Role of trace additions of Ca and Sn in improving the corrosion resistance of Mg-3Al-1Zn alloy

The limited wide applicability of commercial Mg alloys is mainly attributed to the poor corrosion resistance.Addition of alloying elements is the simplest and effective method to improve the corrosion properties.Based on the low-cost alloy composition design,the corro-sion behavior of commercial Mg-3Al-1Zn(AZ31)alloy bearing minor Ca or Sn element was characterized by scanning Kelvin probe force microscopy,hydrogen evolution,electrochemical measurements,and corrosion morphology analysis.Results revealed that the potential differ-ence of Al_(2)Ca/α-Mg and Mg_(2)Sn/α-Mg was(230±19)mV and(80±6)mV,respectively,much lower than that of Al_(8)Mn_(5)/α-Mg(430±31)mV in AZ31 alloy,which illustrated that AZ31-0.2Sn alloy performed the best corrosion resistance,followed by AZ31-0.2Ca,while AZ31 al-loy exhibited the worst corrosion resistance.Moreover,Sn dissolved into matrix obviously increased the potential ofα-Mg and participated in the formation of dense SnO_(2) film at the interface of matrix,while Ca element was enriched in the corrosion product layer,resulting in the cor-rosion product layer of AZ31-0.2Ca/Sn alloys more compact,stable,and protective than AZ31 alloy.Therefore,AZ31 alloy bearing 0.2wt%Ca or Sn element exhibited excellent balanced properties,which is potential to be applied in commercial more comprehensively.

Protein regulation mechanism of cold tolerance in Haemaphysalis longicornis

Ticks are external parasitic arthropods that can transmit a variety of pathogens by sucking blood.Low-temperature tolerance is essential for ticks to survive during the cold winter.Exploring the protein regulation mechanism of low-temperature tolerance of Haemaphysalis longicornis could help to explain how ticks survive in winter.In this study,the quantitative proteomics of several tissues of H.longicornis exposed to low temperature were studied by data independent acquisition technology.Totals of 3699,3422,and 1958 proteins were identified in the salivary gland,midgut,and ovary,respectively.The proteins involved in energy metabolism,cell signal transduction,protein synthesis and repair,and cytoskeleton synthesis changed under low-temperature stress.The comprehensive analysis of the protein regulation of multiple tissues of female ticks exposed to low temperature showed that maintaining cell homeostasis,maintaining cell viability,and enhancing cell tolerance were the most important means for ticks to maintain vital signs under low temperature.The expression of proteins involved in and regulating the above cell activities was the key to the survival of ticks under low temperatures.Through the analysis of a large amount of data,we found that the expression levels of arylamine N-acetyltransferase,inositol polyphosphate multikinase,and dual-specificity phosphatase were up-regulated under low temperature.We speculated that they might have important significance in low-temperature tolerance.Then,we performed RNA interference on the mRNA of these 3 proteins,and the results showed that the ability of female ticks to tolerate low temperatures decreased significantly.

ECGID:a human identification method based on adaptive particle swarm optimization and the bidirectional LSTM model

Physiological signal based biometric analysis has recently attracted attention as a means of meeting increasing privacy and security requirements.The real-time nature of an electrocardiogram(ECG)and the hidden nature of the information make it highly resistant to attacks.This paper focuses on three major bottlenecks of existing deep learning driven approaches:the lengthy time requirements for optimizing the hyperparameters,the slow and computationally intense identification process,and the unstable and complicated nature of ECG acquisition.We present a novel deep neural network framework for learning human identification feature representations directly from ECG time series.The proposed framework integrates deep bidirectional long short-term memory(BLSTM)and adaptive particle swarm optimization(APSO).The overall approach not only avoids the inefficient and experience-dependent search for hyperparameters,but also fully exploits the spatial information of ordinal local features and the memory characteristics of a recognition algorithm.The effectiveness of the proposed approach is thoroughly evaluated in two ECG datasets,using two protocols,simulating the influence of electrode placement and acquisition sessions in identification.Comparing four recurrent neural network structures and four classical machine learning and deep learning algorithms,we prove the superiority of the proposed algorithm in minimizing overfitting and self-learning of time series.The experimental results demonstrated an average identification rate of 97.71%,99.41%,and 98.89% in training,validation,and test sets,respectively.Thus,this study proves that the application of APSO and LSTM techniques to biometric human identification can achieve a lower algorithm engineering effort and higher capacity for generalization.