Giuseppe Di Gironimo | Remote Handling | Excellence in Research Award

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Prof. Giuseppe Di Gironimo | Remote Handling | Excellence in Research Award

Professor | University of Naples Federico II | Italy

Prof. Giuseppe Di Gironimo is a multidisciplinary researcher specializing in advanced mechanical design, digital engineering, and remote-handling technologies for large-scale scientific and industrial systems, with a strong focus on fusion energy devices such as DTT, ITER, and RFX-Mod2. With over 201 publications, 3,031 citations, and an h-index of 28, he has established a significant international presence in the fields of CAD/CAE modeling, electromechanical system analysis, ICRH/ICRF antenna design, and virtual alignment methodologies. His work integrates cloud-based engineering, digital twins, immersive VR training, and under-actuated robotic systems to improve maintainability, safety, and operational efficiency in high-complexity environments. Collaborating with nearly 1,000 co-authors across global research laboratories, universities, and industry partners, he contributes to multidisciplinary advancements in fusion energy development, aerospace manufacturing, and human–machine interaction. His research demonstrates both academic depth and industrial relevance, supporting innovation in automated assembly, virtual design reviews, and remote operations for next-generation scientific infrastructures.

Profiles: Scopus | ORCID | Google Scholar

Featured Publications

1. Grazioso, S., Di Gironimo, G., & Siciliano, B. (2019). A geometrically exact model for soft continuum robots: The finite element deformation space formulation. Soft Robotics, 6(6), 790–811.
Cited by: 253

2. Del Nevo, A., Arena, P., Caruso, G., Chiovaro, P., Di Maio, P. A., Eboli, M., … (2019). Recent progress in developing a feasible and integrated conceptual design of the WCLL BB in EUROfusion project. Fusion Engineering and Design, 146, 1805–1809.
Cited by: 186

3. Singh, G., Akrigg, S., Di Maio, M., Fontana, V., Alitappeh, R. J., Khan, S., Saha, S., … (2022). ROAD: The road event awareness dataset for autonomous driving. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(1), 1036–1054.
Cited by: 159

4. Del Nevo, A., Martelli, E., Agostini, P., Arena, P., Bongiovì, G., Caruso, G., … (2017). WCLL breeding blanket design and integration for DEMO 2015: Status and perspectives. Fusion Engineering and Design, 124, 682–686.
Cited by: 155

5. You, J. H., Mazzone, G., Visca, E., Greuner, H., Fursdon, M., Addab, Y., … (2022). Divertor of the European DEMO: Engineering and technologies for power exhaust. Fusion Engineering and Design, 175, 113010.
Cited by: 151

Prof. Giuseppe Di Gironimo work accelerates progress toward reliable fusion energy systems and next-generation digital engineering, strengthening the technological foundations for future clean-energy infrastructures. Through innovations in virtual design, remote handling, and electromechanical systems, he contributes directly to safer, more efficient, and globally scalable industrial and scientific solutions.

Róbert Bata | Scientific Computing | Best Researcher Award

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Mr. Róbert Bata | Scientific Computing | Best Researcher Award

Assistant Lecturer | University of Debrecen | Hungary

Mr. Róbert Bata is an emerging public health researcher whose work focuses on social epidemiology, gender-related health disparities, and the behavioral and structural determinants of population health. With 5 peer-reviewed publications, 6 citations, and an h-index of 2, he has contributed evidence-based insights into how social perceptions, intimate partner violence, and sexual and reproductive behaviors shape health outcomes, particularly among women in low- and middle-income settings. His research integrates quantitative methods with socio-behavioral analysis to examine sensitive but globally significant issues such as sexually transmitted infections and violence-related health vulnerabilities. Collaborating with 16 co-authors across multidisciplinary fields—including public health, sociology, and international development—he works to illuminate the mechanisms through which social inequality influences individual and community health. Bata’s scholarship, exemplified by his recent open-access study on sexual perceptions, behavior, and intimate partner violence among Filipino women, seeks to inform health policy, strengthen gender-responsive interventions, and support equitable, culturally grounded public health strategies. His growing body of work demonstrates a commitment to advancing global understanding of the social determinants of health and contributing to more inclusive, evidence-driven approaches to improving health outcomes in vulnerable populations worldwide.

