Dr. Chengju Dong | Mechanical Reliability | Best Researcher Award

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Dr. Chengju Dong | Mechanical Reliability | Best Researcher Award

Ministry of Industry and Information Technology | China

Chengju Dong is a researcher specializing in intelligent mechanical systems with a focus on smart robotics, deep learning, and Prognostics and Health Management (PHM). His work integrates advanced signal processing, data-driven modeling, and intelligent diagnostic frameworks to enhance the reliability and autonomy of modern machinery. With expertise grounded in mechanical design theory and computational intelligence, he has developed innovative methods for weak fault detection, tensor decomposition, and multi-modal feature extraction, contributing significantly to predictive maintenance and intelligent monitoring systems. His research aims to bridge the gap between theoretical models and real-world applications by designing algorithms capable of detecting early-stage faults in complex electromechanical systems, particularly rotating machinery and robotic platforms. Chengju Dong’s scholarly output includes 10 peer-reviewed documents that have collectively received 19 citations from 19 citing documents, reflecting a growing impact in the fields of machine health prediction and intelligent diagnostics. His current h-index is 2 based on Scopus data, and Google Scholar also reports consistent citation visibility aligned with these metrics. He continues to expand his research toward more interpretable deep learning models, robust tensor-based diagnostic frameworks, and adaptive PHM systems suitable for industrial environments. His contribution to the scientific community highlights his commitment to advancing predictive intelligence and enhancing machinery health evaluation through data-centric methodologies and engineering innovation.

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Scopus

Featured Publications 

Dong, C., Wu, Y., & Jiang, H. (Year). A novel weak fault feature extraction method based on tensor decomposition model for bearings

Mrs. Maria Fountoulaki | Health Professions | Best Researcher Award

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Mrs. Maria Fountoulaki | Health Professions | Best Researcher Award

University General Hospital Attikon | Greece

Maria Fountoulaki is an emerging clinical researcher whose work spans critical care medicine, anesthesiology, psychiatry, and public health, with growing impact across interdisciplinary healthcare research. Her scholarship focuses on optimizing patient outcomes in intensive care settings, perioperative monitoring, respiratory function assessment, and adolescent health behaviors. Through collaborative investigations, she contributes evidence-based insights into sedation practices, hemodynamic monitoring, mechanical ventilation strategies, and the interplay between sleep habits, academic performance, and well-being among young populations. Her research on dexmedetomidine use in adult ICUs provides an extensive synthesis of two decades of clinical experience, supporting safer sedation protocols and refining current critical care practices. Additionally, her work evaluating tidal volume challenges and preload indices during non-cardiac surgery offers valuable guidance for intraoperative decision-making and personalized monitoring. Across her publications, she demonstrates a strong commitment to advancing clinical standards through rigorous study design, multidisciplinary teamwork, and practical translation of findings into real-world medical practice.Maria Fountoulaki’s scholarly presence continues to grow, as evidenced by her Scopus metrics, which include 33 citations across 33 citing documents, 6 indexed publications, and an h-index of 2. She is also visible on Google Scholar, where her citation impact continues to increase through collaborative and independent research contributions. Her work reflects a balance of clinical relevance and scientific inquiry, positioning her as a developing expert in critical care and perioperative medicine, with expanding influence in health behavior research.

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Scopus

Featured Publications 

Sertaridou, E. N., Fountoulaki, M., Jha, A., Papaioannou, V. E., & Alexopoulou, C. (2025). Dexmedetomidine’s role in adult ICU after 20 years of experience: A narrative review. Healthcare, 13, 2882.

Griva, P., Kapetanakis, E. I., Milionis, O., Panagouli, K., Fountoulaki, M., & Sidiropoulou, T. (2024). Tidal volume challenge to assess volume responsiveness with dynamic preload indices during non-cardiac surgery: A prospective study. Journal of Clinical Medicine, 14, 101.

