Peiyun Zhang | Solid Mechanics | Excellence in Research Award

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Mr. Peiyun Zhang | Solid Mechanics | Excellence in Research Award

Shenzhen University | China

Mr. Peiyun Zhang’s research focuses primarily on the fracture and fatigue behavior of structural materials, with particular emphasis on high-strength steels and composite-reinforced concrete elements. His work delivers quantitative insights into ductile fracture mechanisms, integrating experimental testing with advanced numerical modeling to enhance the predictive accuracy of failure assessments in structural steels. Mr. Peiyun Zhang has contributed significantly to understanding the ductile fracture behavior of Q460C high-strength steel, employing monotonic loading experiments and MMC-based fracture modeling to evaluate damage evolution and fracture initiation under diverse stress states. His studies provide unified methodologies for predicting fracture across various steel grades, enabling more reliable performance evaluations of structural components subjected to extreme mechanical demands. In addition to steel fracture, he has examined fatigue behavior in concrete beams reinforced with CFRP rebars and investigated the influence of different web-anchorage configurations on structural durability. This cohesive body of work advances structural safety design, strengthens material reliability assessments, and supports national-level extreme disaster assessment and mitigation efforts by enabling accurate prediction of failure in large-scale structural systems.

Profile: Google Scholar

Featured Publications

  • Chen, A., Zhang, P., Chen, B., Li, Y., & Xing, J. (2023). A new ductile fracture model for Q460C high-strength structural steel under monotonic loading: Experimental and numerical investigation. Engineering Fracture Mechanics, 288, 109358.

  • Chen, A., Zhang, P., Lin, J., & Xing, J. (2023). Study on ductile fracture of Q460C high-strength structural steel based on MMC fracture model. Engineering Mechanics.

  • Zhang, P. Y., Kim, O. Y., & Cui, X. (2019). Fatigue behavior of concrete beam using CFRP rebar. Journal of the Korea Institute of Building Construction, 19(6), 495–501.

  • Zhang, P., Chen, A., Xing, J., & Wang, Y. (2025). New unified model for predicting ductile fracture in structural steels. International Journal of Mechanical Sciences, Article 111085.

  • Cui, X., & Zhang, P. Y. (2019). Fatigue effect of the type of web anchorage in RC beams bonded with CFRP plate. Journal of the Korean Society for Advanced Composite Structures, 10(6), 21–27.

Hongtao Li | Cyberspace Security | Best Researcher Award

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Prof. Dr. Hongtao Li | Cyberspace Security | Best Researcher Award

Linyi University | China

Prof. Dr. Hongtao Li is a distinguished researcher specializing in cryptography and its applications, IoT security, large model security, and AI security. He serves as a council member of the Shanxi Computer Society and has been recognized under the “Sanjin Talent” Support Program in Shanxi Province. His research contributions encompass privacy-preserving mechanisms for big data, federated learning, blockchain-based auditing, and secure data collection for smart cities and IoT systems. He has led multiple high-profile projects, including a National Natural Science Foundation of China Youth Project on big data security and privacy protection, and several provincial-level initiatives focused on IoT security, medical big data protection, and cyberspace education reform. Prof. Li holds two authorized patents for computer network security and information security devices, reflecting his strong applied research impact. He has published over 50 papers in high-impact journals, addressing differential privacy, location privacy, blockchain protocols, and privacy-preserving schemes for digital communities and healthcare systems. His work demonstrates a consistent focus on safeguarding data and enhancing security frameworks in complex networked environments. With an h-index of 11, 328 citations by 314 documents, and 30 scholarly publications, Prof. Li’s research has significantly influenced both theoretical and applied aspects of cybersecurity, particularly in IoT and large-scale data environments, positioning him as a leading figure in advancing secure and privacy-preserving technologies in China and internationally.

Profile: Scopus

Featured publications

  • Wang, J., Zhang, Z. J., Tian, J., & Li, H. T. (2024). Local differential privacy federated learning based on heterogeneous data multi-privacy mechanism. Computer Networks, 254, 110822.

