The high surface accuracy design of a cable-net antenna structure under the disturbance of the extremely harsh space environment requires the antenna to have good in-orbit adjustment ability for surface accuracy. A shape memory cable-net (SMC) structure is proposed in this paper and believed to be able to improve the in-orbit surface accuracy of the cable-net antenna. Firstly, the incremental stiffness equation of a one-dimensional bar element of the shape memory alloy (SMA) to express the relationship between the force, temperature and deformation was effectively constructed. Secondly, the finite element model of the SMC antenna structure incorporated the incremental stiffness equation of a SMA was established. Thirdly, a shape active adjustment procedure of surface accuracy based on the optimization method was presented. Finally, a numerical example of the shape memory cable net structure applied to the parabolic reflectors of space antennas was analyzed.
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http://dx.doi.org/10.3390/ma12162619 | DOI Listing |
Front Immunol
January 2025
Department of Gastroenterology and Hepatology, Tianjin Third Central Hospital, Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Institute of Hepatobiliary Disease, Tianjin, China.
Objective: Although pegylated interferon α-2b (PEG-IFN α-2b) therapy for chronic hepatitis B has received increasing attention, determining the optimal treatment course remains challenging. This research aimed to develop an efficient model for predicting interferon (IFN) treatment course.
Methods: Patients with chronic hepatitis B, undergoing PEG-IFN α-2b monotherapy or combined with NAs (Nucleoside Analogs), were recruited from January 2018 to December 2023 at Tianjin Third Central Hospital.
RSC Adv
January 2025
Département de Chimie, Faculté des Sciences et de Génie, Université Laval Québec QC G1V 0A6 Canada.
Blood carries some of the most valuable biomarkers for disease screening as it interacts with various tissues and organs in the body. Human blood serum is a reservoir of high molecular weight fraction (HMWF) and low molecular weight fraction (LMWF) proteins. The LMWF proteins are considered disease marker proteins and are often suppressed by HMWF proteins during analysis.
View Article and Find Full Text PDFInt J Clin Pediatr Dent
December 2024
Department of Pediatric Dentistry, Faculty of Dental Medicine, Universitas Airlangga, Surabaya, Indonesia.
Aims And Background: This study aimed to assess the accuracy of digital intraoral scans in capturing the three-dimensional (3D) surface of teeth and dental arches in mixed dentition, compared with conventional plaster models. Intraoral scanning technology has seen rapid advancements in recent years, revolutionizing orthodontic and dental practices. However, its accuracy in mixed dentition remains a subject of investigation.
View Article and Find Full Text PDFACS Cent Sci
January 2025
Chemical Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States.
Inelastic photoelectron scattering (IPES) by gas molecules, a critical phenomenon observed in ambient pressure X-ray photoelectron spectroscopy (APXPS), complicates spectral interpretation due to kinetic energy loss in the primary spectrum and the appearance of additional features at higher binding energies. In this study, we systematically investigate IPES in various gas environments using APXPS, providing detailed insights into interactions between photoelectrons emitted from solid surfaces and surrounding gas molecules. Core-level XPS spectra of Au, Ag, Zn, and Cu metals were recorded over a wide kinetic energy range in the presence of CO, N, Ar, and H gases, demonstrating the universal nature of IPES across different systems.
View Article and Find Full Text PDFBone Rep
March 2025
Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, United States of America.
High resolution peripheral quantitative computed tomography (HRpQCT) offers detailed bone geometry and microarchitecture assessment, including cortical porosity, but assessing chronic kidney disease (CKD) bone images remains challenging. This proof-of-concept study merges deep learning and machine learning to 1) improve automatic segmentation, particularly in cases with severe cortical porosity and trabeculated endosteal surfaces, and 2) maximize image information using machine learning feature extraction to classify CKD-related skeletal abnormalities, surpassing conventional DXA and CT measures. We included 30 individuals (20 non-CKD, 10 stage 3 to 5D CKD) who underwent HRpQCT of the distal and diaphyseal radius and tibia and contributed data to develop and validate four different AI models for each anatomical site.
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