We analyze a finite horizon, single product, periodic review model in which pricing and production/inventory decisions are made simultaneously. Demands in different periods are random variables that are independent of each other and their distributions depend on the product price. Pricing and ordering decisions are made at the beginning of each period and all shortages are backlogged. Ordering cost includes both a fixed cost and a variable cost proportional to the amount ordered. The objective is to find an inventory policy and a pricing strategy maximizing expected profit over the finite horizon. We show that when the demand model is additive, the profit-to-go functions are k-concave and hence an (s,S,p) policy is optimal. In such a policy, the period inventory is managed based on the classical (s,S) policy and price is determined based on the inventory position at the beginning of each period. For more general demand functions, i.e., multiplicative plus additive functions, we demonstrate that the profit-to-go function is not necessarily k-concave and an (s,S,p) policy is not necessarily optimal. We introduce a new concept, the symmetric k-concave functions and apply it to provide a characterization of the optimal policy. / Singapore-MIT Alliance (SMA)

Microcalcification (MC) detection is an important component of breast cancer diagnosis. However, visual analysis of mammograms is a difficult task for radiologists. Computer Aided Diagnosis (CAD) technology helps in identifying lesions and assists the radiologist in making his final decision. This work is a part of a CAD project carried out at the Imaging Science Research Division (ISRD), Digital Medical Imaging Program, Moffitt Cancer Research Center, Tampa, FL. A CAD system had been previously developed to perform the following tasks: (a) pre-processing, (b) segmentation and (c) feature extraction of mammogram images. Ten features covering spatial, and morphological domains were extracted from the mammograms and the samples were classified as Microcalcification (MC) or False alarm (False Positive microcalcification/ FP) based on a binary truth file obtained from a radiologist's initial investigation. The main focus of this work was two-fold: (a) to analyze these features, select the most significant features among them and study their impact on classification accuracy and (b) to implement and compare two machine-learning algorithms, Neural Networks (NNs) and Support Vector Machines (SVMs) and evaluate their performances with these features. The NN was based on the Standard Back Propagation (SBP) algorithm. The SVM was implemented using polynomial, linear and Radial Basis Function (RBF) kernels. A detailed statistical analysis of the input features was performed. Feature selection was done using Stepwise Forward Selection (SFS) method. Training and testing of the classifiers was carried out using various training methods. Classifier evaluation was first performed with all the ten features in the model. Subsequently, only the features from SFS were used in the model to study their effect on classifier performance. Accuracy assessment was done to evaluate classifier performance. Detailed statistical analysis showed that the given dataset showed poor discrimination between classes and proved a very difficult pattern recognition problem. The SVM performed better than the NN in most cases, especially on unseen data. No significant improvement in classifier performance was noted with feature selection. However, with SFS, the NN showed improved performance on unseen data. The training time taken by the SVM was several magnitudes less than the NN. Classifiers were compared on the basis of their accuracy and parameters like sensitivity and specificity. Free Receiver Operating Curves (FROCs) were used for evaluation of classifier performance. The highest accuracy observed was about 93% on training data and 76% for testing data with the SVM using Leave One Out (LOO) Cross Validation (CV) training. Sensitivity was 81% and 46% on training and testing data respectively for a threshold of 0.7. The NN trained using the 'single test' method showed the highest accuracy of 86% on training data and 70% on testing data with respective sensitivity of 84% and 50%. Threshold in this case was -0.2. However, FROC analyses showed overall superiority of SVM especially on unseen data. Both spatial and morphological domain features were significant in our model. Features were selected based on their significance in the model. However, when tested with the NN and SVM, this feature selection procedure did not show significant improvement in classifier performance. It was interesting to note that the model with interactions between these selected variables showed excellent testing sensitivity with the NN classifier (about 81%). Recent research has shown SVMs outperform NNs in classification tasks. SVMs show distinct advantages such as better generalization, increased speed of learning, ability to find a global optimum and ability to deal with linearly non-separable data. Thus, though NNs are more widely known and used, SVMs are expected to gain popularity in practical applications. Our findings show that the SVM outperforms the NN. However, its performance depends largely on the nature of data used.

