A 2-dimension dynamic Bayesian network for large-scale degradation modelling with an application to a bridges network

Modeling the stochastic evolution of a large-scale fleet or network generally proves to be challenging. This difficulty may be compounded through complex relationships between various assets in the network. Although a great number of probabilistic graph-based models (e.g., Bayesian networks) have been developed recently to describe the behavior of single assets, one can find significantly fewer approaches addressing a fully integrated network. It is proposed an extension to the standard dynamic Bayesian network (DBN) by introducing an additional dimension for multiple elements. These elements are then linked through a set of covariates that translate the probabilistic dependencies. A Markov chain is utilized to model the elements and develop a distribution-free mathematical framework to parameterize the transition probabilities without previous data. This is achieved by borrowing from Cooke\u27s method for structured expert judgment and also applied to the quantification of the covariate relationships. Some metrics are also presented for evaluating the sensitivity of information inserted into the covariate DBN where the focus is given on two specific types of configurations. The model is applied to a real-world example of steel bridge network in the Netherlands. Numerical examples highlight the inference mechanism and show the sensitivity of information inserted in various ways. It is shown that information is most valuable very early and decreases substantially over time. Resulting observations entail the reduction of inference combinations and by extension a computational gain to select the most sensitive pieces of information

A 2-dimension dynamic Bayesian network for large-scale degradation modelling with an application to a bridges network

Modeling the stochastic evolution of a large-scale fleet or network generally proves to be challenging. This difficulty may be compounded through complex relationships between various assets in the network. Although a great number of probabilistic graph-based models (e.g., Bayesian networks) have been developed recently to describe the behavior of single assets, one can find significantly fewer approaches addressing a fully integrated network. It is proposed an extension to the standard dynamic Bayesian network (DBN) by introducing an additional dimension for multiple elements. These elements are then linked through a set of covariates that translate the probabilistic dependencies. A Markov chain is utilized to model the elements and develop a distribution-free mathematical framework to parameterize the transition probabilities without previous data. This is achieved by borrowing from Cooke\u27s method for structured expert judgment and also applied to the quantification of the covariate relationships. Some metrics are also presented for evaluating the sensitivity of information inserted into the covariate DBN where the focus is given on two specific types of configurations. The model is applied to a real-world example of steel bridge network in the Netherlands. Numerical examples highlight the inference mechanism and show the sensitivity of information inserted in various ways. It is shown that information is most valuable very early and decreases substantially over time. Resulting observations entail the reduction of inference combinations and by extension a computational gain to select the most sensitive pieces of information

Delayed self-recognition in children with autism spectrum disorder.

This study aimed to investigate temporally extended self-awareness (awareness of one’s place in and continued existence through time) in autism spectrum disorder (ASD), using the delayed self-recognition (DSR) paradigm (Povinelli et al., Child Development 67:1540–1554, 1996). Relative to age and verbal ability matched comparison children, children with ASD showed unattenuated performance on the DSR task, despite showing significant impairments in theory-of-mind task performance, and a reduced propensity to use personal pronouns to refer to themselves. The results may indicate intact temporally extended self-awareness in ASD. However, it may be that the DSR task is not an unambiguous measure of temporally extended self-awareness and it can be passed through strategies which do not require the possession of a temporally extended self-concept

Emergence and spread of two SARS-CoV-2 variants of interest in Nigeria.

Identifying the dissemination patterns and impacts of a virus of economic or health importance during a pandemic is crucial, as it informs the public on policies for containment in order to reduce the spread of the virus. In this study, we integrated genomic and travel data to investigate the emergence and spread of the SARS-CoV-2 B.1.1.318 and B.1.525 (Eta) variants of interest in Nigeria and the wider Africa region. By integrating travel data and phylogeographic reconstructions, we find that these two variants that arose during the second wave in Nigeria emerged from within Africa, with the B.1.525 from Nigeria, and then spread to other parts of the world. Data from this study show how regional connectivity of Nigeria drove the spread of these variants of interest to surrounding countries and those connected by air-traffic. Our findings demonstrate the power of genomic analysis when combined with mobility and epidemiological data to identify the drivers of transmission, as bidirectional transmission within and between African nations are grossly underestimated as seen in our import risk index estimates

