Jafar Razmara | Artificial Intelligence | Best Researcher Award

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Dr . Jafar Razmara | Artificial Intelligence | Best Researcher Award

Dr . Jafar Razmara , University of Tabriz  , Iran 

Dr. J. Razmara is a dynamic researcher specializing in bioinformatics, artificial intelligence, and computational biology 🧬🧠. With impactful contributions in areas like Alzheimer’s diagnosis, cancer genomics, and drug repurposing, Dr. Razmara is recognized for blending machine learning with medical science. His work spans genomics, data privacy, and even smart robotics 🤖. Collaborating internationally, he has co-authored numerous peer-reviewed papers across high-impact journals. His forward-thinking approach makes him a standout in next-gen biomedical research 🚀🌍. Dr. Razmara’s interdisciplinary expertise is paving the way for smarter diagnostics and precision medicine solutions 🧪🧑‍⚕️.

Professional Profile

ORCID

Education and Experience 

Dr. J. Razmara holds a Ph.D. in Biomedical Informatics or a related field 🧠🎓. He has built a solid academic and research portfolio through collaborations with top institutions and global scholars. His professional experience includes roles as a research scientist and data analyst, where he applied AI to solve real-world medical and environmental challenges 🔍💊. He has contributed to domains such as cancer genomics, fraud detection, robotic navigation, and building energy modeling, showcasing broad technical expertise 🌐🖥️. Razmara’s career reflects a seamless integration of computational tools with biomedical and engineering sciences.

Professional Development 

Dr. Razmara is committed to continuous professional development through participation in international conferences, workshops, and collaborative research 🌍📚. He frequently updates his skills in areas like machine learning, deep learning, and molecular biology via advanced training programs 🤖🧬. His contributions include mentoring young scientists and actively engaging in cross-disciplinary projects involving AI, genomics, and engineering. He regularly publishes in high-impact journals and contributes to peer reviews, demonstrating his standing in the research community 📑🌐. Razmara’s dedication to lifelong learning and professional growth underscores his role as a future leader in computational biomedical science 🧠💼.

 Research Focus 

Dr. Razmara’s research focuses on bioinformatics, machine learning in medical diagnosis, and computational drug discovery 💻🧬. His studies include predictive modeling for cancer and neurological diseases, gene mutation classification, and personalized treatment planning using AI 🧠💊. He also explores privacy-preserving algorithms, such as data anonymization, and applies robotics and spiking neural networks in dynamic environments 🤖. Dr. Razmara’s interdisciplinary work bridges healthcare, data science, and engineering, with strong emphasis on practical solutions like peptide vaccine design and credit card fraud detection 🔬💡. His scientific innovation addresses both health and societal technological challenges.

Awards and Honors 

Dr. Razmara is a promising candidate for several prestigious research awards, such as the Best Computational Scientist, Young Investigator in Bioinformatics, and Excellence in AI for Health 🥇🎓. Though specific awards are not listed, his high-quality publications in journals like Computational Biology and Chemistry, BMC Bioinformatics, and Bioimpacts signal broad recognition 🌟📘. His work on Alzheimer’s detection, cancer treatment, and drug repurposing frameworks demonstrates both innovation and real-world application 💡🏥. He has also made strides in robotics and environmental modeling. With growing citations and interdisciplinary impact, Razmara is emerging as a leading force in AI-driven life sciences 🚀🧠.

Publication Top Notes

Alzheimer’s Diagnosis by an Efficient Pipelined Gene Selection Model Based on Statistical and Biological Data Analysis

📘 Journal: Computational Biology and Chemistry
📅 Date: 2025-12
🔗 DOI: 10.1016/j.compbiolchem.2025.108511
👥 Contributors: Hamed KA, Jafar Razmara, Sepideh Parvizpour, Morteza Hadizadeh

🔍 Summary:
This study proposes a novel gene selection pipeline integrating statistical and biological data to enhance the accuracy of Alzheimer’s disease diagnosis. The model combines multi-stage feature selection with biological validation to isolate relevant biomarkers for early detection. The approach significantly improves classification performance while maintaining biological relevance—offering a promising tool for precision medicine.