The South African Weather Service rainfall seasonal forecasts are verified for the period of January-February-March to October-November-December 1998-2004. These forecasts are compiled using different models from different institutions. Probability seasonal forecasts can be evaluated using different skill measures, but in this study the Ranked Probability Skill Score (RPSS), Reliability Diagram (RD) and Relative Operating Characteristics (ROC) are used. The RPSS is presented in the form of maps whereas the RD and ROC are analyses are presented in the form of graphs. The aim of the study is to present skill estimates of operational seasonal forecasts issued at South African Weather Service A limited number of forecasts show positive RPSS value throughout the validation period. From RD and ROC analysis, there is no skill in predicting the normal category as compared to below-normal and above-normal categories. Notwithstanding, the frequency diagrams show that the normal category was often given a large weight in the operational forecasts. The value of verifying seasonal forecast accuracy from the user’s perspective is important. The understanding of seasonal forecast performance helps decision makers to determine when and how to respond to expected climate anomalies. Therefore the frequent update of the seasonal forecast verification is important in order to help Users make better decisions. Copyright / Dissertation (MSc)--University of Pretoria, 2008. / Geography, Geoinformatics and Meteorology / Unrestricted

Title from PDF of title page. / Document formatted into pages; contains 112 pages. / Thesis (M.S.C.S.)--University of South Florida, 2003. / Includes bibliographical references. / Text (Electronic thesis) in PDF format. / ABSTRACT: Breast cancer is the second leading cause of cancer deaths among women in the United States and microcalcifications clusters are one of the most important indicators of breast disease. Computer methodologies help in the detection and differentiation between benign and malignant lesions and have the potential to improve radiologists' performance and breast cancer diagnosis significantly. A Computer-Aided Diagnosis (CAD-Dx) algorithm has been previously developed to assist radiologists in the diagnosis of mammographic clusters of calcifications with the modules: (a) detection of all calcification-like areas, (b) false-positive reduction and segmentation of the detected calcifications, (c) selection of morphological and distributional features and (d) classification of the clusters. Classification was based on an artificial neural network (ANN) with 14 input features and assigned a likelihood of malignancy to each cluster. / ABSTRACT: The purpose of this work was threefold: (a) optimize the existing algorithm and test on a large database, (b) rank classification features and select the best feature set, and (c) determine the impact of single and two-view feature estimation on classification and feature ranking. Classification performance was evaluated with the NevProp4 artificial neural network trained with the leave-one-out resampling technique. Sequential forward selection was used for feature selection and ranking. Mammograms from 136 patients, containing single or two views of a breast with calcification cluster were digitized at 60 microns and 16 bits per pixel. 260 regions of interest (ROI's) centered on calcification cluster were defined to build the single-view dataset. 100 of the 136 patients had a two-view mammogram which yielded 202 ROI's that formed the two-view dataset. Classification and feature selection were evaluated with both these datasets. / ABSTRACT: To decide on the optimal features for two-view feature estimation several combinations of CC and MLO view features were attempted. On the single-view dataset the classifier achieved an AZ =0.8891 with 88% sensitivity and 77% specificity at an operating point of 0.4; 12 features were selected as the most important. With the two-view dataset, the classifier achieved a higher performance with an AZ =0.9580 and sensitivity and specificity of 98% and 80% respectively at an operating point of 0.4; 10 features were selected as the most important. / System requirements: World Wide Web browser and PDF reader. / Mode of access: World Wide Web.

The main objective of our research was to develop a new bankruptcy prediction model for the Czech economy. For that purpose we used the logistic regression and 150,000 financial statements collected for the 2002-2016 period. We defined 41 explanatory variables (25 financial ratios and 16 dummy variables) and used Bayesian model averaging to select the best set of explanatory variables. The resulting model has been estimated for three prediction horizons: one, two, and three years before bankruptcy, so that we could assess the changes in the importance of explanatory variables and models' prediction accuracy. To deal with high skew in our dataset due to small number of bankrupt firms, we applied over- and under- sampling methods on the train sample (80% of data). These methods proved to enhance our classifier's accuracy for all specifications and periods. The accuracy of our models has been evaluated by Receiver operating characteristics curves, Sensitivity-Specificity curves, and Precision-Recall curves. In comparison with models examined on similar data, our model performed very well. In addition, we have selected the most powerful predictors for short- and long-term horizons, which is potentially of high relevance for practice. JEL Classification C11, C51, C53, G33, M21 Keywords Bankruptcy...

