Reconstruction of in-plane strain maps using hybrid dense sensor network composed of sensing skin

The authors have recently developed a soft-elastomeric capacitive (SEC)-based thin film sensor for monitoring strain on mesosurfaces. Arranged in a network configuration, the sensing system is analogous to a biological skin, where local strain can be monitored over a global area. Under plane stress conditions, the sensor output contains the additive measurement of the two principal strain components over the monitored surface. In applications where the evaluation of strain maps is useful, in structural health monitoring for instance, such signal must be decomposed into linear strain components along orthogonal directions. Previous work has led to an algorithm that enabled such decomposition by leveraging a dense sensor network configuration with the addition of assumed boundary conditions. Here, we significantly improve the algorithm's accuracy by leveraging mature off-the-shelf solutions to create a hybrid dense sensor network (HDSN) to improve on the boundary condition assumptions. The system's boundary conditions are enforced using unidirectional RSGs and assumed virtual sensors. Results from an extensive experimental investigation demonstrate the good performance of the proposed algorithm and its robustness with respect to sensors' layout. Overall, the proposed algorithm is seen to effectively leverage the advantages of a hybrid dense network for application of the thin film sensor to reconstruct surface strain fields over large surfaces.This is an author-created, un-copyedited version of an article accepted for publication/published in Measurement Science and Technology. IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record is available online at doi: 10.1088/0957-0233/27/12/124016 ”. Posted with permission.</p

Blade Modal Analysis by Means of Continuous Optical Fiber Sensors

reserved4noA new method for blade modal analysis is introduced in this paper by using continuous optical fiber sensors and optical backscatter reflectometer technology. The main advantage is that the sensor is few invasive and does not affect substantially system parameters. Moreover, the optical fiber sensor can be embedded in composite blades, for instance directly woven in carbon fiber fabric. This allows the sensor to be always installed and ready to use for continuous condition monitoring of the blade. Differently from classical sensors, which can be placed independently from the others, in this case, all the measurement points are placed on the same wire (the fiber itself), characterized by a finite length. Furthermore, due to the physical characteristics of the fiber, some constraints on how the fiber is placed, such as maximum fiber curvature, must be considered. Moreover, strain measurements are collected and precise positioning is required to reconstruct correctly the displacement modal shapes from the strains. In the literature, many optimal placement methods for sensors are proposed, but they are all referred to independent sensors. An optimal method for optical sensor placing on the blade for modal analysis is first introduced in the paper. Then, numerical and experimental tests performed on some blades are shown.mixedPaolo Pennacchi; Gabriele Cazzulani; Martina Chieppi; Andrea ColomboPennacchi, PAOLO EMILIO LINO MARIA; Cazzulani, Gabriele; Chieppi, Martina; Colombo, Andre

Structural Health Monitoring of Wind Turbines Using a Digital Image Correlation System on a UAV

Unmanned aerial vehicles (UAVs) have recently emerged as a robust tool for remote inspection and data acquisition at places that are either inaccessible or riskier to perform measurements. To quantify the level of strain/stress and loading conditions that the rotating structures such as wind turbine experience during operation, an approach is proposed that can perform a nondestructive evaluation of these rotating structures using non-contact, three-dimensional (3D) digital image correlation (DIC). This technique addresses the benefit of non-interference with structure functionality and can be used for rotating or non-rotating structures. In this project, a synchronized set of a stereo camera system is used to acquire the images of a rotating turbine. These images are processed to obtain displacement, geometry, and strain over the wind turbine blades during deformation

MicroRNA-based classification of hepatocellular carcinoma and oncogenic role of miR-517a

Background & Aims Hepatocellular carcinoma (HCC) is a heterogeneous tumor that develops via activation of multiple pathways and molecular alterations. It has been a challenge to identify molecular classes of HCC and design treatment strategies for each specific subtype. MicroRNAs (miRNAs) are involved in HCC pathogenesis, and their expression profiles have been used to classify cancers. We analyzed miRNA expression in human HCC samples to identify molecular subclasses and oncogenic miRNAs. Methods We performed miRNA profiling of 89 HCC samples using a ligation-mediated amplification method. Subclasses were identified by unsupervised clustering analysis. We identified molecular features specific for each subclass using expression pattern (Affymetrix U133 2.0; Affymetrix, Santa Clara, CA), DNA change (Affymetrix STY Mapping Array), mutation (CTNNB1), and immunohistochemical (phosphor[p]\u2013protein kinase B, p\u2013insulin growth factor\u2013IR, p-S6, p\u2013epidermal growth factor receptor, \u3b2-catenin) analyses. The roles of selected miRNAs were investigated in cell lines and in an orthotopic model of HCC. Results We identified 3 main clusters of HCCs: the wingless-type MMTV integration site (32 of 89; 36%), interferon-related (29 of 89; 33%), and proliferation (28 of 89; 31%) subclasses. A subset of patients with tumors in the proliferation subclass (8 of 89; 9%) overexpressed a family of poorly characterized miRNAs from chr19q13.42. Expression of miR-517a and miR-520c (from ch19q13.42) increased proliferation, migration, and invasion of HCC cells in vitro. MiR-517a promoted tumorigenesis and metastatic dissemination in vivo. Conclusions We propose miRNA-based classification of 3 subclasses of HCC. Among the proliferation class, miR-517a is an oncogenic miRNA that promotes tumor progression. There is rationale for developing therapies that target miR-517a for patients with HCC