Mr. Ro-Yu Wu | Computer Science | Research Excellence Award

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Mr. Ro-Yu Wu | Computer Science | Research Excellence Award

Professor | Lunghwa University of Science and Technology | Taiwan

Mr. Ro-Yu Wu is a Taiwan-based researcher recognized for his contributions to combinatorial algorithms, graph theory, and efficient data structures, particularly within the domains of ranking and unranking methods, Hamiltonian graph properties, and algorithmic generation of combinatorial objects. As a productive scholar in theoretical computer science and industrial management, his work emphasizes the design of loopless, lexicographic, and Gray code–based algorithms that enhance computational efficiency and fault-tolerant communication in complex networks. His research is well acknowledged in the global academic community, as reflected in his Scopus record with 45 documents, 308 citations across 189 citing works, and an h-index of 10. On ResearchGate, he maintains an active profile with 47 publications, over 6,300 reads, and 367 citations. These metrics highlight his growing influence, especially in areas involving structured graph traversal, spanning-tree generation, and the analytical foundations supporting optimization and data broadcasting systems. His work frequently explores practical algorithmic strategies for high-performance computing environments, providing innovative insights for fault-tolerant network design and combinatorial enumeration. Mr. Wu’s collaborations span multi-author research teams, contributing to advancements published in high-impact venues such as Theoretical Computer Science, The Journal of Supercomputing, Journal of Combinatorial Optimization, and Optimization Letters. His ongoing research continues to shape efficient computational paradigms for combinatorial structures, making him a relevant contributor to the future of theoretical and applied algorithmic studies.

Profile

Scopus

Featured Publications 

Xie, Z., Wu, R.-Y., & Shi, L. (2025). Ranking and unranking algorithms for derangements based on lexicographical order. Theoretical Computer Science.

Pai, K.-J., Wu, R.-Y., Peng, S. L., & Chang, J. M. (2023). Three edge-disjoint Hamiltonian cycles in crossed cubes with applications to fault-tolerant data broadcasting. The Journal of Supercomputing.

Chang, Y. H., Wu, R.-Y., Chang, R. S., & Chang, J. M. (2022). Improved algorithms for ranking and unranking (k, m)-ary trees in B-order. Journal of Combinatorial Optimization.

Wu, R.-Y., Tseng, C. C., Hung, L. J., & Chang, J. M. (2022). Generating spanning-tree sequences of a fan graph in lexicographic order and ranking/unranking algorithms. International Symposium on Combinatorial Optimization.

Chang, Y. H., Wu, R.-Y., Lin, C. K., & Chang, J. M. (2021). A loopless algorithm for generating (k, m)-ary trees in Gray code order. Optimization Letters.

Prof. Changfang Chen | Medical Image Processing | Research Excellence Award

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Prof. Changfang Chen | Medical Image Processing | Research Excellence Award

Associate Professor | Qilu University of Technology | China

Prof. Changfang Chen is an associate professor at the Shandong Institute of Artificial Intelligence, Qilu University of Technology, where she contributes extensively to medical image processing and artificial intelligence research. She earned her doctorate in control science and engineering from Beihang University in Beijing. Her scholarly influence is supported by citation metrics across major databases, including a Google Scholar record showing more than five hundred citations with strong h-index and i10-index performance, and Scopus-indexed publications appearing in highly ranked journals. Her body of work spans intelligent systems, biomedical signal processing, autonomous control, and deep learning-driven medical applications.

Publication Profile

Google Scholar

Education Background

Prof. Changfang Chen completed her doctoral education at Beihang University with a focus on control science and engineering, where she developed a strong foundation in computational modeling, signal processing, and intelligent system design. Her academic journey fostered a multidisciplinary orientation that later supported her transition into artificial intelligence and medical image analysis. Through advanced coursework, laboratory research, and thesis contributions, she established technical strengths aligned with both theoretical control frameworks and practical biomedical computation, enabling a seamless integration of engineering principles with data-driven medical research applications.

Professional Experience

Prof. Changfang Chen serves as an associate professor at the Shandong Institute of Artificial Intelligence within Qilu University of Technology, contributing to research, postgraduate supervision, and high-impact project development. She has participated in multiple government-supported research programs, including national-level and provincial-level scientific foundations, where her role involved developing algorithms for image analysis, signal denoising, and autonomous systems. Her professional activity extends to collaboration with multidisciplinary teams, publication in leading indexed journals, and engagement in editorial and reviewing tasks, reflecting her sustained commitment to academic service and scientific advancement.

