Wind profiles for WKB Prandtl models based on slope and free air flow

In this article the WKB (Wentzel-Kramers-Brillouin) Prandtl model serves as the baseline for the study of different kinds of slope flows which can occur over inclined surfaces. The Prandtl-type model couples basic boundary-layer dynamics and thermodynamics for pure slope flows. We provide an answer to the question if it is possible to obtain the matching WKB Prandtl model using only friction velocity, friction temperature, and sensible heat flux. This instantly raises the query if there is a transition or combination between the WKB-Prandtl model for slope flows and the Monin-Obukhov similarity theory for free-air flows and vice versa. As a result, we show the difference between friction velocity and friction temperature calculated using the Monin-Obukhov similarity theory and those computed using the WKB Prandtl model. There is ongoing research into hill-perturbed non-neutral wind profiles because of their potential utility in numerous applications. Hence, further discussion includes how the new parametrization of the WKB Prandtl model may be used to calculate slope and free-air flows in a micro-meteorological model of an alpine valley, e.g. for pollutant dispersion calculations

A Bayesian Logistic Regression approach in Asthma Persistence Prediction

Background: A number of models based on clinical parameters have been used for the prediction of asthma persistence in children. The number and significance of factors that are used in a proposed model play a cardinal role in prediction accuracy. Different models may lead to different significant variables. In addition, the accuracy of a model in medicine is really important since an accurate prediction of illness persistence may improve prevention and treatment intervention for the children at risk. Methods: Data from 147 asthmatic children were analyzed by a new method for predicting asthma outcome using Principal Component Analysis (PCA) in combination with a Bayesian logistic regression approach implemented by the Markov Chain Monte Carlo (MCMC). The use of PCA is required due to multicollinearity among the explanatory variables. Results: This method using the most appropriate models seems to predict asthma with an accuracy of 84.076% and 86.3673%, a Sensitivity of 84.96% and 87.25% and a Specificity of 83.22% and 85.52%, respectively. Conclusion: Our approach predicts asthma with high accuracy, gives steadier results in terms of positive and negative patients and provides better information about the influence of each factor (demographic, symptoms etc.) in asthma prediction

Bayesian spatial prediction and sampling design

Das erste Kapitel dieser Dissertation beschreibt neben meinen eigenen BeitrÃ$gen zur Bayesschen Geostatistik die wichtigsten Arbeiten in diesem Gebiet seit 1989. Dieses Kapitel wurde mit besonderem Augenmerk darauf geschrieben, wie man die Unsicherheit der Kovarianzfunktion mittels dem Bayesschen Zugang in der Vorhersage mitberÃcksichtigen kann. Ein weiterer Punkt, der mir wichtig erschien, war die Formulierung von Modellen, welche von den fÃr gewÃhnlich Gausschen Annahmen in der Geostatistik weggehen und abschwÃ$chen. Kapitel 2 ist einem frequentistischen Zugang der BerÃcksichtigung der Unsicherheit der Kovarianzfunktion gewidmet, welcher von mir selbst entwickelt wurde und Kovarianzrobustes Minimax Kriging genannt wird. Im Gegensatz zum Bayesschen Zugang aus Kapitel 1, wo die Kovarianzfunktionen gemÃ$à ihrer a priori Wahrscheinlichkeit gewichtet werden, suchen wir hier nach einem Predictor, welcher den ungÃnstigst mÃglichen mittleren quadratischen Vorhersagefehler in einer Klasse von gleich plausiblen Kovarianzfunktionen minimiert. Kapitel 3 ist Ãber rÃ$umliche Versuchsplanung. Neben meinem eigenen Beitrag zu dieser Theorie, in Form der Approximation eines Zufallfeldes durch ein Regressionsmodell mit stochastischen Koeffizienten und der darauf folgenden Verwendung klassischer Experimental Design Theorie, gebe ich hier auch einen Éberblick Ãber die neuesten Resultate in diesem ansprechenden Gebiet. Inbesondere auch hier, diskutiere ich Methoden, wie man die Unsicherheit der Kovarianzfunktion beim Sampling Design berÃcksichtigen kann.The first chapter of this dissertation discusses besides my own contributions to Bayesian geostatistics the most important papers in this area since 1989. I wrote this chapter with special emphasis on how to take account of the uncertainty of the covariance function by means of the Bayesian approach. Another point that seemed important to me was to formulate models that go away from and weaken the usual Gaussian assumption in geostatistics. Chapter 2 is devoted to a frequentist approach to taking account of the uncertainty of the covariance function developed by myself and called covariance robust minimax kriging. In contrast to the Bayesian approach of chapter 1, where the covariance functions are weighted according to their prior distributions, here we look for a predictor minimizing the worst possible mean square error of prediction among a class of equally possible covariance functions. Chapter 3 is about spatial sampling design. Besides my own contribution to this theory in the form of approximating a stochastic process by a linear regression model with stochastic coefficients and then using classical Bayesian experimental design theory to calculate spatial sampling designs, I give here also a survey of the most recent results in this nice field. Especially also here we discuss some approaches of how to take the uncertainty of the covariance function into account still during spatial sampling design.Gunter SpöckKlagenfurt, Univ., Diss., 2005KB2005 26OeBB(VLID)241232

