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According to the unstable and nonlinear performances of the servo valve-controlled hydraulic motor, classical control methods based on linear theory are gradually unable to meet the high-performance requirements of the system. Using the servo valve-controlled hydraulic motor based on the third-order active disturbance rejection control (ADRC) to improve the dynamic performance of the system is feasible. The mathematical model and the simulation model of the third-order ADRC for the servo valve-controlled hydraulic motor system are established respectively. For the phase lag caused by the third-order ADRC controller, the control performance of the ADRC controller is significantly improved using the advance forecast. The simulation experiment results show that the designed ADRC controller has good tracking performance and stronger robustness of the system than the traditional PID controller.

Landslides cause huge damage to social economy and human beings every year. Landslide susceptibility mapping (LSM) occupies an important position in land use and risk management. This study is to investigate a hybrid model which makes full use of the advantage of supervised learning model (SLM) and unsupervised learning model (ULM). Firstly, ten continuous variables were used to develop a ULM which consisted of factor analysis (FA) and k-means cluster for a preliminary landslide susceptibility map. Secondly, 351 landslides with “1” label were collected and the same number of non-landslide samples with “0” label were selected from the very low susceptibility area in the preliminary map, constituting a new priori condition for a SLM, and thirteen factors were used for the modeling of gradient boosting decision tree (GBDT) which represented for SLM. Finally, the performance of different models was verified using related indexes. The results showed that the performance of the pretreated GBDT model was improved with sensitivity, specificity, accuracy and the area under the curve (AUC) values of 88.60%, 92.59%, 90.60% and 0.976, respectively. It can be concluded that a pretreated model with strong robustness can be constructed by increasing the purity of samples.

The sedimentary structure of tailings is of high significance to the engineering design and safety management of tailings dams. However, due to a lack of accurate measurement techniques for the flow field and hydrodynamic conditions of tailings reservoirs, it is challenging to study the complicated sedimentary structure of tailings dams from the perspective of fluid mechanics. This study focuses on developing a large-scale particle image velocimetry (LSPIV) system in a 20 m long and 2 m wide deposition model flume to measure the flow field characteristics during the ore-drawing process accurately. According to the surface flow field characteristics measured by LSPIV, the tailings in the flume can be divided into three zones, namely the fan-shaped zone, channel zone, and laminar flow zone. Then, a simple method for estimating the flow rate of the slurry was proposed using the surface velocities measured by LSPIV. The flow rate of iron tailings slurry in the flume displays a decreasing trend along the flow direction. The variation of the flow rate of tailings slurry can be described by an exponential function. After the deposition of tailings slurry, the sedimentary characteristics of tailings are investigated, and the distribution of iron tailings particles is discussed in combination with the flow field of the tailings slurry. The LSPIV system can be applied to further deposition model tests of different slurry concentrations, discharge flow rates, and tailings compositions to investigate the effects of these factors on the tailings flow and deposition.

Bronchiolar adenoma (BA) is a rare benign pulmonary neoplasm originating from the bronchial mucosal epithelium and mimics lung adenocarcinoma (LAC) both radiographically and microscopically. This study aimed to develop a nomogram for distinguishing BA from LAC by integrating clinical characteristics and artificial intelligence (AI)-derived histogram parameters across two medical centers. This retrospective study included 215 patients with diagnoses confirmed by postoperative pathology from two medical centers. Medical center 1 provided 151 patients (68 BA and 83 LAC nodules) as the training cohort, while medical center 2 contributed 64 patients (28 BA and 36 LAC nodules) as the external validation cohort. Risk predictors and the nomogram were developed using clinical characteristics and AI-derived histogram parameters. Nodule density (solid, ground glass, and subsolid) exhibited a statistically significant difference between the BA and LAC groups (p < 0.01). The following parameters were significantly higher in the LAC group compared to the BA group (all p < 0.05): 2D long diameter, 2D short diameter, 2D average diameter, 2D maximum surface area, 3D long diameter, 3D surface area, 3D volume, and entropy. In contrast, CT value variance was significantly lower in the LAC group than in the BA group (p < 0.01). A nomogram was constructed incorporating density, 2D short diameter, and CT value variance. The area under the curve (AUC) of the nomogram in the training and validation cohorts were 0.821, 0.811. The AI-based nomogram, as a non-invasive preoperative tool, had the potential to enhance diagnostic accuracy for distinguishing BA from LAC.

With the increase of the requirement for the economy of vehicles and the strengthening of the concept of environmental protection, the development of future vehicles will develop in the direction of high efficiency and cleanliness, and the current power system of vehicles based on traditional fossil fuels will gradually transition to hybrid power. As an essential technological direction for new energy vehicles, the development of fuel cell passenger vehicles is of great significance in reducing transportation carbon emissions, stabilizing energy supply, and maintaining the sustainable development of the automotive industry. To study the fuel economy of a passenger car with the proton exchange membrane fuel cell (PEMFC) during the operating phase, two typical PEMFC passenger cars, test vehicles A and B, were compared and analyzed. The hydrogen consumption and hydrogen emission under two operating conditions, namely the different steady-state power and the Chinese Vehicle Driving Conditions-Passenger Car cycle, were tested. The test results show the actual hydrogen consumption rates of vehicle A and vehicle B are 9.77 g/kM and 8.28 g/kM, respectively. The average hydrogen emission rates for vehicle A and vehicle B are 1.56 g/(kW·h) and 5.40 g/(kW·h), respectively. By comparing the hydrogen purge valve opening time ratio, the differences between test vehicles A and B in control strategy, hydrogen consumption, and emission rate are analyzed. This study will provide reference data for China to study the economics of the operational phase of PEMFC vehicles.