Awards and Honors

Throughout her career, Changfang Chen has been recognized through her involvement in competitive national and provincial research programs, reflecting the scientific value and societal relevance of her contributions. Her patents, including work on wavelet-domain ECG noise elimination, demonstrate innovation in biomedical signal processing. Her publications in prestigious SCI and Scopus-indexed journals such as Neurocomputing, Knowledge-Based Systems, IEEE Transactions on Instrumentation and Measurement, and IEEE Transactions on Intelligent Transportation Systems indicate consistent scholarly excellence. Her citation achievements further validate the long-term influence and recognition of her contributions within the global research community.

Research Focus

Prof. Changfang Chen’s research centers on medical image processing, biomedical signal reconstruction, autonomous control, and artificial intelligence with emphasis on multitask learning and deep neural architectures. Her recent work includes the development of a multi-task consistency learning framework designed to optimize predictions from unlabeled clinical images by integrating segmentation, signed distance mapping, and reconstruction processes. She has also contributed substantially to ECG signal denoising, autonomous vehicle tracking control, and wavelet-based sparse representations. Her research approach blends theoretical rigor with applied innovation to address challenges in modern intelligent healthcare technologies.

Top Publications

Chen, C., Jia, Y., Shu, M., & Wang, Y. (2015). Hierarchical adaptive path-tracking control for autonomous vehicles. IEEE Transactions on Intelligent Transportation Systems, 16(5), 2900–2912. This article has been cited widely for its contribution to autonomous path-tracking control and has received strong scholarly recognition based on citation counts.

Shu, M., Yuan, D., Zhang, C., Wang, Y., & Chen, C. (2015). A MAC protocol for medical monitoring applications of wireless body area networks. Sensors, 15(6), 12906–12931. This publication is frequently cited for its relevance to wireless body area networks and medical monitoring technologies, contributing significantly to wearable-sensing research.

Liu, H., Zhou, S., Chen, C., Gao, T., & Xu, J. (2022). Dynamic knowledge graph reasoning based on deep reinforcement learning. Knowledge-Based Systems, 241, 108235. This work has received strong citation activity and is noted for integrating reinforcement learning with knowledge graph reasoning in intelligent systems.

Hou, Y., Liu, R., Shu, M., Xie, X., & Chen, C. (2023). Deep neural network denoising model based on sparse representation algorithm for ECG signal. IEEE Transactions on Instrumentation and Measurement, 72, 1–11. This article is widely referenced for advancing ECG denoising using deep learning and sparse representation methods.

Hou, Y., Liu, R., Shu, M., & Chen, C. (2023). An ECG denoising method based on adversarial denoising convolutional neural network. Biomedical Signal Processing and Control, 84, 104964. This study has gained citations for its novel adversarial architecture applied to biomedical signal enhancement and reconstruction.

Conclusion

Through her sustained engagement in advanced artificial intelligence research, high-quality publications, and participation in major national science programs, Changfang Chen has established a strong academic profile within the fields of biomedical computation and intelligent systems. Her contributions to medical imaging and signal analysis demonstrate both technical innovation and societal relevance, while her citation record across Google Scholar and Scopus underscores her scholarly influence. Her work continues to advance computational methodologies that support reliability, accuracy, and efficiency in healthcare-oriented artificial intelligence systems.

Assist. Prof. Dr. Jiaxin Li | Metasurfaces | Research Excellence Award

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Assist. Prof. Dr. Jiaxin Li | Metasurfaces | Research Excellence Award

Researcher | Wuhan University of Technology | China

Dr. Li Jiaxin is a researcher in the fields of optical metamaterials and nano-optics, currently working at the China Electric Power Research Institute (Institute of Metrology). Previously, Li obtained a Doctor of Engineering degree from Wuhan University, School of Electronic Information, specializing in Physical Electronics. Her research focuses on metasurfaces and multifunctional metadevices — aiming at micro-nano fabrication and advanced light-wave manipulation. Over the years she has authored/co-authored more than ten SCI articles in high-impact optics and materials-science journals, including a paper selected as an ESI Highly Cited Paper. According to her public profile at a scholarly portal, her publications number around 17, with over 300 citations.

