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Modulus reduction and damping ratio variation with small shear strain amplitudes that are important in dynamic calculations are measured by a resonant column apparatus. These curves are made up of shear modulus, damping ratio and shear strain amplitude at which the shear modulus and damping ratio are measured. There is research into the reliability of the shear modulus and damping ratio measured by the apparatus, but the reliability of shear strain measurement has not been explored. The research questions herein are the reliability of shear strain measurements and the cause of probable deviations. Shear strain amplitudes in the apparatus are measured by its motion transducers. We used additional local strain gauges fixed to dummy specimens to double-check the shear strain amplitude measurements and answer the above questions. The obtained results include satisfying similarity in medium ranges of frequency and shaking amplitudes. However, the key finding is that deviation in the measurement results beyond the above-mentioned frequencies indicates that reliability of shear strain amplitudes in resonant column tests is of major concern; deviation of shear strain amplitudes can affect the interpreted soil properties and the results of the calculations based on these properties. The probable reasons for such differences between measurements of the strain gauge with those of the resonant column accelerometer are mentioned in the paper for further research. The novelty of the study is that it shows the necessity of a more thorough investigation on the measuring method and interpretation of the vibration amplitude in resonant column apparatuses. [ABSTRACT FROM AUTHOR]

This paper investigates the performance of grid and mat foundations under two- and five-storey buildings in an earthquake loading. A detailed study was conducted on a defined area in a northern region of Iran where the ground is covered with thick soft fine-grained soil strata with a potential of cyclic softening. Overall, the observed behavior shows better performance of the grid foundation modeled in this study. Its performance exceeds in its bearing capacity, which is higher than that of the mat foundation with the same size. The relative vertical displacement of the corners of the grid foundation is almost the same or even less than that of the mat foundation after an earthquake. On the contrary, the total average displacement of the mat foundation is larger than that of the grid foundation. Further, there is concentration of stresses in local areas at sides in the mat foundation, in contrast to grid foundation, in which a nearly uniform distribution of stress is observed all over the foundation., Taken together, the structure of the grid foundation employs its capacity nearly fully, making it a more economical option in comparison to the mat foundation of the same size. Based on findings from this study, consideration of grid foundation as an alternative of mat foundation is recommended. [ABSTRACT FROM AUTHOR]

This paper aims to evaluate the general code spectrum of the Iranian code of practice for seismic-resistant design of buildings. The general code spectrum at nine sites with different site classes within the mega city of Tehran is compared with those of ASCE 7–16 and a site-specific design spectrum. It is shown that though the general code spectrum offered by the Iranian code captures far-field site spectra, it cannot address the motion on the bedrock for some of the near-fault sites and thus may not be conservative for near-fault sites of classes B and C. Consideration of proper near-fault correction factors would make the general code spectrum more conservative, as shown via the case study in this paper. The base acceleration on bedrock of the Iranian code is recommended to be a function of the site-to-source distance. In line with ASCE 7–16, the base acceleration can be defined for a short and long period, being adjusted for directivity effects. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the diagnostic performance of susceptibility-weighted imaging (SWI) in identifying acute intraarticular hemorrhages and differentiating blood from other types of joint effusions. Methods: Thirty-two patients (21 men, 11 women; mean age 38.7 ± 16.5 SD) clinically suspected of having joint effusion were prospectively included. All the patients underwent both conventional MRI and SWI. Two radiologists independently reviewed the conventional MRI images and scored the likelihood of intraarticular hemorrhage using a 5-level scoring system. Immediately thereafter, SWI images of each patient were also provided for the radiologists, and the scoring was repeated evaluating the conventional MRI and SWI images together. The patients underwent joint aspiration or surgical operation as the reference standard. The area under the curve (AUC) of conventional MRI and conventional MRI + SWI methods were calculated and compared. The weighted kappa analysis was used to evaluate the interobserver agreement. Results: Traumatic knee injury comprised the majority of study sample. Eighteen out of 32 of the patients were proven to have intraarticular hemorrhage. Using the conventional MRI, reader 1 and 2 achieved AUCs of 0.67 (p = 0.09) and 0.53 (p = 0.76), respectively. Following the addition of SWI, reader 1 and 2 achieved AUCs of 0.96 (p = 0.0001) and 0.95 (p = 0.0001), respectively, and interobserver agreement improved from Κ = 0.61 to Κ = 0.93. Accordingly, difference between the AUCs was 0.28 (p = 0.003) and 0.42 (p = 0.0001) for reader 1 and 2, respectively. Conclusions: If confirmed by future studies, SWI enables the reliable and accurate diagnosis of acute intraarticular hemorrhages. [ABSTRACT FROM AUTHOR]