The purpose of the diploma thesis is the design of condensing steam turbine for driving a compressor with mechanical power of 14,5 MW and operating speed 6800 rotations per minute on the compressor clutch. The main part is the thermodynamic calculation of the blade canal, which is verified with strength calculation to meet the requirements of the standart API 612. The next part is a basic design of gland sealing system, including a piston which is leveling the axial force with reusage of steam back to the blade canal. This thesis includes a calculation of axial and radial forces including the choice of suitable bearing, which both support the turbine. In the end there is an operating characteristics and function reliability of rising rotating speed on the total stress of blades.

Breast cancer is the second leading cause of cancer deaths among women in the United States and microcalcifications clusters are one of the most important indicators of breast disease. Computer methodologies help in the detection and differentiation between benign and malignant lesions and have the potential to improve radiologists' performance and breast cancer diagnosis significantly. A Computer-Aided Diagnosis (CAD-Dx) algorithm has been previously developed to assist radiologists in the diagnosis of mammographic clusters of calcifications with the modules: (a) detection of all calcification-like areas, (b) false-positive reduction and segmentation of the detected calcifications, (c) selection of morphological and distributional features and (d) classification of the clusters. Classification was based on an artificial neural network (ANN) with 14 input features and assigned a likelihood of malignancy to each cluster. The purpose of this work was threefold: (a) optimize the existing algorithm and test on a large database, (b) rank classification features and select the best feature set, and (c) determine the impact of single and two-view feature estimation on classification and feature ranking. Classification performance was evaluated with the NevProp4 artificial neural network trained with the leave-one-out resampling technique. Sequential forward selection was used for feature selection and ranking. Mammograms from 136 patients, containing single or two views of a breast with calcification cluster were digitized at 60 microns and 16 bits per pixel. 260 regions of interest (ROI's) centered on calcification cluster were defined to build the single-view dataset. 100 of the 136 patients had a two-view mammogram which yielded 202 ROI's that formed the two-view dataset. Classification and feature selection were evaluated with both these datasets. To decide on the optimal features for two-view feature estimation several combinations of CC and MLO view features were attempted. On the single-view dataset the classifier achieved an AZ =0.8891 with 88% sensitivity and 77% specificity at an operating point of 0.4; 12 features were selected as the most important. With the two-view dataset, the classifier achieved a higher performance with an AZ =0.9580 and sensitivity and specificity of 98% and 80% respectively at an operating point of 0.4; 10 features were selected as the most important.

This thesis focuses on the design, statistical operating characteristics and interpretation of early phase oncology clinical trials. Anti-cancer drugs are generally highly toxic and it is imperative to deliver a dose to the patient that is low enough to be safe but high enough to produce a clinically meaningful response. Thus, a study of dose limiting toxicities (DLTs) and a determination of the maximum tolerated dose (MTD) of a drug that can be used in later phase trials is the focus of most Phase I oncology trials. We first comprehensively compare the statistical operating characteristics of various early phase oncology designs, finding that all the designs examined select the MTD more accurately when there is a clear separation between the true DLT rate at the MTD and the rates at the dose levels immediately above and below. Among the rule-based designs studied, we found that the 3+3 design under-doses a large percentage of patients and is not accurate in selecting the MTD for all the cases considered. The 5+5 a design picks the MTD as accurately as the model based designs for the true DLT rates generated using the chosen log-logistic and linear dose-toxicity curves, but requires enrolling a larger number of patients. The model based designs examined, mTPI, TEQR, BOIN, CRM and EWOC designs, perform well on the whole, assign the maximum percentage of patients to the MTD, and pick the MTD fairly accurately. However, the limited sample size of these Phase I oncology trials makes it difficult to accurately predict the MTD. Hence, we next study the effect of sample size and cohort size on the accuracy of dose selection in early phase oncology designs, finding that an adequate sample size is crucial. We then propose some integrated Phase 1/2 oncology designs, namely the 20+20 accelerated titration design and extensions of the mTPI and TEQR designs, that consider both toxicity and efficacy in dose selection, utilizing a larger sample size. We demonstrate that these designs provide an improvement over the existing early phase designs. / 2019-12-01T00:00:00Z