Publication Profile

Google Scholar

Education Background

Li Jiaxin obtained her Doctor of Engineering degree under an integrated Master–Doctoral programme at Wuhan University, in the School of Electronic Information focusing on Physical Electronics. Upon completion of her PhD, she made a transition to professional research in metrology and applied optics, combining her academic training with engineering practice.

Professional Experience

After earning her doctorate, Li Jiaxin joined the China Electric Power Research Institute — Institute of Metrology in mid-2023, where she currently serves as an Intermediate Engineer. Prior to that, during 2018–2023 she was a doctoral candidate at Wuhan University, during which period she worked in research on metasurfaces and nano-optical devices. Her dual exposure to academic research and applied metrology places her at the interface of fundamental photonics and practical engineering implementation.

Awards and Honors

Li Jiaxin secured competitive funding early in her career: she led a project supported by the National Natural Science Foundation of China (Young Scientists Fund), another project under the China Postdoctoral Science Foundation (Special Funding), and received support under the Hubei Postdoctoral Cutting-Edge Talent Introduction Program. She also participated in national-level research endeavours including the National Key R&D Program of China and the National Defense Science and Technology 173 Project. To date, she holds eight authorized national invention patents.

Research Focus

Her research centers on metasurface-based micro-nano optical technologies. She investigates mechanisms for manipulating light waves via metasurfaces, with particular emphasis on multifunctional metadevices, advanced imaging, and tunable optoelectronic components. Her work combines design, fabrication, and functional demonstration of metamaterial-based lenses, holograms, encryption metasurfaces and dynamic nanophotonic devices, leveraging micro–nano fabrication processes to realize high-density, multifunctional optical elements. Her most recent work includes an electrically tunable metalens based on PEDOT:PSS.

Publication

Zhang, M., Sun, D., Zhang, S., Deng, L., Li, J., & Guan, J. (2025). Electrically Tunable Metalens Based on PEDOT:PSS. Micromachines, 16(12), 1341.

Conclusion

Dr. Li Jiaxin represents a new generation of photonics researchers who bridge advanced academic research on metasurfaces with practical, engineering-oriented applications. Her strong publication record, supported funding and patents show both scientific creativity and technological relevance. As she advances in her career at the China Electric Power Research Institute, her contributions are likely to further impact the development of compact, reconfigurable, and multifunctional optical devices.

Ms. Ifza Shad | Computer Vision | Research Excellence Award

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Ms. Ifza Shad | Computer Vision | Research Excellence Award

University of Central Punjab | Pakistan

Ms. Ifza Shad is a computer vision and artificial intelligence researcher whose work focuses on real-time object detection, medical image analysis, deep learning optimization, and multimodal perception models for complex environments. Her research integrates advanced machine learning architectures, including YOLO-based detectors, attention-driven fusion networks, and lightweight deep learning frameworks designed for resource-efficient deployment in dynamic real-world scenarios. She has contributed to cutting-edge studies in aquatic and surface litter detection, brain tumor diagnosis, protective workwear recognition, and driver-behavior monitoring systems, demonstrating a strong emphasis on safety, healthcare, and environmental sustainability. Her interdisciplinary approach merges computer vision, robotics, and large-scale data processing, allowing her to design algorithms that address challenges in automation, public health, and smart systems. She has authored impactful publications in reputable international journals indexed in Scopus and Web of Science, with her research widely cited and accessible on Google Scholar. Her scholarly record includes peer-reviewed articles, collaborative projects with international researchers, and contributions to academic seminars and conferences. She continues to advance innovative detection models and AI-driven solutions, aiming to enhance real-time decision support systems through robust, interpretable, and computationally efficient algorithms. Her research output reflects a growing citation count, supported by Scopus metrics, Google Scholar indices, and document-level analytics, emphasizing her active role in the global scientific community and her contribution to emerging intelligent systems.

Profile

ORCID

Featured Publications

Shad, I., Zhang, Z., Asim, M., Al-Habib, M., Chelloug, S. A., & Abd El-Latif, A. (2025). Deep learning-based image processing framework for efficient surface litter detection in computer vision applications. Journal of Radiation Research and Applied Sciences, 18(2), 101534.

Shad, I., Bilal, O., & Hekmat, A. (2025). Attention-driven sequential feature fusion framework for effective brain tumor diagnosis. Significances of Bioengineering & Biosciences, 7(3).

