Aristidis Ilias | University of Patras | Research Excellence Award

Published on by Engineering Scientist

Dr. Aristidis Ilias | Cybersecurity Systems
| Research Excellence Award

University of Patras | Greece

Dr. Aristidis Ilias research centers on strengthening cybersecurity within industrial, cloud, and cyber-physical ecosystems through advanced cryptographic models, secure computation frameworks, and resilient system architectures. A primary contribution lies in enhancing the security of SCADA and industrial control environments by integrating Elliptic Curve Cryptography into the Modbus protocol, enabling strong protection against interception, tampering, and denial-of-service attacks while meeting the performance constraints of real-time operational systems. Further developments address security challenges in microservice-driven data pipelines, focusing on confidentiality, authentication, and integrity in distributed architectures that support critical operations. This includes designing and validating secure communication models that leverage modern cryptographic primitives and Secure Multi-Party Computation (MPC) to ensure privacy-preserving collaborative data processing. Another important research direction advances the fusion of Linear Algebra operations with MPC, examining BLAS-level computational implementations that enable secure matrix operations at scale. These contributions include performance optimization, protocol engineering, and feasibility studies supporting the potential replacement of traditional CBLAS workflows in sensitive analytical contexts. Supported by contributions across cybersecurity projects spanning industrial protection, intelligent transportation, digital transformation, and privacy compliance, the overall research portfolio—reflected in 76 citations, 15 publications, and an h-index of 6—advances secure digital infrastructures and offers scalable, next-generation frameworks for cyber-resilient computing.

Citation Metrics (Scopus)

100

80

60

40

20

0

Citations
76

Documents
15

h-index
6

Citations
Documents
h-index


View Scopus Profile

Featured Publications

Imran Mohammad | Structural Health | Research Excellence Award

Published on by Engineering Scientist

Assist. Prof. Dr. Imran Mohammad | Structural Health
| Research Excellence Award

College of Medicine at Prince Sattam Bin Abdulaziz University | Saudi Arabia

Assist. Prof. Dr. Imran Mohammad is an accomplished microbiology researcher with a strong record of scientific contributions across bacteriology, microbial ecology, natural product research, and medical microbiology. His research spans the discovery of vitamin B12-producing Pseudomonas species, evaluation of marine invertebrate compounds against multidrug-resistant pathogens, and extensive investigations into antibacterial, antibiofilm, and antioxidant activities of medicinal plant extracts, including Salvadora persica, Zingiber officinale, Mukia maderaspatana, Pongamia pinnata, and Tamarindus indica. He has also contributed systematic reviews on the modern medical applications of ginger and the neuroprotective potential of probiotic strains such as Lactobacillus acidophilus. His studies further explore biomarkers like D-Dimer in vaccinated cardiovascular patients during COVID-19, the role of neem as a sustainable biopesticide, and microbial responses under environmental stressors related to food safety. In medical education research, he has assessed the impact of healthcare simulation on practical training in male catheterization procedures. His latest work examines the intersection of emerging pandemics such as Mpox and COVID-19. In addition to publishing widely in peer-reviewed journals, he actively contributes to the scientific community through extensive article review activities across microbiology, epidemiology, sustainability, antioxidants, and clinical research, demonstrating his commitment to advancing global scientific knowledge.

 Profile: Orcid | Scopus

Featured Publications

Mohammad, I., Ansari, M. R., Khan, M. S., Bari, M. N., Kamal, M. A., & Poyil, M. M. (2025). Enhancing food safety: Adapting to microbial responses under diverse environmental stressors. Preprints, 2025091382. https://doi.org/10.20944/PREPRINTS202509.1382.V1

Mohammad, I., Khan, M. S., Ansari, M. R., Kamal, M. A., Bari, M. N., & Anwar, M. (2025). Enhancing food safety: Adapting to microbial responses under diverse environmental stressors. Trends in Ecological and Indoor Environmental Engineering, 3(2), 12–26. https://doi.org/10.62622/TEIEE.025.3.2.12-26

