Surface Defect Classification for Hot-Rolled Steel Strips by Selectively Dominant Local Binary Patterns

Developments in defect descriptors and computer vision-based algorithms for automatic optical inspection (AOI) allows for further development in image-based measurements. Defect classification is a vital part of an optical-imaging-based surface quality measuring instrument. The high-speed production rhythm of hot continuous rolling requires an ultra-rapid response to every component as well as algorithms in AOI instrument. In this paper, a simple, fast, yet robust texture descriptor, namely selectively dominant local binary patterns (SDLBPs), is proposed for defect classification. First, an intelligent searching algorithm with a quantitative thresholding mechanism is built to excavate the dominant non-uniform patterns (DNUPs). Second, two convertible schemes of pattern code mapping are developed for binary encoding of all uniform patterns and DNUPs. Third, feature extraction is carried out under SDLBP framework. Finally, an adaptive region weighting method is built for further strengthening the original nearest neighbor classifier in the feature matching stage. The extensive experiments carried out on an open texture database (Outex) and an actual surface defect database (Dragon) indicates that our proposed SDLBP yields promising performance on both classification accuracy and time efficiencyPeer reviewe

Pan-cancer analysis of super enhancer-induced PRR7-AS1 as a potential prognostic and immunological biomarker

Introduction: Systematic pan-cancer analysis of the roles and regulatory mechanisms for PRR7-AS1 is currently not available.Methods: In the present study, a comprehensive bioinformatic approach was used to mine the underlying oncogenic effects of PRR7-AS1, including expression status, prognostic value and immune characteristics.Results: We discovered that PRR7-AS1 expression was remarkably upregulated in most cancer types and exhibited a negative correlation with the prognosis. Furthermore, PRR7-AS1 expression was inversely connected with the majority of tumor-infiltrating immune cells, immune scores and immune checkpoint gene expression in pancancer. There was also a significant correlation between PRR7-AS1 expression status and tumor mutational burden, microsatellite instability, and neoantigens in certain tumors. PRR7-AS1 had the best predictive power for immune checkpoint blockade efficacy compared to other well-recognized biomarkers. PRR7-AS1 overexpression could affect cytotoxic T cells-mediated antitumor responses. Functional enrichment analysis revealed that PRR7-AS1 might be involved in the metabolic pathways. Super enhancer activity might have participated in the regulation of PRR7-AS1 expression. And we constructed the competitive endogenous RNA networks for PRR7-AS1.Discussion: In general, PRR7-AS1 had the potential to be a diagnostic, prognostic and immune biomarker for pan cancer. PRR7-AS1 was correlated with an immunosuppressive microenvironment and was a new potential target for immunotherapy. Epigenetic factors were the driving forces for PRR7-AS1 overexpression in tumors

Optimal real-time power dispatch of power grid with wind energy forecasting under extreme weather

With breakthroughs in the power electronics industry, the stability and rapid power regulation of wind power generation have been improved. Its power generation technology is becoming more and more mature. However, there are still weaknesses in the operation and control of power systems under the influence of extreme weather events, especially in real-time power dispatch. To optimally distribute the power of the regulation resources in a more stable manner, a wind energy forecasting-based power dispatch model with time-control intervals optimization is proposed. In this model, the outage of the wind energy under extreme weather is analyzed by an autoregressive integrated moving average model (ARIMA). Additionally, the other regulation resources are used to balance the corresponding wind power drop and power mismatch. Meanwhile, an algorithm names weighted mean of vectors (INFO) is employed to solve the real-time power dispatch and minimize the power deviation between the power command and real output. Lastly, the performance of the proposed optimal real-time power dispatch is executed in a simulation model with ten regulation resources. The simulation tests show that the combination of ARIMA and INFO can effectively improve the power control performance of the PD-WEF system

Impact of stent malapposition on intracoronary flow dynamics : An optical coherence tomography-based patient-specific study