Hekmat, A., Zhang, Z., Khan, S. U. R., Shad, I., & Bilal, O. (2024). An attention-fused architecture for brain tumor diagnosis. Biomedical Signal Processing and Control, 101, 107221.

Assoc. Prof. Dr. Ammar Oad | Computer Vision | Research Excellence Award

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Assoc. Prof. Dr. Ammar Oad | Computer Vision | Research Excellence Award

Professor | Shaoyang University | China

Assoc. Prof. Dr. Ammar Oad is an accomplished researcher in Artificial Intelligence with strong expertise in deep learning, computer vision, cybersecurity, and intelligent data-driven systems. His research focuses on designing advanced algorithms for image analysis, object detection, multimodal learning, cross-modal retrieval, and secure AI frameworks capable of addressing modern challenges in threat detection and autonomous systems. Dr. Oad’s scientific contributions span AI-powered fake news detection, plant disease identification using explainable AI, blockchain-enabled cybersecurity mechanisms, sustainable smart grid prediction models, and intelligent pattern recognition. His research impact is reflected in Scopus metrics of 382 citations across 374 documents with an h-index of 9, and Google Scholar metrics of 573 citations, h-index 10, and i10-index 12, demonstrating strong visibility and influence within the scientific community. His work regularly appears in reputable journals such as IEEE Access, Optik, Electronics (MDPI), and leading materials science journals through interdisciplinary collaborations. Dr. Oad also contributes to the academic community as an editorial board member and scientific reviewer for several high-impact journals. His research interests include deep neural architectures, Gaussian mixture models, ensemble learning, blockchain security frameworks, and energy-efficient AI systems for smart cities. By integrating machine learning with cybersecurity principles, he aims to develop intelligent, robust, and transparent AI solutions capable of safeguarding digital infrastructures while advancing the state of automated recognition and decision-making technologies. His growing body of research reflects innovation, rigor, and a commitment to addressing real-world AI challenges.

Profile

Scopus | ORCID | Google Scholar

Featured Publications 

Oad, A., Farooq, H., Zafar, A., Akram, B. A., Zhou, R., & Dong, F. (2024). Fake news classification methodology with enhanced BERT. IEEE Access, 12, 164491–164502.

Oad, A., Abbas, S. S., Zafar, A., Akram, B. A., Dong, F., Talpur, M. S. H., & Uddin, M. (2024). Plant leaf disease detection using ensemble learning and explainable AI. IEEE Access, 12, 156038–156049.

Oad, A., Ahmad, H. G., Talpur, M. S. H., Zhao, C., & Pervez, A. (2023). Green smart grid predictive analysis to integrate sustainable energy of emerging V2G in smart city technologies. Optik, 272, 170146.

Oad, A., Razaque, A., Tolemyssov, A., Alotaibi, M., Alotaibi, B., & Zhao, C. (2021). Blockchain-enabled transaction scanning method for money laundering detection. Electronics, 10(15), 1766.

Li, Y., Liu, W., Pang, X., Oad, A., Liang, D., Zhang, X., Tang, B., Fang, Z., Shi, Z., & Chen, J. (2024). Microwave dielectric properties, Raman spectra and sintering behavior of low loss La7Nb3W4O30 ceramics with rhombohedral structure. Ceramics International.

Ms. Zunaira Khalid | Biophysics | Best Review Paper Award

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Ms. Zunaira Khalid | Biophysics | Best Review Paper Award

Doctoral Researcher | Xi’an jiaotong university | China

Ms. Zunaira Khalid is an emerging biophysics and biosensing researcher whose work spans advanced biosensor design, nanobiosensors, electrochemical sensing platforms, and organoid-integrated diagnostic systems. Her research integrates interdisciplinary approaches from zoology, molecular biology, and biomedical engineering to develop innovative sensing tools for disease detection and environmental health monitoring. She has contributed to the development of biomimetic olfactory and taste-based biosensing systems, label-free detection strategies, and field-effect transistor sensors aimed at improving point-of-care diagnostics. Her foundational research explored parasitology and epidemiology, particularly the prevalence and transmission dynamics of Taenia multiceps and related metacestodes in domestic livestock—helping inform disease management and public health interventions. Building on strong laboratory expertise, she brings hands-on experience with molecular techniques, PCR-based diagnostics, DNA barcoding, microbial analysis, and ecological assessments, complementing her current focus on biosensor innovation. Her scholarly contributions reflect a growing academic footprint, with publications in international journals covering biosensor advancements, nanotechnology, and parasitological epidemiology. She continues to expand her research visibility through scientific presentations, collaborations, and interdisciplinary projects. Her citation record is gradually growing across platforms, with ongoing updates on Scopus and Google Scholar indexing, citation counts, and h-index metrics as additional documents, publications, and citations are processed. Collectively, her work contributes to next-generation diagnostic technologies and promotes translational applications of biosensing in biomedical and ecological domains.

