Crossed Andreev effects in two-dimensional quantum Hall systems

We study the crossed Andreev effects in two-dimensional conductor/superconductor hybrid systems under a perpendicular magnetic field. Both a graphene/superconductor hybrid system and an electron gas/superconductor one are considered. It is shown that an exclusive crossed Andreev reflection, with other Andreev reflections being completely suppressed, is obtained in a high magnetic field because of the chiral edge states in the quantum Hall regime. Importantly, the exclusive crossed Andreev reflection not only holds for a wide range of system parameters, e.g., the size of system, the width of central superconductor, and the quality of coupling between the graphene and the superconductor, but also is very robust against disorder. When the applied bias is within the superconductor gap, a robust Cooper-pair splitting process with high-efficiency can be realized in this system.Comment: 10 pages, 10 figure

OmiEmbed: a unified multi-task deep learning framework for multi-omics data

High-dimensional omics data contains intrinsic biomedical information that is crucial for personalised medicine. Nevertheless, it is challenging to capture them from the genome-wide data due to the large number of molecular features and small number of available samples, which is also called 'the curse of dimensionality' in machine learning. To tackle this problem and pave the way for machine learning aided precision medicine, we proposed a unified multi-task deep learning framework named OmiEmbed to capture biomedical information from high-dimensional omics data with the deep embedding and downstream task modules. The deep embedding module learnt an omics embedding that mapped multiple omics data types into a latent space with lower dimensionality. Based on the new representation of multi-omics data, different downstream task modules were trained simultaneously and efficiently with the multi-task strategy to predict the comprehensive phenotype profile of each sample. OmiEmbed support multiple tasks for omics data including dimensionality reduction, tumour type classification, multi-omics integration, demographic and clinical feature reconstruction, and survival prediction. The framework outperformed other methods on all three types of downstream tasks and achieved better performance with the multi-task strategy comparing to training them individually. OmiEmbed is a powerful and unified framework that can be widely adapted to various application of high-dimensional omics data and has a great potential to facilitate more accurate and personalised clinical decision making.Comment: 14 pages, 8 figures, 7 table

Quasi-Brittle Fracture Modeling of Preflawed Bitumen Using a Diffuse Interface Model

Fundamental understandings on the bitumen fracture mechanism are vital to improve the mixture design of asphalt concrete. In this paper, a diffuse interface model, namely, phase-field method is used for modeling the quasi-brittle fracture in bitumen. This method describes the microstructure using a phase-field variable which assumes one in the intact solid and negative one in the crack region. Only the elastic energy will directly contribute to cracking. To account for the growth of cracks, a nonconserved Allen-Cahn equation is adopted to evolve the phase-field variable. Numerical simulations of fracture are performed in bituminous materials with the consideration of quasi-brittle properties. It is found that the simulation results agree well with classic fracture mechanics

Pulmonary function in primary pulmonary hypertension

AbstractObjectivesThe study was done to ascertain the degree to which abnormalities in resting lung function correlate with the disease severity of patients with primary pulmonary hypertension (PPH).BackgroundPatients with PPH are often difficult to diagnose until several years after the onset of symptoms. Despite the seriousness of the disorder, the diagnosis of PPH is often delayed because it is unsuspected and requires invasive measurements. Although PPH often causes abnormalities in resting lung function, these abnormalities have not been shown to be statistically significant when correlated with other measures of PPH severity.MethodsResting lung mechanics and diffusing capacity for carbon monoxide DLcowere assessed in 79 patients whose findings conformed to the classical diagnostic criteria of PPH and who had no evidence of secondary causes of pulmonary hypertension. These findings were correlated with severity of disease as assessed by cardiac catheterization, New York Heart Association (NYHA) class, and cardiopulmonary exercise testing.ResultsWhen PPH patients were first evaluated at our referral clinic, the DLcoand lung volumes were decreased in approximately three-quarters and one-half, respectively. The decreases in DLco, and to a lesser extent lung volumes, correlated significantly with decreases in peak oxygen uptake (reflecting maximum cardiac output), peak oxygen pulse (reflecting maximum stroke volume), and anaerobic threshold (reflecting sustainable exercise capacity) and higher NYHA class.ConclusionsPatients with PPH commonly have abnormalities in lung mechanics and DLcolevels that correlate significantly with disease severity. These measurements can be useful in evaluating patients with unexplained dyspnea and fatigue

Intelligent ZHENG Classification of Hypertension Depending on ML-kNN and Information Fusion

Hypertension is one of the major causes of heart cerebrovascular diseases. With a good accumulation of hypertension clinical data on hand, research on hypertension's ZHENG differentiation is an important and attractive topic, as Traditional Chinese Medicine (TCM) lies primarily in “treatment based on ZHENG differentiation.” From the view of data mining, ZHENG differentiation is modeled as a classification problem. In this paper, ML-kNN—a multilabel learning model—is used as the classification model for hypertension. Feature-level information fusion is also used for further utilization of all information. Experiment results show that ML-kNN can model the hypertension's ZHENG differentiation well. Information fusion helps improve models' performance