Controlled mechanical loading was applied to Ti-15Mo alloy during annealing at 550 °C. Massive formation of the $\omega_{\textrm{iso}}$ phase from the parent $\beta$-phase occurred during annealing at 550 °C without external stress or with stress well below the yield stress. Moreover, a massive $\alpha$ phase precipitation takes place under simultaneous annealing and plastic deformation. Plastic deformation plays a key role in $\beta\rightarrow\alpha$ transformation and achieving refined $\alpha+\beta$ type microstructure resulted in improved mechanical properties. Studying phase transformations during plastic deformation is critical for understanding and optimizing thermomechanical processing of metastable $\beta$-Ti alloys.

This paper discusses an innovative adaptive heterogeneous fusion algorithm based on estimation of the mean square error of all variables used in real time processing. The algorithm is designed for a fusion between derivative and absolute sensors and is explained by the fusion of the 3-axial gyroscope, 3-axial accelerometer and 3-axial magnetometer into attitude and heading estimation. Our algorithm has similar error performance in the steady state but much faster dynamic response compared to the fixed-gain fusion algorithm. In comparison with the extended Kalman filter the proposed algorithm converges faster and takes less computational time. On the other hand, Kalman filter has smaller mean square output error in a steady state but becomes unstable if the estimated state changes too rapidly. Additionally, the noisy fusion deviation can be used in the process of calibration. The paper proposes and explains a real-time calibration method based on machine learning working in the online mode during run-time. This allows compensation of sensor thermal drift right in the sensors working environment without need of re-calibration in the laboratory.

Robotic process automation (RPA) is a software technology that in recent years has gained a lot of attention and popularity. By now, research on RPA has spread into multiple research streams. This study aims to create a science map of RPA and its aspects by revealing latent topics related to RPA, their research interest, impact, and time development. We provide a systematic framework that is helpful to develop further research into this technology. By using an unsupervised machine learning method based on Latent Dirichlet Allocation, we were able to analyse over 2000 paper abstracts. Among these, we found 100 distinct study topics, 15 of which have been included in the science map we provide.