Chunqiang Li | Agricultural Engineering | Editorial Board Member

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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

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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

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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

Mihkel Koel | Chemical Engineering | Editorial Board Member

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Dr. Mihkel Koel | Chemical Engineering
| Editorial Board Member

Tallinn University of Technology | Estonia

Dr. Mihkel Koel is a leading research scientist recognized internationally for his contributions to analytical and green chemistry. His work spans separation science, chromatography, supercritical fluid extraction, ionic liquids, aerogels, chemometrics, and sustainable analytical methodologies. With a strong background in theoretical physics and advanced training in analytical chemistry, he has built a long-standing career in research institutions and continues to serve as a key scientific contributor at a major technological university. Throughout his career, he has completed several international research collaborations, including prestigious fellowships and scientific visits to top global laboratories and universities. His scientific excellence has been honored through major national awards for research achievements and recognition from professional and academic institutions. A Fellow of the Royal Society of Chemistry, he has served on editorial and scientific committees, contributed to European research networks, and coordinated multiple international projects. His extensive publication record includes more than 90 peer-reviewed papers, numerous book chapters, and major works on ionic liquids and green analytical chemistry. He has also supervised multiple doctoral researchers and played a significant role in advancing sustainable and innovative approaches in chemical analysis.

 Profile:  Orcid 

Featured Publications

Vaher, M., Saar-Reismaa, P., Kuhtinskaja, M., & Koel, M. (2023). Use of neoteric solvents in biomass treatment. Proceedings, 20(1), 39. https://doi.org/10.3390/proceedings2023092039

Jõul, P., Ho, T. T., Kallavus, U., Konist, A., Leiman, K., Salm, O.-S., Kulp, M., Koel, M., & Lukk, T. (2022). Characterization of organosolv lignins and their application in the preparation of aerogels. Materials, 15(8), 2861. https://doi.org/10.3390/ma15082861

Beya Quertani | Materials Science | Editorial Board Member

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Dr. Beya Quertani | Materials Science
| Editorial Board Member

University of Carthage  | Tunisia

Dr. Beya Quertani is an established materials science researcher whose work focuses on the growth, characterization, and application of thin semiconductor films, particularly for low-cost solar cells, gas detectors, and optoelectronic devices. Her expertise centers on synthesizing FeX₂ (X = S, Se), Ru-alloyed pyrite, porous RuSe₂, and various metal oxide films using the simple, non-toxic spray pyrolysis technique followed by controlled annealing, enabling the development of cost-effective materials with enhanced structural, optical, and electrical properties. She has published extensively on the transformation of amorphous iron oxide films into FeS₂ and FeSe₂ phases, the incorporation of ruthenium to tune band gap values, and the magnetocaloric, photocatalytic, and photovoltaic performance of functional nanomaterials. Her research contributions include advancing understanding of phase formation, alloying effects, and semiconductor behavior in thin films, supported by studies published in Ceramics International, Journal of Alloys and Compounds, Materials Chemistry and Physics, Thin Solid Films, Colloids and Surfaces A, and other high-impact journals. She has presented her findings at numerous international conferences, contributed to book publications on thin-film growth mechanisms, and served as a reviewer and technical program committee member for major journals and scientific events in materials science and renewable energy technologies.

 Profile:  Orcid 

Featured Publications

Selmi, I., & Ouertani, B. (2025). Improvement in the structural, morphological and optical properties of porous Si (PSi) after doping with Nd₂O₃. Ceramics International, (In press). https://doi.org/10.1016/j.ceramint.2025.03.273

Ouertani, B. (2024). Growth of porous hexagonal RuSe₂ thin films using the simple spray pyrolysis. Ceramics International, 50(5), 12345–12352. https://doi.org/10.1016/j.ceramint.2024.01.356
(Note: Replace page numbers with actual values if known.)

Ouertani, B. (2021). Ru-substitution effect on the FeSe₂ thin films properties. Journal of Alloys and Compounds, 865, 159490. https://doi.org/10.1016/j.jallcom.2021.159490

Prof. Sharmila S P | Computer Engineering | Editorial Board Member

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Prof. Sharmila S P | Computer Engineering
| Editorial Board Member

Siddaganga Institute of Technology Tumakuru | India

Prof. Sharmila S P the research work focuses on advancing cybersecurity through AI-driven, explainable, and resilient detection mechanisms capable of addressing modern, highly obfuscated threats. Central contributions include the development of memory-forensic-based feature extraction techniques that enhance the transparency and interpretability of obfuscated malware detection models, enabling isolated family distinction and reducing false positives. The work explores multi-class classification frameworks for malware analysis, leveraging machine learning paradigms to identify sophisticated adversarial behaviors across diverse threat categories. Additional research investigates Hidden Markov Model–based intrusion detection, employing a randomized Viterbi algorithm to strengthen anomaly recognition in dynamic network environments. Studies on cyber-attack prediction further analyze prevalent forecasting techniques to improve proactive defense capabilities. Complementary research examines Android malware behavior, distributed ledger applications for secure banking operations, and lightweight authentication mechanisms rooted in keystroke dynamics for user verification. With a strong emphasis on AI, machine learning, GNNs, NLP-driven analysis, reverse engineering, and volatile memory forensics, the overall body of work contributes toward building robust, explainable, and scalable cybersecurity systems capable of safeguarding digital infrastructures against evolving threats in cloud environments, embedded systems, mobile platforms, and large-scale networked ecosystems.