Percutaneous coronary intervention with stent implantation has emerged as a popular approach to treat coronary artery stenosis. Stent malapposition (SM), also referred as incomplete stent apposition, could reduce stent tissue coverage and hence increase the risk of late stent thrombosis. The objective of this study was to investigate the impact of SM on intracoronary flow dynamics by combining optical coherence tomography (OCT) image-based model reconstruction and computational analysis. Firstly, a stenosed coronary artery model was reconstructed from OCT and angiography imaging data of a patient. Two structural analyses were carried out to simulate two types of coronary artery stent implantations: a fully-apposed (FA) case and a SM case. Then, based on the two deformed coronary geometries, two computational fluid dynamics (CFD) analyses were performed to evaluate the differences of hemodynamic metrics between the FA and the SM cases, including wall shear stress (WSS), time-averaged WSS (TWSS), oscillatory shear index (OSI), WSS gradient (WSSG), time-averaged WSSG (TWSSG), and relative residence time (RRT). The results indicated that maximum flow velocity was higher in the SM case than that of the FA case, due to the incomplete expansion of the stent and artery. Moreover, the SM case had a lower percentage of areas of adverse WSS ( 10/Pa) but a higher percentage of areas of adverse OSI (> 0.1) and WSSG (> 5000 Pa/m). Specifically, the differences of OSI, WSSG, and RRT between the two cases were relatively small. It was suggested that SM might not be responsible for negative hemodynamic metrics which would further result in stent thrombosis on the basis of the present specific model.</p

Automated classification of coronary plaque calcification in OCT pullbacks with 3D deep neural networks

Significance: Detection and characterization of coronary atherosclerotic plaques often need reviews of a large number of optical coherence tomography (OCT) imaging slices to make a clinical decision. However, it is a challenge to manually review all the slices and consider the interrelationship between adjacent slices. Approach: Inspired by the recent success of deep convolutional network on the classification of medical images, we proposed a ResNet-3D network for classification of coronary plaque calcification in OCT pullbacks. The ResNet-3D network was initialized with a trained ResNet-50 network and a three-dimensional convolution filter filled with zeros padding and non-zeros padding with a convolutional filter. To retrain ResNet-50, we used a dataset of a1/44860 OCT images, derived by 18 entire pullbacks from different patients. In addition, we investigated a two-phase training method to address the data imbalance. For an improved performance, we evaluated different input sizes for the ResNet-3D network, such as 3, 5, and 7 OCT slices. Furthermore, we integrated all ResNet-3D results by majority voting. Results: A comparative analysis proved the effectiveness of the proposed ResNet-3D networks against ResNet-2D network in the OCT dataset. The classification performance (F1-scores = 94 % for non-zeros padding and F1-score = 96 % for zeros padding) demonstrated the potential of convolutional neural networks (CNNs) in classifying plaque calcification. Conclusions: This work may provide a foundation for further work in extending the CNN to voxel segmentation, which may lead to a supportive diagnostic tool for assessment of coronary plaque vulnerability. </p

Imaging-Based Patient-Specific Biomechanical Evaluation of Atherosclerosis and Aneurysm: A Comparison Between Structural-Only, Fluid-Only and Fluid-Structure Interaction Analysis

Cardiovascular diseases (CVD) are the leading cause of morbidity and mortality worldwide. Atherosclerosis is the dominating underlying cause of CVD, that occurs at susceptible locations such as coronary and carotid arteries. The progression of atherosclerosis is a gradual process and most of the time asymptomatic until a catastrophic event occurs. Similarly, an intracranial aneurysm is the bulging of the cerebral artery due to a weakened area of the vessel wall. The progression of the aneurysm could result in the rupture of the vessel wall leading to a subarachnoid haemorrhage. The formation and progression of atherosclerosis and aneurysm are closely linked to abnormal blood flow behaviour and mechanical forces acting on the vessel wall. Recent technologies in medical imaging, modeling, and computation are used to estimate critical parameters from patient-specific data. However, there is still a need to develop protocols that are reproducible and efficient. This article focuses on the methods for biomechanical analysis of the cerebral aneurysms and atherosclerotic arteries including carotid and coronary. In this study, patient-specific 3D models were reconstructed from optical coherence imaging (OCT) for coronary and magnetic resonance imaging (MRI) for the carotid and cerebral arteries. The reconstructed models were used for computational fluid dynamics (CFD), structural-only, and fluid-structure interaction (FSI) simulations. The results of the FSI were compared against structural and CFD-only simulations to identify the most suitable method for each artery. The comparison between FSI and structural only simulations for the coronary artery showed similar mechanical stress values across the cardiac cycle with a maximum difference of 1.8%. However, the results for the carotid and cerebral arteries showed a maximum difference of 5% and 20% respectively. Additionally, with relation to the hemodynamic WSS calculated from FSI and CFD-only, the coronary artery presented a significant difference of 87%. Conversely, the results for the carotid and cerebral arteries showed a maximum difference of 9 and 6.4% at systole. Based on the results it can be concluded that the shape and location of the artery will influence the selection of the model that can be used for solving the numerical problem.</p

