The empirical risk minimization methods were often used to estimate the multifunctional sensor regression function in signal reconstruction. The small size of sample data would lead to the problem of poor generalization capability and overfitting. Support vector machine (SVM) is a novel machine learning method based on structural risk minimization, and it can improve generalization capability a...

background post-mastectomy breast reconstruction is commonly performed in the united states with numerous options available to patients and providers. this study evaluated patient race and provider in prediction of patient satisfaction following post-mastectomy breast reconstruction. methods the patient satisfaction for women who underwent post mastectomy breast reconstruction at university of ...

the present review classifies and describes the multifactorial causes of anterior cruciate ligament (acl) surgery failure, concentrating on preventing and resolving such situations. the article particularly focuses on those causes that require acl revision due to recurrent instability, without neglecting those that affect function or produce persistent pain. although primary acl reconstruction ...

UNLABELLED To perform a clinical and radiographic assessment of patients undergoing surgical treatment using a cortical structural homologous bone graft for femoral reconstruction following mechanical failure of total hip arthroplasty and periprosthetic fractures. METHODS A retrospective study was conducted on 27 patients who underwent surgical treatment for femoral reconstruction following m...

Phase space reconstruction is investigated as a diagnostic tool for determining the structure of detected nonlinear processes in regression residuals. Empirical evidence supporting this approach is provided using simulations from an Ikeda mapping and the S&P 500. Results in the form of phase portraits (e.g., scatter plots of reconstructed dynamical systems) provide qualitative information to di...

Synchrotron-radiation-based microcomputed-tomography (SR-μCT) is a powerful tool for yielding 3D structural information of high spatial and contrast resolution about a specimen preserved in its natural state. A large number of projection views are required currently for yielding SR-μCT images by use of existing algorithms without significant artifacts. When a wet biological specimen is imaged, ...