Profiles: Scopus | ORCID

Featured Publication

1. Bata, R. (2025). Association of sexual perceptions, behavior, and intimate partner violence with sexually transmitted infection (STI) among Filipino women. BMC Public Health, 25, Article .

Mr. Róbert Bata’s research advances global public health by generating evidence that strengthens women’s health, safety, and autonomy, particularly in marginalized communities. His work contributes to policy-relevant knowledge that supports equitable health systems and promotes social resilience through informed, data-driven interventions.

Zongran Dong | Computer Aided Design | Best Researcher Award

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Assoc. Prof. Dr. Zongran Dong | Computer Aided Design | Best Researcher Award

Teacher | Dalian University of Foreign Languages | China

Assoc. Prof. Dr. Zongran Dong, affiliated with Dalian Neusoft University of Information, China, is an expert in computational optimization and heuristic algorithms, with a focus on practical applications in logistics and industrial engineering. His research emphasizes the development and refinement of metaheuristic methods, including tabu search, to address complex operational challenges such as container-loading optimization, demonstrating both algorithmic innovation and real-world applicability. Despite a concise publication record of 3 peer-reviewed works, his research has garnered 58 citations and of 2 h-index, reflecting recognition and engagement within the scholarly community. Collaborating with six co-authors, Dong has contributed to interdisciplinary knowledge exchange that bridges computer science, applied mathematics, and engineering, enhancing the robustness and impact of his work. His contributions hold relevance for industrial practice, supporting efficiency, resource optimization, and sustainability in logistics and supply-chain systems. Overall, Dong’s research embodies a targeted, application-driven approach that advances intelligent optimization methods and demonstrates measurable influence on both academic research and operational industries worldwide.

Profiles: Scopus | ORCID

Featured Publication

1. A tabu search algorithm for container loading based on homogeneous blocks. (2011). Gaojishu Tongxin
Cited by: 1

Assoc. Prof. Dr. Zongran Dong’s research advances intelligent optimization techniques that enhance efficiency in global logistics and industrial operations. By improving algorithmic performance in container-loading and related systems, his work supports more sustainable, cost-effective, and data-driven decision-making across industries.

Saeid Barshandeh | Optimization | Best Researcher Award

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Dr. Saeid Barshandeh | Optimization | Best Researcher Award

Instructor-Researcher | Afagh Higher Education Institute | Iran

Dr. Saeid Barshandeh is a computational intelligence researcher specializing in metaheuristic optimization, machine learning, and advanced algorithm design, with a research portfolio comprising 14 scientific publications, 12 h-index and over 885 citations. His work focuses on developing innovative, nature-inspired optimization techniques capable of addressing complex, nonlinear problems prevalent in engineering, data science, and industrial systems. A key achievement is the development of the Puma Optimizer (PO), a novel metaheuristic algorithm that has rapidly gained global academic attention and demonstrates his expertise in algorithmic modeling, performance tuning, and machine-learning integration. His research activities are enriched by collaborations with more than 30 co-authors, reflecting active engagement in interdisciplinary networks spanning intelligent systems, evolutionary computation, and data-driven decision-making. Collectively, his contributions enhance the efficiency, scalability, and applicability of optimization methodologies across diverse domains such as energy management, automation, predictive analytics, and industrial process optimization. Through impactful publications and growing scholarly influence, Dr. Barshandeh advances the theoretical and practical foundations of intelligent optimization, contributing to technological innovation and the broader scientific community.

