Prof. Xianglong Xu | Artificial Intelligence Prediction Tools | Research Excellence Award

Published on by Electrical Engineering

Prof. Xianglong Xu | Artificial Intelligence Prediction Tools | Research Excellence Award

Research Professor | Shanghai University of Traditional Chinese Medicine | China

Dr. Xianglong Xu, a Research Professor at Shanghai University of Traditional Chinese Medicine specializing in AI-enabled epidemiology and predictive health analytics, holds a Ph.D. in Epidemiology with advanced expertise in chronic disease modelling and public health informatics. His professional experience spans roles as a young researcher, master’s supervisor, and project leader, contributing to multidisciplinary initiatives that integrate artificial intelligence with clinical practice and community health systems. Dr. Xu has led the development of innovative digital health tools, large-scale preventive health databases, and collaborative programs with hospitals and public health centers, strengthening data-driven decision-making across diverse populations. His research focuses on chronic disease risk assessment, AI-driven health prediction models, digital health evaluation, and the integration of Traditional Chinese Medicine concepts into modern epidemiology, supported by significant scholarly output and impactful scientific contributions. He has published extensively in peer-reviewed journals, contributed to theoretical and applied advancements in intelligent health analytics, and served on editorial boards while reviewing for leading international journals. His professional affiliations include membership in the International Epidemiological Association, IEEE and EMBS, national rehabilitation committees, and multiple scientific societies, complemented by recognitions across national and international platforms. 1,441 Citations, 71 Documents, 21 h-index.

Profiles: Scopus 

Featured Publications

Xu, Xianglong*, Urban and rural disparities in stroke prediction using machine learning among Chinese older adults. Scientific Reports, 2025.

Xu, Xianglong*, External validation of a web- and artificial intelligence-based HIV/STI risk assessment tool: performance evaluation using data from Sydney sexual health centre. BMC Infectious Diseases, 2025.

Xu, Xianglong*, Anxiety responses and testing intentions among gay and bisexual men using an AI-powered HIV/STI risk assessment tool: a quasi-experimental study. BMC Public Health, 2025.

Xu, Xianglong, Comparative analysis of cardiometabolic multimorbidity predictors in China and the USA: A machine learning approach. Diabetes Research and Clinical Practice, 2025.

Xu, Xianglong, Development of a web-based tool using machine learning algorithms to improve adherence to diagnostic colonoscopy among middle-aged and elderly adults in colorectal cancer screening program. Annals of Epidemiology, 2025.

Xu, Xianglong, Determinants and geographical variations in oral traditional Chinese medicine use among middle-aged and elderly chronic adults in China: A cross-sectional study. European Journal of Integrative Medicine, 2025.

Xu, Xianglong, Geographical distribution disparities and prediction of health satisfaction among middle-aged and elderly adults in China: An analysis based on national data. Annals of Epidemiology, 2025.

Dr. Angel Sapena Bano | Modelling Machines for Optimization | Research Excellence Award

Published on by Electrical Engineering

Dr. Angel Sapena Bano | Modelling Machines for Optimization | Research Excellence Award

Associate Professor | Universitat Politecnica de Valencia | Spain

Ángel Sapena Bañó, Profesor Titular at the Universitat Politècnica de València, is a specialist in electrical engineering with expertise in electrical machines, diagnostic methods, numerical modelling, and condition monitoring. He holds degrees in Industrial Engineering, Energy Technology for Sustainable Development, and Secondary Education, complemented by a doctorate in Industrial Engineering focused on advanced diagnostic techniques for electrical machines. His professional trajectory includes roles as Lecturer, Researcher, and Technical Specialist, contributing to major academic initiatives, laboratory modernization, and collaborative research activities. He has participated in multiple competitive and industrial R&D projects, developed fault-diagnosis tools for induction machines and wind-energy systems, and strengthened international cooperation through research stays and Erasmus teaching engagements. His research spans analytical and hybrid modelling, finite-element methods, machine-learning-based diagnostics, and real-time simulation, reflected in numerous high-impact journal articles, conference contributions, book chapters, and patented inventions. He has led and co-led research outputs as first and corresponding author, supervised a wide range of graduate projects, and contributed to organizing scientific conferences and special issues. His distinctions include recognized research merits, invited reviewer roles in indexed journals, participation in prominent research groups, and involvement in impactful national and international scientific initiatives. His scholarly record includes 1,035 citations, 60 documents, and an h-index of 17.

