processing

  • Citation: Ciszewski, T., Gelman, L.M., & Ball, A.D. (2020). Novel fault identification for electromechanical systems via spectral technique and electrical data processing. Electronics, 9(10), 1560. https://doi.org/10.3390/electronics9101560MDPI

  • Summary: This paper introduces an innovative method for fault identification in electromechanical systems by integrating spectral analysis with electrical data processing. The approach enhances the detection and diagnosis of faults, contributing to the reliability and efficiency of electromechanical system operations. MDPI

5. Novel Prediction of Diagnosis Effectiveness for Adaptation of the Spectral Kurtosis Technology to Varying Operating Conditions

  • Citation: Kolbe, S., Gelman, L.M., & Ball, A.D. (2021). Novel prediction of diagnosis effectiveness for adaptation of the spectral kurtosis technology to varying operating conditions. Sensors, 21(20), 6913. https://doi.org/10.3390/s21206913PMC

  • Summary: This study proposes two novel consistency vectors combined with machine learning algorithms to adapt spectral kurtosis technology for optimal gearbox damage diagnosis under varying operating conditions. The approach enables computationally efficient online condition monitoring by predicting diagnosis effectiveness, thereby improving maintenance strategies.

Conclusion

Professor Len Gelman exemplifies the ideal candidate for the Best Researcher Award due to his groundbreaking contributions to condition monitoring, signal processing, and diagnostic technologies. His work not only advances academic knowledge but also addresses critical industry challenges in aerospace, healthcare, and manufacturing. With a sustained record of high-impact research, international leadership, and technological innovation, he stands out as a world-class researcher whose work continues to benefit both academia and society.

 

Dr. MD Shahjalal | cancer epidemiology | Young Researcher Award

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Dr. MD. Shahjalal | cancer epidemiology |Young Researcher Award

Dr. MD Shahjalal, North South University , Bangladesh

Md. Shahjalal is an accomplished public health researcher from Bangladesh 🇧🇩 with a strong academic and research background in cancer epidemiology 🎗️ and global health 🌍. He holds an MPH in Epidemiology from North South University 🎓 and a BUMS from the University of Dhaka 🏥. Currently serving as Research Coordinator at Research Rats, he has contributed to numerous international publications 📚, focusing on healthcare economics and cancer care. He is also an academic editor for PLoS ONE 🖋️ and recipient of national health research grants 🏅. His work aims to reduce disparities in cancer outcomes and improve population health 💡.

Professional Profile

ORCID

Education & Experience 

Md. Shahjalal earned his Master of Public Health (MPH) in Epidemiology 🧠 from North South University (2018–2019) and Bachelor of Unani Medicine & Surgery (BUMS) 🩺 from the University of Dhaka (2012–2017). His research journey includes roles as Research Assistant (2019–2020), Research Associate (2021–2022), and currently as Research Coordinator at Research Rats Bangladesh (2023–Present) 🔬. He also worked as a Teaching Assistant in Biostatistics at NSU 📊. Through these roles, he has developed expertise in grant writing, data analysis, and multi-disciplinary research coordination 🤝, contributing to impactful studies in cancer and public health.

Professional Development 

Shahjalal’s professional growth is rooted in continuous learning and hands-on research 📈. From coordinating multi-site cancer studies to assisting in international grant applications 💼, he has gained advanced skills in public health research, data modeling, and scientific writing 📄. His expertise spans SPSS, STATA, and EXCEL for complex analyses 📊. As an academic editor for PLoS ONE 🧑‍⚖️, he stays engaged with global research trends. His participation in peer-reviewed publications and collaborative projects reflects his commitment to evidence-based solutions in healthcare. With strong communication and leadership skills 💬, he thrives in multidisciplinary teams and policy-relevant research environments 🌐.

Research Focus 

Md. Shahjalal’s research primarily targets cancer epidemiology 🎗️, health-related quality of life, and health economics 💰. His work evaluates systemic and radiation therapy outcomes, mental health disparities 🧠, and economic burdens faced by cancer survivors in Bangladesh. He also explores nutritional issues among vulnerable populations 🥣 and investigates access disparities in cardio-oncology care ❤️. Utilizing statistical modeling and health metrics like EQ-5D-5L, he aims to inform policies for equitable healthcare access 🏥. His contributions support both national and international cancer care reforms 🌍, ensuring that scientific insights lead to real-world impact for underserved communities 📢.

