11 research outputs found

    Vent alignments in San Francisco volcanic field, Arizona : statistical analysis and assessment of structural controls

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    Using cluster analysis, a total of 605 vents in San Francisco Volcanic Field are studied over an area of approximately 5000km2. Application of alignment analysis techniques, including the two-point azimuth analysis and Hough transform analysis, demonstrates that cinder cones are aligned along common orientations within larger clusters. These alignments consist of 9-10 cinder cones, are 20-38 km long, and are regional features. The vent alignments indicate the presence of geological features along which magma ascended more readily than elsewhere. The NE-trending Mesa Butte and Oak Creek Canyon-Doney fault systems seem to control the intermediate to silicic centers which are on the intersection of these fault systems with Cataract Creek fault system and affect the development of cinder cone alignments. Geological maps and geophysical surveys indicate that most vent alignments are parallel or subparallel to these large scale fault systems. This suggests that vent alignment patterns are controlled by regional structures

    Technology of WAVE and Feature Cutter volume of manufacturing

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    A forward and automatic method for in-process model generation is proposed and integrated with three-dimensional computer aided process planning (CAPP) system. The method based on feature cutter volume of manufacturing drives the in-process model to evolve from blank to part, which is coincident with the manufacturing process and ideology of process design. At the same time, technology of What-if Alternative Value Engineering (WAVE) is implanted in the generation to establish the relationship of in-process models, which supports function of the automatic update for models. The paper introduces a theory of the solution to demonstrate the connection between manufacture feature and feature cutter volume, and detailedly presents the technological process in applying the WAVE technology. Examples are completed in a commercial CAPP system to illustrate the feasibility of this approach

    Human Mobility Modeling during the COVID-19 Pandemic via Deep Graph Diffusion Infomax

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    Non-Pharmaceutical Interventions (NPIs), such as social gathering restrictions, have shown effectiveness to slow the transmission of COVID-19 by reducing the contact of people. To support policy-makers, multiple studies have first modelled human mobility via macro indicators (e.g., average daily travel distance) and then study the effectiveness of NPIs. In this work, we focus on mobility modelling and, from a micro perspective, aim to predict locations that will be visited by COVID-19 cases. Since NPIs generally cause economic and societal loss, such a prediction benefits governments when they design and evaluate them. However, in real-world situations, strict privacy data protection regulations result in severe data sparsity problems (i.e., limited case and location information). To address these challenges and jointly model variables including a geometric graph, a set of diffusions and a set of locations, we propose a model named Deep Graph Diffusion Infomax (DGDI). We show the maximization of DGDI can be bounded by two tractable components: a univariate Mutual Information (MI) between geometric graph and diffusion representation, and a univariate MI between diffusion representation and location representation. To facilitate the research of COVID-19 prediction, we present two benchmarks that contain geometric graphs and location histories of COVID-19 cases. Extensive experiments on the two benchmarks show that DGDI significantly outperforms other competing methods

    Table1_Radiomics signatures for predicting the Ki-67 level and HER-2 status based on bone metastasis from primary breast cancer.DOCX

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    This study explores the potential of radiomics to predict the proliferation marker protein Ki-67 levels and human epidermal growth factor receptor 2 (HER-2) status based on MRI images of patients with spinal metastasis from primary breast cancer. A total of 110 patients with pathologically confirmed spinal metastases from primary breast cancer were enrolled between Dec. 2017 and Dec. 2021. All patients underwent T1-weighted contrast-enhanced MRI scans. The PyRadiomics package was used to extract features from the MRI images based on the intraclass correlation coefficient and least absolute shrinkage and selection operator. The most predictive features were used to develop the radiomics signature. The Chi-Square test, Fisher’s exact test, Student’s t-test, and Mann–Whitney U test were used to evaluate the clinical and pathological characteristics between the high- and low-level Ki-67 groups and the HER-2 positive/negative groups. The radiomics models were compared using receiver operating characteristic curve analysis. The area under the receiver operating characteristic curve (AUC), sensitivity (SEN), and specificity (SPE) were generated as comparison metrics. From the spinal MRI scans, five and two features were identified as the most predictive for the Ki-67 level and HER-2 status, respectively. The developed radiomics signatures generated good prediction performance for the Ki-67 level in the training (AUC = 0.812, 95% CI: 0.710–0.914, SEN = 0.667, SPE = 0.846) and validation (AUC = 0.799, 95% CI: 0.652–0.947, SEN = 0.722, SPE = 0.833) cohorts. Good prediction performance for the HER-2 status was also achieved in the training (AUC = 0.796, 95% CI: 0.686–0.906, SEN = 0.720, SPE = 0.776) and validation (AUC = 0.705, 95% CI: 0.506–0.904, SEN = 0.733, SPE = 0.762) cohorts. The results of this study provide a better understanding of the potential clinical implications of spinal MRI-based radiomics on the prediction of Ki-67 levels and HER-2 status in breast cancer.</p