Profiles: Scopus | ORCID | Google Scholar

Featured Publications

1. Abdollahzadeh, B., Khodadadi, N., Barshandeh, S., Trojovský, P., & … (2024). Puma optimizer (PO): A novel metaheuristic optimization algorithm and its application in machine learning. Cluster Computing, 27(4), 5235–5283.
Cited by: 661

2. Barshandeh, S., & Haghzadeh, M. (2021). A new hybrid chaotic atom search optimization based on tree-seed algorithm and Levy flight for solving optimization problems. Engineering with Computers, 37(4), 3079–3122.
Cited by: 129

3. Gharehchopogh, F. S., Abdollahzadeh, B., Barshandeh, S., & Arasteh, B. (2023). A multi-objective mutation-based dynamic Harris Hawks optimization for botnet detection in IoT. Internet of Things, 24, 100952.
Cited by: 128

4. Gharehchopogh, F. S., Nadimi-Shahraki, M. H., Barshandeh, S., & … (2023). Cqffa: A chaotic quasi-oppositional farmland fertility algorithm for solving engineering optimization problems. Journal of Bionic Engineering, 20(1), 158–183.
Cited by: 89

5. Barshandeh, S., Piri, F., & Sangani, S. R. (2022). HMPA: An innovative hybrid multi-population algorithm based on artificial ecosystem-based and Harris Hawks optimization algorithms for engineering problems. Engineering with Computers, 38(2), 1581–1625.
Cited by: 78

Dr. Barshandeh’s research advances the science of optimization by providing robust, efficient algorithms that strengthen machine-learning performance and industrial decision systems. His work contributes to global technological innovation, enabling smarter, more adaptive solutions for complex societal and engineering challenges.

Davoud Shahgholian-Ghahfarokhi | Neural Network | Best Researcher Award

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Dr. Davoud Shahgholian-Ghahfarokhi | Neural Network | Best Researcher Award

Senior Researcher | Tarbiat Modares University | Iran

Dr. Davoud Shahgholian-Ghahfarokhi is a researcher affiliated with Tarbiat Modares University whose work spans advanced structural engineering, materials mechanics, and computational modeling, with a focus on improving the performance, durability, and safety of modern engineering systems. With 20 peer-reviewed publications, 15 h-index and over 792 citations, his research demonstrates sustained scholarly influence and recognition within the global engineering community. His expertise encompasses mechanics-based structural design, vibration analysis, composite and sandwich structures, offshore pipeline integrity, auxetic and graded materials, and artificial-intelligence-assisted engineering assessment. Across his body of work, he has contributed analytical, numerical, and hybrid computational frameworks for understanding complex structural behaviors under dynamic and environmental loads. Notable contributions include the formulation of vibration models for sandwich folded plates with auxetic honeycomb cores and FG-GPLRC coatings, advancing next-generation lightweight, high-performance structures. Additionally, his research on corrosion-induced degradation of offshore pipelines—combining code-based methods, finite-element simulations, and neural-network prediction—provides industry-relevant tools for failure assessment and risk mitigation. Dr. Shahgholian-Ghahfarokhi has collaborated with at least 25 co-authors, reflecting a strong record of interdisciplinary and international engagement. His work supports structural reliability, materials innovation, and infrastructure resilience, offering direct societal benefits in safety-critical sectors such as offshore energy, transportation, and advanced manufacturing. Collectively, his research contributes to a more robust scientific understanding of complex structural systems while fostering emerging engineering solutions that balance performance, sustainability, and safety.

Profiles: Scopus | ORCID | Google Scholar

Featured Publications

1. Shahgholian-Ghahfarokhi, D., Safarpour, M., & Rahimi, A. (2021). Torsional buckling analyses of functionally graded porous nanocomposite cylindrical shells reinforced with graphene platelets (GPLs). Mechanics Based Design of Structures and Machines, 49(1), 81–102.
Cited by: 119

2. Shahgholian, D., Safarpour, M., Rahimi, A. R., & Alibeigloo, A. (2020). Buckling analyses of functionally graded graphene-reinforced porous cylindrical shell using the Rayleigh–Ritz method. Acta Mechanica, 231(5), 1887–1902.
Cited by: 102

3. Shahgholian-Ghahfarokhi, D., & Rahimi, G. (2018). Buckling load prediction of grid-stiffened composite cylindrical shells using the vibration correlation technique. Composites Science and Technology, 167, 470–481.
Cited by: 81

4. Khodadadi, A., Liaghat, G., Taherzadeh-Fard, A., … (2021). Impact characteristics of soft composites using shear thickening fluid and natural rubber – A review of current status. Composite Structures, 271, Article 114092.
Cited by: 80

