Numerical Simulation of Discharge Process in the Single Screw Compressor

In the process of the single screw air compressor, the symmetrical grooves of the compressor are periodically connected with the discharge ports and the gas enters the discharge chamber or flows back from chamber, generating gas pulsation. The gas pulsation increases the flowing loss to bring additional energy loss, meanwhile the gas pulsation is the one of main reasons for the generation of vibration and noise. The thermodynamic model of the compression chamber and the discharge chamber of a single screw air compressor is established in this paper taking the discharge chamber structure and the flow resistance into consideration. The working process in discharge chamber is simulated base on the thermodynamic model. In addition, the influence of speed and back pressure on the pressure in the discharge chamber is investigated in detail. The analysis results provide a basis for optimum design of structure and reduction of vibration and noise

Interactive Speech and Noise Modeling for Speech Enhancement

Speech enhancement is challenging because of the diversity of background noise types. Most of the existing methods are focused on modelling the speech rather than the noise. In this paper, we propose a novel idea to model speech and noise simultaneously in a two-branch convolutional neural network, namely SN-Net. In SN-Net, the two branches predict speech and noise, respectively. Instead of information fusion only at the final output layer, interaction modules are introduced at several intermediate feature domains between the two branches to benefit each other. Such an interaction can leverage features learned from one branch to counteract the undesired part and restore the missing component of the other and thus enhance their discrimination capabilities. We also design a feature extraction module, namely residual-convolution-and-attention (RA), to capture the correlations along temporal and frequency dimensions for both the speech and the noises. Evaluations on public datasets show that the interaction module plays a key role in simultaneous modeling and the SN-Net outperforms the state-of-the-art by a large margin on various evaluation metrics. The proposed SN-Net also shows superior performance for speaker separation.Comment: AAAI 2021 (Accepted

Don't worry about mistakes! Glass Segmentation Network via Mistake Correction

Recall one time when we were in an unfamiliar mall. We might mistakenly think that there exists or does not exist a piece of glass in front of us. Such mistakes will remind us to walk more safely and freely at the same or a similar place next time. To absorb the human mistake correction wisdom, we propose a novel glass segmentation network to detect transparent glass, dubbed GlassSegNet. Motivated by this human behavior, GlassSegNet utilizes two key stages: the identification stage (IS) and the correction stage (CS). The IS is designed to simulate the detection procedure of human recognition for identifying transparent glass by global context and edge information. The CS then progressively refines the coarse prediction by correcting mistake regions based on gained experience. Extensive experiments show clear improvements of our GlassSegNet over thirty-four state-of-the-art methods on three benchmark datasets

Stress Analysis Of Key Components And Vibration Property Research Of The Meshing Pair In Single Screw Compressors

The single screw compressor(SSC) is widely applied to air compression, refrigeration, petrochemical industry, waste heat recovery, etc. Among SSCs, the respective strength and stiffness of the casing, screw rotor and gaterotor, where relative motions occur, play a key role on the machine running and clearance fit. Dynamic properties of the meshing pair directly affect the SSC’s vibration, as well as gaterotor’s wear-out failure. In this thesis, the strength, stiffness and dynamic characteristics of the meshing pair under different operation conditions or with components made of different materials were analyzed. Analytical method and FEM were combined to calculate and analyze the issues above. Main contents and conclusions are as follows: Stress and deformation analysis of key components were implemented by ANSYS Workbench. The results show that both the maximum stress of the casing and the deformation of the gaterotor are basically linear to the discharge pressure. The first six natural frequencies and the corresponding vibration modes of the screw rotor and gaterotor were obtained to analyze and predict their respective vibration properties. It turned out that natural properties of the screw rotor change little on account of rotation speed and damping. Neither the screw rotor nor the gaterotor would resonate. Some exploratory work about the coupling interaction between gaterotor and its support was done. It is concluded that the gaterotor would suffer the support’s collision excitations consequently

A lightweight QRS detector for single lead ECG signals using a max-min difference algorithm