  • Li, H. T., Ma, J. F., & Fu, S. (2015). A privacy-preserving data collection model for digital community. Science China Information Sciences, 58(3), 1–16.

  • Li, H. T., Guo, F., & Wang, L., et al. (2021). A blockchain-based public auditing protocol with self-certified public keys for cloud data. Security and Communication Networks, 2021(3), 1–10.

  • Li, H. T., Wang, Y., & Guo, F., et al. (2021). Differential privacy location protection method based on the Markov model. Wireless Communications and Mobile Computing, 2021, 1–10.

  • Li, H. T., Xue, X., Li, Z., et al. (2021). Location privacy protection scheme for LBS in IoT. Wireless Communications and Mobile Computing, 2021, 1–18.

Peng Su | Machine Learning on Databases | Research Excellence Award

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Prof. Peng Su | Machine Learning on Databases | Research Excellence Award

Hebei University of Technology | China

Prof. Peng Su’s research centers on the advanced design, electromagnetic modeling, and performance optimization of permanent-magnet (PM) electrical machines, with a primary emphasis on flux-switching machine topologies for electric and hybrid-electric vehicle applications. With a citation record of 563 citations in total (431 since 2020), an h-index of 12 (11 since 2020), and an i10-index of 16 (12 since 2020), his contributions are well recognized within the electrical machine research community. His work significantly advances understanding of rotor-PM and stator-PM flux-switching architectures through rigorous analyses of operating principles, air-gap field modulation, hybrid-excitation mechanisms, and multi-phase configurations, enabling improved torque density, efficiency, and thermal robustness. Prof. Peng Su has delivered influential findings on PM eddy-current losses, stator-slot and rotor-pole selection, cogging-torque reduction strategies, and magnetization effects, offering practical design paths for minimizing parasitic losses and enhancing reliability under high-speed and vector-controlled drive conditions. His portfolio extends across diverse machine types—including axial-modular machines, multitooth structures, tubular PM generators, and toroidally wound direct-drive motors—demonstrating comprehensive expertise in advanced electromagnetic machine architectures. He also contributes to loss modeling in soft magnetic composites, fault behavior characterization, and performance evaluation methodologies tailored to transportation electrification requirements. Through systematic comparative studies, innovative structural proposals, and refined analytical models, Prof. Peng Su continues to shape the development of next-generation PM machines and high-efficiency energy-conversion technologies, reinforcing his position as a leading contributor to modern electrical machine engineering.

Profiles: Google Scholar | Orcid

Featured Publications

  • Hua, W., Su, P., Tong, M., & Meng, J. (2016). Investigation of a five-phase E-core hybrid-excitation flux-switching machine for EV and HEV applications. IEEE Transactions on Industry Applications, 53(1), 124–133.

  • Su, P., Hua, W., Wu, Z., Han, P., & Cheng, M. (2017). Analysis of the operation principle for rotor-permanent-magnet flux-switching machines. IEEE Transactions on Industrial Electronics, 65(2), 1062–1073.

  • Su, P., Hua, W., Wu, Z., Chen, Z., Zhang, G., & Cheng, M. (2018). Comprehensive comparison of rotor permanent magnet and stator permanent magnet flux-switching machines. IEEE Transactions on Industrial Electronics, 66(8), 5862–5871.

  • Su, P., Hua, W., Hu, M., Chen, Z., Cheng, M., & Wang, W. (2019). Analysis of PM eddy current loss in rotor-PM and stator-PM flux-switching machines by air-gap field modulation theory. IEEE Transactions on Industrial Electronics, 67(3), 1824–1835.