Mohammad, I., Khan, M. S., Ansari, R., Bari, N., & Anwar, M. (2025). Intersecting pandemics: Analyzing the relationship between Mpox and COVID-19. The New Armenian Medical Journal, 19(2), 4–17. https://doi.org/10.56936/18290825-2.v19.2025-4

Mohammad, I., Ansari, M. R., Bari, M. N., Anwar, M., & Khan, M. S. (2025). The impact of healthcare simulation on practical training: Enhancing medical students’ proficiency in in-vitro male catheterization procedures. Preprints, 2025020300. https://doi.org/10.20944/PREPRINTS202502.0300.V1

Mohammad, I., Ansari, M. R., Khan, M. S., Bari, M. N., Kamal, M. A., & Poyil, M. M. (2025). Beyond digestion: The gut microbiota as an immune–metabolic interface in disease modulation. Gastrointestinal Disorders, 7(4), 77. https://doi.org/10.3390/gidisord7040077

Masaya Yamamoto | Neural Networks | Research Excellence Award

Published on by Engineering Scientist

Dr. Masaya Yamamoto | Neural Networks
| Research Excellence Award

Molecular Neuroscience Systems, Laboratory Medical Institute of Bioregulation, Kyushu University | Japan

Dr. Masaya Yamamoto Current research focuses on uncovering the active roles of astrocytes in regulating synaptic plasticity, learning, and memory, using an integrated, multi-scale approach that combines molecular analysis, in vivo imaging, and spatiotemporal proteomics. Recent work has clarified how astrocytic calcium microdomains, gliotransmitter release, and multisynaptic compartmental signaling coordinate to influence neuronal circuit dynamics. Advanced proteomic profiling is being applied to map activity-dependent changes in astrocyte–neuron communication, revealing novel regulatory proteins and pathways involved in cognitive processing and synaptic remodeling. In vivo imaging techniques are used to track astrocyte and neuronal interactions in real time during learning phases, providing functional insight into the temporal dynamics of memory consolidation. This research reframes astrocytes as essential, active participants in information processing rather than passive support cells. In the context of neurodegeneration, ongoing studies investigate how astrocytic dysfunction contributes to impaired synaptic communication and cognitive decline, offering potential molecular targets for intervention in disorders such as Alzheimer’s disease and vascular dementia. By bridging molecular neuroscience, systems biology, and computational interpretation, this work advances understanding of glial pathology and proposes innovative mechanisms through which astrocytes shape network plasticity and cognitive resilience, contributing significantly to emerging models of brain function and neurological disease progression.

 Profile: Orcid

Featured Publications

Yamamoto, M., & Takano, T. (2025). Astrocyte-mediated plasticity: Multi-scale mechanisms linking synaptic dynamics to learning and memory. Cells, 14(24), Article 1936. https://doi.org/10.3390/cells14241936

Yamamoto, M., Itokazu, T., Uno, H., Maki, T., Shibuya, N., & Yamashita, T. (2025). Anti-RGMa neutralizing antibody ameliorates vascular cognitive impairment in mice. Neurotherapeutics. https://doi.org/10.1016/j.neurot.2024.e00500

Harun Gokce | Mechanical | Best Mechanical Engineering Award

Published on by Engineering Scientist

Assoc. Prof. Dr. Harun Gokce | Mechanical
| Best Mechanical Engineering Award

Gazi University | Turkey

Assoc. Prof. Dr. Harun Gokce Research activities focus on advanced structural and mechanical system design, optimization, and virtual manufacturing, integrating computer-aided engineering, experimental mechanics, and intelligent simulation techniques. Work emphasizes the development of 3D simulation environments for CNC machine tools, virtual machining, and automated process optimization to improve manufacturing accuracy, efficiency, and cost performance. Significant contributions have been made to additive manufacturing, including the design of bio-inspired microstructures and bone scaffolds, enabling improved biomechanical performance in tissue engineering applications. Research also addresses multi-objective optimization of mechanical components such as gearboxes, spur gears, hydrostatic thrust bearings, and diffusers through advanced algorithms including Taguchi methods and grey wolf optimization. Additional studies involve the numerical and experimental investigation of cutting forces, thermal behavior, and tool geometries in high-precision machining processes, contributing to enhanced surface quality and tool life. Expertise in CAD/CAE platforms supports integrated modeling, analysis, and validation of complex assemblies for aerospace, automotive, and defense applications, including guided systems, aerodynamic components, and structural platforms. By combining simulation, reverse engineering, rapid prototyping, and optimization methodologies, this body of work advances smart manufacturing, lightweight design, and digitally driven engineering solutions for high-performance and mission-critical systems.

