Punching Shear Strength Prediction for Reinforced Concrete Flat Slabs without Shear Reinforcement

Failure of flat slabs usually occurs by punching shear mode. Current structural codes provide an experience-based design provision for punching shear strength which is often associated with high bias and variance. This paper investigates the effect of adding a horizontal reinforcement mesh at the top of the slab-column connection zone on punching the shear strength of flat slabs. A new equation considering the effect of adding this mesh was proposed to determine the punching shear strength. The proposed equation is based on the Critical Shear Crack Theory combined with the analysis of results extracted from previous experimental and theoretical studies. Moreover, the equation of load-rotation curves for different steel ratios together with the failure criterion curves were evaluated to get the design points. The investigated parameters were the slab thicknesses and dimensions, concrete strengths, size of the supporting column, and steel ratios. The model was validated using a new set of specimens and the results were also compared with the predictions of different international design codes (ACI318, BS8110, AS3600, and Eurocode 2). Statistical analysis provides that the proposed equation can predict the punching shear strength with a level of high accuracy (Mean Square Error =2.5%, Standard Deviation =0.104, Mean=1.0) and over a wide range of reinforcement ratios and compressive strengths of concrete. Most of the predictions were conservative with an underestimation rate of 12%. Doi: 10.28991/CEJ-2022-08-01-013 Full Text: PDF

Predicting running time of aerodynamic jobs in HPC system by combining supervised and unsupervised learning method

Improving resource utilization is an important goal of high-performance computing systems of supercomputing centers. To meet this goal, the job scheduler of high-performance computing systems often uses backfilling scheduling to fill short-time jobs into job gaps at the front of the queue. Backfilling scheduling needs to obtain the running time of the job. In the past, the job running time is usually given by users and often far exceeded the actual running time of the job, which leads to inaccurate backfilling and a waste of computing resources. In particular, when the predicted job running time is lower than the actual time, the damage caused to the utilization of the system’s computing resources becomes more serious. Therefore, the prediction accuracy of the job running time is crucial to the utilization of system resources. The use of machine learning methods can make more accurate predictions of the job running time. Aiming at the parallel application of aerodynamics, we propose a job running time prediction framework SU combining supervised and unsupervised learning and verify it on the real historical data of the high-performance computing systems of China Aerodynamics Research and Development Center (CARDC). The experimental results show that SU has a high prediction accuracy (80.46%) and a low underestimation rate (24.85%).

Breast lesions excised via vacuum-assisted system: could we get any clues for B3 lesions before excision biopsy?

Background The purpose of this study was to determine the validity of the ultrasound features as well as patient characteristics assigned to B3 (uncertain malignant potential) breast lesions before vacuum-assisted excision biopsy (VAEB). Methods This study population consisted of 2245 women with breast-nodular abnormalities, which were conducted ultrasound-guided VAEB (US-VAEB). Patient’s clinical and anamnestic data and lesion-related ultrasonic feature variables of B3 captured before US-VAEB were compared with those of benign or malignant cases, using histopathological results as a benchmark. Results The proportions of benign, B3 and malignant breast lesions diagnosed post-US-VAEB were 88.5, 8.2 and 3.4% respectively. B3 high frequent occurred in BI-RADS-US grade 3 (7.7%), grade 4a (11.0%) and grade 4b (9.1%). The overall malignancy underestimation rate of B3 was 4.4% (8/183). Malignant lesions were found mostly in the range of BI-RADS grade 4b (27.3%), grade 4c (33.3%) and grade 5 (100%). Multivariate binary logistic regression analyses (B3 vs benign) showed that non-menopausal patients (95% CI 1.628–8.616, P = 0.002), single (95% CI 1.370–2.650, P = 0.000) or vascularity (95% CI 1.745–4.150, P = 0.000) nodules in ultrasonic features were significant risk factors for B3 occurrences. In addition, patients elder than 50 years (95% CI 3.178–19.816, P = 0.000), unclear margin (95% CI 3.571–14.119, P = 0.000) or suspicious calcification (95% CI 4.010–30.733, P = 0.000) lesions were significantly associated with higher risks of malignant potentials for B3 cases (malignant vs B3). Conclusion The results of this study indicate that ultrasound findings and patients’ characteristics might provide valuable information for distinguishing B3 lesions from benign breast abnormalities before VAEB, and help to reduce malignancy underestimation of B3.

