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High-precision and real-time diagnosis of sucker rod pumping system (SRPS) is important for quickly mastering oil well operations. Deep learning-based method for classifying the dynamometer card (DC) of oil wells is an efficient diagnosis method. However, the input of the DC as a two-dimensional image into the deep learning framework suffers from low feature utilization and high computational effort. Additionally, different SRPSs in an oil field have various system parameters, and the same SRPS generates different DCs at different moments. Thus, there is heterogeneity in field data, which can dramatically impair the diagnostic accuracy. To solve the above problems, a working condition recognition method based on 4-segment time-frequency signature matrix (4S-TFSM) and deep learning is presented in this paper. First, the 4-segment time-frequency signature (4S-TFS) method that can reduce the computing power requirements is proposed for feature extraction of DC data. Subsequently, the 4S-TFSM is constructed by relative normalization and matrix calculation to synthesize the features of multiple data and solve the problem of data heterogeneity. Finally, a convolutional neural network (CNN), one of the deep learning frameworks, is used to determine the functioning conditions based on the 4S-TFSM. Experiments on field data verify that the proposed diagnostic method based on 4S-TFSM and CNN (4S-TFSMCNN) can significantly improve the accuracy of working condition recognition with lower computational cost. To the best of our knowledge, this is the first work to discuss the effect of data heterogeneity on the working condition recognition performance of SRPS. [ABSTRACT FROM AUTHOR]

The accurate and intelligent identification of the working conditions of a sucker-rod pumping system is necessary. As onshore oil extraction gradually enters its mid-to late-stage, the cost required to train a deep learning working condition recognition model for pumping wells by obtaining enough new working condition samples is expensive. For the few-shot problem and large calculation issues of new working conditions of oil wells, a working condition recognition method for pumping unit wells based on a 4-dimensional time-frequency signature (4D-TFS) and meta-learning convolutional shrinkage neural network (ML-CSNN) is proposed. First, the measured pumping unit well workup data are converted into 4D-TFS data, and the initial feature extraction task is performed while compressing the data. Subsequently, a convolutional shrinkage neural network (CSNN) with a specific structure that can ablate low-frequency features is designed to extract working conditions features. Finally, a meta-learning finetuning framework for learning the network parameters that are susceptible to task changes is merged into the CSNN to solve the few-shot issue. The results of the experiments demonstrate that the trained ML-CSNN has good recognition accuracy and generalization ability for few-shot working condition recognition. More specifically, in the case of lower computational complexity, only few-shot samples are needed to fine-tune the network parameters, and the model can be quickly adapted to new classes of well conditions. [ABSTRACT FROM AUTHOR]

Phosphatase and tensin homolog (PTEN) is a potent tumor suppressor which regulates various cellular functions. The aim of the present study was to analyze the function of PTEN gene expression in squamous cell carcinoma (SCC) cells. This gene exhibits a unique function in cell migration and proliferation during the early stages of embryonic development. However, its role as a tumor suppressor gene in tongue squamous carcinoma cells remains unclear. In the present study, an SCC-4 cell line stably expressing PTEN was established and the effects of PTEN gene expression on SCC-4 cell proliferation, invasion and apoptosis were investigated. PTEN expression was found to induce apoptosis in SCC-4 cells, possibly via negative regulation of the phosphatidylinositide 3-kinase/Akt signaling pathway and increased expression of Bcl-2-interacting mediator of cell death. In addition, PTEN was found to control the epithelial-mesenchymal transition in SCC cells, thereby reducing their invasive ability. Furthermore, Transwell assay revealed that the expression of E-cadherin was increased, while the expression of vimentin and SNAIL was decreased. This study has provided an important insight into the mechanisms by which PTEN mediates the progression and early metastasis of tongue carcinoma. [ABSTRACT FROM AUTHOR]