Background and objectives - Detection of the R-peak pertaining to the QRS complex of an ECG signal plays an important role for the diagnosis of a patient's heart condition. To accurately identify the QRS locations from the acquired raw ECG signals, we need to handle a number of challenges, which include noise, baseline wander, varying peak amplitudes, and signal abnormality. This research aims to address these challenges by developing an efficient lightweight algorithm for QRS (i.e., R-peak) detection from raw ECG signals. Methods - A lightweight real-time sliding window-based Max-Min Difference (MMD) algorithm for QRS detection from Lead II ECG signals is proposed. Targeting to achieve the best trade-off between computational efficiency and detection accuracy, the proposed algorithm consists of five key steps for QRS detection, namely, baseline correction, MMD curve generation, dynamic threshold computation, R-peak detection, and error correction. Five annotated databases from Physionet are used for evaluating the proposed algorithm in R-peak detection. Integrated with a feature extraction technique and a neural network classifier, the proposed ORS detection algorithm has also been extended to undertake normal and abnormal heartbeat detection from ECG signals. Results - The proposed algorithm exhibits a high degree of robustness in QRS detection and achieves an average sensitivity of 99.62% and an average positive predictivity of 99.67%. Its performance compares favorably with those from the existing state-of-the-art models reported in the literature. In regards to normal and abnormal heartbeat detection, the proposed QRS detection algorithm in combination with the feature extraction technique and neural network classifier achieves an overall accuracy rate of 93.44% based on an empirical evaluation using the MIT-BIH Arrhythmia data set with 10-fold cross validation. Conclusions - In comparison with other related studies, the proposed algorithm offers a lightweight adaptive alternative for R-peak detection with good computational efficiency. The empirical results indicate that it not only yields a high accuracy rate in QRS detection, but also exhibits efficient computational complexity at the order of O(n), where n is the length of an ECG signal

Inhibition of SARS Pseudovirus Cell Entry by Lactoferrin Binding to Heparan Sulfate Proteoglycans

It has been reported that lactoferrin (LF) participates in the host immune response against Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV) invasion by enhancing NK cell activity and stimulating neutrophil aggregation and adhesion. We further investigated the role of LF in the entry of SARS pseudovirus into HEK293E/ACE2-Myc cells. Our results reveal that LF inhibits SARS pseudovirus infection in a dose-dependent manner. Further analysis suggested that LF was able to block the binding of spike protein to host cells at 4°C, indicating that LF exerted its inhibitory function at the viral attachment stage. However, LF did not disrupt the interaction of spike protein with angiotensin-converting enzyme 2 (ACE2), the functional receptor of SARS-CoV. Previous studies have shown that LF colocalizes with the widely distributed cell-surface heparan sulfate proteoglycans (HSPGs). Our experiments have also confirmed this conclusion. Treatment of the cells with heparinase or exogenous heparin prevented binding of spike protein to host cells and inhibited SARS pseudovirus infection, demonstrating that HSPGs provide the binding sites for SARS-CoV invasion at the early attachment phase. Taken together, our results suggest that, in addition to ACE2, HSPGs are essential cell-surface molecules involved in SARS-CoV cell entry. LF may play a protective role in host defense against SARS-CoV infection through binding to HSPGs and blocking the preliminary interaction between SARS-CoV and host cells. Our findings may provide further understanding of SARS-CoV pathogenesis and aid in treatment of this deadly disease