Awards & Honors 

Md. Shahjalal has been recognized for his impactful research through competitive grants and editorial roles 🏆. He received the Bangladesh Health Research Grant (2024) as Principal Investigator for a TB-related nutrition project 🧪 and the General Pharmaceutical Health Research Grant (2023) as Co-Investigator on cardio-oncology care disparities ❤️. With an H-index of 8 on Scopus 📈, his scholarly influence is growing steadily. He serves as an Academic Editor for PLoS ONE 🖋️, a Scopus Q1 journal, reflecting peer recognition of his academic contributions. These honors mark him as a rising leader in public health and medical research 🌟.

Publication Top Notes

1.🎯 Radioimmunotherapy in Cancer Treatment

Title: Assessing the Clinical Effectiveness of Radioimmunotherapy with Combined Radionuclide/Monoclonal Antibody Conjugates in Cancer Treatment: Insights from Randomised Clinical Trials
Journal: Cancers (2025-04-23)
DOI: 10.3390/cancers17091413
➤ Examines advanced radioimmunotherapy effectiveness using combined agents in cancer trials.

2.💰 Cancer Treatment Costs in Bangladesh

Title: Cancer Driven Direct Medical Costs in Bangladesh: Evidence from Patient Perspective
Journal: Journal of Cancer Policy (2025-03)
DOI: 10.1016/j.jcpo.2025.100565
➤ Highlights patient-incurred medical costs and economic stress related to cancer care.

3.🏥 Unplanned Hospitalisation After Cancer Therapy

Title: Emerging Burden of Post-Cancer Therapy Complications on Unplanned Hospitalisation and Costs Among Australian Cancer Patients: A Retrospective Cohort Study Over 14 Years
Journal: Scientific Reports (2025-02-08)
DOI: 10.1038/s41598-025-89247-y
➤ Evaluates long-term health complications and hospital costs post cancer therapy in Australia.

4.🥗 Diet and Dyslipidemia in Type 2 Diabetes

Title: Association between Mediterranean Diet Adherence and Dyslipidemia Among Type-2 Diabetes Mellitus Patients in Dhaka, Bangladesh
Journal: Discover Public Health (2024-10-22)
DOI: 10.1186/s12982-024-00267-x
➤ Investigates the dietary impact on lipid profiles in diabetic patients.5.

5.💸 Economic Burden on Cancer Survivors

Title: Economic Burden of Healthcare Services on Cancer Survivors in Bangladesh
Journal: Cancer Reports (2024-08)
DOI: 10.1002/cnr2.2144
➤ Quantifies healthcare costs and financial strain on cancer survivors.

Conclusion

Md. Shahjalal demonstrates exceptional research potential and output in the interdisciplinary field of cancer epidemiology, public health, and health economics. His impactful publications, leadership in funded research, and commitment to health equity mark him as a leading candidate for the Best Researcher Award. His work not only contributes academically but also has tangible implications for national and global health policy.

 

 

 

 

Ms. Somaye Mohammadi | Vibration Analysis | Best Researcher Award

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Ms. Somaye Mohammadi | Vibration Analysis | Best Researcher Award

Assistant Professor , Sharif University of Technology, Best Researcher Award

Dr. S. Mohammadi is an accomplished mechanical engineer with a strong focus on vibration analysis, acoustics, and machine condition monitoring 🛠️🔍. He earned his Ph.D. from Amirkabir University of Technology, where he specialized in tire/road noise prediction and reduction 🔊🛣️. His research spans intelligent fault diagnosis, dynamic balancing, and advanced signal processing 📊🤖. With a deep commitment to industrial problem-solving and academic excellence, Dr. Mohammadi has published extensively in top-tier journals and conferences 🧠📚. His collaborative work with leading automotive and petrochemical industries demonstrates his practical impact in engineering innovation 🚗🏭.

Professional Profile

ORCID

Education and Experience

Dr. Mohammadi holds a Ph.D. (2016–2021), M.Sc. (2014–2016), and B.Sc. (2010–2014) in Mechanical Engineering from Amirkabir University of Technology 🎓🇮🇷. His doctoral research focused on modeling and predicting tire/road noise using semi-analytical and statistical methods 🔊📈. He has extensive experience in academia and industry, collaborating with IPCO and other companies on dynamic balancing, machine reliability, and condition monitoring ⚙️🏗️. He has published over 25 journal and conference papers and actively participates in technical events and applied engineering research, bridging theory and practice effectively 📚🛠️.

