Lung cancer has one of the highest morbidity and mortality rates in the world. Lung nodules are an early indicator of lung cancer. Therefore, accurate detection and image segmentation of lung nodules is of great significance to the early diagnosis of lung cancer. This paper proposes a CT (Computed Tomography) image lung nodule segmentation method based on 3D-UNet and Res2Net, and establishes a new convolutional neural network called 3D-Res2UNet. 3D-Res2Net has a symmetrical hierarchical connection network with strong multi-scale feature extraction capabilities. It enables the network to express multi-scale features with a finer granularity, while increasing the receptive field of each layer of the network. This structure solves the deep level problem. The network is not prone to gradient disappearance and gradient explosion problems, which improves the accuracy of detection and segmentation. The U-shaped network ensures the size of the feature map while effectively repairing the lost features. The method in this paper was tested on the LUNA16 public dataset, where the dice coefficient index reached 95.30% and the recall rate reached 99.1%, indicating that this method has good performance in lung nodule image segmentation.

Bimetallic oxides have been considered as potential candidates for supercapacitors due to their relatively high electric conductivity, abundant redox reactions and cheapness. However, nanoparticle aggregation and huge volume variation during charging-discharging procedures make it hard for them to be applied widely. In this work, one-dimensional (1D) MnFe₂O₄@C nanowires were in-situ synthesized via a simply modified micro-emulsion technique, followed by thermal treatment. The novel 1D and core-shell architecture, and in-situ carbon coating promote its electric conductivity and porous feature. Due to these advantages, the MnFe₂O₄@C electrode exhibits a high specific capacitance of 824 F·g⁻¹ at 0.1 A·g⁻¹ and remains 476 F·g⁻¹ at 5 A·g⁻¹. After 10,000 cycles, the capacitance retention of the MnFe₂O₄@C electrode is up to 93.9%, suggesting its excellent long-term cycling stability. After assembling with activated carbon (AC) to form a MnFe₂O₄@C//AC device, the energy density of this MnFe₂O₄@C//AC device is 27 W·h·kg⁻¹ at a power density of 290 W·kg⁻¹, and remains at a 10 W·h·kg⁻¹ energy density at a high power density of 9300 W·kg⁻¹.

Fatigue driving has become one of the major causes of traffic accidents. The authors propose an effective method capable of detecting fatigue state via the spatial–temporal feature of driver's eyes. In this work, the authors consider fatigue detection as image‐based sequence recognition and an end‐to‐end trainable convolutional neural network with long short‐term memory (LSTM) units is designed. First, the authors apply a deep cascaded multi‐task framework to extract eye region from infrared videos. Then the spatial features are learned by deep convolutional layers and the relationships between adjacent frames are analysed via LSTM units. Finally, through authors’ model, a sequence‐level prediction for driving state is produced. The proposed method achieves superior accuracy over the state‐of‐the‐art techniques on authors’ own dataset. Experimental results demonstrate the feasibility of authors’ method.