Network-Level Reliability-Based Bridge Inspection, Maintenance and Replacement Optimization Model

This paper presents a reliability-based optimization model of inspection, maintenance and replacement for a system of several highway bridges. The objective in the formulation is to minimize the total expected social cost, including the expected cost of failure. The frequency of inspections is included as a decision variable. The probability of failure is explicitly taken into account in the constraints. A bottom-up approach is used, which allows for bridge-specific details to be taken into account. Most existing system level models assume that component deterioration is memoryless; however, this assumption is relaxed in this paper, and history-dependent deterioration models are used. The formulation is flexible enough to accommodate different types of facilities, deterioration processes and failure modes. A parametric study is conducted to demonstrate the model’s response to different assumptions on the deterioration rates, maintenance costs and efficiency

On the Representability of Complete Genomes by Multiple Competing Finite-Context (Markov) Models

A finite-context (Markov) model of order yields the probability distribution of the next symbol in a sequence of symbols, given the recent past up to depth . Markov modeling has long been applied to DNA sequences, for example to find gene-coding regions. With the first studies came the discovery that DNA sequences are non-stationary: distinct regions require distinct model orders. Since then, Markov and hidden Markov models have been extensively used to describe the gene structure of prokaryotes and eukaryotes. However, to our knowledge, a comprehensive study about the potential of Markov models to describe complete genomes is still lacking. We address this gap in this paper. Our approach relies on (i) multiple competing Markov models of different orders (ii) careful programming techniques that allow orders as large as sixteen (iii) adequate inverted repeat handling (iv) probability estimates suited to the wide range of context depths used. To measure how well a model fits the data at a particular position in the sequence we use the negative logarithm of the probability estimate at that position. The measure yields information profiles of the sequence, which are of independent interest. The average over the entire sequence, which amounts to the average number of bits per base needed to describe the sequence, is used as a global performance measure. Our main conclusion is that, from the probabilistic or information theoretic point of view and according to this performance measure, multiple competing Markov models explain entire genomes almost as well or even better than state-of-the-art DNA compression methods, such as XM, which rely on very different statistical models. This is surprising, because Markov models are local (short-range), contrasting with the statistical models underlying other methods, where the extensive data repetitions in DNA sequences is explored, and therefore have a non-local character

FactSage thermochemical software and databases, 2010–2016

The FactSage computer package consists of a series of information, calculation and manipulation modules that enable one to access and manipulate compound and solution databases. With the various modules running under Microsoft Windows® one can perform a wide variety of thermochemical calculations and generate tables, graphs and figures of interest to chemical and physical metallurgists, chemical engineers, corrosion engineers, inorganic chemists, geochemists, ceramists, electrochemists, environmentalists, etc. This paper presents a summary of the developments in the FactSage thermochemical software and databases during the last six years. Particular emphasis is placed on the new databases and developments in calculating and manipulating phase diagrams

Fixation de protéines chez les ruminants : évolution en fonction du poids des animaux et variations selon la race, le sexe ou le niveau des apports alimentaires

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