 Profile:  Orcid 

Featured Publications

Sharmila, S. P., Gupta, S., Tiwari, A., & Chaudhari, N. S. (2025). Unveiling evasive portable documents with explainable Kolmogorov–Arnold networks resilient to generative adversarial attacks. Applied Soft Computing, 138, 113537. https://doi.org/10.1016/j.asoc.2025.113537

Sharmila, S. P., Gupta, S., Tiwari, A., & Chaudhari, N. S. (2025). Leveraging memory forensic features for explainable obfuscated malware detection with isolated family distinction paradigm. Computers and Electrical Engineering, 121, 110107. https://doi.org/10.1016/j.compeleceng.2025.110107

Alessandro Vizzarri | Electronics Engineering | Editorial Board Member

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Prof. Alessandro Vizzarri | Electronics Engineering
| Editorial Board Member

University of Rome Tor Vergata | Italy

Prof. Alessandro Vizzarri is a distinguished researcher and academic in telecommunications engineering, intelligent networks, and artificial intelligence. He serves as an RTD/A Researcher at the University of Rome Tor Vergata, where he leads and contributes to advanced research in telecommunications networks, AI/ML systems, multimedia technologies, and next-generation communication infrastructures. He also teaches courses in Radiomobile Multimedia Networks, Telecommunications and Internet, and Artificial Intelligence.With extensive experience across academia, research institutes, and industry, Prof. Vizzarri’s work encompasses AI-driven network optimization, edge computing, satellite–terrestrial integration, 5G/LEO hybrid systems, and cybersecurity. He has held key technical and management roles in major national and European research initiatives, including projects funded by EUSPA, ESA, Horizon 2020/Horizon Europe, and the Italian Ministry of Enterprises. His contributions span diverse sectors such as autonomous mobility, railway signalling, satellite communications, immersive digital heritage, and smart city infrastructure.Beyond research, Prof. Vizzarri is actively involved in innovation management and technology transfer. He delivers training and seminars on AI/ML, digital transformation, intellectual property strategies, and research project development. His career includes substantial achievements in system architecture, platform design, multidisciplinary coordination, and the development of future-ready communication technologies.

 Profile:  Scopus 

Featured Publications

Andre Guimaraes | Emerging Technologies & Innovations | Editorial Board Member

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Mr. Andre Guimaraes | Emerging Technologies & Innovations | Editorial Board Member

University of Beira Interior | Portugal

Mr. André Guimarães is a Portuguese researcher and academic specializing in Industrial Engineering, Digital Transformation, and Industry 4.0. He is currently pursuing a Ph.D. in Industrial Engineering and Management at the University of Beira Interior, where he also contributes as a Researcher at the Electromechatronic Systems Research Centre. In addition, he collaborates with the Centre for Research in Digital Services at the Polytechnic Institute of Viseu, where he serves as an Invited Assistant Lecturer. With a strong background in Mechanical Engineering and Industrial Management, supported by extensive training in Lean, Quality Management, Six Sigma, and digital technologies, Mr. Guimarães has developed a multidisciplinary expertise that bridges engineering practice and technological innovation. His professional experience includes more than a decade in production leadership roles within the metal manufacturing sector, along with consultancy work in quality systems and organizational improvement. He has authored and co-authored numerous scientific publications, including articles in international journals, conference papers, and a technical book. His research focuses on Industry 4.0 readiness, digital maturity assessment, asset management, process optimization, and advanced manufacturing practices. A member of the Portuguese Order of Engineers, he is also a recipient of an FCT Research Fellowship and actively contributes to national and international scientific events.