Alexopoulou, C., Fountoulaki, M., Papavasileiou, A., & Kondili, E. (2024). Sleep habits, academic performance and health behaviors of adolescents in Southern Greece. Healthcare, 12, 775.

Mr. Zijian Ding | Philosophy of AI | Best Researcher Award

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Mr. Zijian Ding | Philosophy of AI | Best Researcher Award

University of Edinburgh | United Kingdom

Mr. Zijian Ding is an emerging interdisciplinary researcher whose work bridges analytic philosophy, psychoanalysis, and cognitive science to explore the structures of self-knowledge, affect, and intelligence. His research integrates theoretical frameworks from figures such as Moran, Lacan, Wollheim, and Žižek with contemporary psychological and cognitive models, focusing on how emotions, interpersonal dynamics, and cultural-symbolic structures shape reflective awareness. He has developed a model of self-knowledge that challenges transparency-based accounts by foregrounding evaluative and relational dimensions. His interest in intelligence studies spans both human and artificial cognition, offering a Jungian typological reinterpretation of intelligence while critiquing task-based approaches common in current AI research. Within psychoanalytic theory, his work engages deeply with Lacanian foundations, examining themes such as desire, narcissism, sublimation, and the role of the symbolic order in subject formation. He actively contributes to reading groups, seminars, and public-facing academic communities, facilitating collective inquiry into concepts like the gaze, the Real, and the dialectic between scientific discourse and psychoanalytic practice. He also collaborates on interdisciplinary projects that connect philosophy with psychological research methodologies, including qualitative inquiry and statistical analysis. His contributions extend to academic presentations, peer-led counselling communities, and philosophical dialogues on culture, ethics, and social theory. His citation metrics are forthcoming, with profiles being prepared for indexing on Scopus and Google Scholar, where future h-index values will track his growing scholarly impact. His emerging publication record reflects a commitment to integrating continental and analytic traditions to address questions at the intersection of mind, emotion, and human meaning.

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ORCID

Featured Publications 

Ding, Z. (2025). Intelligence as typological cognition: Revisiting Jungian functions for human and artificial minds. Proceedings.

Mr. Hesham El-Badawy | Performance Modeling | Editorial Board Member

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Mr. Hesham El-Badawy | Performance Modeling | Editorial Board Member

National Telecommunications Institute | Egypt

Hesham M. El-Badawy is a distinguished researcher in wireless and mobile communications, recognized for his extensive contributions to next-generation network performance modeling, intelligent communication systems, and energy-efficient architectures. His work spans B5G/6G systems, MIMO and IRS antenna technologies, channel estimation, visible light communication, cognitive radio, teletraffic models, queuing networks, vehicular technology, and AI-driven optimization techniques. With a strong foundation in telecommunications engineering and over three decades of professional and research experience, he has authored more than 120 scientific publications in high-impact journals and conferences. His research outputs reflect rigorous analytical modeling, advanced simulation frameworks, and the development of practical solutions for spectrum efficiency, network optimization, and seamless heterogeneous communication environments. His contributions have been broadly cited across global research communities, reflected in Scopus metrics of 343 citations across 295 documents with an h-index of 9, and Google Scholar metrics of 618 citations, an h-index of 13, and an i10-index of 19. His research continues to influence areas such as visible light communication, energy-efficient wireless systems, cognitive networks, and emerging vehicular technologies. Beyond his technical achievements, he remains an active scholarly contributor, frequently serving on editorial boards, technical program committees, and peer-review panels for leading journals and conferences. His work is widely recognized through several best paper awards and professional honors, underscoring his impact on advancing the science of modern telecommunications and next-generation wireless networks.

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Scopus | ORCID | Google Scholar

Featured Publications

Rashwan, A. H., El-Badawy, H. M., & Ali, H. H. (2009). Comparative assessments for different WiMAX scheduling algorithms. Proceedings of the World Congress on Engineering and Computer Science, 20–22.