Value of monitoring in asset management: A social costbenefit analysis approach

We present a framework to investigate new monitoring techniques for infrastructures and assess their potential value for the network management. This framework is based on a social cost benefit analysis tool that aims to (i) assist decision makers in selecting and developing cost-effective new monitoring techniques and (ii) provide managers with socially optimal maintenance and rehabilitation strategies that take into account output from these monitoring systems. Potential value of monitoring consists mainly in enabling condition-based strategies and providing more accurate and relevant information that should result in more cost-effective strategies. Monitoring provides information about either the structure degradation level or its environment. The condition of the structure is represented by a set of technical performance indicators that reflect its degradation level and are linked to a set of end-user service levels. Finally, the end-user service levels are valuated to optimize the cost and benefits of maintenance and rehabilitation strategies. Main feature of the tool we develop is to enable optimal, dynamic and reliabilitybased decisions that are reviewed and updated every time a new relevant information is available. Transition probabilities to predict future deterioration levels are estimated and updated using monitoring data to assess risks and optimize its expected cost. Moreover, the derived strategies are socially optimal and take into account indirect impacts of degradations and M&R strategies on the society and the environment. This is done by consideration and valuation of end-user service levels. We use Markov decision processes which are an appropriate framework for decision-making under uncertainty to incorporate reliability and risk measures within the optimization problem. Copyright © 2013, AIDIC Servizi S.r.l

Multidrug resistant Salmonella species isolated from fufu grinding machines in Ghana

Multidrug resistant Salmonella infection has become one of the most dangerous health concerns in Sub-Saharan Africa. Most previous research shows that food and water are the sources of the human Salmonella infection in Ghana. This article examines Salmonella contamination of fufu, a thick paste prepared from pounded boiled tubers, traditionally prepared using pestle and mortar, a common food in West and Central Africa. The fufu grinding machine, a new technology for grinding fufu, is gaining root in many parts of Sub-Saharan Africa, particularly in the urban areas where most people are inclined to use machines to minimize drudgery, leaving behind the traditional way which involves the use of a wooden mortar and pestle. To investigate the sources of these contaminations, 100 samples were collected from 50 randomly sampled fufu grinding machines in the Tamale Metropolis to examine the prevalence and antibiotic susceptibility of Salmonella species. Fufu samples (SA) and fufu wash-out samples (SB) were collected from each grinding machine as described in ISO 6579:2002 protocol for the detection of Salmonella in food. Of the total 100 samples, 27% were confirmed Salmonella positive, of which 16 were fufu samples while 11 were fufu wash-out samples. Forty-eight percent (48%) of the 50 machines were contaminated with Salmonella. Contamination of wooden machines (85.7%) was higher as compared with the metallic machines (41.9%). The resistance levels of the isolates to the various antibiotics used were as follows: gentamicin (7.1%), nitrofurantoin (18.5%), ciprofloxacin (22.2%), erythromycin (81.5%), ceftazidime (85.2%) and ceftriaxone (88.9%). More of the isolates were resistant to three or more antibiotics (81.5% multidrug resistance). From this research, it can be concluded that there is high prevalence of Salmonella isolated from fufu grinding machines in the Tamale metropolis. Measures must be taken by the regulatory authorities to ensure that fufu prepared in grinding machines is safer. Also, awareness creation on antibiotic resistance and strict enforcement of laws on self-prescriptions of drugs would help avert multidrug resistance

Micropiling Two Bridges in Construction in the Motorway M-410 in Madrid (Spain)

International audienceMarkov-based models for predicting deterioration for civil infrastructures are widely recognized as suitable tools addressing this mechanism. The objective of this paper is to provide insights regarding a network of orthotropic steel bridges in terms of degradation. Consequently, a model combining a dynamic Bayesian network and a Markov chain is first introduced that builds up the network in a concise way. In an attempt to represent a network composed of two general classes of orthotropic steel bridges, the classical method of structured expert judgment is carried out as a quantification procedure. The first objective is to elicit indirectly transition probabilities for a Markov chain that describes how each bridge type deteriorates in time. Second, experts are asked to provide estimates on required conditional probabilities related to the Bayesian network. An in-depth analysis of the results is presented so that remarks and observations are subsequently pointed out and, finally conclusions are drawn