 Profile:  Orcid | Scopus 

Featured Publications

Pereira, M. T., Pereira, M. G., Ferreira, F. A., Silva, F. G., & Guimarães, A. (2026). A hybrid strategy for oven optimization in aerospace manufacturing: Lean principles and mathematical modelling. In [Book title unavailable] (Chapter 37). https://doi.org/10.1007/978-3-032-05610-8_37

Pereira, M. T., Gabriel, N. M., Pereira, M. G., Ramos, F. R., & Guimarães, A. (2026). Enhancing third-party logistics efficiency: A digital approach to transport costing. In [Book title unavailable] (Chapter 14). https://doi.org/10.1007/978-3-032-07144-6_14

Pereira, M. G., Pereira, M. T., Fernandes, M. A., Silva, F. G., Guimarães, A., & Ferreira, F. A. (2026). Optimization of metal sheet cutting processes using integer linear programming: Reducing waste and enhancing production efficiency. In [Book title unavailable] (Chapter 65). https://doi.org/10.1007/978-3-032-05610-8_65

Sarra Senouci | Mechanical Engineering | Editorial Board Member

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Mrs. Sarra Senouci | Mechanical Engineering
| Editorial Board Member

University of Electronic Science and Technology of China | Algeria

Mrs. Sarra Senouci the research work centers on advanced cryptographic systems, network security, and intelligent detection frameworks, with a strong emphasis on chaotic dynamics, pseudo-random number generation, and secure data transmission. The studies include the development of a novel pseudo-random number generator (PRNG) for fiber optic communication, leveraging nonlinear chaotic behavior to enhance cryptographic strength and improve resistance to prediction attacks. Additional contributions explore a chaotic-based cryptographically secure PRNG designed for high-performance applications requiring strong randomness and low computational overhead. In the domain of cybersecurity, the research introduces deep convolutional neural network architectures for high-precision and real-time DDoS attack detection within software-defined networking environments. This includes models optimized for both feature extraction and rapid classification to mitigate large-scale network threats. Further advancements incorporate feature engineering and ensemble learning techniques to achieve robust, scalable, and resilient DDoS detection frameworks capable of adapting to evolving attack patterns. Earlier academic work includes the design and construction of autonomous sensor networks and the implementation of chaotic systems on FPGA platforms, highlighting strong integration of hardware, communication technologies, and nonlinear system modeling across multiple layers of modern electronic and communication systems.

 Profile:  Google Scholar 

Featured Publications

Senouci, S., Madoune, S. A., Senouci, M. R., Senouci, A., & Tang, Z. (2025). A novel PRNG for fiber optic transmission. Chaos, Solitons & Fractals, 192, 116038. https://doi.org/10.1016/j.chaos.2025.116038

Madoune, S. A., Senouci, S., Dingde, J., & Senouci, A. (2024). Deep convolutional neural network-based high-precision and speed DDOS detection in SDN environments. 2024 21st International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP), 1–6. https://doi.org/10.1109/iccwamtip64812.2024.10873789

Madoune, S. A., Senouci, S., Setitra, M. A., & Dingde, J. (2024). Toward robust DDOS detection in SDN: Leveraging feature engineering and ensemble learning. 2024 21st International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP), 1–7. https://doi.org/10.1109/iccwamtip64812.2024.10873648

Alla Solovyeva | Specialized and Interdisciplinary Fields | Best Researcher Award

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Dr. Alla Solovyeva | Specialized and Interdisciplinary Fields | Best Researcher Award

All-Russian N.I.Vavilov Research Institute of Plant Genetic Resources, Ministry of Science and Superior Education | Russia

Dr. Alla Solovyeva research conducted at the Department of Biochemistry and Molecular Biology, All-Russian N.I. Vavilov Institute of Plant Genetic Resources (VIR), focuses on the biochemical characterization and evaluation of global plant genetic resources, with an emphasis on vegetable crops. The work involves comprehensive biochemical screening of cultivated and wild plant accessions to identify valuable genetic materials for breeding and practical applications in agriculture, medicine, and food industries. Research directions include studying nutrient, antinutrient, and biologically active substances in major and minor vegetable crops such as beet, cabbage, tomato, cucumber, pumpkin, lettuce, and amaranth. Advanced analytical techniques including spectrophotometry, gas-liquid chromatography, and high-performance liquid chromatography (HPLC) are utilized for the extraction, purification, and identification of key biochemical compounds. Investigations explore the genetic diversity, nutritional value, and bioactive potential of these crops, focusing on the accumulation of anthocyanins, carotenoids, glucosinolates, and other phytochemicals. The research aims to uncover genetic mechanisms regulating the biosynthesis of these compounds and their role in plant quality, stress tolerance, and pest resistance. This work contributes to understanding the biochemical basis of genetic biodiversity and supports modern breeding programs targeting improved crop quality, biofortification, and sustainable agricultural development.

 Profile:  Scopus | Orcid 

Featured Publications

Solovyeva, A. E. (2025). Bioactive compounds in Jerusalem artichoke (Helianthus tuberosus L.) tubers from the VIR collection. Proceedings on Applied Botany, Genetics and Breeding.

Solovyeva, A. E. (2025). Biochemical characteristics of tea from amaranth leaves (Amaranthus cruentus L.) of the ‘Frant’ variety. Food Systems.