Amr, M. N., ElAttar, H. M., Abd El Azeem, M. H., & El-Badawy, H. (2021). An enhanced indoor positioning technique based on a novel received signal strength indicator distance prediction and correction model. Sensors, 21(3), 719.

Mahmoud, H. H., ElAttar, H. M., Saafan, A., & El-Badawy, H. (2017). Optimal operational parameters for 5G energy harvesting cognitive wireless sensor networks. IETE Technical Review, 34(sup1), 62–72.

Farid, S. M., Saleh, M. Z., Elbadawy, H. M., & Elramly, S. H. (2023). ASCO-OFDM based VLC system throughput improvement using PAPR precoding reduction techniques. Optical and Quantum Electronics, 55(5), 410.

Sadat, H., Abaza, M., Gasser, S. M., & El-Badawy, H. (2019). Performance analysis of cooperative non-orthogonal multiple access in visible light communication. Applied Sciences, 9(19), 4004.

Dr. Pavel Horák | Social Sciences | Editorial Board Member

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Dr. Pavel Horák | Social Sciences | Editorial Board Member

Masaryk University | Czech Republic

Dr. Pavel Horák, Ph.D., is a distinguished scholar in public policy, social policy, labour market governance, and public administration reform, with an extensive research record focused on organizational change, public sector modernization, employment policy, and the evolving role of street-level bureaucrats. His work critically examines how policy actors interpret, modify, and implement public policies, particularly within labour market institutions, social services, and administrative systems responding to contemporary societal challenges. He contributes significantly to understanding how digitalization, crises such as COVID-19, and global technological shifts reshape state operations, public service delivery, and citizen engagement. His research also explores social problems, social deviations, governance mechanisms, and the design of social and family policy interventions, with a strong emphasis on evaluative and comparative methodologies. Across collaborative international projects, he has analysed employment transitions, social innovation in care services, organizational resilience, and the implications of the Fourth Industrial Revolution for socio-economic systems. His scholarly impact is demonstrated through Scopus-indexed outputs, with 20 documents, 27 citations, and an h-index of 3, supported by additional citations recorded on Google Scholar, highlighting broader academic reach. His recent publications, appearing in leading journals in public administration and social sciences, reflect his sustained contribution to debates on co-creation, institutional adaptability, homelessness policy, and e-government development, positioning him as a notable researcher shaping contemporary discourse on public governance transformation.

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Scopus | ORCID

Featured Publications 

Horák, P., & Špaček, D. (2025). Examining the Covid 19 driven changes in public administration and their longevity: The case of Czechia. Public Money & Management.

Indra, V., Horáková, M., & Horák, P. (2025). Collaboration, participation, and innovation: Influencing factors of co-creation in a Czech municipality. International Journal of Public Administration.

Horák, P., & Špaček, D. (2025). Organizational resilience of public sector organizations responding to the COVID-19 pandemic in Czechia and key influencing factors: Use of the Nograšek and Vintar model. International Journal of Public Administration.

Horák, P., & Horáková, M. (2025). The framework of family functions and dysfunctions from the perspective of the socialization process for evaluating policy measures. Preprint.

Kedzierski, M., & Horák, P. (2025). The development of e-services in the evolution of e-government in the V4 countries. In Navigating Globotics at the Semi-periphery.

Dr. Inayet Burcu Toprak | Artificial Intelligent | Editorial Board Member

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Dr. Inayet Burcu Toprak | Artificial Intelligent | Editorial Board Member

