For high-resolution borehole images obtained from digital panoramic borehole cameras, a method for recognizing the rock-soil interface and solution fissures based on color is proposed. Owing to the significantly different colors of geotechnical stratifications, such as the rock-soil interface and solution fissures, compared with bedrock, a self-adaptive geotechnical stratification detection model based on the hue, saturation, value (HSV) color space was established. A binarized image of the geotechnical stratification in the borehole was then obtained using this model. The binary image was filtered to depress the noise, and then segmented. The density of pixels in each segment was calculated to determine the depth, area, and orientation of the rock-soil interface and solution fissures, thereby identifying the geotechnical stratification in the borehole image. Verification of the method with many borehole images and comparisons with corresponding borehole radar images illustrates that it can accurately and quickly identify all rock-soil interfaces and solution fissures in a borehole. The proposed method provides a new automated, efficient, and reliable means of identifying borehole rock structures for engineering applications.

Accurate simulation of pollution load at basin scale is very important for controlling pollution. Although data-driven models are increasingly popular in water environment studies, they are not extensively utilized in the simulation of pollution load at basin scale. In this paper, we developed a data-driven model based on Long-Short Term Memory (LSTM)-Back Propagation (BP) spatiotemporal combination. The model comprises several time simulators based on LSTM and a spatial combiner based on BP. The time series of the daily pollution load in the Zhouhe River basin during the period from 2006 to 2017 were simulated using the developed model, the BP model, the LSTM model and the Soil and Water Assessment Tool (SWAT) model, independently. Results showed that the spatial correlation (i.e., Pearson’s correlation coefficient is larger than 0.5) supports using a single model to simulate the pollution load at all sub-basins, rather than using independent models for each sub-basin. Comparison of the LSTM-BP spatiotemporal combination model with the BP, LSTM and SWAT models showed that the performance of the LSTM model is better than that of the BP model and the LSTM model can obtain comparable performance with the SWAT model in most cases, whereas the performance of the LSTM-BP spatiotemporal combination model is much better than that of the LSTM and SWAT models. Although the variation of the simulated pollution load with the LSTM-BP model is high under different hydrological periods and precipitation intensities, the LSTM-BP model can track the temporal variation trend of pollution load accurately (i.e., the RMSE is 6.27, NSE is 0.86 and BIAS is 19.46 for the NH₃ load and the RMSE is 20.27, NSE is 0.71 and BIAS 36.87 is for the TN load). The results of this study demonstrate the applicability of data-driven models, especially the LSTM-BP model, in the simulation of pollution load at basin scale.

Cleft lip and palate is a congenital maxillofacial deformity. Unilateral complete cleft lip and palate is one of the most common clinical types. Nasal alveolar molding (PNAM) is a recognized strategy for the treatment of cleft lip and palate. However, the current design of PNAM devices mainly relies on the subjective experience of doctors. The purpose of this paper is to describe the design and manufacture of a new computer-aided design appliance, which can be applied to the presurgical nasoalveolar molding of unilateral complete cleft lip and palate, eliminate individual differences, and improve production efficiency. In this paper, seven feature points on the healthy side and the affected side are extracted by the method of Gaussian curvature and ridge line extraction, and the healthy side rotation and built-in model are designed by using these seven feature points, which can quickly generate eight treatment stages of PNAM. The correction effects of the PNAM appliance designed in this paper were compared with the original maxillary model and the clinical PNAM appliance (hand-made by subjective experience) from the aspects of alveolar fissure width and symmetry. The PNAM appliance designed in this paper can effectively improve the symmetry of patients with unilateral complete cleft lip and palate (morphological similarity: t = 3.250, p ≤ 0.01; length similarity: t = 1.559, p = 0.150) and reduce the width of alveolar cleft (t = 8.330, p < 0.01). This can fully achieve the therapeutic effect of PNAM appliances prepared by experienced doctors and is more efficient. The method based on complex 3D surface feature point extraction can provide the basis for the design and evaluation of a unilateral complete PNAM correction model, improve the design and production efficiency of unilateral complete cleft lip and palate appliance, eliminate the design problems caused by individual differences, and reduce the burden of doctors.

Modified Electric Convulsive Therapy (MECT) is an efficacious physical therapy in treating mental disorders. The occurrence of epilepsy is a crucial benchmark for evaluating therapeutic effectiveness. However, the medical field still lacks relevant research on automatically detecting epileptic waves in MECT. Therefore, this article proposes a novel automatic detection method of epileptic waves in MECT. In this article, EEG local features (time, frequency, and time-frequency domains) and global features (Pearson correlation coefficient) are combined for epileptic wave detection with SVM (Support Vector Machine). We researched the system with 15 EEG detection channels. The dataset under investigation contains EEG data from 22 patients who received MECT and presented with epileptic seizures. The results revealed that LA (Logarithm of Activity) feature exhibits the best classification significance. When epileptic waves appear, there is a decrease in the power ratio of delta waves and an increase in the power ratio of theta waves. Additionally, the complexity of EEG decreases while the correlation between EEG channels increases. The Cz, F4, and P3 channels exhibit the highest classification significance among all EEG channels. Furthermore, based on the channel classification significance, the EEG detection channels number can be reduced to 8. Similarly, based on the feature classification significance, the local feature number can be reduced from 9 to 3. These conclusions can improve detection efficiency and reduce the cost for MECT. Moreover, the method we proposed can effectively detect epileptic waves in MECT. This work can provide physicians with a reference for evaluating the effectiveness of MECT.

Scientific measurement of the risk degree of traffic scenes and accurate assessment of driver's workload are conducive to reducing driving risk and road traffic accidents. In this paper, EEG signal evaluation method based on "driver's" perspective is used to describe the risk of traffic scene objectively and quantitatively. The traffic scenes with dynamic traffic environment factors are taken as the research objects, including the pedestrian scene and the variable-speed vehicle scene. The drivers’ EEG signals are used as the indicators to evaluate the risk degree of the traffic scene. Based on research objects and indicators, the internal relationship between drivers' EEG signals and traffic dangerous environment factors are explored, and the evaluation models of traffic scene risk degree based on drivers' EEG signals are established.