Profile

Google Scholar

Featured Publications

Khalid, Z., Chen, Y., Liu, X., Noureen, B., Chen, Y., Wang, M., Ma, Y., Du, L., & Wu, C. (2025). Recent advances and unaddressed challenges in biomimetic olfactory and taste-based biosensors: Moving toward integrated AI-powered and market-ready sensing systems. Sensors, 25, 7000.

Khalid, Z., Noureen, B., & colleagues. (2024). Prevalence and epidemiology of coenurosis in domestic bovids of Mianwali, Pakistan. International Journal of Animal Biotechnology.

Khalid, Z., Noureen, B., & colleagues. (2023). Green synthesis of silver nanoparticles and evaluation of their antibacterial activity. International Journal of Cell Science and Biotechnology.

Dr. leila malihi | Knowledge Distillation | Machine Learning Research Award

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Dr. leila malihi | Knowledge Distillation | Machine Learning Research Award

Osnabrück University | Germany

Leila Malihi is a researcher in cognitive science with specialization in computer vision, machine learning, and biomedical image analysis. Her work focuses on developing efficient and controllable deep learning frameworks, particularly model compression techniques such as sequential knowledge distillation and pruning, enabling deployment of high-performance neural networks on edge and resource-limited devices. She has contributed significantly to advancing automated medical image analysis, including wound classification, child face recognition, malaria parasite detection, cancer diagnosis, and ECG signal processing. Her research integrates convolutional neural networks, sparse coding, autoencoders, transfer learning, GAN-based synthetic data generation, and modern pattern-recognition techniques to build interpretable, scalable, and real-time AI systems. She has also explored neural network eigenspaces, principal eigenfeatures, and logistic regression probes to better understand the inner inference behavior of deep models. Leila’s scholarly output reflects her interdisciplinary approach, contributing to journals and international conferences in machine learning, medical informatics, and image processing. Her published work has received 88 Scopus citations from 85 documents, with 10 indexed documents and an h-index of 5, demonstrating a growing impact in the field. On Google Scholar, her research has accumulated 134 citations, with an h-index of 6 and an i10-index of 5, further highlighting the relevance of her contributions to computational healthcare, interpretable AI, and efficient deep learning architectures. Her profile reflects a strong commitment to bridging core AI innovation with real-world biomedical applications.

Profile

Scopus | Google Scholar

Featured Publications

Malihi, L., & Heidemann, G. (2023). Efficient and controllable model compression through sequential knowledge distillation and pruning. Journal of Big Data and Cognitive Computing.

Richter, M. L., Malihi, L., Windler, A. K. P., & Krumnack, U. (2023). Analyzing the inference process in deep convolutional neural networks using principal eigenfeatures, saturation, and logistic regression probes. Journal of Applied Research in Electrical Engineering.

Malihi, L., & Malihi, R. (2020). Single stuck-at faults detection using test generation vector and deep stacked sparse autoencoder. SN Applied Sciences, 2(10), 1–10.

Malihi, L., Ansari-Asl, K., & Behbahani, A. (2015). Improvement in classification accuracy rate using multiple classifier fusion toward computer vision detection of malaria parasite. Jundishapur Journal of Health Sciences, 7(3), 26–32.

Malihi, L., Ansari-Asl, K., & Behbahani, A. (2015). Computer-aided diagnosis of malaria parasite using pattern recognition methods. AJUMS Journals, 14(1), 65–74.

