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The thermal medium oil expansion tank of an offshore platform was corroded and perforated. The cause of the perforation was analyzed through water sample analysis, tank opening inspection and other measures. The results showed that the main cause of the corrosion was the shedding of the oxide scale on the inner wall of the expansion tank head, which was immersed in fresh water, resulting in anaerobic microbial corrosion in the lower part. Maintenance suggestions, daily operation suggestions and new project suggestions, etc. were put forward to solve the corrosion problem of the existing expansion tank and avoid similar problems in new projects. [ABSTRACT FROM AUTHOR]

First-principles calculations are performed, revealing a significant enhancement of the piezoelectric properties of wurtzite Zn 3 6 O 3 6 upon the incorporation of a single Cu atom. Research has demonstrated that the piezoelectric constant d 3 3 reaches its maximum value at a doping concentration of 1.4% for Cu atoms. The lattice parameters a and c of Zn 3 6 O 3 6 are decreased and the piezoelectric strain coefficient d 3 3 is increased by replacing one Cu atom in Zn 3 6 O 3 6 . It is found that elastic softening is the primary factor responsible for the increase of d 3 3 in Zn 3 5 Cu1O 3 6 . By differential charge density analysis, it is found that the covalency between Cu–O bonds is lower than that of Zn–O bonds, and the covalent bonding characteristics are weakened. Bader charge analysis shows that the charge of Cu is higher than that of Zn, indicating a more significant ionic bonding feature than that of Zn. Thus, a weaker covalent and stronger ionic bond are considered to play an essential role in promoting elastic softening for ZnO, which eventually promotes a significant enhancement in piezoelectric properties. [ABSTRACT FROM AUTHOR]

The growing popularity of personalized medicine motivates people to explore individualized treatment regimes according to heterogeneous characteristics of the patients. For the large-scale data analysis, however, the data are collected at different times and different locations, i.e. subjects are usually from a heterogeneous population, which causes that the optimal treatment regimes also vary for patients across different subgroups. In this paper, we mainly focus on the estimation of optimal treatment regimes for subjects come from a heterogeneous population with high-dimensional data. We first remove the main effects of the covariates for each subgroup to eliminate non-ignorable residual confounding. Based on the centralized outcome, we propose a penalized robust learning that estimates the coefficient matrix of the interactions between covariates and treatment by penalizing pairwise differences of the coefficients of any two subgroups for the same covariate, which can automatically identify the latent complex structure of the coefficient matrix with heterogeneous and homogeneous columns. At the same time, the penalized robust learning can also select the important variables that truly contribute to the individualized treatment decisions with commonly used sparsity structure penalty. Extensive simulation studies show that our proposed method outperforms current popular methods, and it is further illustrated in the real analysis of the Tamoxifen breast cancer data. [ABSTRACT FROM AUTHOR]

To explore the effects of the compound fungicide on the microbial life cycle, the improvement of the corrosion inhibition rate and sterilization rate of D-amino acid compound fungicides on carbon steel in different periods was studied using the mixed microflora composed of SRB and IOB produced from the oil and gas field through the weightlessness experiment, electrochemical test, surface analysis technology and other research methods. The results showed that the corrosion rate and sterilization efficiency were improved when D-amino acid compound fungicide was adding in different periods. The function of D-amino acid compound fungicide was optimal, when it was added on the third day. D-amino acid could cause the surface of the bacteria hydrophobic, which affects the material transmission between bacteria. D-amino acid could break up the biofilm and inhibit the growth of biofilm. Microorganisms could multiply rapidly on steel surfaces over time and reach an active peak period of life activity in seven days,during which H2S production will accelerate steel corrosion. [ABSTRACT FROM AUTHOR]

Dynamic treatment regimes are a set of decision rules and each treatment decision is tailored over time according to patients' responses to previous treatments as well as covariate history. There is a growing interest in development of correct statistical inference for optimal dynamic treatment regimes to handle the challenges of nonregularity problems in the presence of nonrespondents who have zero-treatment effects, especially when the dimension of the tailoring variables is high. In this article, we propose a high-dimensional Q-learning (HQ-learning) to facilitate the inference of optimal values and parameters. The proposed method allows us to simultaneously estimate the optimal dynamic treatment regimes and select the important variables that truly contribute to the individual reward. At the same time, hard thresholding is introduced in the method to eliminate the effects of the nonrespondents. The asymptotic properties for the parameter estimators as well as the estimated optimal value function are then established by adjusting the bias due to thresholding. Both simulation studies and real data analysis demonstrate satisfactory performance for obtaining the proper inference for the value function for the optimal dynamic treatment regimes. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]