High-precision tracking of an electro-hydraulic servo material testing machine’s force control system was achieved using a proposed integral sliding mode control method based on feedback linearization to improve the machine’s force control performance and anti-interference ability. First, the electro-hydraulic servo system’s nonlinear mathematical model was established, and its input–output linearization was realized using differential geometry theory. Second, integral sliding mode control was introduced into the controller and the feedback-linearized integral sliding mode controller was designed. The controller’s stability was proven based on the Lyapunov stability principle. Finally, a simulation model of the electro-hydraulic servo material testing machine’s force control system was established using AMESim/Simulink software. The designed controller was simulated and verified, and the control effects of the system’s different amplitudes and frequency signals were analyzed. The results showed that the feedback-linearized integral sliding mode control algorithm could effectively improve the system’s force tracking accuracy and parameter adaptability, yielding better robustness and a better control effect.

The sedimentary structure is important for the engineering design, operation, and safety evaluation of tailing dams. For upstream-method tailing dams, tailing slurry flows and deposits in the pond and forms a complex dam structure. Ore drawing parameters (e.g., slurry concentration and flow rate) have significant influence on the sedimentary structure of tailing dams. However, there is a lack of unified and quantitative understanding of the complicated effects of ore drawing parameters on the deposition behaviour of tailings. In the present study, flume tests were applied to investigate the characteristics of the sedimentary structure of tailing dams. Seven ore drawing experiments were conducted to simulate different slurry concentrations and flow rates. The distribution of characteristic particle sizes d50 and d10 of sediment was obtained. Furthermore, considering two dominant features of particle size distribution, a mathematical model for the equation between characteristic particle size and deposition distance was established. The exponential part of this equation describes the decreasing trend of the characteristic particle size, and a smooth step function is introduced to characterize the abrupt decrease in particle size. The experimental data of d50 and d10 in all these test cases can be approximated by the equation with correlation coefficients R2 greater than 0.861. As the slurry concentration of ore drawing increases, the hydraulic sorting gradually weakens. The characteristic particle size distribution curves corresponding to a larger flow rate are generally located above those corresponding to a small flow rate, indicating that the larger the flow rate is, the coarser the sediment. This study provided useful information for the determination of ore drawing parameters in actual tailing dams. The mathematical model of tailings’ particle size distribution can be further used for refined modelling of tailing dams, so as to analyse the safety and stability of the dams.

The dry beach length determines the hydraulic boundary of tailings impoundments and significantly impacts the infiltration line, which is crucial for the tailings dam. A deep learning method utilizing satellite images is presented to recognize the dry beach area and accurately measure the length of dry beaches in tailing ponds. Firstly, satellite images of various tailing ponds were gathered and the collection was enlarged to create a dataset of satellite images of tailing ponds. Then, a deep learning method was created using YOLOv5-seg to identify the dry beach area of tailing ponds from satellite images. The mask of the dry beach region was segmented and contour extraction was then carried out. Finally, the beach crest line was fitted based on the extracted contour. The pixel distance between the beach crest line and the dry beach boundary was measured and then translated into real distance by ground resolution. This paper’s case study compared the calculated length of dry beach with the real length obtained by field monitoring. The results of the case study showed that the minimum error of the method was 2.10%, the maximum error was 3.46%, and the average error was 2.70%, indicating high precision for calculating dry beach length in tailing ponds.

Hydrogen consumption and mileage are important economic indicators of fuel cell vehicles. Hydrogen consumption is the fundamental reason that restricts mileage. Since there are few quantitative studies on hydrogen consumption during actual vehicle operation, the high cost of hydrogen consumption in outdoor testing makes it impossible to guarantee the accuracy of the test. Therefore, this study puts forward a test method based on the hydrogen consumption of fuel cell vehicles under CLTC-P operating conditions to test the hydrogen consumption of fuel cell vehicles per 100 km. Finally, the experiment shows that the mileage calculated by hydrogen consumption has a higher consistency with the actual mileage. Based on this hydrogen consumption test method, the hydrogen consumption can be accurately measured, and the test time and cost can be effectively reduced.

Unmanned aerial vehicle (UAV) tilt photography technology has gradually become a new technical means of disaster risk identification. This technology combines UAVs, satellite remote sensing, and ground online monitoring systems to establish an integrated space–sky–Earth system that can be used for tailings pond risk identification. With the use of this system for visual interpretation, water body identification, and monitoring data analysis, multiple types of monitoring parameters of a typical tailings pond in China, such as the seepage line and surface deformation, were obtained. Moreover, intelligent fusion analysis was performed of multisource data to outline the problems affecting tailings safety in the process of elevation expansion and irregular ore discharge of the tailings pond. Warning values of different levels were obtained to assess the overall safety condition of the tailings pond, and the proposed technology was verified. The research results could provide a new basis for accurate evaluation of the running state of tailings ponds and offer an effective remote monitoring means for tailings pond enterprises and supervisory departments.