摘要 : Determining soil categories in geotechnical engineering is critical to ensuring building safety, optimizing structural design, and controlling project costs. This study proposes a novel soil classification method based on an integrated machine-learning model. The integrated model synthesizes prediction results from four classification models, assigning weights to each model's probability outputs for various soil categories, then normalizes these aggregated probabilities to ensure that the predicted probabilities of the different soil categories add up to one, ultimately selecting the soil category with the highest normalized probability as the final classification. Initially, data from five distinct engineering projects located in Shanghai were collected, and 70% of data from each site was used to create a comprehensive dataset. It was then divided into training and testing datasets in a ratio of 8:2. Four classification algorithms, including decision tree, Random forest, KNN, and Adaboost, were trained using the training dataset. To enhance their collective predictive capability, Particle Swarm Optimization (PSO) was applied to fine-tune the weight coefficients among these models. The fitness function of PSO was defined as the integrated model's performance on the testing dataset. For the final stage of the study, the efficacy of the integrated model was subsequently validated using the remaining 30% of the data from each site. The experimental results show that the integrated model has high accuracy and robustness in multiple engineering sites compared with individual machine-learning models. In addition, the proposed integrated model exhibits significant superiority over traditional CPT-based direct methods. Overall, the integrated model shows accuracy and significant robustness across different project sites, highlighting its potential for application in geotechnical engineering.

摘要 : Among the products of the electrocatalytic reduction of carbon dioxide (CO2RR), CO is currently the most valuable product for industrial applications. However, poor stability is a significant obstacle to CO2RR. Therefore, we synthesized a series of bimetallic organic framework materials containing different ratios of tungsten to copper using a hydrothermal method and used them as precursors. The precursors were then subjected to pyrolysis at 800 °C under argon gas, and the M-N bimetallic sites were formed after 2 h. Loose porous structures favorable for electrocatalytic reactions were finally obtained. The material could operate at lower reduction potentials than existing catalysts and obtained higher Faraday efficiencies than comparable catalysts. Of these, the current density of WCu-C/N (W:Cu = 3:1) could be stabilized at 7.9 mA ‧ cm-2 and the FE of CO reached 94 % at a hydrogen electrode potential of -0.6 V (V vs. RHE). The novel materials made with a two-step process helped to improve the stability and selectivity of the electrocatalytic reduction of CO2 to CO, which will help to promote the commercial application of this technology.

摘要 : Hydrothermal and ultrasonic processes were used in this study to synthesize a single-atom Cu anchored on t-BaTiO3. The resulting material effectively employs vibration energy for the piezoelectric (PE) catalytic degradation of pollutants. The phase and microstructure of the sample were analyzed using X-ray diffraction (XRD) and scanning electron microscopy (SEM), and it was found that the sample had a tetragonal perovskite structure with uniform grain size. The nanomaterial achieved a considerable increase in tetracycline degradation rate (approximately 95 % within 7 h) when subjected to mechanical vibration. In contrast, pure BaTiO3 demonstrated a degradation rate of 56.7 %. A significant number of piezo-induced negative charge carriers, electrons, can leak out to the Cu-doped BaTiO3 interface due to Cu's exceptional conductivity. As a result, a single-atom Cu catalyst can facilitate the separation of these electrons, resulting in synergistic catalysis. By demonstrating a viable approach for improving ultrasonic and PE materials this research highlights the benefits of combining ultrasonic technology and the PE effect.

摘要 : In recent years, it has been difficult to remove excess N (mainly NO3–-N) in wastewater with a low ratio of biochemical oxygen demand/chemical oxygen demand. The present study combined the operation of electrochemical process (with a modified brass mesh electrode being used as the cathode) and anammox (anaerobic ammonium oxidation) to treat a high nitrate-simulated wastewater and investigated the overall operating parameters of the combined system. Results showed that the efficiency of the combined system for the removal of total nitrogen can be stabilized over 76.35% at an electrochemical feed of 300 mg/L NO3–-N, and the NO2–-N/NH4+-N (around 130 mg/L) in the effluent of the electrochemical part was kept around 1. Although specific anammox activity decreased from 142.07 to 129.23 mg/g VSS-d, the amount of secreted extracellular polymeric substances increased from an initial 152.10 to 204.12 mg/g VSS. The main functional bacteria were Candidatus Brocadia (2.80–8.84%) and Candidatus Jettenia (0.94–1.99%). Compared to traditional denitrification, the composite process lags behind but still holds a certain economic advantage over electrochemical nitrate removal counterpart.

摘要 : Point cloud denoising is crucial for ensuring the quality of three-dimensional point clouds. However, existing denoising methods are extremely prone to error removal for object point clouds while removing noise points, and the error increases with the improvement of noise recognition accuracy. To address this issue, a point cloud denoising algorithm that incorporates improved radius filtering and local plane fitting is proposed. To achieve effective noise point removal, noise points are divided into far- and near-noise points based on their Euclidean distance from the object point clouds and are successively processed using different denoising strategies. First, the far-noise points are removed using improved radius filtering based on the density characteristics of the point clouds. Next, the near-noise points, which are closely located to the object point clouds and attached to their surfaces, are removed using a geometrical feature assessing the deviation of the point cloud from the local fitting plane. Finally, experiments are conducted on common point cloud datasets and the proposed method is validated by comparing its performance with that of three other advanced methods. The results show that the proposed method outperforms all three methods in all indexes under the same noise level. Our proposed method effectively improves the object point cloud recognition accuracy while achieving higher noise recognition accuracy, with the denoising accuracy reaching 95.9%.