5. Ghahfarokhi, D. S., & Rahimi, G. (2018). An analytical approach for global buckling of composite sandwich cylindrical shells with lattice cores. International Journal of Solids and Structures, 146, 69–79.
Cited by: 77

Dr. Shahgholian-Ghahfarokhi’s research advances the scientific foundations and practical tools needed to design safer, lighter, and more resilient engineering structures. By integrating novel materials, computational intelligence, and rigorous mechanics, his work contributes to global innovation in sustainable infrastructure, industrial reliability, and engineering risk reduction.

Vasuk Gautam | Bioinformatics | Best Researcher Award

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Dr. Vasuk Gautam | Bioinformatics | Best Researcher Award

Sr.Scientist | Norton Research Institute | United States

Dr. Vasuk Gautam is an emerging researcher whose work spans metabolomics, biomarker discovery, and computational methods for enhancing analytical confidence in high-throughput biological studies. With a record of 24 peer-reviewed publications, 13 h-index and over 3,102 citations, Gautam has established a strong early-career footprint marked by methodological innovation and extensive interdisciplinary collaboration. His contributions focus particularly on developing frameworks for improving metabolite identification accuracy—an essential challenge in metabolomics that directly influences the reliability of biomedical and environmental research. Notably, his recent work introducing the concept of “Identification Probability” provides a transferable, automated metric for evaluating identification confidence, positioning it as a potentially transformative tool for large-scale metabolomic pipelines. In parallel, Gautam has contributed significantly to the development of MarkerDB 2.0, a comprehensive biomarker database that integrates molecular, clinical, and contextual information to support precision medicine, translational research, and global health initiatives. His scholarship reflects a blend of computational rigor, domain expertise, and a commitment to open-access scientific resources. Gautam’s collaborations include partnerships with over 180 co-authors, underscoring his active engagement with diverse research groups and his ability to contribute meaningfully to multi-institutional projects. This collaborative network spans biochemistry, bioinformatics, systems biology, and clinical sciences, highlighting the broad applicability and relevance of his expertise. Through both his methodological contributions and his involvement in global data-resource efforts, Gautam’s work supports reproducibility, accessibility, and evidence-based discovery in molecular life sciences. His growing citation impact and participation in influential open-access initiatives demonstrate both scientific merit and societal relevance, particularly in areas related to disease diagnostics, personalized healthcare, and data-driven research infrastructures.

Profiles: Scopus | ORCID | Google Scholar

Featured Publications

1. Wishart, D. S., Guo, A. C., Oler, E., Wang, F., Anjum, A., Peters, H., Dizon, R., … (2022). HMDB 5.0: The human metabolome database for 2022. Nucleic Acids Research, 50(D1), D622–D631.

Cited by: 2116

2. Knox, C., Wilson, M., Klinger, C. M., Franklin, M., Oler, E., Wilson, A., Pon, A., Cox, J., … (2024). DrugBank 6.0: The DrugBank knowledgebase for 2024. Nucleic Acids Research, 52(D1), D1265–D1275.

Cited by: 1112

3. Wishart, D. S., Han, S., Saha, S., Oler, E., Peters, H., Grant, J. R., Stothard, P., … (2023). PHASTEST: Faster than PHASTER, better than PHAST. Nucleic Acids Research, 51(W1), W443–W450.

Cited by: 394

4. Wishart, D. S., Tian, S., Allen, D., Oler, E., Peters, H., Lui, V. W., Gautam, V., … (2022). BioTransformer 3.0: A web server for accurately predicting metabolic transformation products. Nucleic Acids Research, 50(W1), W115–W123.

Cited by: 160

5. Wang, F., Allen, D., Tian, S., Oler, E., Gautam, V., Greiner, R., Metz, T. O., … (2022). CFM-ID 4.0: A web server for accurate MS-based metabolite identification. Nucleic Acids Research, 50(W1), W165–W174.

