The casting process and high temperature oxidation resistance of high chromium cast iron grate bar

The solidification process of a high chromium cast iron grate bar used for sinter machine was simulated, and high temperature oxidation resistance was also investigated. The simulation result shows that sequence solidification can be achieved and no shrinkage cavity and porosity were observed. Based on the analysis of the microstructure, it could be known that the grate bar was well protected by the Fe3O4、Fe2O3 and Cr2O3 oxide films at temperatures lower than 800°C

Mycobacterium tuberculosis -Induced Upregulation of the COX-2/mPGES-1 Pathway in Human Macrophages Is Abrogated by Sulfasalazine

Macrophages are the primary human host cells of intracellular Mycobacterium tuberculosis ( M.tb ) infection, where the magnitude of inflammatory reactions is crucial for determining the outcome of infection. Previously, we showed that the anti-inflammatory drug sulfasalazine (SASP) significantly reduced the M.tb bactericidal burden and histopathological inflammation in mice. Here, we asked which genes in human inflammatory macrophages are affected upon infection with M.tb and how would potential changes impact the functional state of macrophages. We used a flow cytometry sorting system which can distinguish the dead and alive states of M.tb harbored in human monocyte-derived macrophages (MDM). We found that the expression of cyclooxygenase-2 and microsomal prostaglandin E 2 synthase (mPGES)-1 increased significantly in tagRFP + MDM which were infected with alive M.tb . After exposure of polarized M1-MDM to M.tb (H37Rv strain)-conditioned medium (MTB-CM) or to the M.tb -derived 19-kD antigen, the production of PGE 2 and pro-inflammatory cytokines increased 3- to 4-fold. Upon treatment of M1-MDM with SASP, the MTB-CM-induced expression of COX-2 and the release of COX products and cytokines decreased. Elevation of PGE 2 in M1-MDM upon MTB-CM stimulation and modulation by SASP correlated with the activation of the NF-κB pathway. Together, infection of human macrophages by M.tb strongly induces COX-2 and mPGES-1 expression along with massive PGE 2 formation which is abrogated by the anti-inflammatory drug SASP

Dephosphorylated Polymerase I and Transcript Release Factor Prevents Allergic Asthma Exacerbations by Limiting IL-33 Release

BackgroundAsthma is a chronic inflammatory disease characterized by airway inflammation and airway hyperresponsiveness (AHR). IL-33 is considered as one of the most critical molecules in asthma pathogenesis. IL-33 is stored in nucleus and passively released during necrosis. But little is known about whether living cells can release IL-33 and how this process is regulated.ObjectiveWe sought to investigate the role of polymerase I and transcript release factor (PTRF) in IL-33 release and asthma pathogenesis.MethodsOvalbumin (OVA)-induced asthma model in PTRF+/− mice were employed to dissect the role of PTRF in vivo. Then, further in vitro experiments were carried out to unwind the potential mechanism involved.ResultsIn OVA asthma model with challenge phase, PTRF+/− mice showed a greater airway hyper-reaction, with an intense airway inflammation and more eosinophils in bronchoalveolar lavage fluid (BALF). Consistently, more acute type 2 immune response in lung and a higher IL-33 level in BALF were found in PTRF+/− mice. In OVA asthma model without challenge phase, airway inflammation and local type 2 immune responses were comparable between control mice and PTRF+/− mice. Knockdown of PTRF in 16HBE led to a significantly increased level of IL-33 in cell culture supernatants in response to LPS or HDM. Immunoprecipitation assay clarified Y158 as the major phosphorylation site of PTRF, which was also critical for the interaction of IL-33 and PTRF. Overexpression of dephosphorylated mutant Y158F of PTRF sequestered IL-33 in nucleus together with PTRF and limited IL-33 extracellular secretion.ConclusionPartial loss of PTRF led to a greater AHR and potent type 2 immune responses during challenge phase of asthma model, without influencing the sensitization phase. PTRF phosphorylation status determined subcellular location of PTRF and, therefore, regulated IL-33 release

