The current study is to investigate the expression pattern and biological function of long non-coding RNA Focally gastric cancer-associated transcript3 (GACAT3) in bladder cancer. Real-time quantitative qPCR was used to detect the expression level of GACAT-3 in tumor tissues and paired normal tissues. Human bladder cancer T24 and 5637 cell lines were transiently transfected with specific CRISPR-Cas13 or negative control CRISPR-Cas13. Cell migration, proliferation, and apoptosis were measured by using wound healing assay CCK-8 assay and Caspase-3 ELISA assay, respectively. The expression changes of p21, Bax, and E-cadherin after knockdown of GACAT3 were detected by using Western blot. The results demonstrated that GACAT3 was up-regulated in bladder cancer tissues than that in the paired normal tissues. Inhibition of cell proliferation, increased apoptosis, and decreased motility were observed in T24 and 5637 cell lines transfected by CRISPR-Cas13 targeting GACAT3. Downregulation of GACAT3 increased p21, Bax, and E-cadherin expression and silencing these genes could eliminate the phenotypic changes induced by knockdown of GACAT3. A ceRNA mechanism for GACAT3 was also revealed. By using CRISPR-Cas13 biotechnology, we suggested that GACAT3 may be a novel target for diagnosis and treatment of bladder cancer.

Single-screw compressor has attracted attentions from the scientific community due to its excellent performance. However, thermal deformations of the star wheel, screw, and casing substantially affect the clearance between the components, and hence reduce the performance of the single-screw compressor. In this study, the thermal deformation of a meshing pair of the single-screw compressor was investigated using a finite-element-based thermo-mechanical coupled model. This model was developed based on measured thermal boundary conditions during compressor operation. The effect of thermal deformation on the compressor sealing clearance was then studied. The results showed that the thermal deformation of the casing, screw, screw shaft, and star wheel significantly affected the clearance between the tooth tip and the groove bottom as well as the meshing pair clearance distribution. The change of clearance between the casing and screw is up to 0.03 mm while the change of the clearance between the star wheel tooth tip and screw groove is up to 0.05 mm. Furthermore, it was found that the spatial position meshing error caused by the thermal deformation was one of the major reasons for the wear of the meshing pair of the single-screw compressor. The simulated thermal deformation results agreed well with the experimental data. The clearances of the compressor were modified based on the thermal deformation in a single-screw compressor with a capacity of 6 m3/min and the results showed that the modified compressor can operate reliably. This indicated that the developed model could be used in the design of the single-screw compressor. It provides guidance for the design and optimization of large single-screw compressors.

According to the statistical results of the reciprocating compressor maintenance in chemical enterprises, the probability of faults caused by wear or damage of vulnerable parts inside the cylinder is close to 80%. Now, fault diagnosis of vulnerable parts inside the cylinder is relied on vibration signal, acoustic emission signal, or thermal parameters. However, it is difficult to extract eigenvalues from vibration signals and acoustic emission signals, which can be disturbed by noise easily. Thermal parameters are relatively stable and less affected by noise. The measurement of thermal parameters requires drilling testing holes through the cylinder wall, which will decrease the strength of the cylinder. According to the working principle of the compressor, fault of the vulnerable parts inside the cylinder would change the pressure and temperature distribution at the suction and the discharge port of the cylinder, while these state parameters are monitored by the parameter monitoring system on most of the compressors. For these reasons, a fault prediction model based on state parameters learning is proposed in this paper aiming to fault diagnosis of vulnerable parts inside the cylinder, which could make fault prediction without adding new hardware cost, such as sensors. Optimized back propagation neural network method by genetic algorithm (GA-BP) is applied to establish the fault prediction model to describe normal working process of the compressor. When fault of vulnerable parts inside the cylinder occurs, monitored pressure and temperature would deviate from the predicted value by the fault prediction. The degree of the deviation is adopted to the fault diagnosis. Then, experiments simulating the fault condition were carried out, and it shows that the accuracy of fault diagnosis using this method could exceed 95%. Verification test shows that the proposed method could successfully predict the fault before the unplanned shutdown.

The fault of vulnerable parts in the cylinder accounts for a large proportion of the fault of the reciprocating compressor. In order to find the fault of the compressor in time, parameter monitoring is usually applied to give forewarning or warning of the compressor fault. Because the setting of forewarning value relies on experiences, over protection and delayed warning events often occur. Recently, the research of fault diagnosis methods combined with artificial intelligence technology is gradually rising. However, most of the fault diagnosis methods based on artificial intelligence technology rely on large number of fault data for fault learning. The problem is that it is almost impossible to obtain the sample data including all of the fault types. To solve above problems, a fault diagnosis method for reciprocating compressor based on the prediction of comprehensive index extracted from the expansion process in indicator diagram is proposed. In this method, a model predicting the comprehensive index of expansion process under normal working conditions is established. The expansion process will change significantly when faults occur, which could result in a deviation between the predicted value and the actual value of the comprehensive index. This deviation is utilized in this paper for fault diagnosis. Fault experiments are carried out to verify the effectiveness of the proposed method. An obvious advantage of this method is that no fault data is needed to establish the fault diagnosis model. It would relatively save the cost of the fault diagnosis and be conducive to application. The accuracy of this method for discriminating fault conditions of an individual vulnerable part is more than 94%. The combined fault conditions with multiple parts under fault state could also be successfully distinguished from normal conditions by this method.

The damage of vulnerable components inside the cylinder of reciprocating compressor, including the valve, piston ring, packing and piston ring, will cause the unexpected shutdown of the compressor unit. The indicator diagram which reflects the thermodynamic process in the cylinder is suitable for fault diagnosis of vulnerable components. However, most of the published fault diagnosis methods based on indicator diagram are aimed at the fault diagnosis of gas valve. In addition, the extracted features lack physical meaning in most fault diagnosis methods using machine learning algorithm, which is not conducive to be widely applied in practical engineering. In this study, features with definite physical meaning, including average suction pressure, average discharge pressure, area of indicator diagram and centroid coordinates of indicator diagram, are extracted from indicator diagram, and the threshold database of features under normal states and various fault states is established according to the contrast experiment. The results of the experiment show that the thresholds of the extracted parameters are obviously different under normal states and various fault states. During fault diagnosis, several groups of indicator diagrams of the compressor to be diagnosed are collected at first. After feature extraction, the extracted features are compared with the thresholds under different compressor states to obtain the average numbers of features within the threshold range under different compressor states to determine the compressor states. The accuracy of the method for judging whether the compressor is faulty or normal could reach 98.3%. Furthermore, the accuracy of identifying individual faulty components and multiple faulty components could reach 86.86%. The reason for the low overall diagnostic accuracy is that certain faults have similar effects on the features extracted from indicator diagram. The proposed method is believed as an excellent fault diagnosis method for the vulnerable components inside the cylinder of reciprocating compressor.