Professional Development

Dr. Mohammadi has significantly contributed to professional development in mechanical engineering through active involvement in research, publications, and conferences 🎤📄. He has attended numerous national and international events such as CMFD, ISAV, and IRNDT, presenting cutting-edge research on condition monitoring, acoustic diagnostics, and vibration analysis 🔍🧠. He continuously updates his skills in AI, machine learning, and signal processing for predictive maintenance and fault detection 🤖📊. His multidisciplinary approach enables practical solutions for complex industrial problems, making him a valuable contributor to academic and engineering communities 🌐🔧.

Research Focus

Dr. Mohammadi’s  research centers on mechanical vibrations, acoustics, and intelligent fault detection using AI and signal processing 🧠🔊. His work addresses real-world engineering challenges like tire noise reduction, gearbox diagnostics, and turbine reliability ⚙️🏭. He combines statistical methods with machine learning to predict failures and optimize performance in rotating machinery, engines, and industrial systems 🤖🔧. His interdisciplinary expertise bridges mechanical design, acoustics, and data analytics to improve machinery health monitoring and performance efficiency 📉📈. His research supports sustainable and cost-effective engineering operations 🔄💡.

Awards and Honors

Dr. Mohammadi has received multiple recognitions for his research excellence and technical contributions 🎖️📚. He has been invited to present at prestigious conferences like CMFD, ISAV, and IRNDT and collaborated with top engineers and institutions on vibration and fault diagnosis projects 🤝🔍. His publications in high-impact journals such as Applied Acoustics, Journal of Vibration and Control, and Machines have earned critical acclaim from the academic community 🌟📰. He was also involved in award-supported industrial collaborations, including projects with IPCO and petrochemical companies, showcasing practical impact and innovation 🏭🏅.

Publication Top Notes

1.🔍 Intelligent Diagnosis of Rolling Element Bearings Under Various Operating Conditions Using an Enhanced Envelope Technique and Transfer Learning
📅 Published: April 2025 – Machines

📌 DOI: 10.3390/machines13050351

👥 Co-authors: Ali Davoodabadi, Mehdi Behzad, Hesam Addin Arghand, Len Gelman

🧠 Key Contribution: Developed an innovative technique combining advanced signal processing (enhanced envelope detection) with transfer learning, significantly improving fault diagnosis accuracy across variable operating conditions in rolling bearings. This paper bridges AI and mechanical reliability – a cutting-edge intersection in engineering diagnostics.

2.📊 A Comprehensive Study on Statistical Prediction and Reduction of Tire/Road Noise
📅 Published: October 2022 – Journal of Vibration and Control

📌 DOI: 10.1177/10775463211013184

👥 Co-authors: Abdolreza Ohadi, Mostafa Irannejad-Parizi

🧠 Key Contribution: Offers a data-driven, statistical framework for predicting and mitigating tire/road interaction noise, addressing environmental and comfort challenges in vehicle design. The study integrates modeling, statistical methods, and experimental validation, making it valuable for the automotive industry.

3.🔉 Effect of Modeling Sidewalls on Tire Vibration and Noise

📅 Published: September 2022 – Journal of Automobile Engineering (IMechE Part D)

📌 DOI: 10.1177/09544070211052368

👥 Co-author: Abdolreza Ohad

🧠 Key Contribution: Investigated how sidewall modeling precision influences vibrational behavior and noise in tires. The research advanced numerical tire modeling techniques, which are essential for designing quieter, more stable vehicles.

Conclusion

Dr. Mohammadi’s blend of deep theoretical knowledge, strong publication output, practical industrial applications, and multidisciplinary research makes him a standout researcher. His work addresses real-world engineering challenges with smart solutions, reinforcing his eligibility for the Best Researcher Award. He not only contributes to advancing scientific understanding but also to improving industrial reliability and performance — hallmarks of a truly impactful researcher 🏅🚀.

