Chuanxi Liu | Cross-disciplinary Synthesis | Cross-disciplinary Excellence Award

Chuanxi Liu | Cross-disciplinary Synthesis | Cross-disciplinary Excellence Award

Mr Chuanxi Liu, State Nuclear Power Information Technology Company.LTD, China

Chuanxi Liu is a prolific researcher with a strong background in astrophysics and artificial intelligence. He has co-authored pivotal papers on Gamma-Ray Burst (GRB) X-Ray flare properties and statistical studies of Swift X-Ray Flash and X-Ray Rich GRBs. Liu’s contributions to electric power technology include developing neural network algorithms for waveform data processing and using image processing techniques for fault detection. He has also innovated in wind power inspection with advancements in oil leakage detection and crack recognition. His notable achievements include a patent on traveling wave ranging and success in the Shandong Provincial AI Competition. πŸ“‘πŸ›°οΈπŸ’‘πŸ’»πŸ”

Publication profile



I earned my BSc in Applied Physics from Shandong Jianzhu University, studying from September 2011 to June 2015. During my time there, I took a variety of courses including Thermodynamics, Mechanics, Optics, Electromagnetism, and Quantum Mechanics πŸ“š. My coursework also covered Higher Mathematics, Linear Algebra, Statistics and Probability Theory, Theoretical Mechanics, Machining Drawing, and Fundamentals of Digital and Analog Circuit Technology πŸ“. Following this, I pursued an MSc in Astrophysics at the University of Chinese Academy of Sciences from September 2015 to June 2019. My graduate studies included courses such as Image Processing, Semiconductor Device Physics, and Astronomical Data Processing 🌌. I also gained expertise in Numerical Simulation Methods for Magnetohydrodynamics and used Linux systems and the Astronomy Software Package IDL for my research πŸ–₯️.


From September 2015 to June 2019, I pursued a Master of Science at Yunnan Observatory, where I conducted extensive research on gamma-ray bursts. My work involved analyzing their spectra and light to validate astrophysical models. During this period, I co-authored several papers, including “GRB X-Ray Flare Properties among Different GRB Subclasses” published in the Astrophysical Journal in 2019 πŸ“š, and “Statistical Study of the Swift X-Ray Flash and X-Ray Rich Gamma-Ray Bursts” in 2018 🌟. Additionally, I contributed to the “Research Progress of GRB X-Ray Flare” in Progress in Astronomy in 2020 🌌.

Research focus

Liu, C., involved in multiple research areas, appears to have a diverse focus. In the field of object detection, Liu contributes to enhancing detection techniques through the development of the TBFF-DAC model, which leverages deformable attention and convolution for improved feature fusion πŸ€–. Additionally, Liu is engaged in electrical engineering, specifically in fault location methods for railway systems, utilizing non-contact measurements πŸš„. In the realm of astrophysics, Liu explores the properties of gamma-ray bursts and X-ray flares, analyzing their characteristics across different subclasses 🌌. This multidisciplinary approach underscores Liu’s expertise in both technological advancements and astrophysical phenomena.

Publication top notes

TBFF-DAC: Two-branch feature fusion based on deformable attention and convolution for object detection

Research on Traveling Wave Fault Location Method of Railway Automatic Blocking/Power Continuous Line Based on Noncontact Measurement

GRB X-Ray Flare Properties among Different GRB Subclasses

Statistical Study of the Swift X-Ray Flash and X-Ray Rich Gamma-Ray Bursts

Cross-disciplinary Excellence Award

Cross-disciplinary Excellence Award


Welcome to the Cross-disciplinary Excellence Award, honoring individuals who bridge the boundaries between disciplines to foster innovation, collaboration, and transformative breakthroughs. This prestigious award celebrates the power of interdisciplinary thinking in addressing complex challenges and driving progress across multiple fields.

Award Overview:

The Cross-disciplinary Excellence Award recognizes individuals who demonstrate outstanding achievements in integrating knowledge, methodologies, and perspectives from diverse disciplines to advance research, scholarship, and societal impact.


  • Open to researchers, scholars, and professionals across all disciplines
  • No age limits apply
  • Qualification: Demonstrated excellence in cross-disciplinary collaboration and innovation
  • Publications: Evidence of interdisciplinary research contributions in peer-reviewed journals or other scholarly outlets
  • Recurrements: Continued commitment to cross-disciplinary exploration and collaboration

Evaluation Criteria:

Candidates will be evaluated based on the following criteria:

  1. Depth and breadth of cross-disciplinary contributions
  2. Impact and significance of interdisciplinary research outcomes
  3. Creativity and innovation in integrating diverse perspectives and methodologies
  4. Potential for future cross-disciplinary collaboration and impact

Submission Guidelines:

  • Submit a comprehensive biography highlighting cross-disciplinary achievements and collaborations
  • Include an abstract summarizing key cross-disciplinary contributions and their significance
  • Provide supporting files such as publications, collaborative projects, or interdisciplinary initiatives


Recipients of the Cross-disciplinary Excellence Award will receive:

  • A prestigious award certificate
  • Recognition at a special ceremony or event
  • Opportunities for networking and collaboration with cross-disciplinary experts and organizations

Community Impact:

Winners of the award are expected to serve as ambassadors for cross-disciplinary collaboration, inspiring others to embrace diverse perspectives and approaches in addressing complex challenges and driving innovation.


A detailed biography outlining the candidate's cross-disciplinary background, collaborative projects, and contributions to interdisciplinary research is required.

Abstract and Supporting Files:

Candidates must submit an abstract summarizing their cross-disciplinary contributions along with supporting files such as publications, collaborative projects, or interdisciplinary initiatives to showcase the depth and breadth of their work.