Rapid Microstructure Homogenization of a Laser Melting Deposition Additive Manufactured Ti-6.5Al-3.5Mo-1.5Zr-0.3Si Alloy by Electropulsing.

The typical microstructure of the laser melting deposition (LMD) additive-manufactured Ti-6.5Al-3.5Mo-1.5Zr-0.3Si alloy (TC11) contains the heat-affected bands (HABs), the narrow bands (NBs) and the melting pools (MPs) that formed due to the reheating and superheating effects during the layer-by-layer manufacturing process. Characterization results indicated that the coarse primary α lath (αp) and transformed ß (ßt) structures were located in the HABs, while the fine basketweave structure was formed inside the MPs. The rapid modifications of microstructure and tensile properties of the LMD-TC11 via electropulsing treatment (EPT) were investigated. The initial heterogeneous microstructure transformed into a complete basketweave structure and the HABs vanished after EPT. Thus, a more homogeneous microstructure was achieved in the EPT sample. The ultrafast microstructural changes were mainly attributed to the solid state phase transformation during electropulsing. The tensile properties of the sample were basically stable, except that the yield strength decreased as EPT voltage increased. This study suggests that EPT could be a promising method to modify the microstructure and mechanical properties of the additive-manufactured alloys in a very short time.

Arsenic speciation transformation in soils with high geological background: New insights from the governing role of Fe.

In soils, the speciation transformation of As were inherently related to the behaviors of iron (oxyhydr) oxides. It is poorly understood that the effects of the transformation of iron (oxyhydr) oxides coupled with As speciation transformation during dissimilatory Fe(III) reduction (DIR) involving with humic substances (HS) as electron donor or shuttle in soils with high arsenic geological background. In this study, the relationships between the transformation of iron (oxyhydr)oxides and As speciation transformation were investigated according to the response between continuously As speciation monitoring and iron (oxyhydr) oxides identification during DIR in the soils. The results showed that F4 (arsenic incorporated with amorphous iron (oxyhydr)oxides including ferrihydrite and schwertmannite) and F5 (arsenic incorporated with crystalline iron (oxyhydr)oxides including hematite and magnetite) were the main source and sink for As(III)Dissolved during DIR. During the incubation period, Fe(II) was the dominant driving force for the reduction of As(V) in the water-soil system. The XRD analysis indicated the changes of iron oxides such as ferrihydrite, schwertmannite, hematite and magnetite were closely related to the release and reduction of As, and those iron oxides could play governing roles for As speciation transformation during DIR in soils. Different from the known mechanism in low As concentrations, a limiting effect of As concentration on iron oxides transformation was found in our incubation experiments using soils with high As geological background (∼1000 mg/kg). This work provides new insights for Fe as governing role in As speciation transformation in soils with high arsenic geological background by firstly identifying the corresponding iron (oxyhydr)oxides in operationally defined arsenic speciation incorporated with iron oxides.

SAFS: Object Tracking Algorithm Based on Self-Adaptive Feature Selection.

Object tracking is one of the most challenging problems in the field of computer vision. In challenging object tracking scenarios such as illumination variation, occlusion, motion blur and fast motion, existing algorithms can present decreased performances. To make better use of the various features of the image, we propose an object tracking method based on the self-adaptive feature selection (SAFS) algorithm, which can select the most distinguishable feature sub-template to guide the tracking task. The similarity of each feature sub-template can be calculated by the histogram of the features. Then, the distinguishability of the feature sub-template can be measured by their similarity matrix based on the maximum a posteriori (MAP). The selection task of the feature sub-template is transformed into the classification task between feature vectors by the above process and adopt modified Jeffreys' entropy as the discriminant metric for classification, which can complete the update of the sub-template. Experiments with the eight video sequences in the Visual Tracker Benchmark dataset evaluate the comprehensive performance of SAFS and compare them with five baselines. Experimental results demonstrate that SAFS can overcome the difficulties caused by scene changes and achieve robust object tracking.