 Profile: Google Scholar

Featured Publications

Top, N., Şahin, İ., & Gökçe, H. (2021). Computer-aided design and additive manufacturing of bone scaffolds for tissue engineering: State of the art. Journal of Materials Research, 36(1), 3725–3745.

Dörterler, M., Şahin, İ., & Gökçe, H. (2018). A grey wolf optimizer approach for optimal weight design problem of the spur gear. Engineering Optimization, 51(1), 1–15.

Yavuz, M., Gökçe, H., Çiftci, I., Yavaş, C., & Şeker, U. (2020). Investigation of the effects of drill geometry on drilling performance and hole quality. International Journal of Advanced Manufacturing Technology, 106(1), 4623–4633.

Top, N., Şahin, İ., & Gökçe, H. (2023). The mechanical properties of functionally graded lattice structures derived using computer-aided design for additive manufacturing. Applied Sciences, 13(21), 1–21

Aytac Aydın | Data Science | Best Researcher Award

Published on by Engineering Scientist

Assoc. Prof. Dr. Aytac Aydın | Data Science
| Best Researcher Award

Karadeniz Technical University | Turkey

Assoc. Prof. Dr. Aytac Aydın Research contributions focus on advancing quality management and performance appraisal within the forest products and wood-based materials industry through the systematic application of statistical analysis and artificial intelligence techniques. The work integrates process optimization, quality control modeling, and ergonomics to enhance productivity, material efficiency, and workplace safety across manufacturing environments. Multiple completed and ongoing research projects have addressed critical challenges in the evaluation of production performance, defect reduction, operational efficiency, and sustainable resource utilization. Innovative methodologies, including multi-criteria decision-making models, predictive analytics, and machine learning algorithms, have been applied to improve decision accuracy in material selection, process planning, and quality inspection. Published studies in SCI-indexed journals contribute valuable data-driven insights to both academia and industry, strengthening standards in forest industrial engineering and promoting evidence-based operational strategies. Research outcomes support environmentally responsible production while increasing economic efficiency in wood-based manufacturing systems. In addition, contributions extend to guiding postgraduate and doctoral-level research focused on quality systems, process improvement, and industrial performance metrics. These efforts collectively strengthen the scientific foundation of forest industry management, drive technological advancement, and support sustainable and intelligent manufacturing practices with a long-term impact on both regional and international forestry-based production sectors.

 Profile: Orcid | Scopus 

Featured Publications

Aydın, A., Temel, B. A., Semercioğlu, İ. N., Başağa, H. B., Toğan, V., & Ağcakoca, E. (2025). Evaluating performance appraisal effects on employee motivation and productivity: Insights from the Turkish construction industry via covariance-based structural equation modeling. Buildings. https://doi.org/10.3390/buildings15224040

Tiryaki, S., Aydın, A., & Ondaral, S. (2025). Process monitoring with individual measurements: A case study in corrugated cardboard industry. Artvin Çoruh Üniversitesi Orman Fakültesi Dergisi. https://doi.org/10.17474/artvinofd.1722824

Tiryaki, S., & Aydın, A. (2024). Orta yoğunlukta liflevha üretiminde çekme direncinin iki aşamalı izlenmesi. Düzce Üniversitesi Orman Fakültesi Ormancılık Dergisi. https://doi.org/10.58816/duzceod.1563540

Aydın, A., & Nemli, G. (2023). Yonga levha endüstrisinde zımparalama sorunlarının ve çözüm önerilerinin belirlenmesine yönelik bir çalışma. Düzce Üniversitesi Orman Fakültesi Ormancılık Dergisi. https://doi.org/10.58816/duzceod.1394936