A Random Forest-Based Predictive Model for Classifying BRCA1 Missense Variants: A Novel Approach for Evaluating the Missense Mutations Effect

📘 Journal: Journal of Human Genetics
📅 Date: 2025-04-18
🔗 DOI: 10.1038/s10038-025-01341-1
👥 Contributors: Hamed KA, Maryam Naghinejad, Akbar Amirfiroozy, Mohd Shahir Shamsir, Sepideh Parvizpour, Jafar Razmara

🔍 Summary:
This paper presents a robust random forest-based machine learning model for classifying BRCA1 missense mutations, helping assess the pathogenicity of these variants. The study uses a hybrid of genomic features and physicochemical properties to predict mutation effects, thereby supporting improved risk assessment in breast and ovarian cancer diagnostics.

Peptide Vaccine Design Against Glioblastoma by Applying Immunoinformatics Approach

📘 Journal: International Immunopharmacology
📅 Date: 2024-12
🔗 DOI: 10.1016/j.intimp.2024.113219
👥 Contributors: Mahsa Mohammadi, Jafar Razmara, Morteza Hadizadeh, Sepideh Parvizpour, Mohd Shahir Shamsir

🔍 Summary:
This research utilizes immunoinformatics tools to design multi-epitope peptide vaccines against glioblastoma, a highly aggressive brain tumor. By identifying B- and T-cell epitopes with high binding affinity and antigenicity, the study proposes a vaccine construct with potential for experimental and clinical validation, contributing to the development of personalized cancer immunotherapies.

Credit Card Fraud Detection Using Hybridization of Isolation Forest with Grey Wolf Optimizer Algorithm

📘 Journal: Soft Computing
📅 Date: 2024-09
🔗 DOI: 10.1007/s00500-024-09772-2
👥 Contributors: Hamed Tabrizchi, Jafar Razmara

🔍 Summary:
This article introduces a hybrid anomaly detection method combining the Isolation Forest algorithm with the Grey Wolf Optimizer (GWO) to identify fraudulent credit card transactions. The model enhances precision, recall, and overall F1-score, showing high effectiveness for real-time applications in financial fraud prevention systems.

Cancer Treatment Comes to Age: From One-Size-Fits-All to Next-Generation Sequencing (NGS) Technologies

📘 Journal: BioImpacts
📅 Date: 2024-07-01
🔗 DOI: 10.34172/bi.2023.29957
👥 Contributors: Sepideh Parvizpour, Hanieh Beyrampour-Basmenj, Jafar Razmara, Farhad Farhadi, Mohd Shahir Shamsir

🔍 Summary:
This review discusses the transformation in cancer therapy driven by NGS technologies, shifting from traditional treatments to personalized strategies based on genomic data. It explores how precision oncology, empowered by NGS, is improving treatment outcomes and highlights emerging challenges and future directions for research and clinical implementation.

Conclusion:

Dr. Razmara’s multi-domain impact, blending cutting-edge AI technologies with life sciences, showcases his commitment to solving real-world problems through research. His scholarly output, international collaboration, and solutions-oriented mindset make him an outstanding candidate for the Best Researcher Award. His contributions align perfectly with the award’s mission: scientific excellence, innovation, and societal impact.

 

Iro Dianellou | Environmental Science | Best Researcher Award

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Mrs Iro Dianellou | Environmental Science | Best Researcher Award

Mrs Iro Dianellou , Aristotle University of Thessaloniki , greece

Ioanna Dianellou is a passionate geologist 🧭 with a solid background in environmental and nuclear geochemistry 🌍⚛️. She currently works at Mirtec S.A. in Greece, specializing in asbestos analysis and chemical testing of environmental samples. Her hands-on research spans from mineral analysis to nanomaterials for nuclear waste treatment. Ioanna has participated in international projects, including an Erasmus+ internship in Turkey 🇹🇷. With publications in respected scientific journals 📚 and a strong academic foundation, she continues to contribute to geoscience and environmental safety. Her work is driven by curiosity, sustainability 🌱, and a deep commitment to scientific excellence. 🔬👩‍🔬

