Automatic Incident Detection Algorithms (AIDA) have been part of freeway management system software from the beginnings of ITS deployment. These algorithms introduce the capability of detecting incidents on freeways using traffic operations data. Over the years, several approaches to incident detection have been studied and tested. However, the size and scope of the urban transportation networks under direct monitoring by transportation management centers are growing at a faster rate than are staffing levels and center resources. This has entailed a renewed emphasis on the need for reliability and accuracy of AIDA functionality. This study investigates a new approach to incident detection that promises a significant improvement in operational performance. This algorithm is formulated on the premise that the current conditions facilitate the prediction of future traffic conditions, and deviations of observations from the predictions beyond a calibrated level of tolerance indicate the occurrence of incidents. This algorithm is specifically designed for easy implementation and calibration at any site. Offline tests with data from the Georgia-Navigator system indicate that this algorithm realizes a substantial improvement over the conventional incident detection algorithms. This algorithm not only achieves a low rate of false alarms but also ensures a high detection rate.

People’s ability to call an experienced item “old” and a novel item “new” is recognition memory. Recognition memory is usually studied by first asking participants to learn a list of words and then make judgments of old (studied) or new (not studied) for test words. It has long been debated whether the underlying process of recognition memory is continuous or discrete. Two types of models are compared specifically that assume either discrete or continuous information states: the 2-high threshold (2HT) model and the unequal variance signal detection (UVSD) model, respectively. Researchers have used the receiver operation characteristic (ROC) function and response time (RT) data to test between the two models. However, both methods have provided evidence for 2HT and UVSD, and the debate has not come to consensus. In this study, we used an alternative approach to look into this issue. After studying the words, participants first made “old/new” judgment for each single test item. Then, if there were falsely identified items, each of them was randomly paired with a correctly identified word of the same response. Participants were asked to choose the studied word from the word pair. Simulation and experimental results were able to discriminate the 2HT and UVSD model. Experimental results showed that the UVSD model fitted the data better than the 2HT model. The forced-choice test paradigm provided an effective way to test between the 2HT and UVSD models.

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Modeling complex cognitive phenomena is a challenging task, especially when it is required to account for the functioning of a cognitive system interacting with an uncertain and changing environment. Psychometrics offers a heterogeneous corpus of computational tools to infer latent cognitive constructs from the observation of behavioral outcomes. However, there is not an explicit consensus regarding the optimal way to properly take into account the intrinsic dynamic properties of the environment, as well as the dynamic nature of cognitive states. In the present dissertation, we explore the potentials of relying on discrete state dynamic models to formally account for the unfolding of cognitive sub-processes in changing task environments. In particular, we propose Probabilistic Graphical Models (PGMs) as an ideal and unifying mathematical language to represent cognitive dynamics as structured graphs codifying (causal) relationships between cognitive sub-components which unfolds in discrete time. We propose several works demonstrating the advantage and the representational power of such a modeling framework, by providing dynamic models of cognition specified according to different levels of abstraction.

The Aldous order is an ordering of representations of the symmetric group motivated by the Aldous Conjecture, a conjecture about random processes proved in 2009. In general, the Aldous order is very difficult to compute, and the proper relations have yet to be determined even for small cases. However, by restricting the problem down to Young-Jucys-Murphy elements, the problem becomes explicitly combinatorial. This approach has led to many novel insights, whose proofs are simple and elegant. However, there remain many open questions related to the Aldous Order, both in general and for the Young-Jucys-Murphy elements.

Aim of this thesis is building of a simulator model of a rotary (Furuta) pendulum and design of appropriate regulators. This paper describes assembly of a nonlinear simulator model, using Matlab–Simulink and its library Simscape–Simmechanics. Furthermore the paper discuss linear discrete model obtained from the system response, using least squares method. This linear model serves as aproximation of the system for designing of two linear discrete state space regulators with sumator. These regulators are supported by a simple swing–up regulator and logics managing cooperation.

This dissertation is concerned with the potential multistability of protein concentrations in the cell that can arise in biochemical networks. That is, situations where one, or a family of, proteins may sit at one of two or more different steady state concentrations in otherwise identical cells, and in spite of them being in the same environment. Models of multisite protein phosphorylation have shown that this mechanism is able to exhibit unlimited multistability. Nevertheless, these models have not considered enzyme docking, the binding of the enzymes to one or more substrate docking sites, which are separate from the motif that is chemically modified. Enzyme docking is, however, increasingly being recognised as a method to achieve specificity in protein phosphorylation and dephosphorylation cycles. Most models in the literature for these systems are deterministic i.e. based on Ordinary Differential Equations, despite the fact that these are accurate only in the limit of large molecule numbers. For small molecule numbers, a discrete probabilistic, stochastic, approach is more suitable. However, when compared to the tools available in the deterministic framework, the tools available for stochastic analysis offer inadequate visualisation and intuition. We firstly try to bridge that gap, by developing three tools: a) a discrete `nullclines' construct applicable to stochastic systems - an analogue to the ODE nullcines, b) a stochastic tool based on a Weakly Chained Diagonally Dominant M-matrix formulation of the Chemical Master Equation and c) an algorithm that is able to construct non-reversible Markov chains with desired stationary probability distributions. We subsequently prove that, for multisite protein phosphorylation and similar models, in the deterministic domain, enzyme docking and the consequent substrate enzyme-sequestration must inevitably limit the extent of multistability, ultimately to one steady state. In contrast, bimodality can be obtained in the stochastic domain even in situations where bistability is not possible for large molecule numbers. We finally extend our results to cases where we have an autophosphorylating kinase, as for example is the case with $Ca^{2+}$/calmodulin-dependent protein kinase II (CaMKII), a key enzyme in synaptic plasticity.