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The lateral loading of a plate anchor is a complicated process that involves complex anchor-soil interactions. The deformation characteristics of the soil around an anchor have an important effect on its lateral bearing capacity. In this paper, the noncontact digital image correlation (DIC) technique is used to study the distribution and variation of the soil deformation field under a laterally loaded anchor in sandy soil. The results show that the sand density and embedment ratio significantly affect the distribution and influence range of the active and passive zones around the anchor. The active zone behind the rod gradually decreases with increasing sand density until ultimately disappearing, and the passive zone increases. The maximum influence height occurs in the passive zone behind the rod in dense sand, and the influence range of the passive zone in front of the rod expands with an increasing embedment ratio. Shear bands form during the lateral loading process, which are accompanied by dilatancy in the shear process. The motion path of the rotation center in loose and medium sand is initially rigid translational and then becomes rigid rotational, while the opposite trend occurs in compact dense sand. The results provide important guidance for the development of predictive models for anchor lateral loading and design. [ABSTRACT FROM AUTHOR]

Background. As a chronic progressive disease, diabetes mellitus (DM) has a high incidence worldwide, and it impacts on cognitive and learning abilities in the lifetime even in the early stage, may degenerate memory in middle age, and perhaps increases the risk of Alzheimer's disease. Method. In this work, we propose a convolutional neural network (CNN) based classification method to help classify diabetes by distinguishing the brains with abnormal functions from the normal ones on resting-state functional magnetic resonance imaging (rs-fMRI). The proposed classification model is based on the Inception-v4-Residual convolutional neural network architecture. In our workflow, the original rs-fMRI data are first mapped to generate amplitude of low-frequency fluctuation (ALFF) images and then fed into the CNN model to get the classification result to indicate the potential existence of DM. Result. We validate our method on a realistic clinical rs-fMRI dataset, and the achieved average accuracy is 89.95 % in fivefold cross-validation. Our model achieves a 0.8690 AUC with 77.50% and 77.51% sensitivity and specificity using our local dataset, respectively. Conclusion. It has the potential to become a novel clinical preliminary screening tool that provides help for the classification of different categories based on functional brain alteration caused by diabetes, benefiting from its accuracy and robustness, as well as efficiency and patient friendliness. [ABSTRACT FROM AUTHOR]

This paper proposed a prewhitening invariance of noise space (PW-INN) as a new magnetoencephalography (MEG) source analysis method, which is particularly suitable for localizing closely spaced and highly correlated cortical sources under real MEG noise. Conventional source localization methods, such as sLORETA and beamformer, cannot distinguish closely spaced cortical sources, especially under strong intersource correlation. Our previous work proposed an invariance of noise space (INN) method to resolve closely spaced sources, but its performance is seriously degraded under correlated noise between MEG sensors. The proposed PW-INN method largely mitigates the adverse influence of correlated MEG noise by projecting MEG data to a new space defined by the orthogonal complement of dominant eigenvectors of correlated MEG noise. Simulation results showed that PW-INN is superior to INN, sLORETA, and beamformer in terms of localization accuracy for closely spaced and highly correlated sources. Lastly, source connectivity between closely spaced sources can be satisfactorily constructed from source time courses estimated by PW-INN but not from results of other conventional methods. Therefore, the proposed PW-INN method is a promising MEG source analysis to provide a high spatial-temporal characterization of cortical activity and connectivity, which is crucial for basic and clinical research of neural plasticity. [ABSTRACT FROM AUTHOR]