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This paper presents a family of novel common-ground-type transformerless photovoltaic (PV) grid-connected inverters, which requires only five power switches, one capacitor, and one filter. A simple dual-closed led-loop control is used to improve control stabilization and accuracy. The main advantages of proposed inverters are: 1) the leakage current is completely eliminated (unlike traditional topologies, which can only suppress leakage current); 2) the devices used are a few and the cost is low; 3) low loss and high efficiency; 4) the ability of realizing reactive power; and 5) there is no need for high DC input voltage compared with half-bridge-type topologies. The operating principle, modulation mode, and control strategy are introduced in detail. The performance of the proposed topology is compared with that of several traditional topologies. The leakage current suppression ability and efficiency of the proposed topology are superior to those of the traditional topologies. The model predictive control (MPC) is applied in the proposed topology, which is easy to realize and can accelerate the dynamic response. Finally, the simulation and experimental results of a 1-kVA prototype are given, which proves the validity of the proposed topology in PV grid-connected system.INDEX TERMS Photovoltaic power generation, transformerless, grid-connected inverter, leakage current, model predictive control.

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Objective: To evaluate the association of Glutathione S-transferase (GST) M1 and T1 genetic polymorphisms and susceptibility to nasopharyngeal carcinoma (NPC) in a high risk area of Guangxi Zhuang Autonomous Region (province), Southwest of China. Methods: A case-control study was conducted to investigate the genetic polymorphisms of these enzymes (GSTM1 and GSTT1 null genotypes). A total of 127 NPC cases and 207 controls were recruited. Results: GSTM1 and GSTT1 null genotype frequencies were higher among NPC patients at a level of statistical significance (P <0.005; P <0.001 respectively), and both GSTM1 and GSTT1 null genotype were even more significant (P <0.001). Conclusion: NPC is the most common cancer in Guangxi. GST enzymes are involved in the detoxification of many environmental carcinogens. Homozygous deletions of GSTM1 and GSTT1 have been associated with several types of cancer. The risk to develop NPC has been associated with environmental factors such as cigarette smoking and EB virus infection. The present results indicate that the GSTM1 and GSTT1 deletion polymorphisms are associated with an increase risk of susceptibility to NPC, and both detoxific enzyme genes deletion is more important than a single gene deletion for the susceptibility to NPC.

Article Highlights• A systematic soft sensor modeling method based on GPR and PCA is proposed • The variance of the predicted output was designed on the output uncertainty of the GPR model • Practical applications show the superiority of the proposed soft sensor method Abstract Erythromycin fermentation is a typical microbial fermentation process. Soft sensors can be used to estimate the biomass of Erythromycin fermentation process due to their relative low cost, simple development, and ability to predict difficult-to-measure variables. However, traditional soft sensors, e.g. artificial neural network (ANN) soft sensors, support vector machine (SVM) soft sensors, etc., cannot represent the uncertainty (measurement precision) of outputs, which results in difficulties in practice. Gaussian process regression (GPR) provides a novel framework to solve regression problems. The output uncertainty of a GPR model follows Gaussian distribution, expressed in terms of mean and variance. The mean represents the predicted output. The variance can be viewed as the measure of confidence in the predicted output that distinguishes the GPR from NN and SVM soft sensor models. We propose a systematic approach based on GPR and principal component analysis (PCA) to establish a soft sensor to estimate biomass of erythromycin fermentation process. Simulations on industrial data from an erythromycin fermentation process show the proposed GPR soft sensor has high performance of modeling the uncertainty of estimates.