Application of Acoustic Emission Technology in Monitoring Corrosion Induced Expansion Cracks of Reinforced Concrete

Acoustic emission (AE) technology is used to monitor the whole process of reinforced concrete rust expanding and cracking, and the accuracy of AE technology to distinguish the location and type of cracks in concrete rust expanding and cracking is studied and verified. The results show that the protective layer is accompanied by a large number of acoustic emission signals in the process of rust expansion and cracking, Moment tensor inversion can more accurately identify crack sources such as shear source, tensile source and mixed source; In the process of corrosion-induced expansion cracking, shear source cracks appear before tensile source cracks and mixed source cracks. It is feasible to use acoustic emission technology to locate and identify cracks

Estimation Method of Path-Selecting Proportion for Urban Rail Transit Based on AFC Data

With the successful application of automatic fare collection (AFC) system in urban rail transit (URT), the information of passengers’ travel time is recorded, which provides the possibility to analyze passengers’ path-selecting by AFC data. In this paper, the distribution characteristics of the components of travel time were analyzed, and an estimation method of path-selecting proportion was proposed. This method made use of single path ODs’ travel time data from AFC system to estimate the distribution parameters of the components of travel time, mainly including entry walking time (ewt), exit walking time (exwt), and transfer walking time (twt). Then, for multipath ODs, the distribution of each path’s travel time could be calculated under the condition of its components’ distributions known. After that, each path’s path-selecting proportion can be estimated. Finally, simulation experiments were designed to verify the estimation method, and the results show that the error rate is less than 2%. Compared with the traditional models of flow assignment, the estimation method can reduce the cost of artificial survey significantly and provide a new way to calculate the path-selecting proportion for URT

Screening, identification and degrading gene assignment of a chrysene-degrading strain

A predominant chrysene-degrading strain named CT was isolated from the activated sludge of Zhenjiang coking plant. The strain was initially identified as Paracoccus aminovorans by the results of morphological observation, physio-biochemical test and 16S rDNA gene sequence analysis. Under the conditions of initial chrysene concentration of 40 mg/l, inoculation amount of 10% (V/V) at pH 7.0 and temperature of 35°C, the degradation efficiency of chrysene by the strain CT reached 85.2% within 8 days. Alkaline lysis was applied to the extract plasmids from strain CT to confirm the location of chrysene-degrading genes. A plasmid, greater than 15 kb, was detected. The transformants obtained the ability to degrade chrysene when the plasmid of strain CT was transformed to competent cell of Escherichia coli DH10B, and could remove 43% of chrysene in the solutions with concentration of 30 mg/l within 8 days. But the mutation lost the ability to degrade chrysene when its plasmid was eliminated by sodium dodecyl sulfonate (SDS) and high temperature. This indicated that the plasmid of strain CT carried chrysene-degrading genes.Key words: Chrysene, degrading strain, Paracoccus, degrading gene,  plasmid

The large terminase DNA packaging motor grips DNA with its ATPase domain for cleavage by the flexible nuclease domain

Many viruses use a powerful terminase motor to pump their genome inside an empty procapsid shell during virus maturation. The large terminase (TerL) protein contains both enzymatic activities necessary for packaging in such viruses: the adenosine triphosphatase (ATPase) that powers DNA translocation and an endonuclease that cleaves the concatemeric genome at both initiation and completion of genome packaging. However, how TerL binds DNA during translocation and cleavage remains mysterious. Here we investigate DNA binding and cleavage using TerL from the thermophilic phage P74-26. We report the structure of the P74-26 TerL nuclease domain, which allows us to model DNA binding in the nuclease active site. We screened a large panel of TerL variants for defects in binding and DNA cleavage, revealing that the ATPase domain is the primary site for DNA binding, and is required for nuclease activity. The nuclease domain is dispensable for DNA binding but residues lining the active site guide DNA for cleavage. Kinetic analysis of DNA cleavage suggests flexible tethering of the nuclease domains during DNA cleavage. We propose that interactions with the procapsid during DNA translocation conformationally restrict the nuclease domain, inhibiting cleavage; TerL release from the capsid upon completion of packaging unlocks the nuclease domains to cleave DNA

Self-supervised Representations and Node Embedding Graph Neural Networks for Accurate and Multi-scale Analysis of Materials

Supervised machine learning algorithms, such as graph neural networks (GNN), have successfully predicted material properties. However, the superior performance of GNN usually relies on end-to-end learning on large material datasets, which may lose the physical insight of multi-scale information about materials. And the process of labeling data consumes many resources and inevitably introduces errors, which constrains the accuracy of prediction. We propose to train the GNN model by self-supervised learning on the node and edge information of the crystal graph. Compared with the popular manually constructed material descriptors, the self-supervised atomic representation can reach better prediction performance on material properties. Furthermore, it may provide physical insights by tuning the range information. Applying the self-supervised atomic representation on the magnetic moment datasets, we show how they extract rules and information from the magnetic materials. To incorporate rich physical information into the GNN model, we develop the node-embedding graph neural networks (NEGNN) framework and show significant improvements in the prediction performance. The self-supervised material representation and the NEGNN framework may investigate in-depth information from materials and can be applied to small datasets with increased prediction accuracy.Comment: 13 pages, 9 figures, 7 table