Cited by: 115

Dr. Vasuk Gautam’s work advances the reliability and scalability of metabolomic and biomarker research, enabling more accurate diagnostics and deeper biological insight. By developing robust identification metrics and contributing to global data resources, he helps accelerate scientific discovery, support precision medicine, and enhance evidence-based decision-making across research, healthcare, and industry.

Xingxing You | Intelligent control | Editorial Board Member

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Assist. Prof. Dr. Xingxing You | Intelligent control | Editorial Board Member

Assistant Professor | Sichuan University | China

Assist. Prof. Dr. Xingxing You is a developing researcher affiliated with Sichuan University, China, whose work spans advanced signal processing, intelligent control, and underwater imaging technologies. With 26 scientific publications, h-index 7and over 408 citations, the author demonstrates an emerging yet steadily growing influence in these fields. His research contributions include multi-level feature fusion strategies for perception-driven underwater image enhancement, advancing the reliability of visual sensing in complex aquatic environments, as well as novel critic-only self-learning optimal control methods for continuum robots operating under unknown disturbances, integrating extended state observer frameworks to elevate robustness and adaptability. These works reflect a broader expertise in machine learning–guided optimization, sensor fusion, and nonlinear dynamical systems, addressing real-world problems where conventional modeling is insufficient. Collaboration is a key dimension of his academic trajectory, with 55 co-authors across disciplines, indicating strong engagement within interdisciplinary research networks and an ability to participate effectively in multi-institutional scientific efforts. His research outcomes demonstrate relevance not only to academic communities working on robotics, automation, and digital signal processing, but also to domains such as marine engineering, environmental monitoring, and intelligent manufacturing. By focusing on interpretable enhancements, computational efficiency, and real-time control, his contributions help bridge theoretical advances and applied technological innovation. Overall, Xingxing You’s scholarly record showcases growing expertise, collaborative capacity, and a commitment to addressing technically challenging problems with practical societal implications.

Profiles: Scopus | ORCID

Featured Publications

1. Perception-driven underwater image enhancement via multi-level feature fusion. (2026). Digital Signal Processing: A Review Journal.

2. Critic-only based self learning optimal control for continuum robots with unknown disturbances via extended state observer. (2025). Nonlinear Dynamics.

Assist. Prof. Dr. Xingxing You’s work advances intelligent sensing and robust control systems, enabling more reliable robotic and imaging technologies in uncertain environments. His research contributes to global innovation by strengthening the scientific foundation for autonomous systems and enhancing their applications in marine exploration, environmental protection, and advanced robotics.

Leonidas Anthopoulos | Smart City | Best Researcher Award

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Prof. Leonidas Anthopoulos | Smart City | Best Researcher Award

Professor | University of Thessaly | Greece

Prof. Leonidas G. Anthopoulos of the University of Thessaly, Greece, is an internationally recognized scholar in the domains of Smart Cities, Digital Transformation, and Emerging Technologies such as Artificial Intelligence, the Internet of Things (IoT), and the Metaverse. With a prolific academic record of 129 publications, 27 h-index and over 2,992 citations, he demonstrates sustained research excellence and global influence in the interdisciplinary field of urban innovation, digital governance, and technology standardization. His research bridges the gap between information systems, urban management, and policy-making, providing actionable frameworks for sustainable and citizen-centric digital ecosystems. Professor Anthopoulos has played a leading role in developing standardization strategies for smart cities at national and international levels, including contributions to the ITU Metaverse Focus Group, where he co-authored the seminal work “Toward a Standardized Metaverse Definition.” His extensive collaborations with 62 co-authors reflect strong interdisciplinary engagement across academia, government, and industry, enhancing the global dialogue on responsible, ethical, and inclusive digital transformation. His scholarship encompasses critical analyses of AI governance, smart city interoperability, and data-driven urban resilience, addressing contemporary challenges such as sustainability, digital equity, and crisis management. In addition to his academic achievements, Professor Anthopoulos’ leadership in conferences such as WebAndTheCity and contributions to open-access research reinforce his commitment to democratizing knowledge and fostering innovation for public good. His work has not only shaped academic discourse but has also informed policy frameworks and strategic planning for smart and resilient cities worldwide, emphasizing technology’s social and economic impact in urban contexts.