The generalization abilities of machine learning algorithms often depend on the algorithms’ initialization, parameter settings, training sets, or feature selections. For instance, SVM classifier performance largely relies on whether the selected kernel functions are suitable for real application data. To enhance the performance of individual classifiers, this dissertation proposes classifier fusion models using computational intelligence knowledge to combine different classifiers. The first fusion model called T1FFSVM combines multiple SVM classifiers through constructing a fuzzy logic system. T1FFSVM can be improved by tuning the fuzzy membership functions of linguistic variables using genetic algorithms. The improved model is called GFFSVM. To better handle uncertainties existing in fuzzy MFs and in classification data, T1FFSVM can also be improved by applying type-2 fuzzy logic to construct a type-2 fuzzy classifier fusion model (T2FFSVM). T1FFSVM, GFFSVM, and T2FFSVM use accuracy as a classifier performance measure. AUC (the area under an ROC curve) is proved to be a better classifier performance metric. As a comparison study, AUC-based classifier fusion models are also proposed in the dissertation. The experiments on biomedical datasets demonstrate promising performance of the proposed classifier fusion models comparing with the individual composing classifiers. The proposed classifier fusion models also demonstrate better performance than many existing classifier fusion methods. The dissertation also studies one interesting phenomena in biology domain using machine learning and classifier fusion methods. That is, how protein structures and sequences are related each other. The experiments show that protein segments with similar structures also share similar sequences, which add new insights into the existing knowledge on the relation between protein sequences and structures: similar sequences share high structure similarity, but similar structures may not share high sequence similarity.

Die vorliegende Arbeit befasst sich mit dem Betriebsverhalten und den Auslegungsrichtlinien für ein hochtemperaturfähiges Übertragungselement aus Stahl für elastischen Klauenkupplungen. Im Grundlagenkapitel wurden hierfür zunächst aus den Eigenschaften und Auslegungsvorschriften handelsüblicher Klauenkupplungen wichtige Kenngrößen und Kennwerte bestimmt und relevante Aspekte der Tribologie und der Federtechnologie beleuchtet. Mittels FEM wird die Spannungsverteilung und das Verformungsverhalten des Übertragungselements bei Drehmomentbelastung untersucht und anhand einer Parameterstudie der Einfluss verschiedener Konstruktionsparameter analysiert. Zusätzlich werden Berechnungen und Simulationen zur Dämpfung und zur Betriebsfestigkeit durchgeführt. Die gewonnenen Erkenntnisse wurden anschließend durch experimentelle Untersuchungen mit statischer und dynamischer Belastung sowie durch Lebensdauerversuche und Betriebslastenversuche verifiziert und ergänzt. Hierbei wurden zu Vergleichszwecken auch handelsübliche Übertragungselemente aus TPE untersucht. Auf Basis der Untersuchungsergebnisse wurden anschließend Richtlinien für die Auslegung und den Betrieb des Übertragungselements abgeleitet. / The present work deals with the operating behaviour and the selection guidelines for a new high-temperature steel transmission element for elastic jaw couplings. In the state oft the art chapter, important parameters and characteristic values were first determined from the properties and selection specifications of commercially available claw couplings and relevant aspects of tribology and spring technology were highlighted. FEM is used to investigate the stress distribution and the deformation behaviour of the transmission element under torque loading and to analyse the influence of various design parameters. In addition, calculations and simulations for damping and fatigue strength are carried out. The knowledge gained was then verified and supplemented by experimental investigations with static and dynamic loads as well as by service life tests and operating load tests. For comparison purposes, commercially available transmission elements made of TPE were also investigated. Based on the test results, guidelines were then derived for the selection and operation of the new transmission element.