Improving K-means clustering with enhanced Firefly Algorithms

In this research, we propose two variants of the Firefly Algorithm (FA), namely inward intensified exploration FA (IIEFA) and compound intensified exploration FA (CIEFA), for undertaking the obstinate problems of initialization sensitivity and local optima traps of the K-means clustering model. To enhance the capability of both exploitation and exploration, matrix-based search parameters and dispersing mechanisms are incorporated into the two proposed FA models. We first replace the attractiveness coefficient with a randomized control matrix in the IIEFA model to release the FA from the constraints of biological law, as the exploitation capability in the neighbourhood is elevated from a one-dimensional to multi-dimensional search mechanism with enhanced diversity in search scopes, scales, and directions. Besides that, we employ a dispersing mechanism in the second CIEFA model to dispatch fireflies with high similarities to new positions out of the close neighbourhood to perform global exploration. This dispersing mechanism ensures sufficient variance between fireflies in comparison to increase search efficiency. The ALL-IDB2 database, a skin lesion data set, and a total of 15 UCI data sets are employed to evaluate efficiency of the proposed FA models on clustering tasks. The minimum Redundancy Maximum Relevance (mRMR)-based feature selection method is also adopted to reduce feature dimensionality. The empirical results indicate that the proposed FA models demonstrate statistically significant superiority in both distance and performance measures for clustering tasks in comparison with conventional K-means clustering, five classical search methods, and five advanced FA variants

Verification of the utility of molecular markers linked to the multiple-allele male-sterile gene Ms in the breeding of male-sterile lines of Chinese cabbage (Brassica rapa)

To verify the molecular markers linked to the genic multiple-allele male-sterile gene Ms, an F1 plant, which was generated by crossing the inbred line a20 and the male-sterile plant of the genic multipleallele male-sterile AB line, was backcrossed with an a20 plant to develop BC4 and BC5 populations. Sequence-characterized amplified region (SCAR) marker syau_scr01 and simple sequence repeat (SSR) marker syau_m13, which were linked to Ms, exhibited polymorphism between the 2 parents. The accuracies of these 2 markers in determining the plant genotype was 85 and 91.7%, respectively. The accuracy reached 100% when the 2 markers were used in combination. These results indicate that these 2 markers can be applied in the marker-assisted selection of the genic multiple-allele male-sterile line of Chinese cabbage.Keywords: Chinese cabbage, genic multiple-allele male sterility, marker-assisted selection, simple sequence repeat, sequence-characterized amplified regionAfrican Journal of Biotechnology Vol. 9(35), pp. 5623-5628, 30 August, 201

Effect of multiple clinical factors on recurrent angina after percutaneous coronary intervention: A retrospective study from 398 ST-segment elevation myocardial infarction patients

Recurrent angina (RA) has an important influence on health status of patients after percutaneous coronary intervention (PCI). This study aimed to retrospectively investigate the effect of multiple clinical factors on both short-term and long-term development of RA.A total of 398 ST-segment elevation myocardial infarction (STEMI) patients were studied for up to 12 months. The primary clinical outcome, RA, was assessed at 1-month and 12-month. In multivariate analyses, the effect of clinical factors, including baseline demographics, medical history, infarction-related arteries, procedural characteristics of PCI, and the use of medicines, was investigated in patients with and without RA.The Logistic regression analysis showed that the patients with treatment through radial approach PCI (odds ratio [OR]: 0.42, 95% confidence interval [CI]: 0.18-0.96, P < 0.05) were less likely to have RA during 1-month assessment. During 12 months after PCI, male patients (OR: 0.53, 95% CI: 0.29-0.96, P < 0.05), and/or those treated with radial approach PCI (OR: 0.45, 95% CI: 0.21-0.97, P < 0.05) were less likely to have RA, whereas the patients with infarction related artery (IRA) in left anterior descending (LAD) (OR: 2.41, 95% CI: 1.20-4.84, P < 0.01) were more likely to have RA at follow-up. The Cox regression analysis further revealed that the patients with infarction of the LAD artery (hazard ratio [HR]: 2.08, 95% CI: 1.10-3.92, P < 0.05), but not with treatment through radial artery during PCI (HR: 0.42, 95% CI: 0.18-0.96, P < 0.05) had higher potential of development of RA during 12 months after PCI.We studied the effects of multiple clinical factors on the development of RA after PCI. Our findings suggest that patients with infarction of the LAD artery, and/or treatment not through radial artery during PCI were associated with higher risk of RA and may require close follow-up