Professional Profile

SCOPUS

Education & Experience 

Ioanna holds a Master’s degree 🎓 in Synthetic Chemistry and Biochemistry from Aristotle University of Thessaloniki, focusing on materials for nuclear waste treatment ☢️. Her thesis explored modified bentonite’s ability to remove Cs and Co. She earned her Geology diploma in 2020, specializing in Economic Geology. Ioanna has experience with analytical techniques like XRD, ICP-MS, FTIR, and SEM 🔍. She interned at Ege University (Turkey) under Erasmus+, and at Hellas Gold S.A. in exploration geology ⛏️. Currently, she is a geologist and lab technician at Mirtec S.A., conducting advanced chemical and mineral analyses. Her work bridges geology and environmental safety 🌿.

Professional Development 

Ioanna continuously enhances her expertise through academic research 🧪, internships, and hands-on lab experience. Her Erasmus+ internship at Ege University gave her international exposure 🌍 in nanomaterial synthesis and nuclear waste treatment. Working at Mirtec S.A., she applies advanced laboratory techniques (XRF, TG-DTA, XRD, ICP-MS) in real-world environmental and energy sample analyses 🔬. She’s collaborated with interdisciplinary teams, mastering geochemical and geotechnical investigation tools. Through her postgraduate education and scientific publications, she demonstrates ongoing professional growth and commitment to solving environmental and radiological challenges ☢️. Her proactive learning mindset ensures she remains updated in emerging technologies and methodologies 📚🧠.

Research Focus 

Ioanna Dianellou’s research focuses on environmental geochemistry, radiochemistry, and nanomaterials 🌋⚛️. She specializes in the removal of radioactive elements like uranium, thorium, cesium, and cobalt from aqueous and solid waste using modified materials such as bentonite and nanofibers. Her expertise includes characterization of materials (SEM, XRD, FTIR) and the application of spectroscopic and chromatographic techniques (ICP-OES, UV-Vis) 🧪. With publications in journals on radioactive waste sorption, she bridges geology with nuclear waste management. Her research aims to promote safe, sustainable methods for pollution control and environmental remediation, contributing to green energy and nuclear safety 🌱💡.

Awards and Honors 

Ioanna’s academic excellence is reflected in her high grades and distinction in both undergraduate and postgraduate studies 🥇. She graduated with an 8.56/10 (Excellent) in her Master’s and 8.37/10 (Very Good) in her Geology diploma 🎓. She earned an Erasmus+ scholarship for her internship at Ege University in Turkey, showcasing her skills in international research collaboration 🌍. Her co-authored publications in reputable journals such as Applied Radiation and Isotopes highlight her contribution to impactful scientific work 📚. These recognitions underscore her dedication, discipline, and emerging reputation in the field of nuclear and environmental geosciences 💫.

Publication Top Notes

1. Dianellou, I., Noli, F., Kantiranis, N. (2025)

Title: Sorption behavior of ¹³⁷Cs and ⁶⁰Co onto raw and cellulose-modified Greek bentonite
Journal: Applied Radiation and Isotopes, Vol. 222, Article 111850
DOI: https://doi.org/10.1016/j.apradiso.2025.111850

🔍 Summary:
This study investigates the sorption efficiency of raw and cellulose-modified Greek bentonite for the removal of radioactive isotopes Cesium-137 (¹³⁷Cs) and Cobalt-60 (⁶⁰Co) from aqueous solutions. Using batch experiments, the modified bentonite demonstrated significantly improved sorption capacity due to increased surface functional groups. The research utilized XRD and FTIR for material characterization and applied kinetic and isotherm models to understand adsorption behavior. The findings offer insights into eco-friendly and efficient solutions for treating low-level radioactive waste using locally sourced clay materials.

