An adaptive minimum spanning tree multi-element method for uncertainty quantification of smooth and discontinuous responses

A novel approach for non-intrusive uncertainty propagation is proposed. Our approach overcomes the limitation of many traditional methods, such as generalised polynomial chaos methods, which may lack sufficient accuracy when the quantity of interest depends discontinuously on the input parameters. As a remedy we propose an adaptive sampling algorithm based on minimum spanning trees combined with a domain decomposition method based on support vector machines. The minimum spanning tree determines new sample locations based on both the probability density of the input parameters and the gradient in the quantity of interest. The support vector machine efficiently decomposes the random space in multiple elements, avoiding the appearance of Gibbs phenomena near discontinuities. On each element, local approximations are constructed by means of least orthogonal interpolation, in order to produce stable interpolation on the unstructured sample set. The resulting minimum spanning tree multi-element method does not require initial knowledge of the behaviour of the quantity of interest and automatically detects whether discontinuities are present. We present several numerical examples that demonstrate accuracy, efficiency and generality of the method.Comment: 20 pages, 18 figure

Modelling Monochamus galloprovincialis dispersal trajectories across a heterogeneous landscape to optimize monitoring by trapping networks

Context 14 The pine wood nematode (PWN), is an invasive species which was introduced into Europe in 15 1999. It represents a major economic and ecological threat to European forests. In Europe, the 16 maritime pine is the main host and Monochamus galloprovinciallis is its only vector. 17 18 Objectives 19 Our goal was to analyze the effect of landscape heterogeneity on the vector’s dispersal. We 20 further aimed at developing a new method to locate the origin of insects captured in a systematic 21 network of pheromone traps. 22 23 Methods 24 A mark-release-recapture experiment was carried out in a heterogeneous landscape combining 25 maritime pine plantations, clear-cuts and isolated patches of broadleaved and mixed forests in 26 the southwest of France. 27 Least-cost path analysis was used to model dispersal trajectories and assign friction values to 28 each land-use type in the landscape. We used the trap’s geographical coordinates, capture levels 29 and mean friction values of neighbouring patches to calculate a weighed barycentre and the 30 position of the release of marked beetlesinfo:eu-repo/semantics/submittedVersio

SEMI-DiffusionInst: A Diffusion Model Based Approach for Semiconductor Defect Classification and Segmentation

With continuous progression of Moore's Law, integrated circuit (IC) device complexity is also increasing. Scanning Electron Microscope (SEM) image based extensive defect inspection and accurate metrology extraction are two main challenges in advanced node (2 nm and beyond) technology. Deep learning (DL) algorithm based computer vision approaches gained popularity in semiconductor defect inspection over last few years. In this research work, a new semiconductor defect inspection framework "SEMI-DiffusionInst" is investigated and compared to previous frameworks. To the best of the authors' knowledge, this work is the first demonstration to accurately detect and precisely segment semiconductor defect patterns by using a diffusion model. Different feature extractor networks as backbones and data sampling strategies are investigated towards achieving a balanced trade-off between precision and computing efficiency. Our proposed approach outperforms previous work on overall mAP and performs comparatively better or as per for almost all defect classes (per class APs). The bounding box and segmentation mAPs achieved by the proposed SEMI-DiffusionInst model are improved by 3.83% and 2.10%, respectively. Among individual defect types, precision on line collapse and thin bridge defects are improved approximately 15\% on detection task for both defect types. It has also been shown that by tuning inference hyperparameters, inference time can be improved significantly without compromising model precision. Finally, certain limitations and future work strategy to overcome them are discussed.Comment: 6 pages, 5 figures, To be published by IEEE in the proceedings of the 2023 ELMAR conferenc

An adaptive minimum spanning tree multielement method for uncertainty quantification of smooth and discontinuous responses

A novel approach for nonintrusive uncertainty propagation is proposed. Our approach overcomes the limitation of many traditional methods, such as generalized polynomial chaos methods, which may lack sufficient accuracy when the quantity of interest depends discontinuously on the input parameters. As a remedy we propose an adaptive sampling algorithm based on minimum spanning trees combined with a domain d

The effect of thyroxin on the renal function of asphyxiated neonates

Thyroxin has been shown to have a beneficial effect on renal function in cases of pending renal failure in animal studies. Studies of the use of thyroxin in humans in pending renal failure are scarce. The aim of this study was to assess the effect of oral thyroxin on the renal function of asphyxiated neonates who often have renal impairment.A randomised controlled trial was conducted. Two groups of 15 term asphyxiated neonates were studied. Thyroxin (50) was given on day 1, 2 and 3 of life for the treatment group and placebo was given for the control group. Renal function was studied on day 1 and day 4 of life. The two groups were not statistically significant different for gestational age, birthweight, severity of asphyxia, pregnancy or delivery complications, fluids administered and drugs used. There was no significant difference in urine output, creatinin clearance and fractional excretion of sodium on day 1 but there was a trend towards a worse renal function on day 1 in the treatment group. The creatinin clearance was significantly better in the treatment group on day 4 (p = 0.017).Urine output and fractional excretion of sodium on day 4 were better in the treatment group but the differences did not reach statistical significance (p of 0.14 and 0.057 respectively). Statistical analysis on the differences between day 4 and day 1 showed statistical significance only for Clcreat : Clcreat day 4 - Clcreat day 4 was 52.6 (+/-32.4)for the thyroxin group and 7.3 (+/-7.8) for the controls (p= 0.006). These data support that thyroxin may have a significant beneficial effect on the renal function in patients with perinatal asphyxia. Thyroxin may be proven useful in future for patients with pending renal failure