Abdul Majeed | Material Sciences | Best Researcher Award

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Dr. Abdul Majeed | Material Sciences | Best Researcher Award

Dr. Abdul Majeed is a dynamic educator, researcher, and science communicator with 6 years of teaching and 11 years of research experience in materials science, blending experimental and theoretical expertise in nanomaterials, complex oxides, and magnetic materials. As an Assistant Professor at The Islamia University of Bahawalpur, he is dedicated to mentoring students and delivering engaging, hands-on physics instruction. His exceptional communication, organizational, and multi-tasking skills complement his academic contributions, fostering a collaborative learning environment and promoting scientific inquiry. Known for simplifying complex scientific concepts, Dr. Majeed is committed to knowledge dissemination, community engagement, and pursuing innovative research for societal benefit.

Profile

Education šŸŽ“

Dr. Majeed earned his Ph.D. in Physics from the University of Malakand, focusing on the effects of cation substitution in hexagonal nanoferrites, preceded by an M.Phil. in Physics from The Islamia University of Bahawalpur on rare-earth doped nanocrystallites, and a BS in Physics from Islamia College University, Peshawar. His education encompasses solid theoretical foundations in materials science, magnetism, quantum mechanics, and computational physics. He is proficient in synthesis techniques like sol-gel auto-combustion, micro-emulsion, and ultrasound-assisted methods, and skilled in material characterization techniques including XRD, SEM, TEM, VSM, UV-Vis, FTIR, and impedance analysis. Dr. Majeed also completed a diploma in English Language, enhancing his academic and professional communication

Experience šŸ‘Øā€šŸ«

Dr. Majeed is currently serving as an Assistant Professor in the Department of Physics at The Islamia University of Bahawalpur since March 2021, teaching undergraduate and MPhil-level physics courses, supervising research students, and coordinating academic and student affairs. Previously, he taught Physics and Mathematics at the Government Higher Secondary School Rehanpur, where he also held the role of Chief Proctor. He has over 11 years of research experience in materials science, particularly in synthesizing and characterizing complex oxide nanomaterials. He actively contributes to administrative duties including BS program coordination, LMS management, and student affairs leadership, reflecting his multifaceted involvement in academia and institutional development.

Awards & Recognitions šŸ…

Dr. Abdul Majeed has received recognition for his impactful research through multiple publications in reputed journals such as Ceramics International, Journal of Alloys and Compounds, and Materials Science in Semiconductor Processing. His work is widely cited in the field of microwave absorption materials and magnetic oxides. He actively collaborates with international scholars and contributes to high-impact studies, particularly in experimental and computational material sciences. His academic excellence is reflected in his outstanding academic grades (CGPA above 3.5 in all degrees) and his leadership in teaching, coordination, and student mentorship roles. His visibility on platforms like Google Scholar and ResearchGate underscores his scholarly influence and engagement.

Research Interests šŸ”¬

Dr. Abdul Majeedā€™s research centers on the synthesis, characterization, and modeling of nanostructured materials, including hexagonal ferrites, multiferroics, dielectric materials, and graphene-ferrite composites, for applications in high-frequency electronics, energy storage, and microwave absorption. His work bridges experimental physics with computational simulations, including DFT-based studies using WIEN2k, exploring the electronic, optical, magnetic, and structural behavior of advanced materials. He specializes in sol-gel, co-precipitation, and ultrasound-assisted synthesis methods and uses a variety of advanced characterization tools. His current projects explore co-substituted magnetic oxides, rare-earth doping effects, and smart materials for energy and sensor technologies.

Publications

Lichen Shi | Mechanical Engineering | Best Researcher Award

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Prof. Lichen Shi | Mechanical Engineering | Best Researcher Award

 

Profile

Education

Lichen Shi (also written as Shi Lichen) is a distinguished Chinese researcher specializing in intelligent measurement, equipment status monitoring, fault diagnosis, and electromechanical system modeling. He was born on June 28, 1972, and is currently affiliated with the School of Mechanical and Electrical Engineering at Xiā€™an University of Architecture and Technology (XAUAT), China.

With a strong academic and research background, Professor Shi has dedicated his career to advancing intelligent measurement techniques through deep learning, as well as improving the reliability of electromechanical systems through fault diagnosis and dynamic analysis.

Academic Contributions

Professor Shi has published extensively in prestigious international journals, particularly in IEEE Sensors Journal, Measurement, and Computer Engineering & Applications. His notable works focus on deep learning-based fault diagnosis, graph neural networks, and AI-driven predictive modeling for mechanical systems.

Some of his key contributions include:

  • Developing an AI-based method for reading pointer meters using human-like reading sequences.
  • Proposing a graph neural network and Markov transform fields approach for gearbox fault diagnosis.
  • Introducing CBAM-ResNet-GCN methods for unbalance fault detection in rotating machinery.
  • Advancing domain transfer learning techniques for mixed-data gearbox fault diagnosis.
  • Pioneering a lightweight low-light object detection algorithm (CDD-YOLO) for enhanced industrial applications.

His research findings have contributed significantly to the optimization of industrial machinery, predictive maintenance, and AI-driven automation in electromechanical systems. Many of his publications are frequently cited, underlining their impact on the field.

Research Interests

Professor Shi’s research spans multiple cutting-edge areas, including:

  • Intelligent Measurement with Deep Learning
  • Equipment Status Monitoring and Fault Diagnosis
  • Electromechanical System Modeling and Dynamic Analysis

Professional Impact

As a leading expert in intelligent diagnostics and mechanical system optimization, Professor Shi has played a crucial role in bridging the gap between artificial intelligence and industrial engineering. His contributions have aided in the development of more efficient, predictive, and adaptive electromechanical systems, helping industries reduce downtime and improve operational efficiency.

Publication

  • [1] Qi Liu, Lichen Shi*. A pointer meter reading method based on human-like readingsequence and keypoint detection[J]. Measurement, 2025(248): 116994. https://doi.org/10.1016/j.measurement.2025.116994
  • [2] Haitao Wang, Zelin. Liu, Mingjun Li, Xiyang Dai, Ruihua Wang and LichenShi*. AGearbox Fault Diagnosis Method Based on Graph Neural Networks and MarkovTransform Fields[J]. IEEE Sensors Journal, 2024, 24(15) ļ¼š25186-25196. doi:
    10.1109/JSEN.2024.3417231
  • [3] Haitao Wang, Xiyang Dai, Lichen Shi*, Mingjun Li, Zelin Liu, Ruihua Wang , XiaohuaXia. Data-Augmentation Based CBAM-ResNet-GCN Method for Unbalance Fault
    Diagnosis of Rotating Machinery[J]. IEEE Sensors Journal, 2024,12:34785-34799. DOI:
    10.1109/access.2024.3368755.
  • 4] Haitao Wang, Mingjun Li, Zelin Liu, Xiyang Dai, Ruihua Wang and Lichen Shi*. RotaryMachinery Fault Diagnosis Based on Split Attention MechanismandGraphConvolutional Domain Adaptive Adversarial Network[J]. IEEE Sensors Journal, 2024,
    24(4) :5399-5413. doi: 10.1109/JSEN.2023.3348597.
  • [5] Haitao Wang, Xiyang Dai, Lichen Shi*. Gearbox Fault Diagnosis Based onMixedData-Assisted Multi-Source Domain Transfer Learning under Unbalanced Data[J]. IEEESensors Journal. doi: 10.1109/JSEN.2024.3477929