Dynamic Performance of Valve in Reciprocating Compressor Used Stepless Capacity Regulation System

Capacity regulation system by controlling suction valve is useful for large scale reciprocating compressor in petrochemical engineering field. The dynamic performance of adjustment device influences the stability and accurancy of this system. In this paper, a mathematical model of adjustment device coupled with the motion of suction valve is built, and the dynamic performances of valve plate are simulated. The results show that the displacement of actuator increases with the hydraulic oil pressure until the valve plate is keeped to be opened. The closing process of valve plate is delayed when the hold time of actuator is larger enough. Although the gas flow rate and power consumption of comressor decrease with the relax angle of actuator, the power is also consumed when the gas is not discharged through the discharge valve. The closing time decreases with the reset spring stiffness but increases with the diameter of hydraulic

Maximum Likelihood Estimation of Model Uncertainty in Predicting Soil Nail Loads Using Default and Modified FHWA Simplified Methods

Accuracy evaluation of the default Federal Highway Administration (FHWA) simplified equation for prediction of maximum soil nail loads under working conditions is presented in this study using the maximum likelihood method and a large amount of measured lower and upper bound nail load data reported in the literature. Accuracy was quantitatively expressed as model bias where model bias is defined as the ratio of measured to predicted nail load. The maximum likelihood estimation was carried out assuming normal and lognormal distributions of bias. Analysis outcomes showed that, based on the collected data, the default FHWA simplified nail load equation is satisfactorily accurate on average and the spread in prediction accuracy expressed as the coefficient of variation of bias is about 30%, regardless of the distribution type. Empirical calibrations were proposed to the default FHWA simplified nail load equation for accuracy improvement. The Bayesian Information Criterion was adopted to perform a comparison of suitability between the competing normal and lognormal statistical models that were intended for description of model bias. Example of reliability-based design of soil nail walls against internal pullout limit state of nails is provided in the end to demonstrate the benefit of performing model calibration and using calibrated model for design of soil nails

Experimental Research on Surge and Stability Enhancement of Centrifugal Compressor

Centrifugal compressors are wildly used in many process industries. The stability of centrifugal compressor is one of the most important performances. When the compressor operates at the small volume flow rate, the working conditions of rotating stall and surge will occur, which lead to the unstable condition for centrifugal compressor. The signals of compressor are tested and analyzed when surge condition occurs in this paper. In addition, a new method to improve the compressor stability is proposed. It is called the active control casing treatment (ACCT) system. The flow in the compressor impeller is changed by the ACCT system and the stability of compressor is improved. The experimental researches have been done in this paper. The test results of ACCT system are also discussed in this paper

Vibration response analysis of wind towers considering coupling effects of wind and tide level

Wind power towers located in the intertidal zone are not only influenced by wind load but also affected by the rising and falling of the tides. Little research examining the dynamic characteristics of wind power towers under multi-factor coupling loads has been conducted. Based on the finite element method and field testing, dynamic characteristics of the wind towers under complex loads were studied. Change laws of natural frequencies of wind power towers under different water levels were analyzed, and change laws of acceleration, displacement, strain and failure characteristics of a wind power tower under the coupling load of wind and tide levels were analyzed. The wind tower blade in expansion and deformation characteristics of two types of feathering were analyzed. The results showed that the influence of water levels on the low order natural frequency of a wind power tower structure was slight, but the influence on the high order frequency was higher. The maximum displacement of a wind power tower structure under a fluctuating wind load increased with increasing height. In extreme cases, a wind power tower will appear to have buckling instability destruction when the blades are in a state of suitable slurry. The top displacement of a wind power tower was less than a state displacement. Field monitoring results validate the correctness of the numerical simulation analysis

You've Got Two Teachers: Co-evolutionary Image and Report Distillation for Semi-supervised Anatomical Abnormality Detection in Chest X-ray

Chest X-ray (CXR) anatomical abnormality detection aims at localizing and characterising cardiopulmonary radiological findings in the radiographs, which can expedite clinical workflow and reduce observational oversights. Most existing methods attempted this task in either fully supervised settings which demanded costly mass per-abnormality annotations, or weakly supervised settings which still lagged badly behind fully supervised methods in performance. In this work, we propose a co-evolutionary image and report distillation (CEIRD) framework, which approaches semi-supervised abnormality detection in CXR by grounding the visual detection results with text-classified abnormalities from paired radiology reports, and vice versa. Concretely, based on the classical teacher-student pseudo label distillation (TSD) paradigm, we additionally introduce an auxiliary report classification model, whose prediction is used for report-guided pseudo detection label refinement (RPDLR) in the primary vision detection task. Inversely, we also use the prediction of the vision detection model for abnormality-guided pseudo classification label refinement (APCLR) in the auxiliary report classification task, and propose a co-evolution strategy where the vision and report models mutually promote each other with RPDLR and APCLR performed alternatively. To this end, we effectively incorporate the weak supervision by reports into the semi-supervised TSD pipeline. Besides the cross-modal pseudo label refinement, we further propose an intra-image-modal self-adaptive non-maximum suppression, where the pseudo detection labels generated by the teacher vision model are dynamically rectified by high-confidence predictions by the student. Experimental results on the public MIMIC-CXR benchmark demonstrate CEIRD's superior performance to several up-to-date weakly and semi-supervised methods

