Automatic defect detection in electroluminescence (EL) images of photovoltaic (PV) modules in production line remains as a challenge to replace time‐consuming and expensive human inspection and improve capacity. This paper presents a deep learning‐based automatic detection of multitype defects to fulfill inspection requirements of production line. At first, a database composed of 5983 labeled EL images of defective PV modules is built, and 19 types of identified defects are introduced. Next, a convolutional neural network is trained on top‐14 defects, and the best model is selected and tested, achieving 70.2% mAP50 (mean average precision with at least 50% localization accuracy). Then, through analyzing an object detection‐based confusion matrix, recognition bias and detection compensation in missed defects that restrain the best model's mAP50 are discovered to be harmless to normal/defective module classification in real production line. Finally, after setting specific screen criteria for different types of defects, normal/defective module classification is conducted on additionally collected 4791 EL images of PV modules on 3 days, and the best model achieves balanced scores of 95.1%, 96.0%, and 97.3%, respectively. As a result, this method surely has a highly promising potential to be adopted in real production line.

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To face the rapid growth of DNA sequencing data, it is of great importance to study high efficiency compression techniques to reduce the cost of storing the massive amount of sequencing data. In this paper, we propose a parallel DNA data compressor/decompressor, PLD-SRC, based on the famous serial DSRC software. We first analyze the compression and decompression algorithm in DSRC and identity three basic operations, namely read, work, and write. Then a single pipeline parallel algorithm is proposed to accelerate the compression/decompression procedure. To further exploit today's popular multi-core, multi-socket systems based on the non-uniform memory access (NUMA) architecture, we extend the single pipeline approach to the multi-pipeline case. Experiments on two different platforms are done and show that PLDSRC in both single and multiple pipeline forms is able to speed up DNA sequencing data compression/decompression greatly, while maintaining the same compressing ratio. Examples indicate that the maximum speedup of PLDSRC on compressing and decompressing is respectively around 24.71x and 22.00x, as compared to the serial DSRC software.

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Large-scale Protein-Protein interaction data sets exist in Saccharomyces cerevisiae due to many interaction detection methods such as yeast two-hybrid assay, mass spectrometry of purified complexes, correlated mRNA expression profile and so on. How to make use of these data sets to understand the protein function is very important. We use the algorithm [17] developed by Stijn van Dongen to describe the functional modules in PPI networks.We analyze four protein-protein networks from Saccharomyces cerevisiae, and our results suggest that the functional modules detected are consistent with the biology knowledge. Protein-Protein interaction network was separated into clusters using MCL algorithm. Based on the clusters resulted from MCL algorithm, we assign the function annotations using Pvalue and majority methods. The majority method is based on the majority rule [15]. The predicted function of proteins provide clue to biology experiments. Two methods are used to assign function annotations for the known clusters and unknown proteins, we compare the two predicted results, the results show that the two methods are consistent with each other.

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