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Twin curved tunnels are often encountered in shield tunnelling, where significant complexities in densely exploited underground space are observed. In this study, the ground settlement and tunnel deformation due to twin-curved shield tunnelling in soft ground were investigated using numerical simulation and field monitoring. Different curvature radii of twin curved tunnels and subsequent effects of tunnel construction were considered to reveal the tunnelling effect on ground surface settlement and tunnel deformation. The results show that the settlement trough yields one offset towards inside of curved shield tunnelling. The location of settlement trough and maximum settlement were affected by curvature radius but except for the shape and width of settlement trough. Adjacent parallel twin-curved shield tunnelling could increase the offset of existing settlement trough and maximum settlement. Then, an empirical prediction of surface settlement trough due to twin-curved shield tunnelling with same tunnel diameters in soft clay was proposed, which was applicable to curvature radius less than 800 m. Finally, a minimum radius of 600 m of curvature tunnel was proposed in terms of allowable convergence deformation of tunnel. The result could provide guidance on safety evaluation for twin curved shield tunnelling construction.

Triaxial compression tests were conducted on frozen sandy soils under a constant minimum principal stress (σ3 = 1.6 MPa) and various intermediate principal stresses (σ2 = 1.6, 3.4, 5.2, 7.0, 8.8, 9.8 MPa). The purpose of the research was to investigate the influence of intermediate principal stress on mechanical properties of frozen soil, and to establish a constitutive model to predict the stress-strain relationship of frozen soil under complex stress state. The test results obtained demonstrated that the crack damage stress and failure stress initially increase and then decrease with an increase in the intermediate principal stress. However, the crack initiation stress exhibits an initial increase up to a specific value, after which it stabilizes. From the perspective of crack propagation, the influence mechanism of intermediate principal stress on the strength was discussed. With the variation of stress state, the difference between intermediate principal strain and minimum principal strain gradually increases. The deformation difference was revealed using the stress superposition principle and Poisson's effect. Finally, the constitutive model based on the Weibull distribution and Drucker-Prager strength criterion can accurately represent the stress-strain relationships of frozen sandy soils under true triaxial stress states.

IntroductionPre-HSCT disease control, suboptimal long-term prognosis, and a high recurrence incidence (RI) continue to pose significant challenges for hematopoietic stem cell transplantation (HSCT) in juvenile myelomonocytic leukemia (JMML) patients.MethodsThis retrospective cohort study assessed the effectiveness of a decitabine (DAC)-based protocol in JMML patients undergoing HSCT. The pre-HSCT treatment includes initial and bridging treatment. The efficacy of DAC monotherapy versus DAC combined with cytotoxic chemotherapy(C-DAC) as initial treatment was compared, followed by DAC plus FLAG (fludarabine, cytarabine, and GCSF) as bridging treatment. The HSCT regimens were based on DAC, fludarabine, and busulfan. Post-HSCT, low-dose DAC was used as maintenance therapy. The study endpoints focused on pretransplantation simplified clinical response and post-HSCT survival.ResultsThere were 109 patients, including 45 receiving DAC monotherapy and 64 undergoing C-DAC treatment. 106 patients completed bridging treatment. All patients were administered planned HSCT regimens and post-HSCT treatment. The initial treatment resulted in 88.1% of patients achieving clinical remission without a significant difference between the DAC and C-DAC groups (p=0.769). Clinical remission rates significantly improved following bridging treatment (p=0.019). The 5-year overall survival, leukemia-free survival, and RI were 92.2%, 88.4%, and 8.0%, respectively. A poor clinical response to pre-HSCT treatment emerged as a risk factor for OS (hazard ratio: 9.8, 95% CI: 2.3-41.1, p=0.002).ConclusionImplementing a DAC-based administration strategy throughout the pre-HSCT period, during HSCT regimens, and in post-HSCT maintenance significantly reduced relapse and improved survival in JMML patients. Both DAC monotherapy and the DAC plus FLAG protocol proved effective as pre-HSCT treatments.

To improve the anti-interference and robustness of the adaptive cruise control system in car-following mode, this paper designs a robust controller for the automobile adaptive cruise control system which contains two layers, the upper and lower structures, based on the μ control theory. On the one hand, the upper controller calculates the theoretical safety distance between two automobiles based on the current working conditions, and it calculates the expected acceleration of the vehicle through an optimal control method based on the safety distance and two vehicle speeds. On the other hand, this paper constructs the lower μ integrated controller of an automobile longitudinal dynamics system based on the performance requirements of an adaptive cruise control system and solves it in Matlab. Then, through calculation and simulation, it demonstrates that the designed dual-layer LQR-μ controller has good performance robustness and robust stability, which can significantly improve the anti-interference ability and driving safety performance of the vehicle during the following cruise process.