Profiles: Scopus | ORCID

Featured Publications

Ángel Sapena Bañó*, Model-based diagnostic techniques for induction machines under transient operational conditions. Int. J. Electr. Power Energy Syst., Accepted.

Ángel Sapena Bañó*, Hybrid FEM–analytical modelling framework for efficient fault detection in eccentric induction motors. Sensors, 2025, 25, 1–28.

Ángel Sapena Bañó, Deep learning–enhanced condition monitoring strategies for electrical machines operating in variable regimes. Mathematics and Computers in Simulation, 2025, 1–28.

Adil Sultan | Environmental Modeling | Best Researcher Award

Published on by Electrical Engineering

Mr. Adil Sultan | Environmental Modeling | Best Researcher Award

PhD Student | National Yunlin University of Science and Technology | Taiwan

Adil Sultan is a dynamic researcher and postgraduate scholar in Computer Science and Information Engineering at the National Yunlin University of Science and Technology, Taiwan, with a Bachelor’s degree in Geophysics from Bahria University Islamabad, Pakistan. His interdisciplinary expertise bridges artificial intelligence, computational modeling, and earth sciences, emphasizing intelligent predictive frameworks for environmental and marine ecosystem dynamics. Adil’s research integrates machine learning, fractional calculus, and neurocomputational modeling to address complex ecological and climatological phenomena, with publications in high-impact Q1 journals such as Water Research, Engineering Applications of Artificial Intelligence, and Process Safety and Environmental Protection. He has contributed to advancing predictive neural architectures for modeling plankton dynamics, environmental toxin propagation, and climate-induced marine variations. His professional experience includes seismic data processing at Oil and Gas Development Company Limited, where he applied geophysical modeling and data analytics for subsurface evaluations, alongside earlier roles in communication and technical support at IBEX Global. Recognized for academic excellence and innovation, he ranked among the top three in his master’s program and earned a Bronze Medal for his undergraduate thesis. Adil has presented his work at international conferences, authored multiple manuscripts under review, and actively engages in interdisciplinary research collaborations. His memberships, scholarly achievements, and leadership in applied machine learning for environmental intelligence underscore his commitment to sustainable scientific innovation and global research excellence. His Scopus profile reflects 17 citations, 6 indexed documents, and an h-index of 2.

Profile: Scopus

Featured Publications

Adil Sultan*, Design of a Fractional-Order Environmental Toxin-Plankton System in Aquatic Ecosystems: A Novel Machine Predictive Expedition with Nonlinear Autoregressive Neuroarchitectures. Water Research, Q1, 12.4 I.F.

Adil Sultan*, Bayesian-Regularized Cascaded Neural Networks for Fractional Asymmetric Carbon-Thermal Nutrient-Plankton Dynamics under Global Warming and Climatic Perturbations. Engineering Applications of Artificial Intelligence, Q1, 8.0 I.F.

Adil Sultan*, Intelligent Predictive Networks for Nonlinear Oxygen-Phytoplankton-Zooplankton Coupled Marine Ecosystems under Environmental and Climatic Disruptions. Process Safety and Environmental Protection, Q1, 7.8 I.F.

Adil Sultan*, Prognostication of Zooplankton-Driven Cholera Pathoepidemiological Dynamics: Novel Bayesian-Regularized Deep NARX Neuroarchitecture. Computers in Biology and Medicine, Q1, 6.3 I.F.

Adil Sultan*, Predictive Modeling of Fractional Plankton-Assisted Cholera Propagation Dynamics Using Bayesian Regularized Deep Cascaded Exogenous Neural Networks. Process Safety and Environmental Protection, Q1, 7.8 I.F.