Aizhen Ren | Machine learning | Excellence in Innovation Award

Published on by Engineering Scientist

Prof. Aizhen Ren | Machine learning
| Excellence in Innovation Award

College of Science, Inner Mongolia Agricultural University | China

Prof. Aizhen Ren research work centers on machine learning, deep learning, statistical inference, and their applications in economics, finance, and computational biology. A significant portion of the research contributes to the development and mathematical validation of advanced bootstrap techniques, including the speedy double bootstrap method, which enhances the statistical reliability of phylogenetic tree estimation and provides third-order accurate unbiased p-values. These methods have been applied to evolutionary analyses of horse breeds, supporting biological and genomic investigations with high-precision statistical tools. In the financial domain, the research explores machine-learning-based trend prediction models, such as multiscale bootstrap-corrected random forest voting systems used to forecast stock index movement with improved accuracy and inference reliability. Additional work includes the construction of financial risk early-warning models for listed companies using multiple machine learning approaches, reflecting an interdisciplinary blend of statistics, computing, and economics. Contributions also extend to consumption behavior analysis employing regression-based models, as well as deep learning ensemble frameworks integrating empirical mode decomposition and temporal convolutional networks for time-series prediction tasks. The released R package SDBP operationalizes the novel bootstrap methodology, enabling researchers to compute unbiased p-values efficiently. Overall, the research advances methodological innovation and practical applications across data-intensive scientific domains.

 Profile: Orcid

Featured Publications

Ren, A., Duan, Y., & Liu, J. (2025). Multiscale bootstrap correction for random forest voting: A statistical inference approach to stock index trend prediction. Mathematics, 13(22), 3601. https://doi.org/10.3390/math13223601

Ren, A., Ishida, T., & Akiyama, Y. (2020). Mathematical proof of the third order accuracy of the speedy double bootstrap method. Communications in Statistics – Theory and Methods, 49(16), 3950–3964. https://doi.org/10.1080/03610926.2019.1594295

Ren, A., Ishida, T., & Akiyama, Y. (2013). Assessing statistical reliability of phylogenetic trees via a speedy double bootstrap method. Molecular Phylogenetics and Evolution, 67(2), 429–435. https://doi.org/10.1016/j.ympev.2013.02.011

Ramzan Kayabaşı | Renewable Energy | Best Researcher Award

Published on by Engineering Scientist

Dr. Ramzan Kayabaşı | Renewable Energy
| Best Researcher Award

Kayseri University | Turkey

Dr. Ramazan Kayabaşı is a dedicated academic and researcher specializing in mechanical engineering, energy systems, renewable energy technologies, geothermal applications, thermodynamics, and HVAC systems. He earned his Ph.D. in Energy Systems Engineering, conducting experimental research on photovoltaic module performance and thermal behavior. His academic journey spans advanced studies in mechanical engineering and energy technologies, complemented by continuous professional training across safety, project management, environmental sustainability, leadership, and educational planning. Dr. Kayabaşı serves as a Lecturer in the Construction Department at Kayseri University, where he has held numerous academic and administrative roles, including Assistant Director of the Vocational School, Erasmus Coordinator, Research and Application Center Manager, and a member of several strategic planning, quality, performance evaluation, and graduation committees. His teaching portfolio covers a wide range of undergraduate and associate-level courses, such as occupational health and safety, geothermal energy, technical drawing, HVAC systems, renewable energy resources, and environmental protection. His research focuses on sustainable materials, geopolymer concrete, solar and wind energy analysis, occupational safety, and energy-efficient building technologies. Dr. Kayabaşı has published extensively in SCI, Scopus, TRDizin, and peer-reviewed journals, contributing to advancements in energy systems, renewable technologies, and workplace safety. His work reflects a strong commitment to scientific innovation, sustainability, and engineering education.