The aldose reductase inhibitor Fidarestat has been noted to have efficacy in treating a variety of tumors. To define its role in hepatocellular carcinoma (HCC), we induced a HCC xenograft model in mice, which were treated with different doses of Fidarestat. The amounts of natural killer (NK) cells and related inflammatory factors were detected in the serum of the mice. Fidarestat inhibited HCC tumor growth and lung metastasis in vivo and increased NK cell number as well as levels of NK cell-related inflammatory factors in mouse serum. NK cells were then co-cultured with the HCC cell line in vitro to detect effects on HCC cell progression after Fidarestat administration. The glycolysis activity of the NK cells was evaluated by extracellular acidification rate, while aldo-keto reductase family 1 member B10 (AKR1B10) expression was detected by western blot analysis. Administration of Fidarestat downregulated the expression of AKR1B10 in NK cells and promoted NK cell glycolysis to enhance their killing activity against HCC cells. However, depletion of NK cells or upregulation of AKR1B10 attenuated the anticancer activity of Fidarestat. Taken together, Fidarestat downregulated AKR1B10 expression in NK cells to promote NK cell glycolysis, thereby alleviating HCC progression.

In this paper, a new collision avoidance switching system is proposed to address the lack of adaptability of intelligent vehicles under different collision avoidance operating conditions. To ensure the rationality of the collision avoidance switching strategy for intelligent vehicles, the NGSIM road dataset is introduced to analyse the driver’s collision avoidance behaviour, and a two-layer fuzzy controller considering the overlap rate is established to design the collision avoidance switching strategy. In order to achieve real-time collision avoidance system activation, a lane change collision avoidance model based on MPC control is also developed. Finally, a simulation environment was created using Matlab/CarSim for simulation verification. The simulation results show that the collision avoidance switching system is more responsive and has a shorter start-up distance and is more adaptable to different driving conditions.

The current vehicle stability judgment methods in the stability control of distributed drive electric vehicles focuses on using the phase plane method, but the existing phase plane stability boundary delineation methods ignore the influence of the front wheel rotation angle. Therefore, a new phase plane partitioning method (NPPPM) is proposed in this paper, and a new phase plane stability domain library is established. Based on this, the direct yaw moment control of distributed drive electric vehicle through phase plane analysis is established. Firstly, the influence of the front wheel turning angle on the phase plane is considered in the design of the phase plane stability boundary, and a new stability boundary function is fitted by the least squares method. This approach avoids the lateral instability caused by too large a front-wheel turning angle and improves the accuracy of vehicle stability judgment. Next, a hierarchical direct transverse moment controller is constructed using Matlab/Simulink. Among them, the upper layer controller adopts sliding mode control with the sideslip angle error of the center of mass as the tracking target. The lower controller adopts the optimal configuration algorithm with the optimal road adhesion utilization as the objective function. Finally, the effectiveness and superiority of the proposed phase plane partitioning method (NPPPM) for distributed drive electric vehicles with sinusoidal and angular order inputs for the front wheel rotation angle are verified by simulation.

The unavoidable speckle in SAR images seriously interferes with image quality and has a negative effect on subsequent image interpretation. The existing filters still need to be strengthened in terms of both noise suppression and edge preservation. Based on this, a novel non-local means filter by multi-directional local plane inclination angle (MDLPIA) is proposed. The proposed filter uses the MDLPIA to reconstruct a new weight function for non-local means filtering. One instance of simulation data and four real SAR images are used for filtering experiments. In the experiment, seven other filters with excellent performance are selected for comparison, and six quantitative indicators are selected for algorithm performance evaluation. The experimental results show that the proposed filter achieves good visual and index evaluation results, and the equivalent number of looks (ENL) is at least 22.13 times higher than the unfiltered image. The effectiveness and superiority of the proposed algorithm are thus verified.

ObjectiveProstate cancer (PCa) is one of the most common malignant tumors, accounting for 20% of total tumors ranked first in males. PCa is usually asymptomatic at the early stage and the specificity of the current biomarkers for the detection of PCa is low. The present study evaluates circulating tumor DNA (ctDNA) in blood or urine, which can be used as biomarkers of PCa and the combination of these markers may increase the sensitivity and specificity of the detection of PCa.MethodsTissue, blood, and urine samples were collected from patients with PCa. All prostate tissue specimens underwent pathological examination. A hybrid-capture-based next-generation sequencing assay was used for plasma and urinary ctDNA profiling. Sequencing data were analyzed by an in-house pipeline for mutation calling. Mutational profiles of PCa and BPH were compared in both plasma and urine samples. Associations of detected mutations and clinical characteristics were statistically analyzed.ResultsA significant association of mutation allele frequencies (MAFs) in the blood samples with patients with metastatic PCa rather than patients with primary PCa, and MAFs are changed after treatment in patients with PCa. Further, the number of mutations in urine is not associated with clinical characteristics of PCa patients, but the frequencies of mutation alleles in the urine are associated with patient age. Comparison of cfDNA aberration profiles between urine and blood reveals more alterations in urine than in blood, including TP53, AR, ATM, MYC, and SPOP mutations.ConclusionThis work provides the potential clinical application of urine, in addition to blood, as a powerful and convenient non-invasive approach in personalized medicine for patients with PCa.