Profiles: Scopus | ORCID | Google Scholar

Featured Publications

1. Anthopoulos, L., Reddick, C. G., Giannakidou, I., & Mavridis, N. (2016). Why e-government projects fail? An analysis of the Healthcare.gov website. Government Information Quarterly, 33(1), 161–173.
Cited by: 606

2. Anthopoulos, L. (2017). Smart utopia VS smart reality: Learning by experience from 10 smart city cases. Cities, 63, 128–148.
Cited by: 514

3. Anthopoulos, L. G. (2015). Understanding the smart city domain: A literature review. In Transforming city governments for successful smart cities (pp. 9–21).
Cited by :497

4. Anthopoulos, L. G. (2017). Understanding smart cities: A tool for smart government or an industrial trick? Springer International Publishing, 22, 293.
Cited by: 477

5. Anthopoulos, L., Janssen, M., & Weerakkody, V. (2018). A Unified Smart City Model (USCM) for smart city conceptualization and benchmarking. In E-Planning and collaboration: Concepts, methodologies, tools.
Cited by: 381

Professor Anthopoulos’ pioneering work advances the global transition toward intelligent, ethical, and sustainable digital societies, where technology serves humanity and governance aligns with social responsibility. His vision promotes the creation of standardized, inclusive, and human-centered smart ecosystems that drive innovation, improve quality of life, and contribute to the digital future of cities worldwide.

Yiliang Jiang | Simulation | Best Researcher Award

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Dr. Yiliang Jiang | Simulation | Best Researcher Award

Researcher | Tsinghua University | China

Dr. Yiliang Jiang is a researcher affiliated with Tsinghua University, Beijing, China, recognized for his interdisciplinary contributions at the intersection of urban sustainability, transport geography, environmental policy, and public health. His research primarily explores the causal relationships between the built environment, transportation behavior, and air quality, with a focus on PM2.5 pollution and sustainable urban mobility. With 7 publications, 122 Citations, and an h-index of 5, Dr. Jiang has established a growing international presence in environmental and urban studies. His recent article, “Built environment, car ownership and PM2.5: Stronger causal estimates from a quasi-experiment” (2025, Journal of Transport Geography), exemplifies his methodological rigor in applying quasi-experimental and causal inference techniques to quantify the environmental impacts of transportation systems. Dr. Jiang’s collaborations span 35 co-authors across multiple disciplines and institutions, reflecting a strong emphasis on interdisciplinary and cross-sectoral research partnerships. His work integrates quantitative modeling, spatial analysis, and policy evaluation, aiming to inform evidence-based urban planning and low-carbon mobility transitions. Beyond academic contributions, his research provides actionable insights for policymakers and city planners, addressing urgent global challenges such as urban air pollution, climate change mitigation, and sustainable transport equity. Through a combination of analytical depth, collaborative engagement, and policy relevance, Dr. Jiang’s scholarship advances both theoretical understanding and real-world applications in sustainable urban development.

Profiles: Scopus | ORCID | Google Scholar

Featured Publications

1. Englmair, G., Jiang, Y., Dannemand, M., Moser, C., Schranzhofer, H., Furbo, S., & … (2018). Crystallization by local cooling of supercooled sodium acetate trihydrate composites for long-term heat storage. Energy and Buildings, 180, 159–171.
Cited by: 57

2. Hu, Y., Jiang, Y., Jensen, J. O., Cleemann, L. N., & Li, Q. (2018). Catalyst evaluation for oxygen reduction reaction in concentrated phosphoric acid at elevated temperatures. Journal of Power Sources, 375, 77–81.
Cited by: 43

3. Zhang, J., Jiang, Y., Wang, Y., Zhang, S., Wu, Y., Wang, S., Nielsen, C. P., & … (2023). Increased impact of aviation on air quality and human health in China. Environmental Science & Technology, 57(48), 19575–19583.
Cited by: 15

4. Jiang, Y., Liang, X., Zhang, S., Hu, Z., Hove, A., & Wu, Y. (2023). The future air quality impact of electric vehicle promotion and coordinated charging in the Beijing-Tianjin-Hebei region. Environmental Pollution, 332, 121928.
Cited by: 12

