Treatment evaluation of Rheumatoid arthritis by in situ fluorescence imaging of the Golgi cysteine.

Rheumatoid arthritis (RA) is a long-term systemic inflammatory disease that causes severe joint pain. Golgi stress caused by redox imbalance significantly involves in acute and chronic inflammatory diseases, in which cysteine (Cys), as a representative reducing agent, may be an effective biomarker for RA. Hence, in order to achieve RA early detection and drugs evaluation, based on our previous work about innovative Golgi-targeting group, we established a phenylsulfonamide-modified fluorescence probe, Golgi-Cys, for the selective fluorescence imaging of Cys in Golgi apparatus in vivo. By application of Golgi-Cys, the Cys changes under Golgi stress in cells were elucidated. More importantly, we found that the probe can be effectively utilized for the RA detection and treatment evaluation in situ.

Improved Analytical Model for Thermal Softening in Aluminum Alloys Form Room Temperature to Solidus.

In advanced solid-state manufacturing processes such as friction stir welding, the metal's temperature ranges from room temperature to the solidus temperature. The material strength in the temperature range is generally required for investigating the mechanical behaviors. In this communication paper, an analytical model is proposed for describing the thermal softening of aluminum alloys for room temperature to solidus temperature, in which the concept of temperature-dependent transition between two thermal softening regimes is implemented. It is demonstrated that the proposed model compares favorably to the well-known Sellars-Tegart model and Johnson-Cook model. The constants of the proposed model for nine typical engineering commercial aluminum alloys are documented.

Towards an Optimized Artificial Neural Network for Predicting Flow Stress of In718 Alloys at High Temperatures.

Artificial neural networks (ANNs) have been an important approach for predicting the value of flow stress, which is dependent on temperature, strain, and strain rate. However, there is still a lack of sufficient knowledge regarding what structure of ANN should be used for predicting metal flow stress. In this paper, we train an ANN for predicting flow stress of In718 alloys at high temperatures using our experimental data, and the structure of the ANN is optimized by comparing the performance of four ANNs in predicting the flow stress of In718 alloy. It is found that, as the size of the ANN increases, the ability of the ANN to retrieve the flow stress results from a training dataset is significantly enhanced; however, the ability to predict the flow stress results absent from the training does not monotonically increase with the size of the ANN. It is concluded that the ANN with one hidden layer and four nodes possesses optimized performance for predicting the flow stress of In718 alloys in this study. The reason why there exists an optimized ANN size is discussed. When the ANN size is less than the optimized size, the prediction, especially the strain dependency, falls into underfitting and fails to predict the curve. When the ANN size is less than the optimized size, the predicted flow stress curves with the temperature, strain, and strain rate will contain non-physical fluctuations, thus reducing their prediction accuracy of extrapolation. For metals similar to the In718 alloy, ANNs with very few nodes in the hidden layer are preferred rather than the large ANNs with tens or hundreds of nodes in the hidden layers.

Numerical Analysis of Keyhole and Weld Pool Behaviors in Ultrasonic-Assisted Plasma Arc Welding Process.

The acoustic radiation force driving the plasma jet and the ultrasound reflection at the plasma arc-weld pool interface are considered to modify the formulas of gas shear stress and plasma arc pressure on the anode surface in ultrasonic-assisted plasma arc welding (U-PAW). A transient model taking into account the dynamic changes of heat flux, gas shear stress, and arc pressure on the keyhole wall is developed. The keyhole and weld pool behaviors are numerically simulated to predict the heat transfer and fluid flow in the weld pool and dynamic keyhole evolution process. The model is experimentally validated. The simulation results show that the acoustic radiation force increases the plasma arc velocity, and then increases both the plasma arc pressure and the gas shear stress on the keyhole wall, so that the keyholing capability is enhanced in U-PAW.