Modeling of axial self-inductive displacement sensor considering fringing effects and eddy current effects.

Axial self-inductive displacement sensor can be used in rotor systems to detect the axial displacement of the rotor. The design and analysis of the sensor are mostly based on the traditional ideal model, which ignores the influence of fringing effects and eddy current effects, resulting in significant discrepancies between theoretical analysis and experimental results. To take into account the influence of fringing effects and eddy current effects, this paper proposed the introduction of the fringing factor and complex permeability and then established an improved model. The results show that the prediction of the sensor's output voltage based on the improved model is in better agreement with the experimental results than the traditional ideal model, and the improved model can analyze the influences of the length of the air gap and excitation frequency on sensitivity. Therefore, the model could provide a significant reference for the design and analysis of the axial self-inductive displacement sensor.

ProS-GNN: Predicting effects of mutations on protein stability using graph neural networks.

Predicting protein stability change upon variation through a computational approach is a valuable tool to unveil the mechanisms of mutation-induced drug failure and develop immunotherapy strategies. Some previous machine learning-based techniques exhibit anti-symmetric bias toward destabilizing situations, whereas others struggle with generalization to unseen examples. To address these issues, we propose a gated graph neural network-based approach to predict changes in protein stability upon mutation. The model uses message passing to encode the links between the molecular structure and property after eliminating the non-mutant structure and creating input feature vectors. While doing so, it also incorporates the coordinates of the raw atoms to provide spatial insights into the chemical systems. We test the model on the Ssym, Myoglobin, Broom, and p53 datasets to demonstrate the generalization performance. Compared to existing approaches, our proposed method achieves improved linearity with symmetry in less time. The code for this study is available at: https://github.com/HongzhouTang/Pros-GNN.

BayeStab: Predicting effects of mutations on protein stability with uncertainty quantification.

Predicting protein thermostability change upon mutation is crucial for understanding diseases and designing therapeutics. However, accurately estimating Gibbs free energy change of the protein remained a challenge. Some methods struggle to generalize on examples with no homology and produce uncalibrated predictions. Here we leverage advances in graph neural networks for protein feature extraction to tackle this structure-property prediction task. Our method, BayeStab, is then tested on four test datasets, including S669, S611, S350, and Myoglobin, showing high generalization and symmetry performance. Meanwhile, we apply concrete dropout enabled Bayesian neural networks to infer plausible models and estimate uncertainty. By decomposing the uncertainty into parts induced by data noise and model, we demonstrate that the probabilistic method allows insights into the inherent noise of the training datasets, which is closely relevant to the upper bound of the task. Finally, the BayeStab web server is created and can be found at: http://www.bayestab.com. The code for this work is available at: https://github.com/HongzhouTang/BayeStab.