Maham Mujahid | Applied Mathematics | Best Researcher Award

Ms. Maham Mujahid | Applied Mathematics | Best Researcher Award

The Islamia University of Bahwalapur | Pakistan

Ms. Maham Mujahid is a mathematics researcher whose work centers on advanced fluid mechanics, with a particular emphasis on viscous and non-Newtonian fluid flows, nanofluid behavior, heat and mass transfer processes, and rheological analysis in complex geometries such as corrugated and curved channels. Her research integrates analytical and computational techniques, including perturbation methods and numerical simulations, to investigate pressure-driven flows, magnetized and hybrid nanofluids, nonlinear fluid models, slip and convective constraints, porous media effects, and entropy production in multiphase or biological flow environments. She has contributed significantly to the understanding of transport phenomena by publishing in high-impact international journals, covering themes such as Casson, Jeffrey, and Carreau-type fluids, thermal radiation, viscous dissipation, permeability, and metachronal wave motion. Her scholarly contributions also extend to hybrid nanofluid modeling based on Yamada–Ota and Xue frameworks, demonstrating her command of emerging areas in thermofluid systems. Alongside her research activities, she has gained substantial teaching experience at both undergraduate and graduate levels in courses related to calculus, linear algebra, integral equations, differential equations, numerical analysis, and fluid mechanics, consistently integrating theoretical knowledge with practical scientific applications. She is skilled in Mathematica, MATLAB, and various computational and productivity tools, enabling precise modeling, visualization, and academic communication. Her broader interests include computational fluid dynamics, non-Newtonian rheology, and the study of thermal and multiphase transport in engineered and natural systems, reflecting a strong commitment to advancing mathematical and physical sciences through research, teaching, and continuous professional development.

Profiles: Scopus | Google Scholar

Featured Publications

Mujahid, M., Abbas, Z., & Rafiq, M. Y. (2024). A study on the pressure‐driven flow of magnetized non‐Newtonian Casson fluid between two corrugated curved walls of an arbitrary phase difference. Heat Transfer, 53(8), 4510–4527.

Mujahid, M., Abbas, Z., & Rafiq, M. Y. (2024). Rheological study of water-based Cu nanofluid between two corrugated curved walls under constant pressure gradient. Alexandria Engineering Journal, 106, 691–703.

Mujahid, M., Abbas, Z., & Rafiq, M. Y. (2025). Flow of hybrid nanofluids between two permeable corrugated curved walls using Yamada–Ota and Xue models with variable viscosity. Physics of Fluids, 37(2).

Mujahid, M., Abbas, Z., & Rafiq, M. Y. (2024). Rheological analysis of pressure-driven Jeffrey fluid flow between corrugated porous curved walls with slip constraints. AIP Advances, 14(9).

Rafiq, M. Y., Abbas, Z., Munawar, F., Mujahid, M., & Durrani, A. (2025). Exploring entropy production in metachronal wave motion of Carreau fluid in a channel under lubrication hypothesis. International Journal of Thermofluids, 27, 101198.

Xiang Li | Computer Science | Best Researcher Award

Dr. Xiang Li | Computer Science | Best Researcher Award

Associate Researcher Qilu University of Technology (Shandong Academy of Sciences) China

Dr. Xiang Li is an accomplished Associate Researcher at the Qilu University of Technology (Shandong Academy of Sciences) in China, where he has been serving since 2019. With a strong academic foundation in computer science and a research focus spanning EEG-based emotion recognition, multimodal sentiment analysis, and contrastive learning, Dr. Li has published widely in high-impact journals and conferences. His work is recognized internationally, with over 1,700 citations on Google Scholar, reflecting his significant influence in the field of artificial intelligence and affective computing.

Profile

Google Scholar

Orcid

🎓 Education

Dr. Li earned his Ph.D. in Computer Science from the College of Intelligence and Computing at Tianjin University in 2019. He previously received his Master’s degree in Computer Science (2014) and a Bachelor’s degree in Network Engineering (2011), both from the School of Information Science and Technology at Shandong University of Science and Technology.

💼 Experience

Since 2019, Dr. Xiang Li has held the position of Associate Researcher at the Qilu University of Technology. In addition to his research role, he contributes significantly to teaching, instructing several undergraduate and graduate-level courses since 2020, including English for Computer Science, Information Retrieval, and Data Mining, Analysis, and Visualization. His multidisciplinary expertise allows him to merge theory with practice, especially in the intersection of artificial intelligence, neuroscience, and ocean data analytics.

🔬 Research Interests

Dr. Li’s research interests lie in EEG-based emotion recognition, multimodal deep learning, contrastive learning, and affective computing. He has also made substantial contributions to intelligent quality control in ocean observation, shipborne wind speed correction, and biomedical signal processing. His innovative approaches often employ supervised and self-supervised learning frameworks, with a focus on enhancing data-driven decision-making using limited or noisy data.

🏆 Awards

  • 🧠 ESI Highly Cited Paper for “EEG based emotion recognition: A tutorial and review” (2022)
  • 🏅 Recognized for over 1,700 citations on Google Scholar
  • 📈 Several publications with >100 citations, such as his works on quantum-inspired sentiment analysis and cross-subject EEG emotion recognition
  • 🧪 Multiple papers published in SCI Tier-1 journals and top CCF-ranked conferences

📚 Publications Top Notes:  

Below is a selection of Dr. Xiang Li’s publications, presented with hyperlinks, publication years, journals/conferences, and citation data (when available):

📘 2025: Multi-Affection Prompt Learning for Sentiment, Emotion and Sarcasm Joint Detection in Conversations – Tsinghua Science and Technology [SCI-1]

📘 2024: A Supervised Information Enhanced Multi-granularity Contrastive Learning Framework for EEG based Emotion Recognition – ICASSP 2024 [CCF-B]

📘 2024: Self-Supervised Pretraining-Enhanced Intelligent Quality Control for Ocean Observations – ICONIP 2024 [CCF-C]

📘 2024: An Adaptive Time-convolutional Network Online Prediction Method for Ocean Observation Data – SEKE 2024 [CCF-C]

📘 2024: Fusion of Time-Frequency Features in Contrastive Learning for Wind Speed Correction – Journal of Ocean University of China [SCI-3]

📘 2023: EEG-based Parkinson Detection through Supervised Contrastive Learning – BIBM 2023 [CCF-B]

📘 2022: EEG based Emotion Recognition: A Tutorial and Review – ACM Computing Surveys, 55(4) [SCI-1, IF=23.8, Cited by: 272]

📘 2021: Emotion Recognition via Dual-pipeline Graph Attention Network – BIBM 2021 [CCF-B]

📘 2020: Latent Factor Decoding of Multi-channel EEG through Neural Networks – Frontiers in Neuroscience, [SCI-2, Cited by: 81]

📘 2018: Exploring EEG Features in Cross-subject Emotion Recognition – Frontiers in Neuroscience, [SCI-2, Cited by: 369]

📘 2016: Emotion Recognition from Multi-channel EEG via CNN-RNN – BIBM 2016 [CCF-B, Cited by: 320]

📘 2015: EEG-based Emotion Identification Using Deep Feature Learning – ACM SIGIR NeuroIR Workshop [CCF-A Workshop, Cited by: 92]