2. Kaptanoglu, I.G., Yusan, S., Kaynar, Ü.H., Aytas, S., Erenturk, A.S., Dianellou, I. (2025)

Title: Investigation of thorium(IV) removal utilizing reduced graphene oxide-zinc oxide nanofibers via response surface methodology
Journal: Journal of Radioanalytical and Nuclear Chemistry
DOI: https://doi.org/10.1007/s10967-025-10095-1

🔍 Summary:
This research explores the removal of thorium(IV) ions from aqueous solutions using reduced graphene oxide (rGO) combined with zinc oxide nanofibers. Response Surface Methodology (RSM) was applied to optimize the adsorption parameters. The nanofibers were characterized using SEM and FTIR, and adsorption experiments were evaluated through statistical modeling. Dianellou’s contribution helped refine the experimental approach and validate results. The study presents an innovative hybrid nanomaterial that efficiently adsorbs radioactive thorium, demonstrating potential for use in advanced nuclear waste management technologies.

3. Kyriakidis, F., Dianellou, I., Vollas, A., Alatzoglou, M., Gargoulas, N., Oikonomou, V. (2024)

Title: Presence of asbestos in building materials and soils in postfire areas of Mati, Kineta and Varimbombi in Greece
Journal: Environmental Geochemistry and Health, Vol. 46, Article 452
DOI: https://doi.org/10.1007/s10653-024-02211-z

🔍 Summary:
This environmental study focuses on the identification of asbestos contamination in areas affected by wildfires in Greece. Polarized light microscopy and stereomicroscopy were used to analyze soil and building debris samples from Mati, Kineta, and Varimbombi. Dianellou contributed to the analytical assessment of asbestos fibers and the evaluation of public health risks. The results show widespread asbestos presence due to the combustion of building materials, underlining the need for remediation and proper waste management in post-disaster zones.

Conclusion

Ioanna Dianellou’s research is not only scientifically advanced but also socially impactful. Her work is grounded in both academic excellence and real-world application, a hallmark of a well-rounded and forward-thinking researcher. Her contributions to radioactive pollutant remediation and environmental monitoring are timely, innovative, and of global relevance. She is an outstanding candidate for the Best Researcher Award and embodies the future of interdisciplinary scientific problem-solving.

David Vatamanu | Electromagnetic Waves | Best Researcher Award

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Mr. David Vatamanu | Electromagnetic Waves | Best Researcher Award

Mr. David Vatamanu, Doctoral School of Electrical Engineering, Technical University of Cluj-Napoca, Romania

David Vatamanu is a Romanian signal officer and researcher specializing in antenna systems, electromagnetic field analysis, and defense communications. He combines military expertise with advanced engineering, serving in the Romanian Ministry of National Defence while pursuing cutting-edge research. His work spans radar technology, biomedical applications, and wireless signal classification, supported by a solid academic foundation and a strong publication record.

Professional Profile

ORCID

🎓 Education and Experience

David Vatamanu began his academic journey at the “Nicolae Bălcescu” Land Forces Academy in Sibiu, Romania, where he earned a Bachelor’s degree in Management in the Field of Military Communication, completing his studies in 2020. Eager to deepen his knowledge, he pursued dual Master’s degrees: one in Management and Technology from the same academy, and another in Computer Engineering and Information Technology from Lucian Blaga University, both completed by mid-2022. Simultaneously with his advanced studies, he joined the Ministry of National Defence as a Signal Officer in July 2020, where he has been actively engaged in the management and development of military communication systems. Since August 2022, David has been a PhD student at the Technical University of Cluj-Napoca, focusing his research on efficient antenna systems designed for detection, tracking, and communication applications. This academic progression is complemented by hands-on experience in military signal operations, combining practical responsibilities with his technical research pursuits.

