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https://www.selleckchem.com/products/ipi-549.html Traffic information is critical for pavement design, management, and health monitoring. Numerous in-pavement sensors have been developed and installed to collect the traffic volume and loading amplitude. However, limited attention has been paid to the algorithm of vehicle speed estimation. This research focuses on the estimation of the vehicle speed based on a cross-correlation method. A novel wireless micro-electromechanical sensor (MEMS), Smartrock is used to capture the triaxial acceleration, rotation, and stress data. The cross-correlation algorithms, i.e., normalized cross-correlation (NCC) algorithm, the smoothed coherence transform (SCOT) algorithm, and the phase transform (PHAT) algorithm, are applied to estimate the loading speed of an accelerated pavement test (APT) and the traffic speed in the field. The signal-noise-ratio (SNR) and the mean relative error (MRE) are utilized to evaluate the stability and accuracy of the algorithms. The results show that both the correlated noise and independent noise have significant influence in the field data. The SCOT algorithm is recommended for speed estimation with reasonable accuracy and stability because of a large SNR value and the lowest MRE value among the algorithms. The loading speed investigated in this study was within 50 km/h and further verification is needed for higher speed estimation.The Italian Sarcoma Group performed this retrospective analysis of patients with advanced soft tissue sarcoma, pretreated with ≥1 anthracycline-based treatment, and treated with trabectedin every three weeks. Primary endpoint was to describe real-life use of trabectedin across Italy. Secondary endpoints included objective response rate (ORR) and safety. Overall, 512 patients from 20 Italian centers were evaluated. Leiomyosarcoma (37.7%)/liposarcoma (30.3%) were the most prevalent histological types (abbreviated as L-sarcoma). Patients received a median of four trabectedin c

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