Akdeniz University | Turkey

Dr. İnayet Burcu Toprak is a multidisciplinary researcher whose work bridges advanced manufacturing, artificial intelligence, materials engineering, and computational modeling. Her research focuses on optimizing additive manufacturing processes—particularly laser powder bed fusion, fused filament fabrication, and melt deposition modeling—using statistical, machine learning, and fuzzy logic–based approaches. She has made significant contributions to improving dimensional accuracy, mechanical performance, hardness prediction, and surface integrity of engineering materials including AlSi10Mg alloys, pure molybdenum, 316L stainless steel, and polymer-based composites. Alongside manufacturing optimization, she has produced impactful studies in signal processing, biomedical engineering, vibration analysis, acoustic emissions, and tool condition monitoring. Her early work on EEG signal classification, artificial neural networks, and neuro-fuzzy systems established a strong computational foundation that continues to underpin her modern engineering solutions. Dr. Toprak’s research has been published across leading international journals and conferences, supported by extensive collaborations in mechanical engineering, electronics, automation, and computational intelligence. Her scholarly impact is evidenced through citations recorded across major indexing platforms including Scopus, Google Scholar, and Web of Science, demonstrating sustained recognition of her contributions to manufacturing science and intelligent systems. She has authored journal articles, book chapters, and conference papers, and serves as a reviewer and editorial board member in engineering journals, contributing to the advancement of scientific publishing and research quality. Her current work continues to integrate intelligent optimization, material characterization, and high-precision manufacturing to develop innovative solutions for next-generation engineering applications.

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ORCID

Featured Publications 

Toprak, I. B., Dogdu, N. (2024). Multi-objective optimization study on production of AlSi10Mg alloy by laser powder bed fusion. Applied Sciences, 14(22), 1–12.

Toprak, I. B. (2025). Fuzzy logic-based prediction of tensile strength in fused filament fabrication: A case study on polylactic acid. Journal of Materials Engineering and Performance.

Fedai, Y., & Toprak, I. B. (2025). Optimization of drilling parameters for glass fiber-reinforced nanocomposite materials using Taguchi-based CRITIC-VIKOR method. Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering.

Toprak, I. B. (2025). Prediction and optimization of hardness in AlSi10Mg alloy produced by laser powder bed fusion using statistical and machine learning approaches. Scientific Reports.

Toprak, I. B., Dogdu, N., & Salamci, M. U. (2025). Numerical optimization of laser powder bed fusion process parameters for high-precision manufacturing of pure molybdenum. Applied Sciences, 15(10).

Dr. Amar Salehi | Microrobotics | Editorial Board Member

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Dr. Amar Salehi | Microrobotics | Editorial Board Member

South China University of Technology | China

Amar Salehi is a multidisciplinary researcher working at the intersection of microrobotics, machine learning, biosensing, and intelligent control systems. His research advances the design, simulation, and real-world implementation of magnetic microrobots, focusing on intelligent navigation, bioinspired control, deep reinforcement learning, and multimodal micro/nanosystems for biomedical and environmental applications. He contributes to emerging microrobotic platforms aimed at targeted therapy, microplastics removal, environmental remediation, and autonomous on-chip diagnostic systems. His work integrates smart materials, fuzzy logic, neural networks, and data-driven modeling to solve complex microscale challenges in biomedicine, biofluidics, and agricultural biosystems. He has also explored microfluidic-spintronic biochips, electrochemical biosensors, and AI-assisted agricultural trait prediction, demonstrating a broad systems-level approach. His publications have appeared in reputable journals in microrobotics, intelligent systems, computational fluid dynamics, and biosystems engineering. According to Google Scholar, he has over 30 citations, with an h-index of 3 and an i10-index of 2; Scopus-indexed documents also contribute to his scholarly visibility through peer-reviewed publications in Advanced Intelligent Systems, Micromachines, and CFD Letters. His conference contributions include work on deep learning-enhanced imaging for microrobots and AI-enabled micro/nano-robotic systems. Overall, his research combines advanced control algorithms, machine learning, and microscale engineering to develop next-generation autonomous robotic platforms for healthcare, agriculture, and environmental sustainability.

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Scopus |  ORCID | Google Scholar

Featured Publications 

Salehi, A., Hosseinpour, S., Tabatabaei, N., Soltani Firouz, M., & Yu, T. (2024). Intelligent navigation of a magnetic microrobot with model-free deep reinforcement learning in a real-world environment. Micromachines, 15(1), 112.

