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This study provides a detailed analysis of the aerodynamic performance of various airfoil configurations, focusing on lift coefficient, stall characteristics, and maximum lift-to-drag ratio. The investigation includes the NACA23012C profile and configurations with different step geometries, ranging from one-step to five-step designs. Experimental measurements were conducted using a well-equipped aerodynamic laboratory, Institute of Aviation Engineering and Technology (IAET), Giza, Egypt. The lab features a wind tunnel, propeller test rig, and data acquisition system. The experiments were conducted meticulously to ensure accuracy and reproducibility, with a standardized method employed for uncertainty analysis. The results reveal distinct aerodynamic behaviors among the different configurations, highlighting the significant impact of design variations on aerodynamic performance. Notably, the three-step configuration consistently exhibited high performance, with a competitive or superior lift coefficient across a range of Reynolds numbers, showing an improvement of up to 35.1 %. Similarly, the four-step configuration demonstrated substantial increases in lift-to-drag ratios, reaching up to 53.2 %, while the five-step configuration exhibited varying trends with a minimum drag coefficient. The study also investigated stall characteristics and sensitivity to Reynolds numbers, revealing the complex trade-offs inherent in airfoil design. The findings provide valuable insights into optimizing airfoil performance under different operational conditions. Additionally, the adoption of two and three stepped airfoils resulted in significant reductions in blade material and associated costs for turbine blades.

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Purpose To investigate the feasibility of using quantitative MR elastography (MRE) to characterize the influence of aging and sex on left ventricular (LV) shear stiffness. Materials and Methods In this prospective study, LV myocardial shear stiffness was measured in 109 healthy volunteers (age range: 18-84 years; mean age, 40 years ± 18 [SD]; 57 women, 52 men) enrolled between November 2018 and September 2019, using a 5-minute MRE acquisition added to a clinical MRI protocol. Linear regression models were used to estimate the association of cardiac MRI and MRE characteristics with age and sex; models were also fit to assess potential age-sex interaction. Results Myocardial shear stiffness significantly increased with age in female (age slope = 0.03 kPa/year ± 0.01, P = .009) but not male (age slope = 0.008 kPa/year ± 0.009, P = .38) volunteers. LV ejection fraction (LVEF) increased significantly with age in female volunteers (0.23% ± 0.08 per year, P = .005). LV end-systolic volume (LVESV) decreased with age in female volunteers (-0.20 mL/m2 ± 0.07, P = .003). MRI parameters, including T1, strain, and LV mass, did not demonstrate this interaction (P > .05). Myocardial shear stiffness was not significantly correlated with LVEF, LV stroke volume, body mass index, or any MRI strain metrics (P > .05) but showed significant correlations with LV end-diastolic volume/body surface area (BSA) (slope = -3 kPa/mL/m2 ± 1, P = .004, r2 = 0.08) and LVESV/BSA (-1.6 kPa/mL/m2 ± 0.5, P = .003, r2 = 0.08). Conclusion This study demonstrates that female, but not male, individuals experience disproportionate LV stiffening with natural aging, and these changes can be noninvasively measured with MRE. Keywords: Cardiac, Elastography, Biological Effects, Experimental Investigations, Sexual Dimorphisms, MR Elastography, Myocardial Shear Stiffness, Quantitative Stiffness Imaging, Aging Heart, Myocardial Biomechanics, Cardiac MRE Supplemental material is available for this article. Published under a CC BY 4.0 license.

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This paper presents a comprehensive dataset comprising experimental test data and numerical simulations of hole bearing tests involving 32 single-bolt, 20 two-bolt, and 20 four-bolt specimens. The dataset encompasses load-deformation curves obtained from experimental tests and displacement data acquired via the Digital Image Correlation (DIC) system, which covers specific regions of the specimens. Additionally, the dataset incorporates force-deformation curves derived from corresponding numerical simulations. The numerical simulation procedure is outlined, involving a simplified model employing solid elements for the specimen and rigid shell elements for the bolts. A "hard-contact" is employed to define the normal behavior of surface-to-surface contact between the specimen and the bolts. Material behavior modeling utilizes true stress-strain curves obtained from experimental tensile tests, encompassing both material properties extracted from these tests and the ensuing input parameters for numerical simulations. Furthermore, the DIC-system measurements provide data on displacements and strain distributions across various regions of the specimens. These strain measurements are meticulously evaluated and presented. The validation of the numerical simulations against experimental results substantiates the robustness of the numerical methodology, instilling confidence in its application for simulating bearing-type connections. Moreover, this dataset serves as a valuable resource for comparative analysis, enhancing the comprehension of these connections, and providing reference points for further numerical simulations.