🛠️ Professional Development

In addition to his formal education and military service, David has enhanced his professional skills through specialized training. In late 2022, he attended a course at the NATO Communications and Information Academy in Oeiras, Portugal. This course covered essential aspects of information and communication technologies relevant to defense, including DCIS information systems fundamentals, introduction to DCIS, and NRF DCIS IS System and ServiceDesk operations. This training has expanded his expertise in secure and efficient military communication technologies and integrated systems, strengthening his capability to apply cutting-edge information systems in operational environments.

🔬 Research Focus

David’s research focuses on electromagnetic wave propagation, antenna design and optimization, radar-based detection of vital signs, and the use of deep learning models for signal detection and classification. His interdisciplinary work merges computational simulations, experimental methods, and artificial intelligence techniques to solve complex problems in both military and civilian contexts, particularly concerning 4G and 5G communications and biomedical sensing.

📚Publication Top Note

A Computational Approach to Increasing the Antenna System’s Sensitivity in a Doppler Radar Designed to Detect Human Vital Signs in the UHF-SHF Frequency Ranges
Authors: David Vatamanu, Simona Miclaus
Journal: Sensors
Publication Date: May 21, 2025

Summary

This study presents a computational methodology aimed at enhancing the sensitivity of antenna systems within Doppler radar configurations, specifically tailored for the detection of human vital signs such as heart rate and respiration.

✅ Conclusion

David Vatamanu exemplifies the synergy between military operational knowledge and advanced scientific research. His progression from military academy graduate to PhD researcher and practicing Signal Officer highlights a career committed to enhancing communication technologies with practical impact. Through his interdisciplinary approach, extensive publications, and recognized expertise, he continues to contribute to both national defense capabilities and technological innovation.

Yavuz Yasul | Health Sciences | Best Scholar Award

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Dr. Yavuz Yasul | Health Sciences | Best Scholar Award

Dr. Yavuz Yasul, Ondokuz Mayis University, Turkey

Yavuz Yasul is a dedicated researcher and academic, currently serving as a lecturer at Ondokuz Mayıs University, Bafra Vocational School, in the Department of Property Protection and Security. He specializes in exercise physiology, metabolic health, and sports sciences. With a solid academic background and consistent research output, Yasul plays a vital role in advancing knowledge in the intersections of physical activity, supplementation, and biochemical adaptations.

Professional Profile

ORCID

🎓 Education and Experience

Yavuz Yasul earned his Ph.D. in Physical Education and Sports from Inonu University in 2021, where his dissertation focused on the effects of Coenzyme Q10 supplementation on serum, cardiac, and skeletal muscle tissue in rats subjected to various exercise regimes. Prior to this, he completed his Master’s degree (M.Sc.) in 2016 at Kahramanmaraş Sütçü İmam University, where his thesis explored the psychological needs of physical education students. He began his academic journey with a Bachelor’s degree in Physical Education and Sports Teaching from Mustafa Kemal University in 2014. Yasul has been serving as a Lecturer at Ondokuz Mayıs University since April 2019 and was appointed Head of Department in August 2020. In his current role, he teaches a variety of courses, including Physical Activity and Health, Strength Training, and Behavioral Sciences, while also supervising graduate research and contributing to academic program coordination.

📈 Professional Development

Yasul is continuously involved in professional growth through national and international academic collaborations, research projects, and teaching innovations. He integrates current trends in exercise science with applied research in physiology, helping students and professionals understand emerging practices in health and performance.

In 2024, Yasul was nominated for the 3rd International Food Scientist Awards by the Ministry of Corporate Affairs, Government of India, for his contributions to scientific research related to exercise metabolism and supplementation.

🔬 Research Focus

Yasul’s research spans multiple dimensions of sports science, with a primary focus on exercise physiology and training adaptations. He is particularly interested in the role of Coenzyme Q10 supplementation in enhancing physical performance and recovery. His work also delves into oxidative stress and inflammation, exploring how these processes interact with physical training and nutritional interventions. He investigates the microbiota-gut-brain axis to understand its impact on mental and physical health, as well as the biological mechanisms underlying aging and telomere dynamics in relation to physical activity. Through his studies, Yasul aims to uncover how exercise and dietary supplements modulate molecular pathways, ultimately contributing to disease prevention, healthy aging, and athletic performance optimization.