Classifying Image Stacks of Specular Silicon Wafer Back Surface Regions: Performance Comparison of CNNs and SVMs

In this work, we compare the performance of convolutional neural networks and support vector machines for classifying image stacks of specular silicon wafer back surfaces. In these image stacks, we can identify structures typically originating from replicas of chip structures or from grinding artifacts such as comets or grinding grooves. However, defects like star cracks are also visible in those images. To classify these image stacks, we test and compare three different approaches. In the first approach, we train a convolutional neural net performing feature extraction and classification. In the second approach, we manually extract features of the images and use these features to train support vector machines. In the third approach, we skip the classification layers of the convolutional neural networks and use features extracted from different network layers to train support vector machines. Comparing these three approaches shows that all yield an accuracy value above 90%. With a quadratic support vector machine trained on features extracted from a convolutional network layer we achieve the best compromise between precision and recall rate of the class star crack with 99.3% and 98.6%, respectively

Evaluation of Bayesian classifiers in asthma exacerbation prediction after medication discontinuation

Abstract Objective The achievement of the optimal control of the disease is of cardinal importance in asthma treatment. As the control of the disease is sustained the medication should be gradually reduced and then stopped. Nevertheless, the discontinuation of asthma medication may lead to loss of disease control and eventually to an exacerbation of the disease. The goal of this paper is to examine the performance of Bayesian network classifiers in predicting asthma exacerbation based on several patient’s parameters such as objective measurements and medical history data. Results In this study several Bayesian network classifiers are presented and evaluated. It is shown that the proposed semi-naive network classifier with the use of Backward Sequential Elimination and Joining algorithm is able to predict if a patient will have an exacerbation of the disease after his last assessment with 93.84% accuracy and 90.9% sensitivity. In addition, the resulting structure and the conditional probability tables give a clear view of the probabilistic relationships between the used factors. This network may help the clinicians to identify the patients who are at high risk of having an exacerbation after stopping the medication and to confirm which factors are the most important

Assessing the impacts of temperature extremes on agriculture yield and projecting future extremes using machine learning and deep learning approaches with CMIP6 data

Climate change, particularly extreme weather events, has significantly affected various sectors, including agriculture, human health, water resources, sea levels, and ecosystems. It is anticipated that the intensity, duration, and frequency of these extremes will escalate in the future. This study aims to discover the association between temperature extremes and agricultural yield and to project these extremes using machine learning (ML) and deep learning (DL) models with CMIP6 (Coupled Model Intercomparison Project Phase 6) data under two SSPs (Shared Socioeconomic Pathways). A bi-wavelet coherence technique is employed to investigate the association, providing detailed information in both the frequency and time domains for the period of 1980–2014. Various ML and DL models are trained and tested for the periods of 1985–2004 and 2005–2014, respectively, with gradient boosting machine chosen for projecting temperature extremes based on its superior performance. Mann-Kendall test is used for trend analysis in the projected temperature extremes. The results indicate strong negative and positive associations between TN10p (Cold nights) and TN90p (Warm nights), respectively, with wheat production. Additionally, there is a long-term negative association of CSDI (Cold Spell Duration Indicator) and strong positive association of WSDI (Warm Spell Duration Indicator) with rice yield. Projected results show an increase and decrease under SSP2-4.5 and SSP5-8.5, respectively, in DTR (Diurnal Temperature Range) at most stations. TN10p will increase in the future at most stations, with exceptions such as Muree station where it decreases during 2025–2049 and then increases under both SSPs. Projections show that TXn (annual or monthly minimum value of daily maximum temp) will increase in the future, with Muree station exhibiting the lowest value close to zero, while the average maximum value is around 20 °C at Khanpur station. Trend analysis reveals significantly increasing trend in TR20 (Tropical nights) and decreasing trend in CSDI in future durations under both SSPs. These findings hold implications for policymakers and stakeholders in various departments, including agriculture, health, and water resources management