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Recently, there has been a growing interest in the use of sandwich panels that, beyond handling well-known bending stress, can withstand torsional stresses. This is particularly relevant for wall applications when the panels are equipped with photovoltaic or supplemental curtain wall modules. This research article presents a detailed exploration of the structural behavior of eccentrically loaded sandwich panels, with a specific focus on warping torsion. Experimental and numerical studies were conducted on polyisocyanurate (PU) core sandwich panels, commonly employed in building envelopes. These studies involved various dimensions and material properties, while omitting longitudinal joints. The experimental study provided essential insights and validated the numerical model in ANSYS. Enabling parametric variation, the numerical analysis extends the analysis beyond the experimental scope. Results revealed a high degree of correlation between experimental, numerical, and analytical solutions, regarding the rotation, as well as the normal and shear stress of the panel. Confirming the general applicability of warping torsion in sandwich panels with certain limitations, the study contributes valuable data for applications and design of eccentrically loaded sandwich panels, laying the foundation for potential engineering calculation methods.

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In the modern theory of compressed concrete elements, the most attention is paid to longitudinal deformations, whereas transverse ones are rarely considered and just within Poisson's coefficient limits (i.e., elastic concrete behavior in the transverse direction). However, transverse deformations significantly develop beyond the limits corresponding to Poisson's coefficient, where they lead to longitudinal crack initiation and development. In-depth experimental and numerical investigations of transverse deformations in the inelastic stage showed that it is necessary to consider crack propagation. The present study proposes simultaneous consideration of longitudinal and transverse deformations, as well as the appearance of cracks and their widths and depths. This allowed us to obtain a complete compressed concrete element behavior pattern at all performance stages in two types of limit states (based on longitudinal and transverse deformations). Consequently, new ultimate limit states by the depth and width of cracks caused by transverse deformations are proposed to be included in modern design practices and codes.

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Radiographic markers contain protected health information that must be removed before public release. This work presents a deep learning algorithm that localizes radiographic markers and selectively removes them to enable de-identified data sharing. The authors annotated 2000 hip and pelvic radiographs to train an object detection computer vision model. Data were split into training, validation, and test sets at the patient level. Extracted markers were then characterized using an image processing algorithm, and potentially useful markers (eg, "L" and "R") without identifying information were retained. The model achieved an area under the precision-recall curve of 0.96 on the internal test set. The de-identification accuracy was 100% (400 of 400), with a de-identification false-positive rate of 1% (eight of 632) and a retention accuracy of 93% (359 of 386) for laterality markers. The algorithm was further validated on an external dataset of chest radiographs, achieving a de-identification accuracy of 96% (221 of 231). After fine-tuning the model on 20 images from the external dataset to investigate the potential for improvement, a 99.6% (230 of 231, P = .04) de-identification accuracy and decreased false-positive rate of 5% (26 of 512) were achieved. These results demonstrate the effectiveness of a two-pass approach in image de-identification. Keywords: Conventional Radiography, Skeletal-Axial, Thorax, Experimental Investigations, Supervised Learning, Transfer Learning, Convolutional Neural Network (CNN) Supplemental material is available for this article. © RSNA, 2023 See also the commentary by Chang and Li in this issue.