Predicting Running Time of Aerodynamic Jobs in HPC System by Combining Supervised and Unsupervised Learning Method

Improving resource utilization is an important goal of high-performance computing systems of supercomputing centers. In order to meet this goal, the job scheduler of high-performance computing systems often use backfilling scheduling to fill short-time jobs into the gaps of jobs at the front of the queue. Backfilling scheduling needs to obtain the running time of the job. In the past, the job running times are usually given by users and often far exceeded the actual running time of the job, which leads to inaccurate backfilling and a waste of computing resources. In particular, when the predicted job running time is lower than the actual time, the damage caused to the utilization of the system’s computing resources becomes more serious. Therefore, the prediction accuracy of the job running time is crucial to the utilization of system resources. The use of machine learning methods can make more accurate predictions of the job running time. Aiming at the parallel application of aerodynamics, we propose a job running time prediction framework SU combining supervised and unsupervised learning, and verifies it on the real historical data of the high-performance computing systems of China erodynamics Research and Development Center(CARDC). The experimental results show that SU has a high prediction accuracy(80.46%) and a low underestimation rate(24.85%).

Criteria for Excision of Suspicious Calcifications Using the Breast Lesion Excision System (BLES)

Background: The aim of the study was to retrospectively evaluate possible imaging and histopathology criteria that can be used in a clinical basis to assess the success of excision of suspicious calcifications using the breast lesion excision system (BLES).Methods: We investigated 400 BLES stereotactic biopsies of suspicious calcifications with the mean size of 15.38 mm (st. dev.= 13.579 mm, range 3-78 mm) using a 20 mm probe performed in our department between January 2014 and 2016. The mean age of our population was 58.5 years old (range 39-78 years). The pathology results of BLES specimens were compared with the final surgical results to assess excision success rates. Possible imaging and histopathology criteria for removal were statistically analyzed (mammographic size, disease free margins, grade, comedo phenotype, molecular type).Results: The results showed that 90/400 (22.5%) biopsies were cancers (80% DCIS) and 38/400 were lesions with cell atypia (9.5%) of which 29/38 had subsequent surgery and were included in the study. Excision was achieved in 31/90 cancers (34.4%) and in 23/29 lesions with cell atypia (76.3%). The imaging and histopathology criteria for BLES excision that could be potentially clinically assessed were the initial mammographic size (p<0.001), the distance of the lesion from the specimen margins (p<0.001), the presence of comedo necrosis (p=0.014) and the grade of the cancers (p=0.021). The underestimation rate was 15.5%. Conclusion: the mammographic size, grade, comedo presence and disease-free margins, were the main criteria for BLES success rate of excision of suspicious calcifications.

Predicting running time of aerodynamic jobs in HPC system by combining supervised and unsupervised learning method

Improving resource utilization is an important goal of high-performance computing systems of supercomputing centers. In order to meet this goal, the job scheduler of high-performance computing systems often use backfilling scheduling to fill short-time jobs into the gaps of jobs at the front of the queue. Backfilling scheduling needs to obtain the running time of the job. In the past, the job running times are usually given by users and often far exceeded the actual running time of the job, which leads to inaccurate backfilling and a waste of computing resources. In particular, when the predicted job running time is lower than the actual time, the damage caused to the utilization of the system’s computing resources becomes more serious. Therefore, the prediction accuracy of the job running time is crucial to the utilization of system resources. The use of machine learning methods can make more accurate predictions of the job running time. Aiming at the parallel application of aerodynamics, we propose a job running time prediction framework SU combining supervised and unsupervised learning, and verifies it on the real historical data of the high-performance computing systems of China Aerodynamics Research and Development Center(CARDC). The experimental results show that SU has a high prediction accuracy(80.46%) and a low underestimation rate(24.85%).

Breast lesions excised via Vacuum-Assisted System: Could We Get Any Clues for B3 Lesions Before Excision Biopsy?