 Profile: Orcid | Google Scholar

Featured Publications

Ozturk, M., Kayabaşı, R., Yildizhan, H., & Ameen, A. (2025). Resource use efficiency and environmental impacts in strawberry production: An energy–exergy analysis. Energies, 18(21), 5572. https://doi.org/10.3390/en18215572

Özdemir, H., & Kayabaşı, R. (2025). Analysis of work accidents experienced by female employees based on SGK data: 2013–2022. Beykoz Academy Journal. https://doi.org/10.14514/beykozad.1546813

Özdemir, H., Kayabaşı, R., & Cündübeyoğlu, İ. (2025). Analysis of work accidents in the furniture industry in Türkiye between 2013–2023. Bartın Journal of Forestry Faculty. https://doi.org/10.24011/barofd.1596284

Chunqiang Li | Agricultural Engineering | Editorial Board Member

Published on by Engineering Scientist

Dr. Chunqiang Li | Agricultural Engineering
| Editorial Board Member

Shenyang Agricultural University | China

Dr. Chunqiang Li’s research focuses on the structural, functional, and physicochemical behavior of food proteins, with particular emphasis on protein oxidation, cross-linking mechanisms, and their role in improving food quality, stability, and functionality. His work extensively explores myofibrillar protein modification, transglutaminase-catalyzed cross-linking, and protein self-assembly under various processing conditions, including oxidation, freeze–thaw cycles, pH shifting, and phosphate regulation. He has significantly advanced understanding of how microstructural transitions—such as β-sheet to α-helix transformations and protein unfolding—impact emulsification, gelation, hydration behavior, and meat product quality. His studies on Pickering emulsions, modified plant proteins, and composite particles provide innovative strategies for developing low-fat and functional meat products with enhanced texture, stability, and nutritional value. Through both experimental and molecular-level investigations, he has clarified oxidative effects on actomyosin, light meromyosin, myosin S2, and other muscle proteins, revealing how controlled oxidation can improve enzymatic cross-linking efficiency. His body of work also examines phosphates, pyrophosphates, and ultrasound-assisted treatments that modulate protein structure and improve water-holding capacity, emulsification behavior, and gel network integrity. Collectively, his research contributes important insights for designing healthier, stable, and high-quality food products through targeted protein modification and processing innovations.

 Profile: Orcid 

Featured Publications

Chen, S., Yu, D., Fu, X., Xie, X., Shao, J.-H., Zhao, H., & Li, C. (2025). The β-sheet to α-helix transition of modified soy glycinin particles at the oil–water interface promoted the stability of Pickering emulsion and the quality of pork sausages. Food Hydrocolloids, 111290. https://doi.org/10.1016/j.foodhyd.2025.111290

Liu, J., Yu, Z., Xie, W., Yang, L., Zhang, M., Li, C., & Shao, J.-H. (2023). Effects of tetrasodium pyrophosphate coupled with soy protein isolate on the emulsion gel properties of oxidative myofibrillar protein. Food Chemistry, 135208. https://doi.org/10.1016/j.foodchem.2022.135208

Li, C., Xie, W., Zhang, X., Liu, J., Zhang, M., & Shao, J.-H. (2023). Pickering emulsion stabilized by modified pea protein–chitosan composite particles as a new fat substitute improves the quality of pork sausages. Meat Science, 109086. https://doi.org/10.1016/j.meatsci.2022.109086