  • Su, P., Hua, W., Hu, M., Wu, Z., Si, J., Chen, Z., & Cheng, M. (2019). Analysis of stator slots and rotor pole pairs combinations of rotor-permanent magnet flux-switching machines. IEEE Transactions on Industrial Electronics, 67(2), 906–918.*

Adugna Nigatu Alene | Graph Databases | Editorial Board Member

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Mr. Adugna Nigatu Alene | Graph Databases | Editorial Board Member

Bahir Dar University | Ethiopia

Mr. Adugna Nigatu Alene is an emerging researcher whose work is grounded in sustainable materials development, environmental remediation, and green nanotechnology, supported by a growing scholarly impact reflected in 475 citations, an h-index of 8, and an i10-index of 6. His research integrates organic chemistry, nanomaterials engineering, and environmental science to create low-cost, high-performance adsorbents and nanocatalysts for the removal of hazardous dyes and industrial pollutants from aqueous environments. He frequently employs agricultural and industrial wastes—including kaolin, waste ash, Catha edulis stem, seed peel residues, and tella by-products—to develop eco-friendly bio-adsorbents with enhanced adsorption efficiency, while his mechanistic investigations elaborate on adsorption kinetics, isotherms, equilibrium behavior, and thermodynamic characteristics. In parallel, he advances functional nanocomposites such as boron-doped cobalt oxide nanoparticles, silver-loaded magnetic nanostructures, and humic acid-modified magnetite, demonstrating their catalytic and electrochemical performance in applications spanning reductive dye degradation and supercapacitor enhancement. His work also includes biodegradable film development using natural fillers and plant-based biopolymers, contributing to circular materials innovation and sustainable product design. Further contributions involve studies on phytochemical profiles, nutraceutical potential, and plant-derived extracts relevant to health and biochemical research. Across his publication record, Mr. Alene emphasizes cost-effective synthesis, resource valorization, and environmentally responsible technologies that deliver scalable solutions to industrial pollution challenges. His research trajectory positions him as a promising contributor to green chemistry, nanotechnology-driven remediation, and the development of sustainable material systems for improved environmental quality.

Profile: Google Scholar

Featured Publications

  • Abate, G. Y., Alene, A. N., Habte, A. T., & Getahun, D. M. (2020). Adsorptive removal of malachite green dye from aqueous solution onto activated carbon of Catha edulis stem as a low-cost bio-adsorbent. Environmental Systems Research, 9(1), 29.

  • Aragaw, T. A., & Alene, A. N. (2022). A comparative study of acidic, basic, and reactive dyes adsorption from aqueous solution onto kaolin adsorbent: Effect of operating parameters, isotherms, kinetics, and thermodynamics. Emerging Contaminants, 8, 59–74.

  • Alene, A. N., Abate, G. Y., & Habte, A. T. (2020). Bioadsorption of basic blue dye from aqueous solution onto raw and modified waste ash as an economical alternative bioadsorbent. Journal of Chemistry, 2020, 8746035.

  • Abate, G. Y., Alene, A. N., Habte, A. T., & Addis, Y. A. (2021). Adsorptive removal of basic green dye from aqueous solution using humic acid modified magnetite nanoparticles: Kinetics, equilibrium, and thermodynamic studies. Journal of Polymers and the Environment, 29(3), 967–984.

  • Alene, A. N., Abate, G. Y., Habte, A. T., & Getahun, D. M. (2021). Utilization of a novel low-cost Gibto (Lupinus albus) seed peel waste for the removal of malachite green dye: Equilibrium, kinetic, and thermodynamic studies. Journal of Chemistry, 2021, 1–16.

Tamirat Aye | AI Powered Database Systems | Editorial Board Member

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Mr. Tamirat Aye | AI Powered Database Systems | Editorial Board Member