Abnormal Cockpit Pilot Driving Behavior Detection Using YOLOv4 Fused Attention Mechanism

The abnormal behavior of cockpit pilots during the manipulation process is an important incentive for flight safety, but the complex cockpit environment limits the detection accuracy, with problems such as false detection, missed detection, and insufficient feature extraction capability. This article proposes a method of abnormal pilot driving behavior detection based on the improved YOLOv4 deep learning algorithm and by integrating an attention mechanism. Firstly, the semantic image features are extracted by running the deep neural network structure to complete the image and video recognition of pilot driving behavior. Secondly, the CBAM attention mechanism is introduced into the neural network to solve the problem of gradient disappearance during training. The CBAM mechanism includes both channel and spatial attention processes, meaning the feature extraction capability of the network can be improved. Finally, the features are extracted through the convolutional neural network to monitor the abnormal driving behavior of pilots and for example verification. The conclusion shows that the deep learning algorithm based on the improved YOLOv4 method is practical and feasible for the monitoring of the abnormal driving behavior of pilots during the flight maneuvering phase. The experimental results show that the improved YOLOv4 recognition rate is significantly higher than the unimproved algorithm, and the calling phase has a mAP of 87.35%, an accuracy of 75.76%, and a recall of 87.36%. The smoking phase has a mAP of 87.35%, an accuracy of 85.54%, and a recall of 85.54%. The conclusion shows that the deep learning algorithm based on the improved YOLOv4 method is practical and feasible for the monitoring of the abnormal driving behavior of pilots in the flight maneuvering phase. This method can quickly and accurately identify the abnormal behavior of pilots, providing an important theoretical reference for abnormal behavior detection and risk management

Research on Human-Error Factors of Civil Aircraft Pilots Based On Grey Relational Analysis

In consideration of the situation that civil aviation accidents involve many human-error factors and show the features of typical grey systems, an index system of civil aviation accident human-error factors is built using human factor analysis and classification system model. With the data of accidents happened worldwide between 2008 and 2011, the correlation between human-error factors can be analyzed quantitatively using the method of grey relational analysis. Research results show that the order of main factors affecting pilot human-error factors is preconditions for unsafe acts, unsafe supervision, organization and unsafe acts. The factor related most closely with second-level indexes and pilot human-error factors is the physical/mental limitations of pilots, followed by supervisory violations. The relevancy between the first-level indexes and the corresponding second-level indexes and the relevancy between second-level indexes can also be analyzed quantitatively

Improved LS-SVM Method for Flight Data Fitting of Civil Aircraft Flying at High Plateau

High-plateau flight safety is an important research hotspot in the field of civil aviation transportation safety science. Complete and accurate high-plateau flight data are beneficial for effectively assessing and improving the flight status of civil aviation aircrafts, and can play an important role in carrying out high-plateau operation safety risk analysis. Due to various reasons, such as low temperature and low pressure in the harsh environment of high-plateau flights, the abnormality or loss of the quick access recorder (QAR) data affects the flight data processing and analysis results to a certain extent. In order to effectively solve this problem, an improved least squares support vector machines method is proposed. Firstly, the entropy weight method is used to obtain the index weights. Secondly, the principal component analysis method is used for dimensionality reduction. Finally, the data are fitted and repaired by selecting appropriate eigenvalues through multiple tests based on the LS-SVM. In order to verify the effectiveness of this method, the QAR data related to multiple real plateau flights are used for testing and comparing with the improved method for verification. The fitting results show that the error measurement index mean absolute error of the average error accuracy is more than 90%, and the error index value equal coefficient reaches a high fit degree of 0.99, which proves that the improved least squares support vector machines machine learning model can fit and supplement the missing QAR data in the plateau area through historical flight data to effectively meet application needs