How getting twisted in scaffold design can promote bone regeneration : A fluid–structure interaction evaluation

Bone tissue engineering (BTE) uses engineering principles to repair large bone defects, which requires effective mass transport ability of scaffolds to support cellular activities during bone regeneration. Since the implanted BTE scaffolds keep deforming under physiological loading which influences the fluid flow and mass transport within the scaffold and surrounding tissue, thus, scaffold design needs to consider the mass transport behavior under the physiological loading. This work proposed a novel twist scaffold, and its mass transport efficiency under physiological loading conditions was evaluated by a fluid–structure interaction analysis. The results showed that compared to the non-twist scaffold, the twist scaffold could form a rotating flow under the physiological loading, which enhanced the mass transport and generated more appropriate wall shear stress (WSS) to promote bone regeneration. This highlighted the better mass transport efficiency of the twist scaffold. Therefore, getting twist may be a promising design strategy for future BTE scaffolds, and the fluid–structure interaction approach may be a more reliable method for bone regeneration studies in either in vivo or in vitro systems.</p

Atherosclerotic Plaque Tissue Characterization:An OCT-Based Machine Learning Algorithm With ex vivo Validation

There is a need to develop a validated algorithm for plaque characterization which can help to facilitate the standardization of optical coherence tomography (OCT) image interpretation of plaque morphology, and improve the efficiency and accuracy in the application of OCT imaging for the quantitative assessment of plaque vulnerability. In this study, a machine learning algorithm was implemented for characterization of atherosclerotic plaque components by intravascular OCT using ex vivo carotid plaque tissue samples. A total of 31 patients underwent carotid endarterectomy and the ex vivo carotid plaques were imaged with OCT. Optical parameter, texture features and relative position of pixels were extracted within the region of interest and then used to quantify the tissue characterization of plaque components. The potential of individual and combined feature set to discriminate tissue components was quantified using sensitivity, specificity, accuracy. The results show there was a lower classification accuracy in the calcified tissue than the fibrous tissue and lipid tissue. The pixel-wise classification accuracy obtained by the developed method, to characterize the fibrous, calcified and lipid tissue by comparing with histology, were 80.0, 62.0, and 83.1, respectively. The developed algorithm was capable of characterizing plaque components with an excellent accuracy using the combined feature set.</p

Prediction of atherosclerotic plaque life - Perceptions from fatigue analysis

Cardiovascular diseases are the leading causes of morbidity and mortality globally. Heart disease and stroke contribute to most fatalities in which atherosclerotic plaque disruption is the underlying pathology. The pulsatile blood flow in the arteries generates mechanical stresses that affect the rupture of the atherosclerotic plaque. Fatigue failure being the accumulation of the damage due to repeated loading that occurs when the stresses are much lower than those needed to rupture the plaque with normal loading. Therefore, fracture mechanics concepts were used to investigate the impact of morphology and blood pressure on the plaque life. Incremental fatigue crack propagation simulations were performed on idealized geometries based on the maximum circumferential stress criteria by using a finite element solver. XFEM, which extends the standard finite element formulation by introducing additional enrichment functions was used to model the fatigue crack growth simulations. Paris’ Law was used to determine the fatigue crack growth rate. Cracks extended radially and fatigue crack growth rate increased with increase in pulse pressure. Further validation studies on the 3D printed arteries are necessary for better understanding the factors contributing to plaque rupture. The results could help in assessing the atherosclerotic plaque life under the fatigue environment of the cardiovascular system