In the scramjet engine, the injection and mixing of fuel are the basis for organizing efficient combustion. The shock wave/jet shear layer interaction is one of the methods used to enhance fuel mixing in supersonic flow. In this research, the effect of the induced shock wave on the mixing augmentation of fuel counter-flow jet is studied in the supersonic crossflow. The results show that the symmetrical setting of the shock wave generators (SWGs) has the highest mixing efficiency but brings a greater total pressure loss. In the specific range, the mixing efficiency increases with the increase in the angle of the SWG, while the total pressure recovery coefficient is the opposite. The induced oblique shock wave acting on the front of the fuel jet has an obvious influence on the flow field, resulting in the highest mixing efficiency. In the end, the analytic hierarchy process (AHP) is used to evaluate the extent of mixing augmentation by mixing parameters, which provides a decision-making idea for the scheme decision of mixing augmentation.

The current commercial intelligent driving systems still take the optimal strategy judged by the machine to be the only goal. Therefore, in order to improve the driving experience of the intelligent driving following scene, based on the assumption that environmental factors remain unchanged for a short time, five important parameters affecting the following scene are selected through correlation analysis, and vehicle-following research is carried out. This paper adopts a driver-following model based on a Genetic Algorithm (GA)-optimized Back Propagation (BP) neural network. Based on the data of next-generation simulation (ngsim), this paper selects vehicle 32 (32 represents the ID of the vehicle in the ngsim project) as the main vehicle in order to study short-term driving habits. A BP neural network is built using MATLAB; 60% of the data of vehicles 32 and 29 is used for the training set, 20% is used for the verification set, and 20% for the test set. Because short-term prediction requires high timeliness, the genetic algorithm is used to optimize the initial weights of the neural network, which not only accelerates the convergence speed but also plays a role in avoiding the local optimal solution. The experimental results show that compared with the traditional stimulus-response vehicle-following model, this model has a following ability that is more in line with the driver’s driving habits in terms of ensuring following safety.

This article focuses on the trajectory tracking problem in the actuation control section of autonomous vehicles. Based on a two-degrees-of-freedom dynamics model, this paper combines adaptive preview control with a second-order sliding mode control method to develop a new control method. By designing an objective function based on lateral deviations, road boundaries, and the corresponding characteristics of the overall vehicle motion, the method adaptively adjusts the preview time to obtain the ideal yaw rate and then uses a second-order sliding mode control algorithm named Super-Twisting to calculate the steering wheel angle. Combining the low-pass filter with this controller can effectively suppress the chattering caused by the switching of the sliding mode plane while proposing a concept of smoothing based on gradient derivative, the smoothness after filtering is one-seventeenth of that before filtering, whereas the phase plane is used to prove its effectiveness and stability, it can be seen from the phase diagrams that all the state points are in the stable region. A joint simulation model of Matlab/Simulink and Carsim was built to verify the control effectiveness of the controller under the double-shift road, and the simulation results show that the designed controller has good control effect and high tracking accuracy. Meanwhile, the simulation model is also used for other simulations, firstly, simulation comparison tests were carried out with the Model Predictive Control algorithm at speeds of 36 and 54 km/h, compared to the MPC controller, the tacking accuracy of the ST controller has improved to 64.42% and 51.02% at 36 and 54 km/h; secondly, taking simulation of the designed controller against a conventional sliding mode controller based on isokinetic law of convergence, compared to the CSMC controller, the tracking accuracy of the ST controller has improved 41.78% at 54 km/h, and the smoothness of the ST controller is one-nineteenth of that of the CSMC controller; thirdly, carrying out simulations on parameter uncertainties, and replacing parameter uncertainty with Gaussian white noise, the maximum tracking error at 36 and 54 km/h did not exceed 0.3 m, and tracking remains good. Small fluctuations in the steering wheel angle do not affect the normal actuation of the actuator.

In the study of autonomous obstacle avoidance of intelligent vehicles, the traditional artificial potential field method has the problem that the vehicle may fall into the local minima and lead to obstacle avoidance failure. Therefore, this paper improves the traditional potential field function. Based on the vehicle dynamics model, a strategy of jumping out of local minima based on smaller steering angles is proposed. By finding a smaller steering angle and setting a suitable jump out step length, the intelligent vehicle is enabled to jump out of the local minima. Simulation experiments by MATLAB show that the improved method can jump out of the local minima. By comparing the planned trajectories of the traditional method and the improved method in static and dynamic obstacles situations, the trajectory planned by the improved method is smooth and the curvature is smaller. The planned trajectory is tracked by the Carsim platform, and the test results show that the improved method reduces the front steering wheel angle while the intelligent vehicle satisfies the vehicle dynamics constraints during active obstacle avoidance, which verifies the stability and rationality of the improved method.