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The exploration of cost-effective and sustainable biomass-derived carbon materials as electrodes for energy conversion and storage has gained extensive attention in recent research studies. However, the selection of the biomass and the electrochemical performance regulation of the derived biochar, as well as their interrelationship still remain challenging for practical application. Herein, corn wastes with high carbon content (>40%), corn cob and corn silk, were selected as precursors for the preparation of high value-added and high yield carbon materials via a modified synthetic process. Uniquely, this work put emphasis on the theoretical and experimental investigations of how the biomass properties influence the composition and nanostructure regulation, the electrolyte ion adsorption free energy, and the electrical conductivity of the derived carbon materials as well as their electrochemical performance optimization. Owing to the favorable specific surface area, the hierarchical porous structure, and the diverse elemental distribution, corn cob and corn silk derived carbon materials (CBC and SBC) present great potential as promising electrodes for alkaline aqueous zinc batteries and supercapacitors. The assembled CBC//Zn and SBC//Zn zinc batteries deliver high energy densities of 63.0 Wh kg-1 and 39.1 Wh kg-1 at a power density of 575 W kg-1, with excellent cycling performance of 91.1% and 84.3% capacitance retention after 10,000 cycles. As for the assembled symmetric supercapacitors, high energy densities of 14.9 Wh kg-1 and 13.6 Wh kg-1, and superior long-term cycling stability of 99.3% and 96.6% capacitance retention after 20,000 cycles could be achieved. This study highlights the advantages of utilizing corn cob and corn silk as carbon sources on the designed synthesis of carbon electrodes, and presents a meaningful perspective in the investigation of biomass-derived carbon materials and their potential applications in rechargeable devices.

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The purpose of this paper is to present a new approach for the modelling of post-tensioned beams with calibration of the FE model to experimental results until the load capacity and post-critical state are reached. Two post-tensioned beams with different nonlinear tendon layouts were analysed. Material testing for concrete, reinforcing steel and prestressing steel was performed prior to the experimental testing of the beams. The Hypermesh program was used to define the geometry of the spatial arrangement of the finite elements of the beams. The Abaqus/Explicit solver was used for numerical analysis. The concrete damage plasticity model was used to describe the behaviour of concrete with different laws of elastic-plastic stress-strain evolution for compression and tension. Elastic-hardening plastic constitutive models were used to describe the behaviour of steel components. An effective approach to modelling the load was developed, supported by the use of Rayleigh mass damping in an explicit procedure. The presented model approach ensures good agreement between numerical and experimental results. The crack patterns obtained in concrete reflect the actual behaviour of structural elements at every loading stage. Random imperfections found during experimental studies on the results of numerical analyses were also discussed.

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It is widely recognized that the quality of fruits and vegetables can be altered during transportation and storage. Firmness and loss of weight are the crucial attributes used to evaluate the quality of various fruits, as many other quality attributes are related to these two attributes. These properties are influenced by the surrounding environment and preservation conditions. Limited research has been conducted to accurately predict the quality attributes during transport and storage as a function of storage conditions. In this research, extensive experimental investigations have been conducted on the changes in quality attributes of four fresh apple cultivars (Granny Smith, Royal Gala, Pink Lady, and Red Delicious) during transportation and storage. The study evaluated the weight loss and change in firmness of these apples varieties at different cooling temperatures ranging from 2 °C to 8 °C to assess the impact of storing at these temperatures on the quality attributes. The results indicate that the firmness of each cultivar continuously decreased over time, with the R2 values ranging from 0.9489-0.8691 for red delicious, 0.9871-0.9129 for royal gala, 0.9972-0.9647 for pink lady, and 0.9964-0.9484 for granny smith. The rate of weight loss followed an increasing trend with time, and the high R2 values indicate a strong correlation. The degradation of quality was evident in all four cultivars, with temperature having a significant impact on firmness. The decline in firmness was found to be minimal at 2 °C, but increased as the storage temperature increased. The loss of firmness also varied among the four cultivars. For instance, when stored at 2 °C, the firmness of pink lady decreased from an initial value of 8.69 kg·cm2 to 7.89 kg·cm2 in 48 h, while the firmness of the same cultivar decreased from 7.86 kg·cm2 to 6.81 kg·cm2 after the same duration of storage. Based on the experimental results, a multiple regression quality prediction model was developed as a function of temperature and time. The proposed models were validated using a new set of experimental data. The correlation between the predicted and experimental values was found to be excellent. The linear regression equation yielded an R2 value of 0.9544, indicating a high degree of accuracy. The model can assist stakeholders in the fruit and fresh produce industry in anticipating quality changes at different storage stages based on the storage conditions.

