Software development for analysis of stochastic petri nets using transfer functions

This thesis research is an implementation of a closed-form analytical technique for study, evaluation and analysis of Stochastic Petri Nets (SPN). The technique is based on a theorem that an isomorphism exists between an SPN and a Markov Chain. The procedure comprises five main steps: reachability graph generation of the underlying Petri net, transformation of the reachability graph to a state machine Petri net, calculation of transfer functions, computation of equivalent transfer functions via Mason\u27s rule, and computation of performance parameters of the SPN model from the equivalent transfer functions and their derivatives. The software is developed in UNIX using C and applied to various SPN models. Future research includes implementation of Mason\u27s rule for complex cases and symbolic derivation of equivalent transfer functions

Rutting assessment of crumb rubber modifier modified warm mix asphalt incorporating warm asphalt additive

Warm Mix Asphalt (WMA) as a green technology, permits production of asphalt mixtures at lower temperatures compared to conventional HMA; emissions and energy consumption reduction, were among the key success of this technology, thus, enhancing social, economic, and environmental sustainability. But due to the reduced production temperature, WMA are more prone to rutting, to improve the rutting resistance of WMA mixtures and minimize pollution resulting from waste rubber tire. Therefore, the effect of wet processed Crumb Rubber Modifier (CRM) on rutting depth of WMA mixtures incorporating 2.5% Sasobit by weight of base binders were assessed in the laboratory. In this study, the asphalt mixtures were fabricated in accordance with Superpave, using; crush granite aggregate of 9.5mm NMAS and the four binders that were produced by blending the PG 64 binder with different contents of 40 mesh size CRM (0%, 5%, 10%, and 15%, by weight of the base binder). Rutting depths of the mixtures were assessed on 150mm diameter and 70mm thick cylindrical samples using wheel tracker, the wheel tracking test were carried out at 45oC and 60oC, in accordance with BS 598 Part 110 (1998). Based on the results of wheel tracking tests, CRM could improve the resistance of the WMA mixtures to rutting. It was also found from statistical Analysis of Variance (ANOVA), that the two influence factors; CRM, and the test temperature both having p-values less than the assumed significance at 95% confidence level, therefore they have significant effect on rutting in WMA

Psychological Parameters for Crowd Simulation: From Audiences to Mobs

In the social psychology literature, crowds are classified as audiences and mobs. Audiences are passive crowds, whereas mobs are active crowds with emotional, irrational and seemingly homogeneous behavior. In this study, we aim to create a system that enables the specification of different crowd types ranging from audiences to mobs. In order to achieve this goal we parametrize the common properties of mobs to create collective misbehavior. Because mobs are characterized by emotionality, we describe a framework that associates psychological components with individual agents comprising a crowd and yields emergent behaviors in the crowd as a whole. To explore the effectiveness of our framework we demonstrate two scenarios simulating the behavior of distinct mob types. © 2015 IEEE

Impact of race, ethnicity, and BMI on achievement of pathologic complete response following neoadjuvant chemotherapy for breast cancer: a pooled analysis of four prospective Alliance clinical trials (A151426)

Previous studies demonstrated poor response to neoadjuvant systemic therapy (NST) for breast cancer among black women and women who are overweight or obese but this may be due to chemotherapy under dosing. We assessed associations of race, ethnicity and body mass index (BMI) with pathologic complete response (pCR) in clinical trial populations

Atomistic modeling of dopant segregation in alpha-alumina ceramics: Coverage dependent energy of segregation and nominal dopant solubility

Microstructural control is a key aspect in producing ceramics with tailored properties and is often achieved by using dopants in a rather empirical fashion. Atomic scale simulations could provide much needed insight but the long-standing challenge of linking simulation results on isolated grain boundaries to those measured in real ceramics needs to be resolved. Here a novel Monte-Carlo simulation method based on a microstructural model in combination with energies obtained from atomic scale energy minimization is presented. This approach allows, for the first time, the prediction of the nominal solubility of dopants (Y, La and Mg) in a ceramic purely from theory. Results compare well with segregation/precipitation data as a function of grain size, found in the literature. The method can therefore be used in developing experimental guidelines for the effective use of dopants in ceramic production, thus accelerating the development of novel materials required for innovative applications

Automated detection of calcified plaque using higher-order spectra cumulant technique in computer tomography angiography images

Cardiovascular disease continues to be the leading cause of death globally. Often, it stems from atherosclerosis, which can trigger substantial variations in the coronary arteries, possibly causing coronary artery disease (CAD). Coronary artery calcification is known to be a strong and independent forecaster of CAD. Hence, coronary computer tomography angiography (CTA) has become a fundamental noninvasive imaging tool to characterize coronary artery plaques. In this article, an automated algorithm is presented to uncover the presence of a calcified plaque, using 2060 CTA images acquired from 60 patients. Higher-order spectra cumulants were extracted from each image, thereby providing 2448 descriptive features per image. The features were then reduced using numerous well-established techniques, and ranked according to t value. Subsequently, the reduced features were input to several classifiers to achieve the best diagnostic accuracy with a minimum number of features. Optimal results were obtained using the support vector machine with a radial basis function, having 22 features obtained with the multiple factor analysis feature reduction algorithm. The accuracy, positive predictive value, sensitivity, and specificity obtained were 95.83%, 97.05%, 94.54%, and 97.13%, respectively. Based on these results, the technique could be useful to automatically and accurately identify calcified plaque evident in CTA images, and may therefore become an important tool to help reduce procedural costs and patient radiation dose