Dr. Soumaya Hechmi | Economics | Best Researcher Award

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Dr. Soumaya Hechmi | Economics | Best Researcher Award

Assistant Professor | Imam Mohammad Ibn Saud Islamic University (IMSIU) | Saudi Arabia

Dr. Soumaya Hechmi is an accomplished finance scholar whose research spans corporate finance, private equity, sustainability economics, and macro-financial analysis. Her work investigates how investment behavior, value creation, corporate performance, and governance mechanisms shape firm-level outcomes across both emerging and developed markets. She has developed a strong empirical orientation, applying advanced econometric techniques such as ARDL, FMOLS, DOLS, CCR, fixed-effects modeling, and quantile regression to study financial dynamics, environmental sustainability, and real-estate market behavior. Her research also explores energy economics, CO₂ emissions, renewable and non-renewable energy interactions, and the role of financial inclusion, trade, tourism, and institutional quality in economic development. Across interdisciplinary contributions, she consistently bridges finance, sustainability, and macroeconomic policy. Dr. Hechmi’s scholarly output includes studies on non-performing loans, bank capital adequacy, technological innovation, human development, and market stability, with publications featured in Scopus-indexed, ESCI-indexed, and ARCIF-indexed journals. Her analytical rigor and consistent contributions have earned measurable academic impact, reflected in Scopus metrics of 19 citations across 4 documents with an h-index of 2, and Google Scholar metrics of 59 citations, an h-index of 4, and an i10-index of 2. Her work is recognized for providing actionable insights for policymakers, investors, and financial institutions. Dr. Hechmi continues to expand her research integrating financial development, sustainability challenges, technological innovation, and economic growth, making her a notable contributor to modern financial and economic scholarship.

Profile

Scopus | ORCID | Google Scholar

Featured Publications 

Hechmi, S. (2025). Beyond sunlight: How CO₂ emissions, coal lock-in, and global finance shape Australia’s solar energy consumption – An ARDL analysis with robustness checks. Economics – Innovative and Economics Research Journal.

Ben Saanoun, I., & Hechmi, S. (2025). How can corporate governance moderate the relationship between private benefits of control and firm performance in the French context? Journal of Cultural Analysis and Social Change.

Hechmi, S. (2025). PropTech in the Saudi real estate market: Case studies of NEOM and Qiddiya. Edelweiss Applied Science and Technology, 9(11), 1087–1095.

Hechmi, S. (2024). Impact of profitability, leverage and corporate governance on value creation: Empirical study of Saudi real estate companies. Open Journal of Business and Management, 12(3), 1403–1410.

Abid, I., Hechmi, S., & Chaabouni, I. (2024). Impact of energy intensity and CO₂ emissions on economic growth in Gulf Cooperation Council countries. Sustainability, 16(23), 10266.

Prof. Wenfeng Ding | Computational Hydrology | Editorial Board Member

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Prof. Wenfeng Ding | Computational Hydrology | Editorial Board Member

Changjiang River Scientific Research Institute | China

Prof. Wenfeng Ding is a distinguished researcher in soil erosion science, hydrodynamics, and watershed environmental processes, with extensive contributions to understanding slope-gully erosion mechanisms, sediment transport, and non-point source pollution. His work focuses on the physical mechanisms that drive soil detachment, sediment yield, and runoff behavior under varying topographic, vegetation, and rainfall conditions. He has advanced the field by integrating experimental hydrodynamics, erosion modeling, fractal soil structure analysis, and GIS-based environmental assessment. His research has played a pivotal role in improving soil and water conservation practices, particularly in the Loess Plateau, the Yangtze River Basin, and purple soil regions of Southwest China. Through sustained scientific inquiry, he has contributed to the development and validation of predictive models across multiple spatial scales, including rill erosion processes, slope-gully couplings, and large watershed sediment dynamics. His studies involving rare earth element tracers, erosion-runoff interaction simulations, and long-term hydrological trend assessments have strengthened the scientific basis for ecological restoration and erosion mitigation in fragile environments. With a substantial body of peer-reviewed publications, Wenfeng Ding has achieved strong scholarly impact, reflected in Scopus metrics of 792 citations across 48 documents with an h-index of 14. His influence extends further on Google Scholar, where his citation counts are typically higher due to broader indexing of regional and conference literature. His research continues to support national efforts in soil conservation, watershed rehabilitation, and sustainable land management.

Profile

Scopus

Featured Publications 

Ding, W., & Alkenbyt, H. (2011). Annual discharge and sediment load variation in Jialing River during the past 50 years. Journal of Mountain Science, 8, 664–676.

Ding, W., & Zhang, P. (2012). Fractal dimension features of soil aggregate distribution with different reclamation years on the Loess Plateau. Sensor Letters, 10, 1–7.