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Structures in various industries are exposed to dynamic loads. The dissipative properties of adhesively bonded joints can contribute to the damping of dynamically stressed structures. Dynamic hysteresis tests are carried out to determine the damping properties of adhesively bonded overlap joints by varying the geometry and test boundary conditions. The dimensions of the overlap joints are full-scale and thus relevant for steel construction. Based on the results of experimental investigations, a methodology is developed for the analytic determination of the damping properties of adhesively bonded overlap joints for various specimen geometries and stress boundary conditions. For this purpose, dimensional analysis is carried out using the Buckingham Pi Theorem. In summary, it can be stated that the loss factor of adhesively bonded overlap joints investigated within this study is in the range between 0.16 and 0.41. The damping properties can particularly be increased by increasing the adhesive layer thickness and reducing the overlap length. The functional relationships of all the test results shown can be determined by dimensional analysis. An analytical determination of the loss factor considering all identified influencing factors is enabled by derived regression functions with a high coefficient of determination.

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Purpose To investigate ferumoxytol (FMX)-enhanced MRI as a pretreatment predictor of response to liposomal irinotecan (nal-IRI) for thoracoabdominal and brain metastases in women with metastatic breast cancer (mBC). Materials and Methods In this phase 1 expansion trial (ClinicalTrials.gov identifier, NCT01770353; 27 participants), 49 thoracoabdominal (19 participants; mean age, 48 years ± 11 [SD]) and 19 brain (seven participants; mean age, 54 years ± 8) metastases were analyzed on MR images acquired before, 1-4 hours after, and 16-24 hours after FMX administration. In thoracoabdominal metastases, tumor transverse relaxation rate (R*2) was normalized to the mean R*2 in the spleen (rR*2), and the tumor histogram metric rR*2,N, representing the average of rR*2 in voxels above the nth percentile, was computed. In brain metastases, a novel compartmentation index was derived by applying the MRI signal equation to phantom-calibrated coregistered FMX-enhanced MRI brain scans acquired before, 1-4 hours after, and 16-24 hours after FMX administration. The fraction of voxels with an FMX compartmentation index greater than 1 was computed over the whole tumor (FCIGT1) and from voxels above the 90th percentile R*2 (FCIGT1 R*2,90). Results rR*2,90 computed from pretherapy MRI performed 16-24 hours after FMX administration, without reference to calibration phantoms, predicted response to nal-IRI in thoracoabdominal metastases (accuracy, 74%). rR*2,90 performance was robust to the inclusion of some peritumoral tissue within the tumor region of interest. FCIGT1 R*2,90 provided 79% accuracy on cross-validation in prediction of response in brain metastases. Conclusion This first in-human study focused on mBC suggests that FMX-enhanced MRI biologic markers can be useful for pretherapy prediction of response to nal-IRI in patients with mBC. Keywords: MRI Contrast Agent, MRI, Breast, Head/Neck, Tumor Response, Experimental Investigations, Brain/Brain Stem Clinical trial registration no. NCT01770353 Supplemental material is available for this article. © RSNA, 2023 See also commentary by Daldrup-Link in this issue.

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A cable-stiffened steel column (CSSC) possesses superior stability behaviour compared to ordinary compression columns. In the past, the research emphasis has focused on the behaviours of stiffened columns under axial compression; investigations into their behaviour under eccentric loading is scant. This study aims to examine the buckling behaviour of CSSCs under eccentric loading using experimental and numerical investigations. The effects of pretension in cables and eccentricity on stability behaviours were studied. According to the current investigation, it can be demonstrated that the capacities of CSSCs are higher than those of ordinary compression columns. It has also been illustrated that both the buckling loads and modes of CSSCs can be changed by changing the load eccentricity; however, the modes of ordinary columns cannot be changed. These results could be of theoretical and engineering significance in the exploration of the behaviours of cable-stiffened columns.