Mizan Tepi University | Ethiopia

Mr. Tamirat Taye is an emerging EFL researcher with a growing scholarly footprint, reflected in 101 citations, an h-index of 4, and an i10-index of 2, and his work consistently contributes to the advancement of evidence-based language pedagogy. His research focuses on reading comprehension strategies, extensive reading, academic writing development, and the pedagogical integration of literary texts within EFL contexts. Through publications in journals such as Heliyon, Social Sciences & Humanities Open, Discover Education, and English Education Journal, he has examined how extensive reading practices enhance reading comprehension and overall language development among undergraduate learners. A central theme of his scholarship is the analysis of common writing challenges, the role of grammatical accuracy, and the comparative effectiveness of direct, indirect, and peer-feedback error-correction techniques in improving students’ writing proficiency. His studies on speaking-skill challenges, cultural awareness, and the instructional value of literary texts further highlight his commitment to a holistic understanding of language learning that integrates cognitive, linguistic, and socio-cultural dimensions. Additionally, his work on Grade 12 textbook content and the use of literary texts in Ethiopian classrooms provides context-driven insights for curriculum reform and pedagogical improvement. Employing mixed-method methodologies, he generates practical, classroom-relevant findings that support teachers, curriculum designers, and policy stakeholders in enhancing EFL instruction. His collaborative research with co-authors across multiple studies reflects sustained engagement in strengthening EFL pedagogy, student competencies, and evidence-based teaching strategies in higher education settings.

Profile: Google Scholar

Featured Publications

  • Taye, T., & Mengesha, M. (2024). Identifying and analyzing common English writing challenges among regular undergraduate students. Heliyon, 10(17), e36876.

  • Taye, T., & Teshome, G. (2025). The efficacy of extensive reading strategies for enhancing reading comprehension among 4th year EFL students at Mizan Tepi University. Social Sciences & Humanities Open, 11, 101616.

  • Taye, T., & Teshome, G. (2025). The impact of extensive reading on academic writing proficiency in EFL undergraduate students. Discover Education, 4(1), 264.

  • Simel, T. T. (2024). Assessing the role of literary texts in students’ cultural awareness, historical understanding, and challenges faced by EFL students. English Education Journal, 15(4), 208–228.

  • Taye, T., & Teshome, G. (2025). The benefits and challenges of integrating literary texts in English language textbooks in Ethiopian Grade 12 English language classrooms. Social Sciences & Humanities Open, 11, 101545.

Iman Hajird | Irrigation and Drainage | Editorial Board Member

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Dr. Iman Hajird | Irrigation and Drainage | Editorial Board Member

University of Tehran | Iran

Dr. Iman Hajirad is a water engineering researcher whose contributions to irrigation science and sustainable water management are reflected in his growing scholarly impact, with 60 citations, an h-index of 5, and an i10-index of 1. His work focuses on advancing irrigation efficiency, improving crop water productivity, and developing climate-resilient water strategies for arid and semi-arid regions. He has conducted extensive research on pulsed and continuous drip irrigation systems, analyzing their effects on yield, evapotranspiration, soil moisture dynamics, and crop response factors, particularly for silage maize and wheat. Dr. Hajirad employs soil water balance models, crop coefficient estimation methods, and advanced simulation tools such as HYDRUS-2D to deepen understanding of soil–water interactions under variable irrigation regimes. His studies also integrate remote sensing data from platforms like WaPOR, alongside IoT-enabled irrigation systems, to support precision agriculture and smart water management. He has explored nonlinear growth modeling, irrigation scheduling, and practical strategies for optimizing yield under saline and water-scarce conditions, contributing valuable insights for sustainable agricultural planning. In recent work, he has addressed broader themes such as climate-resilient water infrastructure, global water resource adaptation, and innovative irrigation practices for environmental sustainability. Dr. Hajirad also plays an active role in academic communication through editorial leadership in several scientific journals and has received multiple national awards and best paper recognitions for his impactful research. His body of work advances efficient irrigation, data-driven water management, and resilient agricultural systems.

Profile: Google Scholar

Featured Publications

  • Mohammadi, S., Mirlatifi, S. M., Homaee, M., Dehghanisanij, H., & Hajirad, I. (2024). Evaluation of silage maize production under pulsed drip irrigation in a semi-arid region. Irrigation Science, 42(2), 269–283.

  • Hajirad, I., Mohammadi, S., & Dehghanisanij, H. (2023). Determining the critical points of a basin from the point of view of water productivity and water consumption using the WaPOR database. Environmental Sciences Proceedings, 25(1), 86.