5. Zhang, S., Jiang, Y., Zhang, S., & Choma, E. F. (2024). Health benefits of vehicle electrification through air pollution in Shanghai, China. Science of The Total Environment, 914, 169859.
Cited by: 11

Dr. Jiang’s research advances global efforts toward sustainable, low-emission urban environments by linking data-driven insights with practical policy solutions. His vision is to harness scientific evidence and interdisciplinary collaboration to design healthier, more resilient, and environmentally responsible cities that enhance quality of life and reduce ecological footprints worldwide.

Amirhossein Ghasemi Abyaneh | Machine Learning | Best Researcher Award

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Mr. Amirhossein Ghasemi Abyaneh | Machine Learning | Best Researcher Award

Researcher | Kharazmi University | Iran

Mr. Amirhossein Ghasemi Abyaneh is an emerging scholar in the field of artificial intelligence applications in sustainable supply chains, affiliated with Kharazmi University, Tehran, Iran. His academic endeavors focus on integrating advanced data analytics, optimization techniques, and machine learning frameworks to enhance decision-making, efficiency, and sustainability across complex supply chain networks. With 3 published research papers and an h-index of 1, Mr. Abyaneh has begun establishing a scholarly footprint that bridges technology-driven innovation with environmental and operational resilience. His work, including the open-access article “An Analytical Review of Artificial Intelligence Applications in Sustainable Supply Chains” (2025, Supply Chain Analytics), provides critical insights into the evolving intersection of AI and sustainability, emphasizing how digital intelligence can optimize resource utilization, reduce carbon footprints, and strengthen circular economy practices. Having received citations from international scholars, he actively contributes to the global academic dialogue on sustainable logistics, smart manufacturing, and responsible innovation. Mr. Abyaneh’s collaborative research network includes seven co-authors from diverse academic and institutional backgrounds, reflecting a strong interdisciplinary approach that combines engineering, data science, and environmental management. His studies aim to foster both theoretical advancement and practical applicability, offering valuable implications for policymakers, corporations, and researchers seeking to transition toward greener, data-driven supply chains. Beyond academic impact, his contributions align with global sustainability goals, promoting knowledge transfer, digital equity, and responsible AI adoption for societal benefit.

Profiles: Scopus | ORCID | Google Scholar

Featured Publications

1. Sharbati, A., Movahed, A. B., Abyaneh, A. G., & Rahmanian, F. (2025). Risk assessment of healthcare systems using the FMEA method: Medication management process. Journal of Future Digital Optimization, 1(1), 71–85.
Cited by: 4

2. Abyaneh, A. G., Movahed, A. B., Abyari, A., Nodehfarahani, A., & Khakbazan, M. (2025). Evaluating the RFID technology in Costco Company: A focus on logistics and supply chain management. Applied Innovations in Industrial Management, 5(2), 34–51.
Cited by: 2

3. Movahed, A. B., Abyaneh, A. G., Khakbazan, M., & Movahed, A. B. (2025). Smart economy cybersecurity: AI-driven risk management in digital markets. In Dynamic and Safe Economy in the Age of Smart Technologies (pp. 49–72).
Cited by: 2

4. Abyaneh, A. G., Ghanbari, H., Mohammadi, E., Amirsahami, A., & Khakbazan, M. (2025). An analytical review of artificial intelligence applications in sustainable supply chains. Supply Chain Analytics, 100173.
Cited by: 1

5. Abyaneh, A. G., Khakbazan, M., & Movahed, A. B. (2026). Artificial intelligence in digital marketing: Trends, challenges, and strategic opportunities. In Improving Consumer Engagement in Digital Marketing Through Cognitive AI (pp. 225–260)

Mr. Amirhossein Ghasemi Abyaneh envisions a future where artificial intelligence empowers sustainable industrial transformation, enabling supply chains to become more adaptive, transparent, and environmentally responsible. His research advances the integration of smart analytics and sustainability principles, fostering innovation that supports global climate resilience and ethical technological progress.