Assoc. Prof. Dr .VietTran | Emergency Medicine | Excellence in Research

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Assoc. Prof. Dr. VietTran | Emergency Medicine | Excellence in Research

Director, Tasmanian Emergency Medicine Research Institute ,Tasmanian Emergency Medicine Research Institute , Australia


A/Prof Viet Tran 🇦🇺 is a distinguished Emergency Physician and academic leader based in Tasmania. As Associate Professor at the University of Tasmania and Deputy Director at Royal Hobart Hospital’s Emergency Department, he blends clinical excellence with impactful research. Founder of TASER and DoctorsWriting.com, he champions innovation in emergency care 🏥, knowledge translation 📚, and medical education 👩‍⚕️. With board roles and leadership across research and health governance, he shapes the future of emergency medicine both nationally and globally 🌏. His commitment to clinical advancement, policy influence, and teaching has earned him widespread respect and recognition 🏅.

Professional Profile:

ORCID

Education & Experience:

Dr. Viet Tran holds a Bachelor of Medical Science, Bachelor of Medicine & Surgery 🎓 from the University of Tasmania, and is a Fellow of the Australasian College for Emergency Medicine (FACEM) 🩺. With over a decade in emergency care, he currently serves as Deputy Director at Royal Hobart Hospital and Associate Professor at UTAS. His expertise spans clinical research, trauma care, and medical education 💉📘. He also leads state-level initiatives such as the Tasmanian Emergency Departments Network and the Health Senate, reflecting his deep commitment to system-wide healthcare improvement in Australia 🇦🇺.

Professional Development

A/Prof Tran is actively involved in professional leadership and governance 🏛️. He holds board positions with the Post Graduate Medical Council of Tasmania and the Emergency Medicine Foundation 🌐. As a founding chair of multiple research and clinical networks in Tasmania, he fosters collaboration across institutions 🧠. His work with national bodies like the Australasian College for Emergency Medicine highlights his role in shaping emergency medicine policy and clinical trials 🤝. Through mentoring PhD students and developing health pathways, he cultivates the next generation of clinician-researchers while advancing medical knowledge and innovation 🧬📊.

Research Focus

A/Prof Tran’s research spans emergency medicine systems, trauma care 🚑, infectious disease diagnostics (e.g., COVID-19 rapid testing 🧪), implementation science, and health services reform 🏥. As Director of the TASER institute and Chair of ACEM’s Research Committee, he champions data-driven clinical improvement. His work integrates epidemiology 📊, clinical trials, and knowledge translation with a focus on real-world impact—ranging from trauma outcomes to opioid prescribing and elder care 🧓. He also contributes to national surveillance systems and predictive analytics, ensuring timely, evidence-based interventions that enhance emergency care in Australia and beyond 🌏.

Awards & Honors

A/Prof Tran’s dedication to excellence has earned him prestigious accolades, including the 2023 Vice Chancellor’s Award for Teaching Excellence 🥇 and the College of Health and Medicine’s Teaching and Early Career Research Awards 🧠📖. His impactful work is backed by over $3 million in grants as Chief Investigator and $23 million as Associate Investigator 💰. Recognized for both leadership and innovation, he serves on editorial boards and research councils, and was named Oceanic Representative for the International Federation for Emergency Medicine Research 🌐. These honors reflect his influence in academic medicine, teaching, and global emergency care advancement 🚨.

Publication Top Notes:

📚 Notable Recent Publications (Selected)

1.Emergency Department Clinical Quality Registries: A Scoping Review
Healthcare, 2025
🔍 A comprehensive scoping review exploring the current landscape, challenges, and opportunities of clinical quality registries in emergency departments.
👉 DOI: 10.3390/healthcare13091022

2.Incidence and 12-Month Outcomes of Fracture Types Associated with Abuse in Children Under Three
Future, 2025
🧸 Investigates patterns and long-term outcomes of abuse-related fractures in young children, highlighting the critical role of emergency departments in early detection.
👉 DOI: 10.3390/future3010003

3.Making the Most of What We Have: The Future of Emergency Department Data
Emergency Medicine Australasia, 2024
📊 Discusses the strategic potential of ED data to enhance patient outcomes and system efficiency across Australasia.
👉 DOI: 10.1111/1742-6723.14475

4.Use and Impact of Clinical Pathways Across Healthcare Settings: Protocol for an Umbrella Review
Journal of Evaluation in Clinical Practice, 2024
🌐 Protocol for a high-level synthesis of global evidence regarding clinical pathways and their outcomes in diverse healthcare environments.
👉 DOI: 10.1111/jep.14201

5.Procedural Sedation and Analgesia in an Australian ED: Insights from a New Registry
Anesthesia Research, 2024
💉 Presents data from a novel sedation registry, enhancing understanding of practice patterns and safety in procedural sedation.
👉 DOI: 10.3390/anesthres1030015

Conclusion:

A/Prof Viet Tran exemplifies the qualities of a modern academic clinician-researcher: intellectually rigorous, clinically relevant, and impact-driven. His translational research, innovative leadership, and dedication to both scholarly excellence and public service make him a prime candidate for the Excellence in Research Award.