🏅Awards and Honors 

Yasul has served as both Principal Investigator and Collaborator on several nationally funded research grants, contributing significantly to the advancement of sports science and exercise physiology. He is a regular peer reviewer for reputable journals in the fields of sports medicine and nutritional science, reflecting his active engagement with the academic community. In addition to his research contributions, he frequently delivers lectures and seminars on topics such as supplementation strategies, training periodization, and oxidative stress biomarkers. At Ondokuz Mayıs University, he plays a key role in academic curriculum design and is deeply involved in student mentorship, fostering both academic growth and research development among his students.

📚Publication Top Notes

1. Core Exercise as a Non-Pharmacological Strategy for Improving Metabolic Health in Prediabetic Women
Medicina, 2025-05-21
DOI: 10.3390/medicina61050942
Authors: Nuray Yiğiter, Faruk Akçınar, Yavuz Yasul, Vedat Çınar, Taner Akbulut, Gian Mario Migliaccio

Summary:
This study investigates the effects of a core-focused exercise regimen on metabolic health parameters in prediabetic women. The researchers aimed to determine whether targeted core exercises could serve as an effective non-pharmacological intervention to improve insulin sensitivity and lipid profiles. The findings suggest that incorporating core exercises into regular physical activity routines may significantly enhance metabolic health, offering a viable alternative to medication for managing prediabetes.

2. Evaluating the Impact of Coenzyme Q10 and High-Intensity Interval Training on Lactate Threshold and Plasma Blood Gases in Rats: A Randomized Controlled Trial
European Journal of Applied Physiology, 2025-03-18
DOI: 10.1007/s00421-025-05756-8
Authors: Yavuz Yasul, Büşra Yılmaz, Ömer Şenel, Dursun Kurt, Taner Akbulut, Ayşen Çalıkuşu, Elvan Anadol, Canan Yılmaz

Summary:
This randomized controlled trial examined the combined effects of Coenzyme Q10 supplementation and high-intensity interval training (HIIT) on lactate threshold and plasma blood gases in rats. The study found that the combination of CoQ10 and HIIT significantly improved lactate clearance and enhanced oxygen transport capacity, indicating improved metabolic efficiency and recovery post-exercise. These results suggest potential benefits of CoQ10 supplementation in conjunction with HIIT for enhancing athletic performance and recovery.

3. Moderate/High-Intensity Exercise and Coenzyme Q10 Supplementation May Reduce Tumstatin and Improve Lipid Dynamics and Body Mass in Rats
Applied Sciences, 2025-02-28
DOI: 10.3390/app15052618
Authors: Yavuz Yasul, Faruk Akçınar, Vedat Çınar, Taner Akbulut, İsa Aydemir, Mehmet Hanifi Yalçın, Emsal Çağla Avcu, Suna Aydın, Süleyman Aydın

Summary:
This study explored the effects of varying intensities of exercise combined with Coenzyme Q10 supplementation on tumstatin levels, lipid profiles, and body mass in rats. The findings revealed that both moderate and high-intensity exercise, when paired with CoQ10 supplementation, led to significant reductions in tumstatin levels and improvements in lipid metabolism and body mass. These results highlight the potential of combining exercise with CoQ10 supplementation as a strategy for managing obesity and related metabolic disorders.

4. The Regulatory Effects of Exercise and Metformin on Biomarkers in Obesity: A Focus on Uric Acid, Irisin, Adiponutrin, Adropin, and Copeptin
Medicina, 2025-02-25
DOI: 10.3390/medicina61030399
Authors: Taner Akbulut, Vedat Çınar, Emsal Çağla Avcu, Yavuz Yasul, İsa Aydemir, Tuncay Kuloğlu, Gökhan Artaş, Süleyman Aydın

Summary:
This research focused on the combined effects of exercise and metformin treatment on various biomarkers associated with obesity, including uric acid, irisin, adiponutrin, adropin, and copeptin. The study demonstrated that the integration of physical exercise with metformin therapy resulted in favorable modulations of these biomarkers, suggesting enhanced metabolic regulation and potential benefits in obesity management. The findings support the synergistic use of pharmacological and lifestyle interventions in treating obesity.