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Purpose: To train a deep natural language processing (NLP) model, using data mined structured oncology reports (SOR), for rapid tumor response category (TRC) classification from free-text oncology reports (FTOR) and to compare its performance with human readers and conventional NLP algorithms. Materials and Methods: In this retrospective study, databases of three independent radiology departments were queried for SOR and FTOR dated from March 2018 to August 2021. An automated data mining and curation pipeline was developed to extract Response Evaluation Criteria in Solid Tumors-related TRCs for SOR for ground truth definition. The deep NLP bidirectional encoder representations from transformers (BERT) model and three feature-rich algorithms were trained on SOR to predict TRCs in FTOR. Models' F1 scores were compared against scores of radiologists, medical students, and radiology technologist students. Lexical and semantic analyses were conducted to investigate human and model performance on FTOR. Results: Oncologic findings and TRCs were accurately mined from 9653 of 12 833 (75.2%) queried SOR, yielding oncology reports from 10 455 patients (mean age, 60 years ± 14 [SD]; 5303 women) who met inclusion criteria. On 802 FTOR in the test set, BERT achieved better TRC classification results (F1, 0.70; 95% CI: 0.68, 0.73) than the best-performing reference linear support vector classifier (F1, 0.63; 95% CI: 0.61, 0.66) and technologist students (F1, 0.65; 95% CI: 0.63, 0.67), had similar performance to medical students (F1, 0.73; 95% CI: 0.72, 0.75), but was inferior to radiologists (F1, 0.79; 95% CI: 0.78, 0.81). Lexical complexity and semantic ambiguities in FTOR influenced human and model performance, revealing maximum F1 score drops of -0.17 and -0.19, respectively. Conclusion: The developed deep NLP model reached the performance level of medical students but not radiologists in curating oncologic outcomes from radiology FTOR.Keywords: Neural Networks, Computer Applications-Detection/Diagnosis, Oncology, Research Design, Staging, Tumor Response, Comparative Studies, Decision Analysis, Experimental Investigations, Observer Performance, Outcomes Analysis Supplemental material is available for this article. © RSNA, 2022.

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PURPOSE: Despite the proven effectiveness of rehabilitation robots (RR) in the literature, they are still little used in clinical rehabilitation. The aim of this study was to analyse the factors influencing the use of RR and the perception of therapists who used RR. METHOD: In order to characterize the factors influencing the use of RR by therapists, a semi-structured interview was conducted with 18 therapists. These interviews are based on an interview guide inspired by the Unified Theory of Acceptance and Use of Technology model. The interviews were recorded and then transcribed, summarized and finally synthesized cross-sectionally. In addition and in parallel, the System Usability Scale (SUS) was also proposed to clinicians in order to collect quantitative data. RESULTS: The interviews highlight the facilitators perceived by the therapists, such as the intensity of the movement, the complementarity with conventional rehabilitation. The results also showed the possible barriers perceived, these can be sometimes inconclusive (e.g., bugs). The SUS results show no effect, either on the gender of the users, their therapists, or the duration of use of the tool. CONCLUSION: Better communication on the functionality of the robot and the construction of achievable goals would lead to more results that are conclusive but also better patient care. To date, and despite the evidence for the effectiveness of RRs, therapists believe that there are still many barriers to their use. They agree, however, that if changes are made, RRs will become an integral part of therapy.IMPLICATIONS FOR REHABILITATIONThe study idenfied and highlighted the factors influencing the use of the rehabilitation robot in the clinics through metric and ergonomic evaluations.The study allowed to quantify the level of acceptance of the Lokomat among therapists.This study allowed to identify negative factors that could be resolved through the implementation of a structured and generalized protocol for patients and thus improve their care.