  • Pourgholam-Amiji, M., Hajirad, I., Nayebi, J., Alavi, S. R., Nozari, F., & Akbarpour, M. (2024). Improving wheat irrigation productivity in Iran (Part one: From the viewpoint of irrigation system and water management). Water and Soil Management and Modelling, 4(1), 171–193.

  • Hajirad, I., Mirlatifi, S. M., Dehghanisanij, H., & Mohammadi, S. (2021). Determining yield response factor (Ky) of silage maize under different irrigation levels of pulsed and continuous irrigation management. Central Asian Journal of Plant Science Innovation, 1(4), 214–220.

  • Hajirad, I., Mirlatifi, S. M., Dehaghani, S. H., & Mohammadi, S. (2021). Investigating the effect of deficit irrigation on yield and water productivity of silage maize under pulsed and continuous drip irrigation management. Iranian Water Research Journal, 15(342), 15–23.*

Ali Rafe | Food Sciecne and Technology | Editorial Board Member

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Prof. Dr. Ali Rafe | Food Sciecne and Technology | Editorial Board Member

Research Institute of Food Sciecne and Technology | Iran

Prof. Dr. Ali Rafe is a leading researcher in food engineering whose work focuses on sustainable food systems, plant proteins, biopolymer interactions, and the rheological and interfacial properties of complex food matrices. His research advances the understanding of protein–polysaccharide complexation, electrostatic coacervation, and structure–function relationships, supporting the development of clean-label, high-performance, and nutritionally enhanced food formulations. Prof. Dr. Ali Rafe has made major contributions to encapsulation science by designing complex coacervates and nanophytosomes for protecting and delivering sensitive bioactive compounds such as saffron constituents, barberry anthocyanins, and pomegranate extracts, achieving improved stability, controlled release, and bioavailability. His work extensively investigates how pH, ionic environments, and processing conditions affect the physicochemical and rheological behaviors of plant and dairy protein systems—including canola, sesame, and wheat germ proteins—thereby enhancing texture, sensory attributes, and overall functionality in diverse food products. He also explores advanced processing methods such as pulsed electric fields, cold plasma, and enzymatic treatments to improve extraction efficiency, antioxidant retention, and techno-functional performance of food ingredients. Beyond fundamental research, he contributes to food product development in areas such as pickled cucumbers, dairy creams, ketchup systems, and hydrocolloid-stabilized formulations. With 2743 citations, an h-index of 33, and an i10-index of 55, Prof. Dr. Ali Rafe has established significant scientific influence and continues to shape innovative, sustainable, and health-promoting solutions within the field of food engineering.

Profile: Google Scholar

Featured Publications

  • Moghadam, A., Ghorbani-HasanSaraei, A., Rafe, A., Fazeli, F., & Shahidi, S. A. (2025). Improving the functional properties of canola protein isolate through electrostatic coacervation with soluble fraction of Tragacanth gum. International Journal of Biological Macromolecules, 148, 103.

  • Dara, A., Feizy, J., Naji-Tabasi, S., Fooladi, E., & Rafe, A. (2023). Intensified extraction of anthocyanins from Berberis vulgaris L. by pulsed electric field, vacuum-cold plasma, and enzymatic pretreatments: Modeling and optimization. Chemical and Biological Technologies in Agriculture, 10(1), 93.

  • Jamshidian, H., & Rafe, A. (2024). Complex coacervate of wheat germ protein/high methoxy pectin in encapsulation of d-limonene. Chemical and Biological Technologies in Agriculture, 11(1), 60.

  • Ardestani, F., Haghighi Asl, A., & Rafe, A. (2024). Characterization of caseinate-pectin complex coacervates as a carrier for delivery and controlled-release of saffron extract. Chemical and Biological Technologies in Agriculture, 11(1), 118.