Wave propagation in piezoelectric rings with rectangular cross-sections

The ring ultrasonic transducers are widely used in the ocean engineering and medical fields.This paper employs an extended orthogonal polynomial approach to solve the guided wave propagation in two-dimensional structures, i.e. piezoelectric rings with rectangular cross-sections. The extended polynomial approach can overcome the drawbacks of the conventional orthogonal polynomial approach which can be used to solve wave propagation in one-dimensional structures. Through numerical comparison with the available results for a rectangular aluminum bar, the validity of the present approach is illustrated. The dispersion curves and displacement and electric potential distributions of various rectangular piezoelectric rings are calculated, and the effects of different radius to thickness ratios, width to height ratios and polarizing directions on the dispersion curves are illustrated

Correction and Fitting Civil Aviation Flight Data EGT Based on RPM: Polynomial Least Squares Analysis

There are different missing flight data due to various reasons in the process of acquisition and storage, especially in general aviation, which cause inconvenience for flight data analysis. Effectively explaining the relationship between flight data parameters and selecting a simple and effective method for fitting and correcting flight data suitable for engineering applications are the main points of the paper. Herein, a convenient and applicable approach of missing data correction and fitting based on the least squares polynomial method is introduced in this work. Firstly, the polynomial fitting model based on the least squares method is used to establish multi-order polynomial by existing flight data since the order of the least squares polynomial has a direct impact on the fitting effect. The order is too high or too small, over-fitting or deviation will occur, resulting in improper data. Therefore, the optimization and selection of the model order are significant for flight data correction and fitting. Because the flight data of the aircraft engine exhaust gas temperature (EGT) are often lost because of the immature detection technology, a series of the multi-order polynomial are established by the relationship of aircraft engine exhaust gas temperature and Revolutions Per Minute (RPM). Case study results confirm the optimal model order is four for the fitting and correction of aircraft engine exhaust temperature, and the least squares polynomial method is applicable and effective for EGT flight data correction and fitting based on RPM data

Improved LS-SVM Method for Flight Data Fitting of Civil Aircraft Flying at High Plateau

High-plateau flight safety is an important research hotspot in the field of civil aviation transportation safety science. Complete and accurate high-plateau flight data are beneficial for effectively assessing and improving the flight status of civil aviation aircrafts, and can play an important role in carrying out high-plateau operation safety risk analysis. Due to various reasons, such as low temperature and low pressure in the harsh environment of high-plateau flights, the abnormality or loss of the quick access recorder (QAR) data affects the flight data processing and analysis results to a certain extent. In order to effectively solve this problem, an improved least squares support vector machines method is proposed. Firstly, the entropy weight method is used to obtain the index weights. Secondly, the principal component analysis method is used for dimensionality reduction. Finally, the data are fitted and repaired by selecting appropriate eigenvalues through multiple tests based on the LS-SVM. In order to verify the effectiveness of this method, the QAR data related to multiple real plateau flights are used for testing and comparing with the improved method for verification. The fitting results show that the error measurement index mean absolute error of the average error accuracy is more than 90%, and the error index value equal coefficient reaches a high fit degree of 0.99, which proves that the improved least squares support vector machines machine learning model can fit and supplement the missing QAR data in the plateau area through historical flight data to effectively meet application needs

Do higher value firms voluntarily disclose more information? Evidence from China

This paper examines the effect of guanxi on the relation between firm value and voluntary disclosure of information about new investment projects in China's institutional setting. We find a negative relation between firm value and voluntary disclosure for firms that rely heavily on guanxi in their value creation (e.g. non-high-tech firms, and firms located in regions with underdeveloped institutions). By contrast, for firms that rely less heavily on guanxi and more on other sources of core competencies (e.g. high-tech firms, and firms in high-marketisation regions), we find a positive relation between firm value and voluntary disclosure. The moderating role of guanxi on the relation between firm value and voluntary disclosure is explained by firms conscientiously balancing the costs and benefits of voluntary disclosure relative to guanxi. Specifically, high guanxi-dependence firms refrain from detailed voluntary disclosures for fear of revealing sensitive information that may harm their guanxi. In contrast, low guanxi-dependence firms rely more heavily on voluntary disclosures to reduce information asymmetry and financing cost, with such incentives being particularly strong for high value firms. Our evidence has implications for research on motives for disclosure and regulation of financial reporting