Prof. Dr. Jose Vicente | Civil Aviation | Best Researcher Award

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Prof. Dr. Jose Vicente | Civil Aviation | Best Researcher Award

Somaye Mohammadi | Engineering | Best Researcher Award

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Ms .  Somaye Mohammadi | Engineering | Best Researcher Award

Assistant Professor at Sharif University of Technology , Iran

Dr. Somaye Mohammadi, an Assistant Professor at Sharif University of Technology, is an accomplished researcher in mechanical engineering, specializing in acoustics, vibrations, condition monitoring, and intelligent diagnostics. With over 13 journal publications and more than 30 conference papers, her research spans both theoretical and applied domains. She has collaborated extensively with major industries, contributing to real-world problem-solving and development of intelligent diagnostic systems. Recognized nationally, she has received multiple best paper awards and was honored for the best Ph.D. thesis in acoustics and vibration. Her leadership roles in major conferences and active teaching at top universities further reflect her academic influence. Technically proficient in a wide range of engineering and AI software, she demonstrates strong research and teaching capabilities. While expanding international collaborations and citation metrics would further enhance her profile, her current achievements and impact make her a highly deserving candidate for the Best Researcher Award.

Professional Profile  

Education🎓

Dr. Somaye Mohammadi holds a Ph.D. in Mechanical Engineering from Amirkabir University of Technology (Tehran Polytechnic), completed in 2021 with an exceptional GPA of 19.58/20. Her doctoral research, supervised by Prof. Abdolreza Ohadi, focused on modeling and predicting tire/road noise using semi-analytical and statistical methods, earning national recognition as the best Ph.D. thesis by the Iranian Society of Acoustics and Vibration. She also earned her M.Sc. and B.Sc. degrees in Mechanical Engineering from the same university with GPAs of 19.17/20 and 18.71/20, respectively. Her M.Sc. thesis addressed the dynamic balancing of a three-cylinder engine and was supported by IranKhodro Powertrain Company. As an undergraduate, her thesis explored the dynamic response of a passive biped robot. Throughout her academic journey, Dr. Mohammadi consistently ranked among the top students and gained admission into advanced programs through Iran’s elite talent quotas, reflecting her strong academic performance and dedication to engineering research.

Professional Experience📝

Dr. Somaye Mohammadi has built a robust professional profile through diverse academic and industrial roles. She currently serves as an Assistant Professor at Sharif University of Technology, where she also holds leadership positions as the Director of Public Relations and Deputy of the Continuous Professional Development Center. Previously, she was a postdoctoral researcher under Prof. Mehdi Behzad, contributing to advanced diagnostics in mechanical systems. She has worked as a CAE expert at Zamyad Company and has collaborated with Behravesh Vibration Engineering Company. Her consulting experience includes industrial vibration analysis, diagnostics, and reliability enhancement across sectors such as petrochemicals, water utilities, and mining. She has held key roles in organizing major national conferences, including Executive Secretary and Session Chair. Additionally, she has taught at top Iranian universities and delivered specialized workshops. Her hands-on engagement with industry, academia, and research leadership showcases a well-rounded and impactful professional journey in mechanical engineering.

Research Interest🔎

Dr. Somaye Mohammadi’s research interests lie at the intersection of mechanical engineering, signal processing, and artificial intelligence, with a strong focus on vibration and noise analysis, condition monitoring, and intelligent fault diagnostics. Her work addresses real-world challenges in rotating machinery, industrial gearboxes, and vehicle dynamics, aiming to enhance system reliability and operational efficiency. She is particularly interested in applying machine learning and deep learning techniques to predict mechanical failures and monitor the health of complex systems. Her expertise also includes tire/road noise modeling, dynamic balancing, modal analysis, and the design of experiments. By integrating traditional engineering methods with modern AI-driven approaches, Dr. Mohammadi develops innovative diagnostic solutions for use in industries such as oil and gas, petrochemicals, water systems, and transportation. Her interdisciplinary research not only advances theoretical understanding but also has significant practical applications, making her a key contributor to the evolving field of intelligent mechanical systems.