  • Ghorbani, A., Rafe, A., Hesarinejad, M. A., & Lorenzo, J. M. (2025). Impact of pH on the physicochemical, structural, and techno-functional properties of sesame protein isolate. Food Science & Nutrition, 13(1), e4760.

Michal Haindl | Visual Texture Inpainting | Best Researcher Award

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Prof. Dr. Michal Haindl | Visual Texture Inpainting | Best Researcher Award

Institute of Information Theory and Automation of the Czech Acadaemy of Sciences | Czech Republic

Prof. Dr. Michal Haindl is a leading Czech researcher recognized internationally for his extensive contributions to pattern recognition, texture analysis, material appearance modeling, and computational imaging, with a prolific body of work spanning more than two decades and over 130 publications. As a pioneer in advanced texture modeling, he has developed influential methods such as Bidirectional Texture Function (BTF) models, multispectral and 3D causal random field texture representations, anisotropic BRDF modeling, and rotationally invariant textural features that have shaped modern approaches in computer vision. His research addresses fundamental challenges in texture fidelity, similarity criteria, segmentation, scale and illumination invariance, and material recognition, providing robust frameworks widely applied in remote sensing, biomedical imaging, cultural heritage restoration, forestry classification, and disease detection. Prof. Dr. Haindl has also significantly advanced unsupervised learning and benchmarking for image segmentation, contributing datasets, evaluation metrics, and criteria that have become reference standards in the field. His work on medical imaging—including mammogram enhancement, melanoma recognition, and disease survival modeling—reflects his interdisciplinary impact across health analytics and AI-driven diagnostic support. Additionally, he has contributed to computational methods for evaluating physical and rendered materials, transfer learning for texture models, and structural detection in archeology. Through sustained innovation, extensive collaborations, and consistent publication in high-impact journals and conferences, Prof. Dr. Michal Haindl has established himself as a foundational figure in texture-based pattern recognition and material appearance research, continuously driving forward the scientific understanding and practical applications of computational vision.

Profiles: Orcid | Scopus

Featured Publications

  • Haindl, M., & Mikes, S. (2023). Optimal activation function for anisotropic BRDF modeling. Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP), 1–9.

  • Mikes, S., & Haindl, M. (2022). Texture segmentation benchmark. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(12), 1–15.

  • Vacha, P., & Haindl, M. (2023). Texture recognition under scale and illumination variations. Journal of Information and Telecommunication, 7(4), 1–14.

  • Remes, V., & Haindl, M. (2019). Bark recognition using novel rotationally invariant multispectral textural features. Pattern Recognition Letters, 128, 1–8.

  • Haindl, M. (2022). Bidirectional texture function modeling. In Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging (pp. 1–15). Springer.

Nikolaos Varotsis | Technological Change | Best Researcher Award

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Dr. Nikolaos Varotsis | Technological Change | Best Researcher Award

Lonian University | Greece

Dr. Nikolaos Varotsis is a multidisciplinary researcher whose work spans tourism management, behavioral economics, and knowledge management, focusing on how individuals, tourists, and organizations make decisions under complex socio-economic and informational conditions. With 203 citations, an h-index of 8, and an i10-index of 8, his research output demonstrates strong and steadily growing academic influence in both tourism studies and behavioral sciences. His contributions advance understanding of tourist information search behavior, destination brand equity, cultural tourism development, and digital entrepreneurship within the tourism sector. Drawing from behavioral economics and social simulation methodologies, Dr. Nikolaos Varotsis investigates mental accounting, organizational motivation, tax behavior, and the interplay of psychological, economic, and social factors influencing tax planning and tax compliance. His work also provides significant insights into telecommuting performance, work–family conflict, and public sector attitudes during the COVID-19 era, along with fiscal foresight models, shadow economy reduction strategies, and e-payment institutionalization. In tourism research, he has produced influential studies on information service management, wedding tourism decision motives, alternative tourism models for island destinations, and quality standards in hospitality services. His publications appear in respected journals such as Cogent Business & Management, Journal of Convention & Event Tourism, Nonlinear Dynamics Psychology and Life Sciences, Journal of Economic Structures, Digital Policy, Regulation and Governance, and Theoretical Economics Letters. Dr. Nikolaos Varotsis diverse research interests continue to evolve around tourism behavior, social simulations, organizational change, knowledge management, and the integration of behavioral insights into tourism innovation and public administration.

