A Hyper-Solution Framework for SVM Classification:Improving Damage Detection on Helicopter Fuselage Panels

Abstract The on-line assessment of structural health of aircraft fuselage panels and their remaining useful life is crucial both in military and civilian settings. This paper presents an application of a Support Vector Machines (SVM) classification framework aimed at improving the diagnosis task based on the strain values acquired through a monitoring sensor network deployed on the helicopter fuselage panels. More in details, diagnosis is usually defined as detecting a damage, identifying the specific component affected (i.e., bay or stringer) and then characterizing the damage in terms of center and size. Here, the first two steps are performed through the SVM classification framework while the last one is based on an Artificial Neural Network (ANN) hierarchy already presented in a previous authors' work. The training dataset was built through Finite Elements Method (FEM) based simulation, able to simulate the behavior of any type of panel and damage according to specific parameters to set up; the result of FE simulation consists of the strain fields on different locations. As results, the proposed SVM classification framework permits to improve reliability of detection and characterization tasks respect to the previous approach entirely based on ANN hierarchies. Finally, the remaining useful life is estimated by using another ANN, different for damage on bay and stringer, able to predict the values of two parameters of the NASGRO equation which is used to estimate the damage propagation

The B-cell receptor controls fitness of MYC-driven lymphoma cells via GSK38\u9f inhibition

Similar to resting mature B cells, where the B-cell antigen receptor (BCR) controls cellular survival, surface BCR expression is conserved in most mature B-cell lymphomas. The identification of activating BCR mutations and the growth disadvantage upon BCR knockdown of cells of certain lymphoma entities has led to the view that BCR signalling is required for tumour cell survival. Consequently, the BCR signalling machinery has become an established target in the therapy of B-cell malignancies. Here we study the effects of BCR ablation on MYC-driven mouse B-cell lymphomas and compare them with observations in human Burkitt lymphoma. Whereas BCR ablation does not, per se, significantly affect lymphoma growth, BCR-negative (BCR(-)) tumour cells rapidly disappear in the presence of their BCR-expressing (BCR(+)) counterparts in vitro and in vivo. This requires neither cellular contact nor factors released by BCR(+) tumour cells. Instead, BCR loss induces the rewiring of central carbon metabolism, increasing the sensitivity of receptor-less lymphoma cells to nutrient restriction. The BCR attenuates glycogen synthase kinase 3 beta (GSK3β) activity to support MYC-controlled gene expression. BCR(-) tumour cells exhibit increased GSK3β activity and are rescued from their competitive growth disadvantage by GSK3β inhibition. BCR(-) lymphoma variants that restore competitive fitness normalize GSK3β activity after constitutive activation of the MAPK pathway, commonly through Ras mutations. Similarly, in Burkitt lymphoma, activating RAS mutations may propagate immunoglobulin-crippled tumour cells, which usually represent a minority of the tumour bulk. Thus, while BCR expression enhances lymphoma cell fitness, BCR-targeted therapies may profit from combinations with drugs targeting BCR(-) tumour cells

Proceedings Of The 23Rd Paediatric Rheumatology European Society Congress: Part Two

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