141 research outputs found

    Research and Design on Navigation Electronic Map System

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    This paper puts forward the new definition on the basis of the original concept of the navigation electronic map, designs the structure of the navigation electronic map system which contains three parts: hardware equipment, data system and software system, and analyzes each part of them in detail, finally this paper discusses the functional framework of the navigation electronic map

    Data Drift Monitoring for Log Anomaly Detection Pipelines

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    Logs enable the monitoring of infrastructure status and the performance of associated applications. Logs are also invaluable for diagnosing the root causes of any problems that may arise. Log Anomaly Detection (LAD) pipelines automate the detection of anomalies in logs, providing assistance to site reliability engineers (SREs) in system diagnosis. Log patterns change over time, necessitating updates to the LAD model defining the `normal' log activity profile. In this paper, we introduce a Bayes Factor-based drift detection method that identifies when intervention, retraining, and updating of the LAD model are required with human involvement. We illustrate our method using sequences of log activity, both from unaltered data, and simulated activity with controlled levels of anomaly contamination, based on real collected log data

    Absolute frequency measurement of the 87Sr optical lattice clock at NTSC using International Atomic Time

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    We report the absolute frequency measurement of the 5s2 1S0-5s5p 3P0 transition in 87Sr optical lattice clock (Sr1) at National Time Service Center (NTSC). Its systematic frequency shifts are evaluated carefully with a total relative uncertainty of 5.1E10-17. The measured absolute frequency is 429 228 004 229 872.91(18) Hz with a relative uncertainty of 4.13E10-16, with reference to the ensemble of primary and secondary frequency standards published in the Circular T bulletin by BIPM through a global navigation satellite system (GNSS) link

    Effect of Electric Field on the Degradation Process of Reinforced Mortar under Chloride and Sulfate Attack

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    This study investigated the degradation mechanism behind the reinforced mortar exposed to chloride, sulfate and electric field. The steel-mortar samples were exposed to 5% Na2SO4, 5% NaCl + 5% Na2SO4 solutions and deionized water in two regimes (full immersion and direct current electric field). The efficiencies of three current densities were compared as well. The total and free sulfate ion content in the mortar were measured. The microstructural analysis by scanning electron microscopy (SEM), and energy dispersive X-ray spectroscopy (EDS) were conducted. The results indicated that the electric field drastically increased the ingress of sulfate, as well as the sulfate reaction. Meanwhile, the current attenuated the interaction between chloride and sulfate. The increase in current density decreased the efficiency of degradation acceleration. An acceleration factor (AF) was proposed based on the comparison between the number of ions in the mortar under electric field and immersion. Findings from this study are beneficial to develop a reliable acceleration method for the long-term performance of RC structures under chloride and sulfate attack

    One for Multiple: Physics-informed Synthetic Data Boosts Generalizable Deep Learning for Fast MRI Reconstruction

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    Magnetic resonance imaging (MRI) is a principal radiological modality that provides radiation-free, abundant, and diverse information about the whole human body for medical diagnosis, but suffers from prolonged scan time. The scan time can be significantly reduced through k-space undersampling but the introduced artifacts need to be removed in image reconstruction. Although deep learning (DL) has emerged as a powerful tool for image reconstruction in fast MRI, its potential in multiple imaging scenarios remains largely untapped. This is because not only collecting large-scale and diverse realistic training data is generally costly and privacy-restricted, but also existing DL methods are hard to handle the practically inevitable mismatch between training and target data. Here, we present a Physics-Informed Synthetic data learning framework for Fast MRI, called PISF, which is the first to enable generalizable DL for multi-scenario MRI reconstruction using solely one trained model. For a 2D image, the reconstruction is separated into many 1D basic problems and starts with the 1D data synthesis, to facilitate generalization. We demonstrate that training DL models on synthetic data, integrated with enhanced learning techniques, can achieve comparable or even better in vivo MRI reconstruction compared to models trained on a matched realistic dataset, reducing the demand for real-world MRI data by up to 96%. Moreover, our PISF shows impressive generalizability in multi-vendor multi-center imaging. Its excellent adaptability to patients has been verified through 10 experienced doctors' evaluations. PISF provides a feasible and cost-effective way to markedly boost the widespread usage of DL in various fast MRI applications, while freeing from the intractable ethical and practical considerations of in vivo human data acquisitions.Comment: 22 pages, 9 figures, 1 tabl

    Selective disrupted gray matter volume covariance of amygdala subregions in schizophrenia

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    ObjectiveAlthough extensive structural and functional abnormalities have been reported in schizophrenia, the gray matter volume (GMV) covariance of the amygdala remain unknown. The amygdala contains several subregions with different connection patterns and functions, but it is unclear whether the GMV covariance of these subregions are selectively affected in schizophrenia.MethodsTo address this issue, we compared the GMV covariance of each amygdala subregion between 807 schizophrenia patients and 845 healthy controls from 11 centers. The amygdala was segmented into nine subregions using FreeSurfer (v7.1.1), including the lateral (La), basal (Ba), accessory-basal (AB), anterior-amygdaloid-area (AAA), central (Ce), medial (Me), cortical (Co), corticoamygdaloid-transition (CAT), and paralaminar (PL) nucleus. We developed an operational combat harmonization model for 11 centers, subsequently employing a voxel-wise general linear model to investigate the differences in GMV covariance between schizophrenia patients and healthy controls across these subregions and the entire brain, while adjusting for age, sex and TIV.ResultsOur findings revealed that five amygdala subregions of schizophrenia patients, including bilateral AAA, CAT, and right Ba, demonstrated significantly increased GMV covariance with the hippocampus, striatum, orbitofrontal cortex, and so on (permutation test, P< 0.05, corrected). These findings could be replicated in most centers. Rigorous correlation analysis failed to identify relationships between the altered GMV covariance with positive and negative symptom scale, duration of illness, and antipsychotic medication measure.ConclusionOur research is the first to discover selectively impaired GMV covariance patterns of amygdala subregion in a large multicenter sample size of patients with schizophrenia

    The oyster genome reveals stress adaptation and complexity of shell formation

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    The Pacific oyster Crassostrea gigas belongs to one of the most species-rich but genomically poorly explored phyla, the Mollusca. Here we report the sequencing and assembly of the oyster genome using short reads and a fosmid-pooling strategy, along with transcriptomes of development and stress response and the proteome of the shell. The oyster genome is highly polymorphic and rich in repetitive sequences, with some transposable elements still actively shaping variation. Transcriptome studies reveal an extensive set of genes responding to environmental stress. The expansion of genes coding for heat shock protein 70 and inhibitors of apoptosis is probably central to the oyster's adaptation to sessile life in the highly stressful intertidal zone. Our analyses also show that shell formation in molluscs is more complex than currently understood and involves extensive participation of cells and their exosomes. The oyster genome sequence fills a void in our understanding of the Lophotrochozoa. © 2012 Macmillan Publishers Limited. All rights reserved
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