Profiles: Google Scholar | Scopus | Orcid

Featured Publications

  • Varotsis, N., & Mylonas, N. (2024). A systematic literature review on information service management and information-seeking behavior in tourism. Cogent Business & Management, 11(1), 2385731.

  • Mylonas, N., Varotsis, N., & Vozinidou, I.-M. (2024). Unveiling the relationship between travel decision motives and destination brand equity in wedding tourism. Journal of Convention & Event Tourism, 25(2), 233–248.

  • Kontogeorgis, G., & Varotsis, N. (2022). Cultural tourism in developed island tourist destinations: The development of an alternative tourism model in Corfu. Journal of Environmental Management and Tourism, 13(2), 490–503.

  • Varotsis, N. (2022). A fiscal policy foresight tax model, shadow economy reduction, and e-payment institutionalization as a result of knowledge management. Theoretical Economics Letters, 12(6), 1710–1728.

  • Varotsis, N. (2022). Exploring the influence of telework on work performance in public services: Experiences during the COVID-19 pandemic. Digital Policy, Regulation and Governance, 24(3), 248–263.

Lijuan Wu | Power Device | Best Researcher Award

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Mrs. Lijuan Wu | Power Device | Best Researcher Award

Changsha University of Science and Technology | China

Mrs. Lijuan Wu is a distinguished researcher specializing in power semiconductor devices and power integrated circuits, with a strong focus on the design, simulation, and optimization of SiC and GaN-based electronic components such as MOSFETs, HEMTs, and IGBTs. Her work emphasizes enhancing energy efficiency, switching controllability, and device reliability for next-generation semiconductor applications. With 69 research publications, 317 citations, and an h-index of 9, she has made impactful contributions through innovative designs, including Double-RESURF SiC MOSFETs and self-clamped P-shield trench MOSFETs. Mrs. Wu has successfully led 10 funded research projects, including one supported by the National Natural Science Foundation of China, and multiple provincial and industry collaborations, bridging theoretical modeling with applied semiconductor technology. She has published 24 first-author papers in high-impact journals such as IEEE Transactions on Electron Devices and Microelectronics Journal, and authored the national monograph Optimization Technology for Charge Field in Heterogeneous Voltage-Resistant Layers of Power Semiconductors. Additionally, she has filed 12 invention patents, with 2 granted, showcasing her commitment to innovation and technology transfer. Her academic influence extends to mentoring graduate research, guiding numerous students in publishing high-quality papers, and contributing to the advancement of semiconductor device education and research. Through her sustained scholarly excellence and leadership in semiconductor innovation, Mrs. Wu continues to make significant strides toward energy-efficient and reliable power electronics.

Profile: Scopus

Featured Publications

  • Wu, L., et al. (2025). Simulation study of a 1200V 4H–SiC lateral MOSFETs with Double-RESURFs technology for reducing saturation current. Micro and Nanostructures.

  • Wu, L., et al. (2025). The ESD robustness of Schottky-gate p-GaN HEMT under different states. IEEE Transactions on Electron Devices.

  • Wu, L., et al. (2024). Self-clamped P-shield 4H-SiC trench MOSFET for low turn-off loss and suppress switching oscillation. Microelectronics Journal, 142, 106901.

  • Wu, L., et al. (2024). A novel 4H–SiC IGBT with double gate PMOS for improving the switch controllability and FBSOA. Microelectronics Journal, 141, 106820.

  • Wu, L., et al. (2024). Study on the hydrogen effect and interface/border traps of a depletion-mode AlGaN/GaN high-electron-mobility transistor with a SiNx gate dielectric at different temperatures. Micromachines, 15(2), 301.