Li, M., Yao, W. Y., Ding, W. F., et al. (2009). Effect of grass coverage on sediment yield in the hillslope-gully side erosion system. Journal of Geographical Sciences, 19, 321–330.

Zhang, X. C., Li, Z. B., & Ding, W. F. (2005). Validation of WEPP sediment feedback relationships using spatially distributed rill erosion data. Soil Science Society of America Journal, 69, 1440–1447.

Li, M., Li, Z. B., & Ding, W. F., et al. (2006). Using rare earth element tracers and neutron activation analysis to study rill erosion processes. Applied Radiation and Isotopes, 64, 402–408.

Prof. Vincenzo Maria Romeo | Psychoanalysis | Innovative Research Award

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Prof. Vincenzo Maria Romeo | Psychoanalysis | Innovative Research Award

Researcher | University of Palermo | Italy

Prof. Vincenzo Maria Romeo, MD, PhD, is an accomplished Italian psychiatrist, clinical psychologist, and psychoanalytic scholar whose research spans addiction medicine, psychopathology, psychopharmacology, body-image disturbance, eating disorders, and psychoanalytic models of subjectivation. His scientific work integrates biological, psychological, and socio-cultural dimensions of mental health, with a consistent focus on complex comorbidities such as substance use, behavioral addictions, depression, schizophrenia, and antisocial behavior in adolescents. He has contributed extensively to understanding the intersections between personality structure, executive functioning, emotional processing, and maladaptive behaviors. His scholarship further explores contemporary psychoanalytic anthropology, post-digital identity formation, and innovative conceptual models for interpreting psychological development and psychopathology. Prof. Romeo’s clinical research includes randomized controlled trials, case studies, and cross-sectional investigations on emerging pharmacological treatments, psychodynamic interpretations, and novel therapeutic approaches for addiction and mood disorders. His contributions to pandemic-related psychiatric research have provided valuable insights into mental health vulnerabilities during social restrictions. He has produced influential work on the psychodiagnostic use of the Rorschach test and has authored notable theoretical contributions including the Tripartite Triangle Model and studies on intermittent attachment. His research output reflects strong academic impact with Scopus reporting 285 citations across 13 documents with an h-index of 7, and Google Scholar listing 617 citations, an h-index of 10, and an i10-index of 10. Prof. Romeo’s interdisciplinary approach positions him as a leading voice advancing integrative, psychodynamic, and evidence-based perspectives in contemporary psychiatry and psychology.

Research Publication Profile

Scopus | ORCID | Google Scholar

Featured Publications 

Muscatello, M. R. A., Bruno, A., Pandolfo, G., Mico, U., Scimeca, G., Romeo, V. M., Santoro, V., Settineri, S., Spina, E., & Zoccali, R. A. (2011). Effect of aripiprazole augmentation in treatment-resistant obsessive-compulsive disorder. Journal of Clinical Psychopharmacology, 31(2), 174–179.

Mico, U., Bruno, A., Pandolfo, G., Maria Romeo, V., Mallamace, D., D’Arrigo, C., Spina, E., & Zoccali, R. A., Muscatello, M. R. (2011). Duloxetine as adjunctive treatment to clozapine in schizophrenia. International Clinical Psychopharmacology, 26(6), 303–310.

Scimeca, G., Bruno, A., Pandolfo, G., Mico, U., Romeo, V. M., Abenavoli, E., Schimmenti, A., Zoccali, R., & Muscatello, M. R. (2013). Alexithymia, negative emotions, and sexual behavior in university students. Archives of Sexual Behavior, 42(1), 117–127.

Bruno, A., Quattrone, D., Scimeca, G., Cicciarelli, C., Romeo, V. M., Pandolfo, G., Zoccali, R. A., & Muscatello, M. R. (2014). Exercise addiction, narcissism, and self-esteem. Journal of Addiction, 2014, 1–6.

Martinotti, G., Alessi, M. C., Di Natale, C., Sociali, A., Ceci, F., Lucidi, L., Picutti, E., Di Carlo, F., Corbo, M., Vellante, F., Tourjansky, G., Catalano, G., Carenti, M. L., Incerti, C. C., Bartoletti, L., Barlati, S., Romeo, V. M., Verrastro, V., De Giorgio, F., … di Giannantonio, M. (2020). Psychopathological burden and quality of life in substance users during COVID-19 lockdown. Frontiers in Psychiatry, 11, 572245.