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.

Profile

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.

Profile

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.

Dr. Hem Bahadur Motra | Rock Mechanics| Excellence in Research Award

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Dr. Hem Bahadur Motra | Rock Mechanics| Excellence in Research Award

University of Kiel | Germany

Dr. Hem Bahadur Motra is a distinguished researcher in the field of geomechanics, rock mechanics, and structural reliability with a strong interdisciplinary background bridging engineering computing, uncertainty modeling, and geostatistics. His research primarily focuses on reliable engineering computation, structural reliability, risk and hazard analysis, and quality evaluation of numerical, mathematical, and experimental models and methods. Dr. Motra has made significant contributions to rock physics, reservoir characterization, and the study of elastic and seismic anisotropy of rocks under extreme pressure and temperature conditions. His work also encompasses advanced uncertainty modeling in structural and geotechnical engineering, enhancing the understanding of rock behavior and material properties under variable environmental influences. He has an impressive academic record with 47 Scopus-indexed publications, accumulating 625 citations from 537 documents and an h-index of 14. On Google Scholar, his research impact is even broader, with 899 citations, an h-index of 16, and an i10-index of 23, reflecting the depth and influence of his scholarly contributions. Dr. Motra’s research excellence lies in combining experimental, computational, and probabilistic approaches to assess material behavior, measurement uncertainty, and geomaterial modeling, contributing to both fundamental understanding and industrial applications.

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Scopus | ORCID | Google Scholar

Featured Publications

Motra, H. B., Hildebrand, J., & Dimmig-Osburg, A. (2014). Assessment of strain measurement techniques to characterise mechanical properties of structural steel. Engineering Science and Technology, an International Journal, 17(4), 260–269.

Ji, S., Li, L., Motra, H. B., Wuttke, F., Sun, S., Michibayashi, K., & Salisbury, M. H. (2018). Poisson’s ratio and auxetic properties of natural rocks. Journal of Geophysical Research: Solid Earth, 123(2), 1161–1185.

Khalifeh, M., Saasen, A., Hodne, H., & Motra, H. B. (2019). Laboratory evaluation of rock-based geopolymers for zonal isolation and permanent P&A applications. Journal of Petroleum Science and Engineering, 175, 352–362.

Motra, H. B., & Stutz, H. H. (2018). Geomechanical rock properties using pressure and temperature dependence of elastic P- and S-wave velocities. Geotechnical and Geological Engineering, 36(6), 3751–3766.

Motra, H. B., Hildebrand, J., & Wuttke, F. (2016). The Monte Carlo Method for evaluating measurement uncertainty: Application for determining the properties of materials. Probabilistic Engineering Mechanics, 45, 220–228.

Dr. Friba Nurmukhamma | Medicine | Best Researcher Award

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Dr. Friba Nurmukhamma | Medicine | Best Researcher Award

Khoja Akhmet Yassawi International Kazakh-Turkish University | Kazakhstan

Dr. Friba NurmukhammadNasrullakyzy is a cardiologist, senior researcher, and PhD scholar specializing in cardiovascular pathology, with a particular focus on coronary artery disease (CAD), stenting, and post-coronary artery bypass grafting (CABG) outcomes. Her research explores the mechanisms of high residual platelet reactivity (HRPR) and dyslipidemia, contributing to improved treatment algorithms and predictive models aimed at reducing mortality in patients with complex cardiovascular conditions. Dr. Friba’s innovative work integrates clinical insights with advanced diagnostic methodologies and personalized therapeutic approaches, emphasizing non-statin therapies, platelet reactivity modulation, and functional diagnostics in cardiology. She has authored multiple peer-reviewed publications indexed in Scopus and other scientific databases, reflecting a growing international research presence. Her Scopus h-index is 3, and her research impact is further supported by citations across both Scopus and Google Scholar databases. Dr. Friba’s current projects involve developing mortality prediction scales and individualized treatment algorithms for patients with HRPR, aiming to advance precision medicine in cardiology. Her scientific contributions underscore her commitment to translational cardiovascular research and her role in fostering evidence-based practices within clinical cardiology.

Profile

Scopus | ORCID

Featured Publications

Mussagaliyeva, A., Zhangelova, S., Danyarova, L., Nurmukhammad, F., Kapsultanova, D., Sakhov, O., Rustamova, F., Sugraliyev, A., & Akhmentayeva, D. (2025). Residual platelet reactivity and dyslipidemia in post-CABG patients undergoing repeat revascularization: Insights from Kazakhstan. Diseases, 13(11), 365.

Nurmukhammad, F. N., Zhangelova, S. B., & Kapsultanova, D. A. (2025). Non-statin therapy in patients with elevated LDL-C and high platelet reactivity: A narrative review. Medical Journal of Malaysia, 80(2), 258–265.

Zhangelova, S. B., Nurmukhammad, F. N., & Nurdinov, N. (2023). High residual platelet reactivity as a predictor of atherothrombosis in patients with coronary heart disease. Science and Healthcare, 25(3), 25–31.