摘要 : A multistep spatiotemporal forecasting (MSTF) network is developed through incorporating the graph convolutional network (GCN) and the long short-term memory (LSTM) network within a sequence-to-sequence (seq2seq) framework. The MSTF method can not only extract spatial and temporal information from the input data but also make multistep-ahead and continuous predictions. An MSTF-based harmful algal bloom (HAB) forecasting model is then formulated to predict the chlorophyll-a (Chl-a) concentration of the Dianchi Lake (China). The integrated gradients (IG) method is employed to interpret the trained MSTF model and quantify the attribution of each input dimension to the Chl-a prediction. Results indicate that (i) the coefficient of determination (R2) of the MSTF model in 24-h-ahead Chl-a prediction reaches 0.926, 28.4% higher than that of the traditional LSTM model; (ii) the ammonia nitrogen (12.3%), the total phosphorus (10.2%), the total nitrogen (9.9%), and the temperature (8.6%) are significant variables for Chl-a prediction; (iii) the spatial information from neighbor lake and river stations plays an important role in the HAB forecasting, with an average contribution of 35.0%; (iv) the proposed MSTF model is also skillful in the 72-h-ahead Chl-a prediction. Results presented highlight the importance of considering both spatial and temporal dependency of monitoring data in HAB forecasting and mechanism interpreting.

摘要 : As a strong competitor of the next-generation high-dynamic-range (HDR) display, liquid crystal displays (LCDs) with a minilight-emitting-diode (Mini-LED) backlight are an essential approach for developing new display technologies. LCDs with a Mini-LED backlight inevitably introduce the halo effect because of their structural characteristics, such as local dimming and light leakage. Therefore, to investigate the effect of ambient light on the halo perception of LCDs with a Mini-LED backlight, we propose a fundamental LCD with a Mini-LED backlight simulation model and design a systematic visual perception experiment based on the proposed model. Furthermore, we investigate halo perception under different ambient illuminances, Mini-LED backlight block sizes, and contrast ratios of liquid crystal panels. Additionally, we explore the influence mechanism of ambient light on halo perception based on actual light measurements. The results show that ambient light has a substantial effect on halo perception; ambient light can affect the contrast ratio of the image; and ambient illuminance is negatively correlated with halo perception intensity. Therefore, this study provides a reference for designing and optimizing LCDs with a Mini-LED backlight under different ambient light conditions.

摘要 : Natural gas hydrates have significant economic and environmental potential, and their exploitation and utilization are of strategic significance for national energy security and realizing "dual carbon goals"of peak carbon and carbon neutrality. The majority of hydrates in China is distributed in the South China Sea. The complexity and concealment of the marine environment result in highly uncertainty of engineering measurement data, which affects drilling safety. However, there is a lack of reliability analysis for wellbore stability in hydrate reservoirs. Based on the analytical model of wellbore stability in hydrate reservoirs and the specific geological conditions of the Shenhu area in the South China Sea, the reliability analysis methods including the advanced first-order second-moment method and the response surface method are employed to quantitatively assess the probability of wellbore instability during drilling, and to analyze the sensitivity of the wellbore instability probability and safe drilling fluid pressure window to the mean and uncertainty of the main parameters. The research results indicate that: (1) If the measurement data are accurate enough, the drilling in hydrate reservoirs in the Shenhu area of the South China Sea is highly safe and the safe drilling fluid pressure window is also large. However, the increased uncertainty of measurement data can significantly increase the probability of wellbore instability and narrow the safe drilling fluid pressure window. (2) A lower drilling fluid temperature can slightly reduce the probability of wellbore instability and significantly increase the safe drilling fluid pressure window. (3) The mean and uncertainty of the five main parameters have the same order of influence on the probability of wellbore instability, that is: initial in situ stress > initial internal friction angle > elastic modulus ratio > initial cohesion > initial elastic modulus. Accurate measurement of initial in situ stress in practical engineering can significantly improve the wellbore stability in hydrate reservoirs.

摘要 : Three-dimensional (3D) imaging technology is widely used in augmented, virtual, and mixed realities. Dynamic virtual spatial distortion is an important factor that affects visual comfort. This study analyzes the processes involved in 3D image acquisition, display, and human eye perception to quantify the spatial distortion of virtual space in 3D imaging accurately. This study also simulates different spatial distortions that may occur in the process. The point cloud data of the object in the virtual space before and after distortion are compared and analyzed by first dividing and then aggregating. The quantitative model of static geometric distortion is thus established. The dynamic geometric distortion quantification model is obtained by combining the static model and the object motion attributes. The effectiveness of the proposed method is verified by simulating 10 different degrees of geometric distortion based on six groups of point clouds and comparing the subjective and objective consistencies between the proposed and classical method through subjective evaluation experiments. The results demonstrate that the proposed method has the best index performance in quantifying the geometric distortion of virtual space, and the Pearson's linear correlation coefficient obtained is 0.93, which accurately reflects the geometric distortion perceived by the test subjects. The research will provide a theoretical reference for the research in geometric distortion optimization and visual comfort improvement of 3D displays.

摘要 : A van der Waals (vdW) heterostructure is formed by combining multiple materials through vdW bonds. It can combine the advantages of electronic, optical, thermal, and magnetic properties of different 2D materials and has the potential to develop into the next generation of high-performance functional devices. Herein, the current research advances of vdW heterostructures are reviewed. First, current fabrication methods and physical structures of vdW heterostructures are summarized. The 2D/nD (n = 0, 1, 2, 3) mixed-dimensional heterostructures are discussed in detail. Second, a new type of vdW heterostructure is introduced based on two-dimensional electron gas with a nanoscale junction interface. Finally, the application prospects of vdW heterostructures in photoelectric and memory devices are further outlined by combing new applications in the neural networks. This review shows that vdW heterostructures have great advantages in high integration, energy harvesting, and logical operations, and it provides directions and suggestions for the future research and application of environmentally friendly, high-performance, and smart functional devices.