Acoustic emission characterization of microcracking in laboratory-scale hydraulic fracturing tests

Understanding microcracking near coalesced fracture generation is critically important for hydrocarbon and geothermal reservoir characterization as well as damage evaluation in civil engineering structures. Dense and sometimes random microcracking near coalesced fracture formation alters the mechanical properties of the nearby virgin material. Individual microcrack characterization is also significant in quantifying the material changes near the fracture faces （i.e. damage）. Acoustic emission （AE） monitoring and analysis provide unique information regarding the microcracking process temporally, and infor- mation concerning the source characterization of individual microcracks can be extracted. In this context, laboratory hydraulic fracture tests were carried out while monitoring the AEs from several piezoelectric transducers. In-depth post-processing of the AE event data was performed for the purpose of under- standing the individual source mechanisms. Several source characterization techniques including moment tensor inversion, event parametric analysis, and volumetric deformation analysis were adopted. Post-test fracture characterization through coring, slicing and micro-computed tomographic imaging was performed to determine the coalesced fracture location and structure. Distinct differences in fracture characteristics were found spatially in relation to the openhole injection interval. Individual microcrack AE analysis showed substantial energy reduction emanating spatially from the injection interval. It was quantitatively observed that the recorded AE signals provided sufficient information to generalize the damage radiating spatially away from the injection wellbore.

Multi-Model Ensemble Deep Learning Method to Diagnose COVID-19 Using Chest Computed Tomography Images

Deep learning based analyses of computed tomography(CT)images contribute to automated diagnosis of COVID-19,and ensemble learning may commonly provide a better solution.Here,we proposed an ensemble learning method that integrates several component neural networks to jointly diagnose COVID-19.Two ensemble strategies are considered:the output scores of all component models that are combined with the weights adjusted adaptively by cost function back propagation;voting strategy.A database containing 8347 CT slices of COVID-19,common pneumonia and normal subjects was used as training and testing sets.Results show that the novel method can reach a high accuracy of 99.37%(recall:0.9981;precision:0.9893),with an increase of about 7% in comparison to single-component models.And the average test accuracy is 95.62%(recall:0.9587;precision:0.9559),with a corresponding increase of 5.2%.Compared with several latest deep learning models on the identical test set,our method made an accuracy improvement up to 10.88%.The proposed method may be a promising solution for the diagnosis of COVID-19.

ARSlice:Head-Mounted Display Augmented with Dynamic Tracking and Projection

Computed tomography(CT)generates cross-sectional images of the body.Visualizing CT images has been a challenging problem.The emergence of the augmented and virtual reality technology has provided promising solutions.However,existing solutions suffer from tethered display or wireless transmission latency.In this paper,we present ARSlice,a proof-of-concept prototype that can visualize CT images in an untethered manner without wireless transmission latency.Our ARSlice prototype consists of two parts,the user end and the projector end.By employing dynamic tracking and projection,the projector end can track the user-end equipment and project CT images onto it in real time.The user-end equipment is responsible for displaying these CT images into the 3D space.Its main feature is that the user-end equipment is a pure optical device with light weight,low cost,and no energy consumption.Our experiments demonstrate that our ARSlice prototype provides part of six degrees of freedom for the user,and a high frame rate.By interactively visualizing CT images into the 3D space,our ARSlice prototype can help untrained users better understand that CT images are slices of a body.

TbF3 nanoparticles as dual-mode contrast agents for ultrahigh field magnetic resonance imaging and X-ray computed tomography

Considering the development of magnetic resonance imaging （MRI） under ultrahigh magnetic field （〉3 T）, the exploration of novel contrast agents （CAs） for ultrahigh field MRI is urgently needed. Herein, we report polyethyleneimine （PEI）-coated TbF3 nanoparticles （NPs）, which were synthesized by a facile solvothermal method, as potential dual-mode CAs for ultrahigh field MRI and X-ray computed tomography （CT）. Owing to their strong paramagnetism, the TbF3 NPs showed excellent transverse relaxivity （395.77 mM-l.s-1） and negligible longitudinal relaxivity under an ultrahigh magnetic field （7 T） with a great potential as a T2-weighted MRI contrast agent. Furthermore, by comparison with the clinically used CT CAs （iohexol）, the TbF3 NPs showed superior X-ray attenuation ability. The practical application for T2-weighted MRI and CT imaging was demonstrated with an animal model. Moreover, cell cytotoxicity and in vivo toxicity assessments implied the low toxicity of TbF3 NPs. In summary the above results indicate that TbF3 NPs are promising candidates for ultrahigh field MRI and CT dual-mode imaging.