The research on damages of structures that are supported by deep foundations has been quite intensive in the past decade. Kinematic interaction in soil-pile interaction is evaluated based on the p-y curve approach. Existing p-y curves have considered the effects of relative density on soil-pile interaction in sandy soil. The roughness influence of the surface wall pile on p-y curves has not been emphasized sufficiently. The presented study was performed to develop a series of p-y curves for single piles through comprehensive experimental investigations. Modification factors were studied, namely, the effects of relative density and roughness of the wall surface of pile. The model tests were subjected to lateral load in Johor Bahru sand. The new p-y curves were evaluated based on the experimental data and were compared to the existing p-y curves. The soil-pile reaction for various relative density (from 30% to 75%) was increased in the range of 40-95% for a smooth pile at a small displacement and 90% at a large displacement. For rough pile, the ratio of dense to loose relative density soil-pile reaction was from 2.0 to 3.0 at a small to large displacement. Direct comparison of the developed p-y curve shows significant differences in the magnitude and shapes with the existing load-transfer curves. Good comparison with the experimental and design studies demonstrates the multidisciplinary applications of the present method.
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http://dx.doi.org/10.1155/2014/917174 | DOI Listing |
Acad Radiol
December 2024
Department of Otorhinolaryngology, Head and Neck Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai, China (Y.W., P.Y., J.W., Z.Z., G.W., Y.Z., Y.Y., Y.M., X.S.); Shandong Provincial Clinical Research Center for Otorhinolaryngologic Diseases, Yantai Yuhuangding Hospital, Yantai, China (Y.W., P.Y., J.W., Z.Z., G.W., Y.Z., Y.Y., Y.M., X.S.); Yantai Key Laboratory of Otorhinolaryngologic Diseases, Yantai Yuhuangding Hospital, Yantai, China (Y.W., P.Y., J.W., Y.Z., Y.Y., Y.M., X.S.). Electronic address:
Rationale And Objectives: Nasal polyps (NP) and inverted papilloma (IP) are benign tumors within the nasal cavity, each necessitating distinct treatment approaches. Herein, we investigate the utility of a deep learning (DL) model for distinguishing between NP and IP.
Materials And Methods: A total of 1791 patients with nasal benign tumors from two hospitals were retrospectively enrolled.
ACS Nano
October 2024
Key Laboratory of Precision and Intelligent Chemistry, School of Nuclear Science and Technology, Hefei National Research Center for Physical Sciences at the Microscale, National Synchrotron Radiation Laboratory, University of Science and Technology of China, Hefei 230026, P. R. China.
Acad Radiol
August 2024
Department of Radiology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang, China (P.Y., Z.F.). Electronic address:
Rationale And Objectives: Sarcopenia, as measured at the level of the third lumbar (L3) has been shown to predict the survival of cancer patients. However, many patients with advanced non-small cell lung cancer (NSCLC) do not undergo routine abdominal imaging. The objective of this study was to investigate the association of thoracic sarcopenia with survival outcomes among patients who underwent immunotherapy for NSCLC.
View Article and Find Full Text PDFCell Rep Med
July 2024
Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Recurrent high-grade gliomas (rHGGs) have a dismal prognosis, where the maximum tolerated dose (MTD) of IV terameprocol (5 days/month), a transcriptional inhibitor of specificity protein 1 (Sp1)-regulated proteins, is 1,700 mg/day with median area under the plasma concentration-time curve (AUC) of 31.3 μg∗h/mL. Given potentially increased efficacy with sustained systemic exposure and challenging logistics of daily IV therapy, here we investigate oral terameprocol for rHGGs in a multicenter, phase 1 trial (GATOR).
View Article and Find Full Text PDFDiagnostics (Basel)
June 2024
Center of Cohort Studies, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul 04514, Republic of Korea.
The purposes of this study were to develop an artificial intelligence (AI) model for future breast cancer risk prediction based on mammographic images, investigate the feasibility of the AI model, and compare the AI model, clinical statistical risk models, and Mirai, a state of-the art deep learning algorithm based on screening mammograms for 1-5-year breast cancer risk prediction. We trained and developed a deep learning model using a total of 36,995 serial mammographic examinations from 21,438 women (cancer-enriched mammograms, 17.5%).
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