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BACKGROUND: When performing computed tomography (CT)-guided biopsy procedures with non-disposable, automatic biopsy instruments, the actual course of the biopsy needle is not registered. PURPOSE: To evaluate the ability to visualize the sampling location after CT-guided biopsy in vitro using a novel method, where the space between the inner needle and the outer cannula in a core biopsy needle is filled with contrast media; and to compare the grade of visibility for two different concentrations of contrast media. MATERIAL AND METHODS: Core needle biopsies were performed in a tissue phantom using biopsy needles primed with two different iodine contrast media concentrations (140 mg I/mL and 400 mg I/mL). Commercially available needle-filling contraptions with sealing membranes were used to fill the needles. Each biopsy was imaged with CT, and the visibility was evaluated twice by three senior radiologists in a randomized order. RESULTS: The presence of traces was confirmed after biopsy, almost without exception for both concentrations. The visibility was sufficient to determine the biopsy location in all observations with the 400 mg I/mL filling, and in 7/10 observations with the 140 mg I/mL filling. The grade of visibility of the trace and the proportion of the biopsy needle course outlined were higher with the 400 mg I/mL filling. CONCLUSION: With CT-guided biopsy in vitro, the sampling location can be visualized using a novel method of priming the biopsy needle with iodine contrast media, specifically highly concentrated contrast media.

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PURPOSE: To reconstruct virtual MR elastography (MRE) images based on traditional MRI inputs with a machine learning algorithm. MATERIALS AND METHODS: In this single-institution, retrospective study, 149 patients (mean age, 58 years ± 12 [standard deviation]; 71 men) with nonalcoholic fatty liver disease who underwent MRI and MRE between January 2016 and January 2019 were evaluated. Nine conventional MRI sequences and clinical data were used to train a convolutional neural network to reconstruct MRE images at the per-voxel level. The architecture was further modified to accept multichannel three-dimensional inputs and to allow inclusion of clinical and demographic information. Liver stiffness and fibrosis category (F0 [no fibrosis] to F4 [significant fibrosis]) of reconstructed images were assessed by using voxel- and patient-level agreement by correlation, sensitivity, and specificity calculations; in addition, classification by receiver operator characteristic analyses was performed, and Dice score was used to evaluate hepatic stiffness locality. RESULTS: The model for predicting liver stiffness incorporated four image sequences (precontrast T1-weighted liver acquisition with volume acquisition [LAVA] water and LAVA fat, 120-second-delay T1-weighted LAVA water, and single-shot fast spin-echo T2 weighted) and clinical data. The model had a patient-level and voxel-level correlation of 0.50 ± 0.05 and 0.34 ± 0.03, respectively. By using a stiffness threshold of 3.54 kPa to make a binary classification into no fibrosis or mild fibrosis (F0-F1) versus clinically significant fibrosis (F2-F4), the model had sensitivity of 80% ± 4, specificity of 75% ± 5, accuracy of 78% ± 3, area under the receiver operating characteristic curve of 84 ± 0.04, and a Dice score of 0.74. CONCLUSION: The generation of virtual elastography images is feasible by using conventional MRI and clinical data with a machine learning algorithm.Keywords: MR Imaging, Abdomen/GI, Liver, Cirrhosis, Computer Applications/Virtual Imaging, Experimental Investigations, Feature Detection, Classification, Reconstruction Algorithms, Supervised Learning, Convolutional Neural Network (CNN) Supplemental material is available for this article. © RSNA, 2021.

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Cardiac MRI is clinically feasible in the setting of submassive pulmonary embolism and is able to demonstrate measurable differences of right heart function before and after catheter-directed therapy.

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PURPOSE: To develop a proof-of-concept convolutional neural network (CNN) to synthesize T2 maps in right lateral femoral condyle articular cartilage from anatomic MR images by using a conditional generative adversarial network (cGAN). MATERIALS AND METHODS: In this retrospective study, anatomic images (from turbo spin-echo and double-echo in steady-state scans) of the right knee of 4621 patients included in the 2004-2006 Osteoarthritis Initiative were used as input to a cGAN-based CNN, and a predicted CNN T2 was generated as output. These patients included men and women of all ethnicities, aged 45-79 years, with or at high risk for knee osteoarthritis incidence or progression who were recruited at four separate centers in the United States. These data were split into 3703 (80%) for training, 462 (10%) for validation, and 456 (10%) for testing. Linear regression analysis was performed between the multiecho spin-echo (MESE) and CNN T2 in the test dataset. A more detailed analysis was performed in 30 randomly selected patients by means of evaluation by two musculoskeletal radiologists and quantification of cartilage subregions. Radiologist assessments were compared by using two-sided t tests. RESULTS: The readers were moderately accurate in distinguishing CNN T2 from MESE T2, with one reader having random-chance categorization. CNN T2 values were correlated to the MESE values in the subregions of 30 patients and in the bulk analysis of all patients, with best-fit line slopes between 0.55 and 0.83. CONCLUSION: With use of a neural network-based cGAN approach, it is feasible to synthesize T2 maps in femoral cartilage from anatomic MRI sequences, giving good agreement with MESE scans.See also commentary by Yi and Fritz in this issue.Keywords: Cartilage Imaging, Knee, Experimental Investigations, Quantification, Vision, Application Domain, Convolutional Neural Network (CNN), Deep Learning Algorithms, Machine Learning Algorithms© RSNA, 2021.