Ghiasi, P., Salehi, A., Hoseini, S. S., Najafi, G., Mamat, R., & Balkhaya, B. (2020). Investigation of the effect of flow rate on fluid heat transfer in counter-flow helical heat exchanger using CFD method. CFD Letters, 12(3), 98–111.

Salehi, A., Hosseinpour, S., Tabatabaei, N., Soltani Firouz, M., Zadebana, N., & Yu, T. (2024). Advancements in machine learning for microrobotics in biomedicine. Advanced Intelligent Systems.

Zhu, B., Salehi, A., Xu, L., Yuan, W., & Yu, T. (2025). Multi-module micro/nanorobots for biomedical and environmental remediation applications. Advanced Intelligent Systems.

Salehi, A., Hosseinpour, S., Tabatabaei, N., & Soltani Firouz, M. (2024). Smart control of a microrobot for navigation on fluid surface and simulation of its application in microplastics removal. Iranian Journal of Biosystems Engineering.

Dr. Ehsan Adibnia | Computer Science | Editorial Board Member

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Dr. Ehsan Adibnia | Computer Science | Editorial Board Member

University of Sistan and Baluchestan | Iran

Dr. Ehsan Adibnia is a dedicated researcher in Electrical Engineering with a strong interdisciplinary focus spanning artificial intelligence, machine learning, deep learning, nanophotonics, optics, plasmonics, and photonic device engineering. His research primarily explores the integration of AI-driven approaches in nanophotonic design, optical switching, and biosensing applications, enabling significant advancements in optical computing and sensing technologies. He has made notable contributions to the fields of photonics and deep learning-based optical system design through innovative studies on inverse design, nonlinear plasmonic structures, and photonic crystal encoders. His expertise extends to advanced simulation tools such as Lumerical, COMSOL, and RSoft, as well as programming in MATLAB and Python for modeling and data analysis. Dr. Adibnia has actively contributed to scientific research through multiple peer-reviewed publications in prestigious international journals. According to Google Scholar, he has accumulated 6,540 citations, an h-index of 45, and an i10-index of 156, reflecting his significant academic influence. His Scopus profile records 70 citations across 53 documents with an h-index of 5, highlighting his growing global research impact.

Profiles

Scopus | ORCID | Google Scholar

Featured Publications

Adibnia, E., Mansouri-Birjandi, M. A., & Ghadrdan, M. (2024). A deep learning method for empirical spectral prediction and inverse design of all-optical nonlinear plasmonic ring resonator switches. Scientific Reports, 14, 5787.

Adibnia, E., Ghadrdan, M., & Mansouri-Birjandi, M. A. (2024). Nanophotonic structure inverse design for switching application using deep learning. Scientific Reports, 14, 21094.

Adibnia, E., Ghadrdan, M., & Mansouri-Birjandi, M. A. (2025). Chirped apodized fiber Bragg gratings inverse design via deep learning. Optics & Laser Technology, 181, 111766.

Jafari, B., Gholizadeh, E., Jafari, B., & Adibnia, E. (2023). Highly sensitive label-free biosensor: graphene/CaF2 multilayer for gas, cancer, virus, and diabetes detection. Scientific Reports, 13, 16184.

Soroosh, M., Al-Shammri, F. K., Maleki, M. J., Balaji, V. R., & Adibnia, E. (2025). A compact and fast resonant cavity-based encoder in photonic crystal platform. Crystals, 15, 24.