Background: The purpose of this study was to determine the validity of the ultrasound features as well as patient characteristics assigned to B3 (uncertain malignant potential) breast lesions before vacuum-assisted excision biopsy (VAEB).Methods: This study population consisted of 2245 women with breast-nodular abnormalities, which were conducted ultrasound-guided VAEB (US-VAEB). Patient’s clinical and anamnestic data and lesion-related ultrasonic feature variables of B3 were compared with those of benign or malignant cases, using histopathological results as a benchmark. Results: The proportions of benign, B3 and malignant breast lesions were 88.5%, 8.2% and 3.4% respectively. B3 high frequent occurred in BI-RADS-US grade 3 (7.7%), grade 4 (9.3%), grade 4a (11.3%) and grade 4b (9.1%). The overall malignancy underestimation rate of B3 cases was 4.4% (8/183). The more common malignancy underestimation of B3 subtypes were PL/ADH 50% (1/2), ADH 23.1% (3/13), and CSL/RS 16.7% (1/6). And the highly frequent recurrence rates of B3 subtypes were ADH 10% (1/10), PL 4.9% (4/82), and PT 8.6% (6/70). Multivariate binary logistic regression analyses (B3 vs benign) showed that non-menopausal patients (95% CI 1.628-8.616, P = 0.002), single (95% CI 1.370-2.650, P = 0.000) or abundant blood supply (95% CI 1.745-4.150, P = 0.000) nodules via ultrasound examination were significant risk factors for B3 occurrences. Moreover, patients elder than 50 years (95% CI 3.178-19.816, P = 0.000), unclear margin (95% CI 3.571-14.119, P = 0.000) or microcalcification (95% CI 4.010-30.733, P = 0.000) nodules were significantly associated with higher risks of malignant potentials (B3 vs malignant).Conclusion: The results of this study indicate that ultrasound findings and patients’ characteristics could provide valuable information for distinguishing B3 cases from benign or malignant cases before VAEB.

Ductal Carcinoma In Situ Underestimation of Microcalcifications Only by Stereotactic Vacuum-Assisted Breast Biopsy: A New Predictor of Specimens without Microcalcifications

The mammographic appearance of ductal carcinoma in situ (DCIS) is mostly observed as microcalcifications. Although stereotactic vacuum-assisted breast biopsy (VABB) is a reliable alternative to surgical biopsy for suspicious microcalcifications, underestimation of VABB-proven DCIS is inevitable in clinical practice. We therefore retrospectively analyzed the variables in the prediction of DCIS underestimation manifesting as microcalcifications only proved by stereotactic VABB. In 1147 consecutive VABB on microcalcification-only lesions from 2010 to 2016, patients diagnosed with DCIS were selected to evaluate the underestimation rate. The analyzed variables included clinical characteristics, mammographic features, VABB procedure, and biomarkers. Univariate and multivariate analyses were used, and a p value < 0.05 was considered statistically significant. Of the 131 VABB-proven DCIS, 108 cases were diagnosed with DCIS and 23 were upgraded to invasive ductal carcinoma (IDC) after subsequent surgery. The small extent of microcalcification, grouped microcalcifications distribution, nearly complete microcalcification removal, and non-calcified specimens without DCIS were low for DCIS underestimation. Among them, the results of non-calcified specimens with or without DICS were the only statistically significant variables by multivariate logistic regression. These results indicate that the histology of non-calcified specimens was highly predictive of DCIS underestimation. Specimens without DCIS had a low upgrade rate to IDC.

Tumor size estimation of the breast cancer molecular subtypes using imaging techniques

Background and aim. In medical practice the classification of breast cancer is most commonly based on the molecular subtypes, in order to predict the disease prognosis, avoid over-treatment, and provide individualized cancer management. Tumor size is a major determiner of treatment planning, acting on the decision-making process, whether to perform breast surgery or administer neoadjuvant chemotherapy. Imaging methods play a key role in determining the tumor size in breast cancers at the time of the diagnosis. We aimed to compare the radiologically determined tumor sizes with the corresponding pathologically determined tumor sizes of breast cancer at the time of the diagnosis, in correlation with the molecular subtypes. Methods. Ninety-one patients with primary invasive breast cancer were evaluated. The main molecular subtypes were luminal A, luminal B, HER-2 positive, and triple-negative. The Bland–Altman plot was used for presenting the limits of agreement between the radiologically and the pathologically determined tumor sizes by the molecular subtypes. Results. A significantly proportional underestimation was found for the luminal A subtype, especially for large tumors. The p-values for the magnetic resonance imaging, mammography, and ultrasonography were 0.020, 0.030, and <0.001, respectively. No statistically significant differences were observed among the radiologic modalities in determining the tumor size in the remaining molecular subtypes (p > 0.05). Conclusion. The radiologically determined tumor size was significantly smaller than the pathologically determined tumor size in the luminal A subtype of breast cancers when measured with all three imaging modalities. The differences were more prominent with ultrasonography and mammography. The underestimation rate increases as the tumor gets larger.