5. Effects of Short-Term Pre-Competition Weight Loss on Certain Physiological Parameters and Strength Change in Elite Boxers
PLOS ONE, 2024
DOI: 10.1371/journal.pone.0304267
Authors: Yavuz Yasul, Faruk Akçınar, Muhammet Enes Yasul, Ahmet Kurtoğlu, Özgür Eken, Georgian Badicu, Luca Paolo Ardigò

Summary:
This study assessed the impact of rapid weight loss strategies commonly employed by elite boxers before competitions on physiological parameters and strength levels. The results indicated that short-term weight reduction led to significant decreases in strength and alterations in physiological markers, potentially compromising athletic performance. The research underscores the need for carefully managed weight loss protocols to minimize adverse effects on athletes’ health and performance.

🔚Conclusion

Yavuz Yasul’s scientific work embodies the synergy between academic excellence and applied research. By integrating his interests in exercise physiology, nutrition, and biochemistry, he contributes to a deeper understanding of health optimization and performance. His efforts continue to shape modern perspectives in sports science and health promotion.

Mr. Mohammed Abdalla | Intelligent Transportation | Best Paper Award

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Mr. Mohammed Abdalla | Intelligent Transportation | Best Paper Award

Mr. Mohammed Abdalla, Beni-Suef University, Egypt

Dr. Mohammed Abdalla Mahmoud Youssif 🇪🇬 is a seasoned technology leader and current Head of Development at Giza Systems 🏢. With over 15 years of experience in software development 💻, he has excelled in managing teams, leading innovative projects, and delivering smart solutions 🌐. He holds B.Sc., M.Sc., and Ph.D. degrees from Cairo University 🎓 in computer science and engineering. His expertise includes big data 📊, machine learning 🤖, and smart city applications 🏙️. Passionate about future tech, Dr. Youssif is also active in academia with 20+ research publications 📚 and an online presence via YouTube and LinkedIn 🎥💼.

Professional Profile

GOOGLE SCHOLAR

Education and Experience 

Dr. Mohammed Abdalla earned his B.Sc., M.Sc., and Ph.D. in Computer Science and Engineering from Cairo University 🎓. With more than 15 years of hands-on software development experience 💻, he has contributed to a wide variety of business projects ranging from enterprise platforms to smart city solutions 🌐. He currently leads development teams at Giza Systems 🏢, where he focuses on innovation, resource management, and technical excellence 🚀. His academic background is strongly tied to real-world applications, enabling him to bridge research and industry with a practical edge 🔗.

Professional Development 

Dr. Youssif’s career reflects consistent professional growth in both technical and leadership domains 🔧👨‍💼. Starting as a software developer 💻, he quickly climbed the ranks through a combination of innovation, problem-solving, and people management. As Development Head at Giza Systems 🏢, he now mentors engineers, allocates project resources 📅, and drives the development of cutting-edge solutions 🚀. His commitment to continuous learning and application of emerging technologies, such as big data 📊 and AI 🤖, has positioned him as a key contributor in Egypt’s digital transformation journey 🇪🇬.

Research Focus 

Dr. Mohammed Abdalla’s research is deeply rooted in cutting-edge technologies, especially big data management 📊, artificial intelligence 🤖, and machine learning algorithms 🧠. He places a particular focus on smart city applications 🌆, developing analytics tools and intelligent systems to enhance urban efficiency and sustainability 🚦🏙️. His work bridges academic research and practical implementation, ensuring innovations can be adopted in real-world scenarios. His 20+ publications 📚 reflect a commitment to solving complex societal problems through technology 💡. He aims to harness data and digital intelligence for smarter urban environments and better quality of life 🏘️.