Performance Analysis of Centrifugal Compressor under Multiple Working Conditions Based on Time-weighted Average

A method of compressor performance analysis under multiple working conditions is present based on the Time-weighted average (TWA). The main operation parameters can be obtained based the estimate of the working conditions and times of compressors. Then the comprehensive analysis method can be used to get the overall performance of compressor. The performance of a basic centrifugal compressor was simulation by CFD method in this paper. The overall performance of the centrifugal compressor is calculated under different working conditions. The TWA analysis method can be used as a tool to evaluate the overall performance of compressor. And it can also be used during the design phase to improve the performance of compressor fundamentally

Anti-HER-2 engineering antibody ChA21 inhibits growth and induces apoptosis of SK-OV-3 cells

<p>Abstract</p> <p>Background and Aims</p> <p>Anti-HER-2 antibodies targeting distinct epitopes have different biological functions on cancer cells. In a previous study, we demonstrated that anti-HER-2 engineering antibody ChA21 was able to bind to subdomain I of HER-2 extracellular domain. In this study, The effects of ChA21 on growth and apoptosis against ovarian carcinoma cell SK-OV-3 over-expressing HER-2 <it>in vitro </it>and <it>in vivo </it>were investigated.</p> <p>Methods</p> <p>Cell growth inhibition was evaluated by MTT assay. Apoptosis was detected by TUNEL stain, transmission electron microscopy and flow cytometry on cultured cells and tissue sections from nude mice xenografts. The apoptosis-related proteins Bax and Bcl-2 were assessed by immunohistochemistry.</p> <p>Results</p> <p>We found that treatment of ChA21 caused a dose-dependent decrease of cell proliferation <it>in vitro </it>and a significant inhibition of tumor growth <it>in vivo</it>. ChA21 therapy led to a significant increase in the induction of apoptosis, and up-regulated the expression of Bax, while the expression of Bcl-2 was down-regulated.</p> <p>Conclusion</p> <p>These data suggest that ChA21 inhibits the growth and induces apoptosis of SK-OV-3 via regulating the balance between Bax and Bcl-2.</p

Nested Dilation Networks for Brain Tumor Segmentation Based on Magnetic Resonance Imaging

Aim: Brain tumors are among the most fatal cancers worldwide. Diagnosing and manually segmenting tumors are time-consuming clinical tasks, and success strongly depends on the doctor's experience. Automatic quantitative analysis and accurate segmentation of brain tumors are greatly needed for cancer diagnosis.Methods:This paper presents an advanced three-dimensional multimodal segmentation algorithm called nested dilation networks (NDNs). It is inspired by the U-Net architecture, a convolutional neural network (CNN) developed for biomedical image segmentation and is modified to achieve better performance for brain tumor segmentation. Thus, we propose residual blocks nested with dilations (RnD) in the encoding part to enrich the low-level features and use squeeze-and-excitation (SE) blocks in both the encoding and decoding parts to boost significant features. To prove the reliability of the network structure, we compare our results with those of the standard U-Net and its transmutation networks. Different loss functions are considered to cope with class imbalance problems to maximize the brain tumor segmentation results. A cascade training strategy is employed to run NDNs for coarse-to-fine tumor segmentation. This strategy decomposes the multiclass segmentation problem into three binary segmentation problems and trains each task sequentially. Various augmentation techniques are utilized to increase the diversity of the data to avoid overfitting.Results: This approach achieves Dice similarity scores of 0.6652, 0.5880, and 0.6682 for edema, non-enhancing tumors, and enhancing tumors, respectively, in which the Dice loss is used for single-pass training. After cascade training, the Dice similarity scores rise to 0.7043, 0.5889, and 0.7206, respectively.Conclusion: Experiments show that the proposed deep learning algorithm outperforms other U-Net transmutation networks for brain tumor segmentation. Moreover, applying cascade training to NDNs facilitates better performance than other methods. The findings of this study provide considerable insight into the automatic and accurate segmentation of brain tumors