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Previous studies have primarily investigated the seismic behavior of nailed and anchored walls under only a single seismic excitation. This study aims to prepare a precise numerical model, being capable of accurately simulating a fully instrumented 50g centrifuge test on a soil-nailed wall in medium-dense sand subjected to three sequential cyclic loadings. Both two-dimensional and three-dimensional analyses were performed, and the results were compared to assess the impact of neglecting the third dimension. The results of the numerical simulations, i.e., wall deformation, ground settlement, soil acceleration, and failure pattern of the excavation wall, compared well with those observed in the previously conducted centrifuge test. Additionally, a sensitivity analysis of the soil constitutive model was conducted to evaluate the influence of the model parameters on simulation results. The simulations showed, firstly that soil acceleration in a free field area, away from the excavation wall, matched the recordings from the centrifuge tests with a higher degree of accuracy, compared to observations in the area adjacent to the wall that included soil–nail-wall interactions. Secondly, examination of the model's plastic points during cyclic loadings indicated that failure was initiated around the nails, leading to the slide of the nails through the soil. The results of this study confirmed that a three-dimensional numerical model, given the actual position of the nails in all directions, and benefiting from proper soil constitutive model and structural elements for nails, can predict deformations of an excavation in a seismic event relatively satisfactorily.

This paper describes a newly generated ground response analysis software package at the International Institute of Earthquake Engineering and Seismology, called SeisGRASP. The package is user-friendly benefiting from graphical interfaces in MATLAB programming environment, calling an upgraded version of the SHAKE program to perform equivalent-linear analysis in frequency domain. The package contains a preprocessor, capable of easy production of plenty of geotechnical profiles with numerous layers along with definition and correction of several input motions. The package also offers a library of soil models and input motions. After applying SHAKE code to analyze the profiles, a postprocessor enables the user to manage a huge number of outputs generated by the code. It also creates spreadsheets of the selective outputs that makes it straightforward to use. Overall, SeisGRASP facilitates performance of huge amounts of site response analyses as effortlessly as possible.

The abnormality of mastoid air cells represents various types of ear maladies. The current traditional manual analyzing of huge amount of collected images from ear cavity is time consuming and the low accuracy of diagnosing these abnormalities by humans is inevitable, thus the subsequent consequences could threaten the patient's health. This study presents an automated machine learning based method to classify normal and abnormal mastoid air cells using CT images procured in the clinical center. Methods This paper introduces the first robust method based on convolutional layers and deep neural network to classify normal and abnormal mastoid air cells. The used dataset is comprised of total of 24,800 (right and left mastoid) CT slides of 152 patients who have been referred to the Tabriz Golgasht Imaging Center(TGIC) at the request of the ENT specialist which include the mastoid air cells from most upper to the lowest part of the ear cavity. Results The proposed fully automatic classification and diagnosing method provides a promising result compared to the manual classification by ENT specialists. In our classification algorithm the accuracy, f 1_score, Precision, Recall, were 98.10%, 98.05%, 98.32%, 97.89% respectively(over the five-fold cross-validation on validation dataset) and the accuracy of this method on test data was 97.56% (the average of 5 times running of five-fold cross-validation). The robustness and efficiency of the proposed method are demonstrated by comparison with some of most common deep learning architectures ResNet50 and AlexNet. Conclusions The proposed machine learning method directly learned from B-scan labels, requiring no manual detailed annotations at image. Medically, the image investigation of ear CT scan images mainly remains at the doctor’s manual diagnosis stage, but manual examination and diagnosis could be labor intensive and time consuming. In this paper, a deep convolutional neural network (ConvNet) is used to achieve automatic classification of mastoid air cells using CT images by analyzing the characteristics of the patient’s CT images of the ear cavity.

This paper focuses on generating a set of equations to estimate the amplitude and number of equivalent uniform shear stress cycles caused by earthquakes in the core of embankment dams. The set of equations of the estimate is verified by two reference case studies of Alborz and Polrood Dams, in Iran. Based on the findings from this investigation, an estimate of the shear stress variation at the middle levels of a dam core caused by an input motion is suggested, drawing on a few geometric parameters of the dam, a number of general constitutive parameters of the core material and particular seismic parameters of the input motion. The investigation has implications for the following areas: first, the generated estimation is a useful tool to inspect seismic stability of embankment dams. Second, uniform cycles of loading estimated by the equations proposed in this investigation can be exerted on soil elements in laboratory experiments; such experiments are necessary to calibrate the advanced constitutive models that are dependent on a loading path.

Background: Diffusion-weighted imaging (DWI) is a novel imaging technique with growing application in onco-imaging. This modality evaluates the diffusion of water molecules in various tissues, which is restricted in hyper cellular regions such as malignant tissue. Apparent diffusion co-efficient (ADC) is a method which can quantify the degree of restriction in tissues and can have diagnostic roles in characterization of hepatic lesions. In this study, 93 patients with proven hepatic lesions were included. These patients had undergone initial evaluation via ultrasonography and dynamic CT scan, and had a definite diagnosis confirmed by biopsy. These patients underwent DW imaging and ADC values of their lesions were calculated. Patients were divided into two groups, benign and malignant groups, based on their biopsy results; and ADC values of hepatic lesions were compared in the two groups. Results: The two groups were gender matched. There was a significant difference in the age distribution between the two groups. Mean ADC values for benign and malignant hepatic lesions were 1.58 ± 0.35 (10-3mm2/s) and 0.87 ± 0.16 (10-3mm2/s), respectively. There was a statistically significant differences between benign and malignant hepatic lesions (pvalue < 10-3). DW imaging had a sensitivity of 97.6% and specificity of 98.7% in detecting malignant hepatic lesions from benign ones (p= 0.0001, AUC = 0.99). Conclusion: DW MRI imaging can differentiate malignant and benign liver lesions with high sensitivity and specificity using ADC values generated; furthermore, each subgroup of hepatic lesions could be determined based on ADC values.