Award and Honor🏆

Dr. Somaye Mohammadi has received numerous awards and honors in recognition of her academic excellence and impactful research. She was awarded the Best Ph.D. Thesis by the Iranian Society of Acoustics and Vibration in 2021 for her groundbreaking work on tire/road noise modeling. Her research papers have been consistently recognized, with two of her articles winning Best Academic Paper Awards in Condition Monitoring at the CMFD conferences in 2023 and 2024. Throughout her academic journey, she has demonstrated outstanding performance, earning first rank in both her Ph.D. and M.Sc. programs and second rank in her undergraduate studies. She was also admitted to both her master’s and doctoral programs without entrance exams under Iran’s elite talent quotas. Additionally, she received the prestigious National Elite Foundation Scholarship for three consecutive years. These honors reflect her dedication, intellectual capabilities, and significant contributions to the field of mechanical engineering and applied diagnostics.

Research Skill🔬

Dr. Somaye Mohammadi possesses a diverse and advanced set of research skills that span both theoretical and applied aspects of mechanical engineering. Her core expertise includes engineering acoustics, industrial vibrations, condition monitoring, and dynamic system modeling. She is proficient in utilizing statistical analysis, design of experiments, and optimization techniques to solve complex engineering problems. Dr. Mohammadi has a strong command of artificial intelligence and machine learning tools, which she effectively applies in predictive maintenance, fault diagnostics, and noise reduction studies. Her skills extend to deep learning, signal processing, and modal analysis, enabling her to develop intelligent systems for health monitoring of machinery and rotating equipment. Additionally, she is experienced in using advanced engineering software such as MATLAB/Simulink, Python, ANSYS, ADAMS, LMS Virtual.Lab, and Design-Expert. This comprehensive skill set allows her to integrate traditional mechanical principles with modern computational techniques, making her a highly capable and innovative researcher in her field.

Conclusion💡 

In conclusion, Dr. Somaye Mohammadi is a distinguished researcher and academic whose work bridges the gap between theoretical research and industrial application in mechanical engineering. With a strong foundation in acoustics, vibrations, and intelligent diagnostics, she has made significant contributions to the development of predictive maintenance systems and noise reduction technologies. Her excellence is reflected in numerous high-impact publications, multiple best paper awards, and recognition for her Ph.D. thesis. Dr. Mohammadi’s ability to lead interdisciplinary projects, collaborate with industry, and engage in high-level academic and organizational roles demonstrates her well-rounded expertise and dedication. Her proficiency in advanced analytical tools and AI-based methods enhances her capacity to innovate and solve real-world engineering challenges. As a top-ranked student, award-winning researcher, and active educator, she exemplifies the qualities of a leading academic in her field. Dr. Mohammadi is undoubtedly a strong candidate for the Best Researcher Award, with a promising trajectory of continued impact and excellence.

Publications Top Noted✍

  • Title: How local slopes stabilize passive bipedal locomotion
    Authors: AT Safa, S Mohammadi, SE Hajmiri, M Naraghi, A Alasty
    Year: 2016
    Citations: 29

  • Title: A novel approach to design quiet tires, based on multi-objective minimization of generated noise
    Authors: S Mohammadi, A Ohadi
    Year: 2021
    Citations: 28

  • Title: Stability improvement of a dynamic walking system via reversible switching surfaces
    Authors: AT Safa, S Mohammadi, M Naraghi, A Alasty
    Year: 2018
    Citations: 13

  • Title: A comprehensive study on statistical prediction and reduction of tire/road noise
    Authors: S Mohammadi, A Ohadi, M Irannejad-Parizi
    Year: 2022
    Citations: 8

  • Title: Introducing a procedure for predicting and reducing tire/road noise using a fast-computing hybrid model
    Authors: S Mohammadi, A Ohadi
    Year: 2022
    Citations: 8

  • Title: Effect of modeling sidewalls on tire vibration and noise
    Authors: S Mohammadi, A Ohadi
    Year: 2022
    Citations: 4

  • Title: Multi-objective optimization of counterweights: a substitute for the balance shaft or mass unbalancing in three-cylinder engines
    Authors: S Mohammadi, A Ohadi, R Keshavarz
    Year: 2018
    Citations: 3