The application of NiCo-LDHs as electrode materials for supercapacitors has attracted widespread attention. However, pristine NiCo-LDHs always suffer from low conductivity and instability that limit their electrochemical performance in supercapacitors. Herein, we rationally engineered a novel KCu₇S₄@NiCo-LDH core–shell structure with good capacitive performance using a simple one-step solvothermal method and applied this structure as an electrode material for supercapacitors. KCu₇S₄ nanowires act as a dependable “skeleton” for supporting the NiCo-LDH nanosheets, thus preventing the aggregation of NiCo-LDHs and improving their conductivity. By controlling the reaction time and the ratio of Ni/Co in the precursor solution, according to the results of electrochemical testing, an optimal composite was obtained with Ni/Co = 2/1 and the reaction time of 8 h; the final KCu₇S₄@Ni₂Co₁-LDH composite possessed the high specific capacitance of 1104.5 F g⁻¹ (122.7 mA h g⁻¹) at 2 A g⁻¹, good rate capability of 65.9% when the current density reached up to 10 A g⁻¹, and a long-term stability (an 83.5% capacitance retention after 1000 cycles).

Oil and gas wastewater refers to the waste stream produced in special production activities such as drilling and fracturing. This kind of wastewater has the following characteristics: high salinity, high chromaticity, toxic and harmful substances, poor biodegradability, and a difficulty to treat. Interestingly, nanomaterials show great potential in water treatment technology because of their small size, large surface area, and high surface energy. When nanotechnology is combined with membrane treatment materials, nanofiber membranes with a controllable pore size and high porosity can be prepared, which provides more possibilities for oil–water separation. In this review, the important applications of nanomaterials in wastewater treatment, including membrane separation technology and photocatalysis technology, are summarized. Membrane separation technology is mainly manifested in ultrafiltration (UF), nanofiltration (NF), and reverse osmosis (RO). It also focuses on the application of semiconductor photocatalysis technology induced by TiO₂ in the degradation of oil and gas wastewater. Finally, the development trends of nanomaterials in oil and gas wastewater treatment are prospected.

The dynamic yaw motion of the wind turbine will affect the overall aerodynamic performance of the impeller and the corresponding wake flow, but the current research on this issue is inadequate. Thus, it is very necessary to study the complicated near-wake aerodynamic behaviors during the yaw process and the closely related blade aerodynamic characteristics. This work utilized the multi-relaxation time lattice Boltzmann (MRT-LBM) model to investigate the integral aerodynamic performance characteristics of the specified impeller and the dynamic changes in the near wake under a sine yawing process, in which the normalized result is adopted to facilitate data comparison and understanding. Moreover, considering the complexity of the wake flows, the large eddy simulation (LES) and wall-adapting local eddy-viscosity (WALE) model are also used in this investigation. The related results indicate that the degree of stability of tip spiral wake in the dynamic yaw condition is inversely related to the absolute value of the change rate of yaw angular speed. When the wind turbine returns to the position with the yaw angle of 0 (deg) around, the linearized migration of tip vortex is changed, and the speed loss in the wake center is reduced at about the normalized velocity of 0.27, and another transverse expansion appeared. The directional inducing downstream of the impeller sweep surface for tip vortex is clearly reflected on the entering side and the exiting side. Additionally, the features of the static pressure on the blade surface and the overall aerodynamic effects of the impeller are also discussed, respectively.

In this paper, based on density functional theory, the adsorption mechanism and gas sensitivity of Ag₂O/CuO-modified SnS₂ were analyzed. The results were analyzed according to the adsorption energy, total density of states, partial density of states, and frontier molecular orbital theory. The results show that the adsorption of all gas molecules is exothermic. NH₃, Cl₂, and C₂H₂ gases are chemisorbed on the modified SnS₂ surfaces. After gas adsorption, the energy gap of the base changes by more than 10%, which fully shows that the conductivity changes greatly after gas adsorption, which can be reflected in the macroscopic resistance change. Ag₂O-SnS₂ is suitable as a gas sensor for NH₃ gas sensors in terms of moderate adsorption distance, large adsorption energy, charge transfer, and frontier molecular orbital theory, while CuO-SnS₂ is more suitable as a C₂H₂ gas sensor.

Relational triple extraction, a fundamental procedure in natural language processing knowledge graph construction, assumes a crucial and irreplaceable role in the domain of academic research related to information extraction. In this paper, we propose a Double-Headed Entities and Relations Prediction (DERP) framework, which divides the entity recognition process into two stages: head entity recognition and tail entity recognition, using the obtained head and tail entities as inputs. By utilizing the corresponding relation and the corresponding entity, the DERP framework further incorporates a triple prediction module to improve the accuracy and completeness of the joint relation triple extraction. We conducted experiments on two English datasets, NYT and WebNLG, and two Chinese datasets, DuIE2.0 and CMeIE-V2, and compared the English dataset experimental results with those derived from ten baseline models. The experimental results demonstrate the effectiveness of our proposed DERP framework for triple extraction.

Melanoma segmentation based on a convolutional neural network (CNN) has recently attracted extensive attention. However, the features captured by CNN are always local that result in discontinuous feature extraction. To solve this problem, we propose a novel multiscale feature fusion network (MSFA-Net). MSFA-Net can extract feature information at different scales through a multiscale feature fusion structure (MSF) in the network and then calibrate and restore the extracted information to achieve the purpose of melanoma segmentation. Specifically, based on the popular encoder-decoder structure, we designed three functional modules, namely MSF, asymmetric skip connection structure (ASCS), and calibration decoder (Decoder). In addition, a weighted cross-entropy loss and two-stage learning rate optimization strategy are designed to train the network more effectively. Compared qualitatively and quantitatively with the representative neural network methods with encoder-decoder structure, such as U-Net, the proposed method can achieve advanced performance.