Mussagaliyeva, A., Zhangelova, S., Nurmukhammad, F., & Kapsultanova, D. (2025). Residual platelet reactivity and dyslipidemia in post-CABG patients: Preprint insights from Kazakhstan. Preprints, 2025(09), 2498.

Prof. Dr. Abdel-Aziz Sharabati | Business Intelligence | Best Researcher Award

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Prof. Dr. Abdel-Aziz Sharabati | Business Intelligence | Best Researcher Award

Middle East University | Jordan

Prof. Dr. Abdel-Aziz Ahmad Sharabati is a distinguished scholar in Business Administration, specializing in intellectual capital, total quality management, corporate social responsibility, supply chain management, and digital business strategies. His research bridges management theory and practical applications, focusing on how digital transformation, innovation, and sustainability drive organizational performance across industries. He has made extensive academic contributions with over 80 publications in reputed international journals and more than 30 conference presentations globally. His work often integrates qualitative and quantitative methodologies to explore emerging trends such as artificial intelligence, digital marketing, and blockchain in business ecosystems. Prof. Sharabati’s research impact is reflected in his strong citation metrics, including 1,179 Scopus citations across 56 documents with an h-index of 18, and 6,243 Google Scholar citations with an h-index of 28 and an i10-index of 50, highlighting his sustained scholarly influence and interdisciplinary reach.

Research Profile

Scopus | ORCID | Google Scholar

Featured Publications

Aristovnik, A., Keržič, D., Ravšelj, D., Tomaževič, N., & Umek, L. (2020). Impacts of the COVID-19 pandemic on life of higher education students: A global perspective. Sustainability, 12(20), 8438.

Sharabati, A. A. A., Jawad, S. N., & Bontis, N. (2010). Intellectual capital and business performance in the pharmaceutical sector of Jordan. Management Decision, 48(1), 105–131.

Sharabati, A. A. A., Ali, A. A. A., Allahham, M. I., Hussein, A. A., & Alheet, A. F. (2024). The impact of digital marketing on the performance of SMEs: An analytical study in light of modern digital transformations. Sustainability, 16(19), 8667.

Al-Haddad, S., Sharabati, A. A. A., Al-Khasawneh, M., Maraqa, R., & Hashem, R. (2022). The influence of corporate social responsibility on consumer purchase intention: The mediating role of consumer engagement via social media. Sustainability, 14(11), 6771.

Atieh Ali, A. A., Sharabati, A. A. A., Allahham, M., & Nasereddin, A. Y. (2024). The relationship between supply chain resilience and digital supply chain and the impact on sustainability: Supply chain dynamism as a moderator. Sustainability, 16(7), 3082.

Prof. Zhiguo Zhao | Machine Learning | Best Researcher Award

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Prof. Zhiguo Zhao | Machine Learning | Best Researcher Award

Professor | Huaiyin Institute of Technology | China

Prof. Zhiguo Zhao is a distinguished academic and researcher in automotive engineering, currently serving as Dean at the School of Traffic Engineering, Huaiyin Institute of Technology. His research primarily focuses on automotive system dynamics and control, intelligent connected vehicles, new energy vehicle technology, and energy equipment fault diagnosis. He has made significant contributions to battery State of Health (SOH) estimation, vehicle safety, and energy management systems, developing advanced models integrating artificial intelligence and optimization algorithms. Professor Zhao has authored over 20 high-impact publications in leading SCI and EI journals, alongside securing 10 invention patents. His research outputs have received provincial and national recognition, particularly for their practical applications in intelligent transportation and energy-efficient vehicle systems. He has successfully led multiple national and provincial research projects and has cultivated innovative industry-university collaboration models for talent development. According to Scopus, his academic record includes 36 indexed documents with 147 citations and an h-index of 7, while Google Scholar reports higher citation metrics, reflecting his growing international academic influence. His interdisciplinary expertise bridges theoretical modeling and industrial applications, fostering advancements in intelligent mobility, new energy systems, and vehicular safety technology.

Profile

Scopus

Featured Publications

Zhao, Z. (2025). Estimation of lithium battery state of health using hybrid deep learning with multi-step feature engineering and optimization algorithm integration. Energies, 18(21), 5849.

Zhao, Z. (2019). Construction and verification of equivalent mechanical model for liquid sloshing in hazardous material tankers. Journal of Huaiyin Institute of Technology, 5, 1–10.

Zhao, Z. (2023). Integrated energy management strategy for hybrid electric vehicles based on adaptive control and machine learning. Journal of Energy Storage, 59, 106781.

Zhao, Z. (2022). Fault diagnosis of power equipment using hybrid neural network and sensor fusion techniques. IEEE Transactions on Industrial Electronics, 69(8), 8123–8134.

Zhao, Z. (2021). Dynamic modeling and control optimization for intelligent connected vehicles in complex traffic environments. Vehicle System Dynamics, 59(4), 613–631.