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Point-of-care detection of tumor biomarkers with high sensitivity remains an enormous challenge in the early diagnosis and mass screening of cancer. Fluorescent lateral flow immunoassay (LFA) is an attractive platform for point-of-care testing due to its inherent advantages. Particularly, a fluorescent probe is crucial to improving the analytical performance of the LFA platform. Herein, we developed an enhanced second near-infrared (NIR-II) LFA (ENIR-II LFA) platform based on supramolecular host–guest self-assembly for detection of the prostate-specific antigen (PSA) as a model analyte. In this platform, depending on the effective supramolecular surface modification strategy, cucurbit[7]uril (CB[7])-covered rare-earth nanoparticles (RENPs) emitting in the NIR-II (1000–1700 nm) window were prepared and employed as an efficient fluorescent probe (RENPs-CB[7]). Benefiting from its superior optical properties, such as low autofluorescence, excellent photostability, enhanced fluorescence intensity, and increased antibody-conjugation efficiency, the ENIR-II LFA platform displayed a wide linear detection range from 0.65 to 120 ng mL–1, and the limit of detection was down to 0.22 ng mL–1for PSA, which was 18.2 times lower than the clinical cutoff value. Moreover, the testing time was also shortened to 6 min. Compared with the commercial visible fluorescence LFA kit (VIS LFA) and the previously reported NIR-II LFA based on a RENPs-PAA probe, this ENIR-II LFA demonstrated more competitive advantages in analytical sensitivity, detection range, testing time, and production cost. Overall, the ENIR-II LFA platform offers great potential for the highly sensitive, rapid, and convenient detection of tumor biomarkers and is expected to serve as a useful technique in the general population screening of the high-incidence cancer region.

The pressure in the wheel cylinder plays an important role in the anti-lock braking system, but direct measurement usually needs expensive sensors. Therefore, estimation of the pressure in the wheel cylinder is of great significance to optimize the algorithm of the anti-lock braking system. There are two commonly used estimation methods: one is based on the hydraulic model, and the other is based on the equation for the tyre dynamics. Both of these, however, suffer from inadequacies. The former method is sensitive to the accuracy of the model in practical applications, and an accumulated error appears if some parameters of the process are not sufficiently accurate. In the latter method the pressure in the wheel cylinder is calculated for each cycle but, when the vehicle is running, the fluctuations in the deceleration are relatively large. To estimate the pressure more accurately, a novel method is proposed in this paper, which combines these two methods on the basis of the extended Kalman filter. The method can compensate for the inadequacies of the two methods mentioned above by using the variation in the wheel speed as the observation variable to modify the hydraulic model. It can decrease its sensitivity to the accuracy of the hydraulic model and improve the accuracy of the estimated magnitude of the pressure. The estimated results are compared with the real values which are obtained by using a pressure sensor on a typical low-friction road and on an asphalt road; in addition, the effectiveness of the novel method is validated by hardware-in-the-loop tests and vehicle tests. The results show that the proposed method can provide an accurate estimation of the pressure in the wheel cylinder during hydraulic control of the anti-lock braking system or any other brake control system.

As is known, estimation of the dynamic states of a vehicle plays an important role in safety control, but it is difficult to obtain the vehicle states accurately without expensive measurement instruments because of the non-linear and huge hysteretic characteristics. Nowadays, many methods have been adopted to solve this problem, the results of which are not ideal because it is assumed that the key parameters are constant, in particular in severe manoeuvres. This paper develops a novel estimation method for the vehicle states using the extended Kalman filter with a fusion algorithm. First, the optimal key parameters (the equivalent roll stiffness and the equivalent roll damping) are identified by genetic algorithms using the data from the relationship between the key parameters and the vehicle real-time states. Then, a novel non-linear observer for the side-slip angle and the roll angle is established on the basis of a four-degree-of-freedom vehicle model by utilizing the identified key parameters and the sensors mounted on normal vehicles. The performance of the observer is investigated using both simulations and real-vehicle experiments. The results demonstrate the reasonable accuracy of the estimation method proposed in this paper.

As a typical mechatronics numerical control equipment, CNC lathes are widely used in various processing industries. To obtain better machining accuracy and effect of parts, there is a direct relationship between the structural configuration of the CNC lathe, the input and output mode of the machining information, the arrangement of the operating mechanism, the difficulty of the operation of the equipment, and the accuracy of the operator's operation. When designing a CNC lathe, we must consider the relationship between the human-machine-environment and apply the ergonomic methods and principles to the product design. This paper is based on the individual components of the appearance of the CY-PTC5655 CNC lathe. It proposes the ergonomic design points based on the appearance, creates a good human-computer interaction relationship, and effectively improves the operator's working environment. Reduce the operator's fatigue and improve the production efficiency of the product. The main contents and analysis methods of the thesis have certain reference for the designers of related majors.