Prof. Vali Rasooli Sharabiani | Biological Sciences | Editorial Board Member

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Prof. Vali Rasooli Sharabiani | Biological Sciences | Editorial Board Member

University of Mohaghegh Ardabili | Iran

Dr. Vali Rasooli Sharabiani is a distinguished Professor at the University of Mohaghegh Ardabili, Iran, whose research centers on precision agriculture, smart farming technologies, and non-destructive measurement methods for sustainable crop production and food processing. His scientific work integrates artificial intelligence, hyperspectral imaging, and multivariate data analysis to enhance agricultural efficiency, resource management, and environmental protection. Dr. Sharabiani’s contributions have significantly advanced the understanding of variable rate technology, remote sensing, and the application of machine learning models such as ANNs, ANFIS, and fuzzy logic in agricultural systems. His interdisciplinary approach bridges engineering, agronomy, and data science, making his research highly influential in both academic and industrial sectors. With more than 1,500 citations, an h-index of 21, and an i10-index of 37 on Google Scholar, along with high-impact publications indexed in Scopus, his scholarly achievements reflect strong global recognition. Dr. Sharabiani’s research outputs are widely referenced in the fields of agricultural mechanization, energy-efficient drying systems, and precision monitoring of crop and soil properties.

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Google Scholar

Featured Publications

Kaveh, M., Sharabiani, V. R., Chayjan, R. A., & Taghinezhad, E. (2018). ANFIS and ANNs model for prediction of moisture diffusivity and specific energy consumption of potato, garlic, and cantaloupe drying under convective hot air dryer. Information Processing in Agriculture, 5(3), 372–387.

Kaveh, M., Chayjan, R. A., Taghinezhad, E., Sharabiani, V. R., & Motevali, A. (2020). Evaluation of specific energy consumption and GHG emissions for different drying methods (Case study: Pistacia Atlantica). Journal of Cleaner Production, 259, 120963.

Jahanbakhshi, A., Kaveh, M., Taghinezhad, E., & Rasooli Sharabiani, V. (2020). Assessment of kinetics, effective moisture diffusivity, and specific energy consumption in the pistachio kernel drying process in microwave drying. Journal of Food Processing and Preservation, 44(6), e14449.

Jahedi Rad, S., Kaveh, M., Sharabiani, V. R., & Taghinezhad, E. (2018). Fuzzy logic, artificial neural network, and mathematical model for prediction of white mulberry drying kinetics. Heat and Mass Transfer, 54(11), 3361–3374.

Rasooli Sharabiani, V., Kaveh, M., Abdi, R., Szymanek, M., & Tanaś, W. (2021). Estimation of moisture ratio for apple drying by convective and microwave methods using artificial neural network modeling. Scientific Reports, 11(1), 9155.

Mr. Junde Lu | Artificial Neural Networks | Best Researcher Award

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Mr. Junde Lu | Artificial Neural Networks | Best Researcher Award

Beijing Information Science and Technology University | China

Mr. Junde Lu is a promising early-career researcher specializing in optical communication systems and signal processing, with a focus on developing efficient equalization algorithms for high-speed data transmission. His research interests center around enhancing the performance and reliability of optical communication links through advanced digital signal processing and AI-empowered equalization methods. He has contributed to the design of low-complexity receiver-side equalizers and has explored the potential of machine learning in nonlinear compensation for coherent optical systems. His scholarly contributions have been published in reputable international journals and conferences, particularly within the fields of photonics and communication technology. Junde Lu has authored and co-authored several scientific documents, with a citation record demonstrating growing recognition in his domain. According to Scopus and Google Scholar metrics, his academic record includes 13 research documents, 1 citation, and an h-index of 1, highlighting his emerging influence in optical communication research. His collaborative works with distinguished researchers underscore his commitment to advancing next-generation high-speed optical transmission technologies.

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Scopus

Featured Publications

Lu, J., Sun, Y., Qin, J., & Lu, G.-W. (2025). A low-complexity receiver-side lookup table equalization method for high-speed short-reach IM/DD transmission systems. Photonics.

Chen, L., Sun, Y., Shi, J., Lu, J., & Qin, J. (2025). Exploring 400 Gbps/λ and beyond with AI-accelerated silicon photonic slow-light technology. Photonics.