Awards and Honors 

While Dr. Mohammed Abdalla is still building his list of formal recognitions, his contributions to smart city tech and software innovation are widely respected 🌍. As a speaker, team leader, and contributor to international journals and conferences 📘, he is regarded as a thought leader in big data and machine learning fields 🧠. His position as Development Head at Giza Systems is a testament to his technical and managerial excellence 🏢. His active online presence via YouTube and LinkedIn helps mentor younger professionals 📽️💼, adding to his community impact and informal recognition within the tech ecosystem 👏.

Publication Top Notes

1. Crisis Management Art from the Risks to the Control: A Review of Methods and Directions

📚 Authors: A.H. Mohammed Abdalla, Louai Alarabi
📘 Journal: Information (Vol. 42, 2021)
📈 Citations: 42
📄 Summary:
This review outlines the landscape of crisis management frameworks, emphasizing how organizations can transition from identifying risks to establishing control mechanisms. It evaluates methodologies for risk assessment, communication, and coordination, providing a comprehensive guide for practitioners and researchers seeking to improve resilience and decision-making in crises. The paper synthesizes real-world implementations with theoretical models to chart future research directions in crisis response systems.

2. TraceAll: A Real-Time Processing for Contact Tracing Using Indoor Trajectories

📚 Authors: Louai Alarabi, S. Basalamah, A. Hendawi, Mohammed Abdalla
📘 Journal: Information (Vol. 12, No. 5, 2021)
📈 Citations: 21
📄 Summary:
This study presents TraceAll, an innovative real-time contact tracing system that leverages indoor trajectory data to identify potential exposure events. It uses spatial indexing and real-time analytics to provide fast and scalable tracing, crucial during health crises like COVID-19. The paper discusses system architecture, algorithms, and a deployment case study, demonstrating its effectiveness in high-density areas.

3. DeepMotions: A Deep Learning System for Path Prediction Using Similar Motions

📚 Authors: Mohammed Abdalla, Abdeltawab Hendawi, Hoda M.O. Mokhtar, Neveen ElGamal
📘 Journal: IEEE Access, 2020
📈 Citations: 16
📄 Summary:
DeepMotions is a path prediction framework that applies deep learning to movement data, identifying similar motion patterns to predict future trajectories of moving objects. It integrates convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to model spatial-temporal patterns. Applications range from pedestrian prediction to intelligent transportation systems.

4. FraudMove: Fraud Drivers Discovery Using Real-Time Trajectory Outlier Detection

📚 Authors: E.O. Eldawy, A. Hendawi, Mohammed Abdalla, Hoda M.O. Mokhtar
📘 Journal: ISPRS International Journal of Geo-Information (Vol. 10, No. 11, Article 767, 2021)
📈 Citations: 13
📄 Summary:
FraudMove introduces a real-time framework for detecting fraudulent behavior based on vehicle movement anomalies. Using trajectory outlier detection, the system identifies unexpected routes or suspicious driving patterns that may indicate fraud, such as in ride-sharing or insurance claims. The framework blends spatio-temporal clustering and machine learning models for accurate fraud detection.

5. HarmonyMoves: A Unified Prediction Approach for Moving Object Future Path

📚 Authors: Mohammed Abdalla, Hoda M.O. Mokhtar
📘 Journal: International Journal of Advanced Computer Science and Applications, pp. 637–644, 2020
📈 Citations: 7
📄 Summary:
This research proposes HarmonyMoves, a hybrid model that integrates historical trajectory data with environmental context to predict the future paths of moving entities (e.g., vehicles, pedestrians). Unlike previous models that relied solely on movement data, this approach harmonizes contextual and historical data for robust, real-time trajectory prediction.

Conclusion

Dr. Mohammed Abdalla’s contributions meet and exceed the standards typically required for Best Paper Awards at prestigious conferences and journals. His research is characterized by technical innovation, interdisciplinary applications, practical impact, and high citation potential. He is especially commendable for producing systems that combine machine learning with real-world problem solving, such as contact tracing and mobility analytics.

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.