2025-02-12

The paper "Fukushima Contaminated Water Risk Factor: Global Implications" has been published in the international journal Environmental Science & Technology. 

The discharge of contaminated water from Fukushima poses a comprehensive threat to the global marine ecosystem and human health. Currently, discussions on the risks of discharging the contaminated water from Fukushima mostly lack quantitative indicators and fail to comprehensively consider various factors. To address these deficiencies, we innovatively proposed the Fukushima Contaminated Water Risk Factor (FCWRF), which integrates the risks of radionuclide diffusion, bioaccumulation, and global seafood trade to quantitatively assess the risks of discharging the contaminated water into the sea. The results show that the measurable comprehensive risk will spread to six continents around the world, and the diffusion of this comprehensive risk will be six times faster than that driven by ocean currents. FCWRF bridges the gap between different fields of radioactive nuclide risk assessment and provides support for timely and effective global response measures. Additionally, we have developed a visualization website for the dynamic database of the spatiotemporal distribution of FCWRF, named "Fukushima Risk", and made it available globally. This website visually presents the spatiotemporal evolution process of the global distribution of FCWRF and provides users with channels to select, view, and download the data they are concerned about. 

The Environmental Science & Technology Journal is a top-tier publication in both the JCR and the Chinese Academy of Sciences' Engineering Technology category 1, with an impact factor of 10.8. Professor Zhang Jianmin and I are the corresponding authors of the paper, while the first author is doctoral student Liu Yi, with Li Yilin and Min Yantao as co-authors. The research was funded by the Guangdong Provincial Basic and Applied Basic Research Foundation, the National Natural Science Foundation of China, and the Liao Shan Laboratory.

2025-01-23

Recently, I was honored to be awarded the title of "Outstanding Reviewer of 2024". I am grateful to the "Construction Technology (Chinese and English)" magazine for recognizing my work achievements. At the same time, I am deeply aware of the great responsibility and will continue to contribute to the development of the construction technology field in our country. 

Construction Technology  is a national-level professional scientific and technological journal in China, supervised by China Construction Technology Group Co., Ltd., and jointly hosted by Asia-Pacific Construction Technology Information Research Institute Co., Ltd., China State Construction Engineering Corporation Limited, and the China Civil Engineering Society. In 2023, it was selected as a high-quality scientific and technological journal in the field of architecture by the China Association for Science and Technology.

2025-01-17

"A dynamic cable dynamic response monitoring method, computer readable storage medium and program product based on deep learning" has obtained the invention patent certificate (patent number: ZL202411609188.X).

The invention discloses a dynamic cable dynamic response monitoring method based on deep learning, which collects the 6-DOF motion time history data of floating platform and the 3-DOF motion time history data of float section through the monitoring system, and combines the dynamic response data of each monitoring node along the cable length to form a training data set for different fatigue hot spots. Next, a multi-task integrated model is constructed with multiple parallel regression modules, each focused on predicting the tension or bending moment of a specific monitoring node. Each regression module is trained separately using training data sets for different fatigue hotspots and integrated to form a complete multi-task integration model. Finally, the real-time motion history data is input into the model to predict the tension and bending moment of each monitoring node, and realize the dynamic response monitoring of the dynamic cable. The invention realizes the health monitoring of the dynamic response in the dynamic cable operation and maintenance stage, reduces the monitoring cost, and improves the economic benefit of the floating wind farm.

Note: The inventors of this patent also include Associate Professor Li Binbin and master student Liu Jin.

2025-01-15

Recently, China National Knowledge Infrastructure (CNKI) released the list of highly cited scholars for 2024, and I was selected as one of the "Top 1% Highly Cited Scholars of CNKI in 2024".

The "2024 CNKI Highly Cited Researchers Top 1%" is the first quantitative assessment of scholars' academic influence conducted by the China Science Literature Metrology and Evaluation Research Center of CNKI based on the domestic academic papers and conference papers included in CNKI in 2024. It selected the list of "2024 CNKI Highly Cited Researchers" to promote the domestic first publication of outstanding academic achievements. This selection activity aims to objectively and scientifically measure and honor the group of scholars with rich research achievements and outstanding academic influence in China over the past decade.

2025-01-02

The paper "Geometrized Task Scheduling and Adaptive Resource Allocation for Large-Scale Edge Computing in Smart Cities" has been published in the IEEE Internet of Things Journal. 

Edge computing is vital in developing smart cities by providing on-site computational resources to support the surging Internet of Things demands. However, the distributed nature of edge nodes and large scale of tasks distributed in expansive urban spaces challenge task scheduling and resource allocation. In this paper, a novel framework is developed to achieve efficient task scheduling (assignment and offloading) and resource allocation for large-scale edge computing in both wired and wireless smart-city applications. To overcome overparameterization in existing optimization-based heuristic algorithms, the geometrized task scheduling problem is addressed by transforming the assignment of clustered tasks into a regional partition problem in a two-dimensional graph and applying a Tetris-like task offloading strategy for edge-cloud cooperation. These approaches avoid combinatorial explosion and NP-hardness, and the regional partition problem is solved by multiplicative weighted Voronoi diagrams with polynomial computational complexity. Furthermore, an adaptive resource allocation algorithm is proposed to overcome the dynamic, uncertain, and highly concurrent task requests. An online learning algorithm is adopted to adjust the sliding window length according to the evolving conditions. Comparison results show that the proposed framework significantly reduces the average task deadline violation rate, i.e., up to 4.72% of (more than 20 times better than) those using the other schemes, especially when handling large-scale workloads.

Note: The IEEE Internet of Things Journal is a JCR first-tier and Chinese Academy of Sciences first-tier TOP journal with an impact factor of 8.2. The first author of the paper is Chen Yang, a master's student of the 2022 grade. The research results were funded by National Key R&D Program of China.