Haifeng Zhai | Mechanical Engineering | Editorial Board Member

Published on by Engineering Scientist

Dr. Haifeng Zhai | Mechanical Engineering
| Editorial Board Member

Dalian University of Technology | China

Dr. Haifeng Zhai the research focuses on advancing the fundamental and applied understanding of the mechanical behavior of metallic materials, with emphasis on fatigue life prediction, microstructural evolution, and deformation mechanisms under complex loading conditions. The work integrates experimental investigations with high-fidelity computational modeling approaches—including crystal plasticity finite element modeling (CPFEM) and phase-field (PF) simulations—to uncover the interactions between microstructure, defects, and loading history in determining fatigue performance. Significant contributions include developing predictive frameworks for fatigue life in additive-manufactured alloys by examining the role of defects, anisotropy, and overload effects on crack initiation and propagation. The research further establishes rapid fatigue prediction methodologies using phase-field models to capture microstructural evolution under varied laser scanning strategies, enabling improved process–structure–property relationships. Systematic experimental and simulation studies on 316L stainless steel under multiple overload conditions have provided new insights into cyclic deformation behavior and damage evolution pathways. Active involvement in national research projects has supported the formulation of new computational–experimental strategies for modeling fatigue mechanisms with enhanced accuracy and efficiency. Overall, the research advances the scientific understanding of fatigue behavior in engineered materials and contributes to the development of predictive tools essential for structural reliability, durability assessment, and materials design.

 Profile: Orcid 

Featured Publications

Zhai, H., Wang, Y., & Yang, Y. (2025). Rapid prediction of overload fatigue life based on phase-field modeling of microstructures under different scanning strategies. Additive Manufacturing, 104, 104771. https://doi.org/10.1016/j.addma.2025.104771

Jiang, W., Wu, H., Zhai, H., Wang, Y., Li, D., & Dong, C. (2025). The influence of microstructural features on the fracture performance of specimens fabricated by SLM. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science. https://doi.org/10.1177/09544062251336549

Zhai, H., Jiang, W., Wang, Y., Yang, Y., & Lv, H. (2025). Experimental and simulation study on the microstructural evolution and fatigue life of 316L stainless steel under different periodic overload conditions. Engineering Failure Analysis, 109, 109475. https://doi.org/10.1016/j.engfailanal.2025.109475

Jamshaid Ul Rahman | Artificial Intelligence | Editorial Board Member

Published on by Engineering Scientist

Dr. Jamshaid Ul Rahman | Artificial Intelligence
| Editorial Board Member

Abdus Salam School of Mathematical Sciences, GC University Lahore | Pakistan

Dr. Jamshaid Ul Rahman’s research spans artificial intelligence, deep learning, mathematical modeling, and scientific computing, with a strong emphasis on applying advanced neural architectures to complex problems in molecular modeling, epidemiology, biomechanics, and computational physics. His work integrates graph neural networks, wavelet neural networks, periodic neural networks, and deep optimization frameworks to improve prediction accuracy and interpretability in molecular property analysis, virus transmission modeling, and dynamic system simulation. He has contributed significantly to AI-driven analysis of infectious diseases such as Marburg, Ebola, and Hepatitis C through nonlinear modeling, hybrid optimization, and neural simulation techniques. His research also advances computational mechanics, including deep learning-based vibration analysis in tapered beams, micro-electromechanical systems, and nonlinear oscillators. In cheminformatics, he has enhanced molecular property prediction using quantized GNNs, optimized activation functions, and improved training strategies for message-passing networks. His contributions extend to biophysical modeling, where neural networks simulate glycolysis, biochemical oscillators, and biological system dynamics with high fidelity. He has also explored angular softmax variations, Laplacian smoothing, and stochastic gradient methodologies in deep convolutional networks, advancing core theoretical aspects of machine learning. Across more than fifty publications, his work consistently integrates mathematical rigor with cutting-edge AI to address interdisciplinary scientific and engineering challenges.

 Profile:  Google Scholar

Featured Publications

Rahman, J. U., Noureen, I., Mannan, A., & Uwitije, R. (2025). PhyFold: Environment aware physics informed neural network for protein folding dynamics.

Ul Rahamn, J., Iqbal, M. A., Rasool, A., & Uwitije, R. (2025). p-GIN: A graph isomorphism network based on p-Laplacian operator to enhance molecular property prediction. Discover Applied Sciences, 7(11), 1251.

Shoket, N., Ul Rahman, J., & Zafar, N. (2025). Mathematical modeling and computational investigation of the COVID-19 epidemic using wavelet neural networks and coupled optimization. SN Soft Computing, 1–18.

Rahman, J. U., Ali, H., & Rassol, A. (2025). A comprehensive analysis of optimizers in message passing neural networks for molecular property prediction task. Computational Biology and Chemistry, 108556