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PURPOSE: To evaluate the feasibility and accuracy of simulated postcontrast T1-weighted brain MR images generated by using precontrast MR images in patients with brain glioma. MATERIALS AND METHODS: In this retrospective study, a three-dimensional deep convolutional neural network was developed to simulate T1-weighted postcontrast images from eight precontrast sequences in 400 patients (mean age, 57 years; 239 men; from 2015 to 2020), including 332 with glioblastoma and 68 with lower-grade gliomas. Performance was evaluated by using quantitative image similarity and error metrics and enhancing tumor overlap analysis. Performance was also assessed on a multicenter external dataset (n = 286 from the 2019 Multimodal Brain Tumor Segmentation Challenge; mean age, 60 years; ratio of men to women unknown) by using transfer learning. A subset of cases was reviewed by neuroradiologist readers to assess whether simulated images affected the ability to determine the tumor grade. RESULTS: Simulated whole-brain postcontrast images were both qualitatively and quantitatively similar to the real postcontrast images in terms of quantitative image similarity (structural similarity index of 0.84 ± 0.05), pixelwise error (symmetric mean absolute percent error of 3.65%), and enhancing tumor compartment overlap (Dice coefficient, 0.65 ± 0.25). Similar results were achieved with the external dataset (Dice coefficient, 0.62 ± 0.27). There was no difference in the ability of the neuroradiologist readers to determine the tumor grade in real versus simulated images (accuracy, 87.7% vs 90.6%; P = .87). CONCLUSION: The developed model was capable of producing simulated postcontrast T1-weighted MR images that were similar to real acquired images as determined by both quantitative analysis and radiologist assessment.Keywords: MR-Contrast Agent, MR-Imaging, CNS, Brain/Brain Stem, Contrast Agents-Intravenous, Neoplasms-Primary, Experimental Investigations, Technology Assessment, Supervised Learning, Transfer Learning, Convolutional Neural Network, Deep Learning Algorithms, Machine Learning Algorithms Supplemental material is available for this article. © RSNA, 2021.

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The purpose of the work is an experimental analysis of the behavior of reinforced concrete beams with a new, patented system of truss-shaped reinforcement. Experimental tests of reinforced concrete beams with conventional reinforcement and with truss-shaped, mass equivalent reinforcement, with two different values of longitudinal reinforcement ratio, were carried out. The testing results of the load-carrying capacity and displacements of beams are presented. The cracking and failure mechanism of beams with a new truss-shaped reinforcement system was also analyzed. The test results for conventionally reinforced beams and with truss-shaped reinforcement were compared. The test results show that the use of the truss reinforcement has an influence on increasing the load-carrying capacity of beams. The amount of this increase depends on the total longitudinal reinforcement ratio and reaches as much as 95% for beams with a low reinforcement ratio and 12% for beams with a higher reinforcement ratio. Based on the investigation of the cracking mechanism, it can be concluded that the failure of the beams with transverse truss-shaped reinforcement occurs with a greater number of smaller cracks, which are more evenly distributed along the length of the cracking zone, and have a shorter range over the cross-section depth, which results in their smaller opening widths. The comparative analysis shows the effectiveness of the proposed reinforcement system, justifying the high potential possibilities of its use for the reinforcement of concrete structural elements.