The monitoring of loads and displacements during service life is proving to be crucial for developing a modern Structural Health Monitoring framework. The continuous monitoring of these physical quantities can provide fundamental information on the actual health status of the structure and can accurately guide pro-active condition-based maintenance operations, thus reducing the maintenance costs and extending the service life of the monitored structures. Pushed by these needs and by the simultaneous development in the field of strain sensing technologies, several displacement reconstruction and load identification methods have been developed that are based on discrete strain measurements. Among the different formulations, the inverse Finite Element Method (iFEM), the Modal Method (MM) and the 2-step method, the latter being the only one able to also compute the loads together with the displacements, have emerged as the most accurate and reliable ones. In this paper, the formulation of the three methods is summarized in order to set the numerical framework for a comparative study. The three methods are tested on the reconstruction of the external load and of the displacement field of a stiffened aluminium plate starting from experimentally measured strains. A fibre optic sensing system has been used to measure surface strains and an optimization procedure has been performed to provide the best fibre pattern, based on five lines running along the stiffeners' direction and with a back-to-back measuring scheme. Additional sensors are used to measure the applied force and the plate's deflection in some locations. The comparison of the results obtained by each method proves the extreme accuracy and reliability of the iFEM in the reconstruction of the deformed shape of the panel. On the other hand, the Modal Method leads to a good reconstruction of the displacements, but also exhibits a sensitivity to the choice of the modes considered for the specific application. Finally, the 2-step approach is able to correctly identify the loads and to reconstruct the displacements with an accuracy that depends on the modeling of the experimental setup.
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http://dx.doi.org/10.3390/s22031064 | DOI Listing |
Clin Exp Med
January 2025
Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong, China.
The role of metabolic reprogramming of the tumor immune microenvironment in cancer development and immune escape has increasingly attracted attention. However, the predictive value of differences in metabolism-immune microenvironment on the prognosis of colon cancer (CC) and the response to immunotherapy have not been elucidated. The aim of this study was to investigate changes in metabolism and immune profile of CC and to identify a reliable signature for predicting prognosis and therapeutic response.
View Article and Find Full Text PDFToxics
January 2025
Beijing Key Laboratory of Resource-Oriented Treatment of Industrial Pollutants, School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China.
Road dust carries various contaminants and causes urban non-point source pollution in waterbodies through runoff. Road dust samples were collected in each month in two years and then sieved into five particle size fractions. The concentrations of ten heavy metals (As, Cd, Cr, Cu, Hg, Mn, Ni, Pb, Zn, Fe) in each fraction were measured.
View Article and Find Full Text PDFFront Plant Sci
January 2025
College of Information Technology, Jilin Agricultural University, Changchun, China.
Introduction: Potatoes and tomatoes are important Solanaceae crops that require effective disease monitoring for optimal agricultural production. Traditional disease monitoring methods rely on manual visual inspection, which is inefficient and prone to subjective bias. The application of deep learning in image recognition has led to object detection models such as YOLO (You Only Look Once), which have shown high efficiency in disease identification.
View Article and Find Full Text PDFBMC Genomics
January 2025
Key Laboratory of Animal Genetics and Breeding on Tibetan Plateau, Ministry of Agriculture and Rural Affairs, Lanzhou Institute of Husbandry and Pharmaceutical Sciences of Chinese Academy of Agricultural Sciences, Lanzhou, 730050, China.
Background: Prolonged natural selection and artificial breeding have contributed to increased uniformity within the Tibetan sheep population, resulting in a reduction in genetic diversity and the establishment of selective signatures in the genome. This process has led to a loss of heterozygosity in specific genomic regions and the formation of Runs of Homozygosity (ROH). Current research on ROH predominantly focuses on inbreeding and the signals of selection; however, there is a paucity of investigation into the genetic load and selective pressures associated with ROH, both within these regions and beyond.
View Article and Find Full Text PDFJ Proteome Res
January 2025
Corelabs, King Abdullah University of Science and Technology, Thuwal 23500-6900, Kingdom of Saudi Arabia.
We introduce here a novel approach, termed time-segmented acquisition (Seg), to enhance the identification of peptides and proteins in trapped ion mobility spectrometry (TIMS)-time-of-flight (TOF) mass spectrometry. Our method exploits the positive correlation between ion mobility values and reversed-phase liquid chromatography (LC) retention time to improve ion separation and resolution. By dividing the LC retention time into multiple segments and applying a segment-specific narrower ion mobility range within the TIMS tunnel, we achieved better separation and higher resolution of ion mobility.
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