Radio frequency interference (RFI) detection and excision is one of the key steps in the data processing pipeline of the Five-hundred-meter Aperture Spherical radio Telescope (FAST). The FAST telescope, due to its high sensitivity and large data rate, requires more accurate and efficient RFI flagging methods than its counterparts. In the last decades, approaches based upon artificial intelligence (AI), such as codes using Convolutional Neural Network (CNN), have been proposed to identify RFI more reliably and efficiently. However, RFI flagging of FAST data with such methods has often proved to be erroneous, with further manual inspections required. In addition, network construction as well as training dataset preparation for effective RFI flagging has imposed significant additional workloads. Therefore, rapid deployment and adjustment of AI approaches for different observations is impractical to implement with existing algorithms. To overcome such problems, we propose a model named RFI-Net. With the input of raw data without any processing, RFI-Net can detect RFI automatically, producing corresponding masks without any alteration of the original data. Experiments with RFI-Net using simulated astronomical data show that our model has outperformed existing methods in terms of both precision and recall. Besides, compared with other models, our method can obtain the same relative accuracy with less training data, thus saving effort and time required to prepare the training set. Further, the training process of RFI-Net can be accelerated, with overfittings being minimised, compared with other CNN codes. The performance of RFI-Net has also been evaluated with observing data obtained by FAST and Bleien Observatory. Our results demonstrate the ability of RFI-Net to accurately identify RFI with fine-grained, high-precision masks that required no further modification.

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With the arrival of cloud computing and Big Data, many scientific applications with large amount of data can be abstracted as scientific workflows and running on a cloud environment. Distributing these datasets intelligently can decrease data transfers efficiently during the workflow's execution. In this paper, we proposed a 2-stage data placement strategy. In the initial stage, we cluster the datasets based on their correlation, and allocate these clusters onto data centers. Compared with existing works, we have incorporated the data size into correlation calculation, and have proposed a new type of data correlation for the intermediate data named "the first order conduction correlation". Hence the data transmission cost can be measured more reasonable. In the runtime stage, the re-distribution algorithm can adjust data layout according to the changed factors, and the overhead of re-layout itself has also been measured. Compared with previous work, simulation results show that our proposed strategy can effectively reduce the time consumption of data movements during the workflow execution.

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Modern power systems are continuously developing to large and interconnected ones. The power industry restructuring and the reduced investment in transmission system expansion make power systems operate closer and closer to their limits, and hence lead to larger possibility of fault outages than before. Therefore, the protection and control in power systems become more and more important as well as complicated. On the other hand, the continuous technological development in communication and measurement accelerates the occurrence and applications of wide-area protection, a kind of advanced protections based on wide-area measurements. The blackouts happened in North America as well as other countries in the past few years also provide more and more incentive for scientists and engineers in the power system circle to devote to the study on wide-area protection and control systems. In this paper, a comprehensive bibliographical survey is made on recent development in this field, and the survey is done from seven relevant aspects.