Background: It has been reported that acute stroke services were compromised during COVID-19 due to various pandemic-related issues. We aimed to investigate these changes by recruiting centers from different countries.Methods: Eight countries participated in this cross-sectional, observational, retrospective study by providing data from their stroke data base. We compared 1 year before to 1 year during COVID-19 as regards onset to door (OTD), door to needle (DTN), door to groin (DTG), duration of hospital stay, National Institute of Health Stroke Scale (NIHSS) at baseline, 24 h, and at discharge as well as modified Rankin score (mRS) on discharge and at 3 months follow-up.Results: During the pandemic year, there was a reduction in the number of patients, median age was significantly lower, admission NIHSS was higher, hemorrhagic stroke increased, and OTD and DTG showed no difference, while DTN time was longer, rtPA administration was decreased, thrombectomy was more frequent, and hospital stay was shorter. mRS was less favorable on discharge and at 3 months.Conclusion: COVID-19 showed variable effects on stroke services. Some were negatively impacted as the number of patients presenting to hospitals, DTN time, and stroke outcome, while others were marginally affected as the type of management. [ABSTRACT FROM AUTHOR]

Purpose: Mastoid abnormalities show different types of ear illnesses, however inadequacy of experts and low accuracy of diagnostic demand a new approach to detect these abnormalities and reduce human mistakes. The manual analysis of mastoid CT scans is time-consuming and labor-intensive. In this paper the first and robust deep learning-based approaches is introduced to diagnose mastoid abnormalities using a large database of CT images obtained in the clinical center with remarkable accuracy. Methods: In this paper, mastoid abnormalities are classified using the Xception based Convolutional Neural Network (CNN) model, with optimizer Adamax into five categories (Complete pneumatized, Opacification in pneumatization, Partial pneumatization, Opacification in partial pneumatization, None pneumatized). For this reason, a total of 24,800 slides of 152 patients were selected that include the mastoid from most upper to the lowest part of the middle ear cavity to complete the construction of the proposed deep neural network model. Results: The proposed model had the best accuracy of 87.80% (based on grader 1) and 88.44% (based on grader 2) on the 20th epoch and 87.70% (based on grader 1) and 87.56% (based on grader 2) on average and also significantly faster than other types of implemented architectures in terms of the computer running time (in seconds). The 99% confidence interval of the average accuracy was 0.012 which means that the true accuracy is 87.80% and 87.56% ± 1.2% that indicates the power of the model. Conclusions: The manual analysis of ear cavity CT scans is often time-consuming and prone to errors due to various inter- or intra operator variability studies. The proposed method can be used to automatically analyze the middle ear cavity to classify mastoid abnormalities, which is markedly faster than most types of models with the highest accuracy. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the safety and effectiveness of core needle biopsy (CNB) under the assistance of hydrodissection (HDS). Materials and Methods: Of 2325 patients requiring biopsy of thyroid lesions, 21 high-risk patients with subcapsular nodules smaller than 10 mm were recruited into this study. All patients underwent HDS with 0.9% saline solution followed by ultrasound (US)-guided CNB with an 18-gauge semi-automated biopsy needle. The separation success rate (SSR) of the HDS, technical success rate (TSR) of CNB, histopathologic success rate (HSR), and complications were assessed. Results: Both the SSR of HDS and TSR of CNB were 100% (21/21). The HSR of the thyroid nodules was 85.7% (18/21). No major complications were recorded. Conclusion: HDS before CNB can successfully lead to safe biopsy of small subcapsular thyroid nodules. Level of Evidence: Level 4, Case Series [ABSTRACT FROM AUTHOR]