Radiating amorphous In–Ga–Zn–O (a-IGZO) thin-film transistors (TFTs) with deep ultraviolet light (λ = 175 nm) is found to induce rigid negative threshold-voltage shift, as well as a subthreshold hump and an increase in subthreshold-voltage slope. These changes are attributed to the photo creation and ionization of oxygen vacancy states (VO), which are confined mainly to the top surface of the a-IGZO film (backchannel). Photoionization of these states generates free electrons and the transition from the neutral to the ionized VO is accompanied by lattice relaxation, which raises the energy of the ionized VO. This and the possibility of atomic exchange with weakly bonded hydrogen leads to metastability of the ionized VO, consistent with the rigid threshold-voltage shift and increase in subthreshold-voltage slope. The hump is thus a manifestation of the highly conductive backchannel and its formation can be suppressed by reduction of the a-IGZO film thickness or application of a back bias after radiation. These results support photo creation and ionization of VO as the main cause of light instability in a-IGZO TFTs and provide some insights on how to minimize the effect.

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On 25 July 2021, the AUV of the Marine Science and Technology Research Center was lost under the sea due to a fracture of the wire rope when it was performing a mission offshore of China. A model is presented in the paper for predicting the trajectory of a lost AUV based on ABiLSTM. To increase the precision of model prediction, the model incorporates the soft attention mechanism and is based on the bidirectional Long Short-Term Memory (BiLSTM) network. In comparison to LSTM, BiLSTM, and attention-LSTM models, experiments have demonstrated that the proposed model enhanced prediction accuracy in terms of longitude, latitude, and altitude by 0.009° E, 0.008° N, and 2 m using representative root mean squared error as an assessment indicator. The findings of the study can improve marine rescue efforts and aid in the search and recovery of AUVs that have crashed.

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The semantic segmentation of a brain tumor is the essential stage in medical treatment planning. Due to the different characteristics of tumors, one of the main difficulties in image segmentation is the severe imbalance between classes. Also, a dataset with imbalanced classes is a common problem in multimodal 3D brain MRIs. Despite these problems, most studies in brain tumor segmentation are biased toward the overrepresented tumor class (majority class) and ignore the small size tumor class (minority class). In this paper, we propose an improved loss function Weighted Focal Loss (WFL), based on 3D U-Net to enhance the prediction of brain tumor segmentation. Using our proposed loss function (WFL) solves the imbalance between classes and the imbalance between weights by giving higher weights to the minority and lower weights to the majority. After assigning these weights to different pixel values, our work is able to resolve pixel degradation, which is one of the limitations of the loss function during model training. Based on our experiments, the proposed function (WFL) on the 3D U-Net model for high-grade glioma (HGG) and low-grade glioma (LGG) in the Brain Tumor Segmentation Challenge (BraTS) 2019 dataset has shown promising results for tumor core (TC), whole tumor (WT) and enhanced tumor (ET) with average dice scores of HGG: 0.