Bayesian networks for assessment of disruption to school systems under combined hazards

Exposure of school buildings to floods and earthquakes poses significant risk to the vulnerable population of students and their education process. In regions of high exposure, these hazards may often act concurrently, whereby yearly flood events weaken masonry school buildings, rendering them more vulnerable to frequent earthquake shaking. This recurring damage, combined with other functional losses, ultimately result in disruption to education delivery. The socio-economic condition of the users-community also plays a role in the extent of such disruption. A complex problem of this nature demands consideration of a large number of dimensions, to estimate the impact to the school system infrastructure in a locality. To handle the qualitative and quantitative nature of these variables, a Bayesian network (BN) model is proposed representing multiple schools in a locality as a system. Three dimensions are considered to contribute to the system disruption, namely, schools’ physical functionality loss, accessibility and use loss, and social vulnerability. The impact is quantified through the probability of the system being in various states of disruption. The BN also explores mitigating measures, such as the mobility of students between schools in the system. The general methodology is illustrated by a case-study of school buildings in Guwahati, India, whereby the majority of buildings is constructed in confined masonry with varying level of seismic performance. The physical effects of combined flood and seismic action on confined masonry buildings is assessed by nonlinear numerical modelling, and their probabilistic occurrence is expressed in terms of fragility functions corresponding to varying flood depth and peak ground acceleration

Bayesian networks for the multi-risk assessment of road infrastructure

The purpose of this study is to develop a methodological framework for the multi-risk assessment of road infrastructure systems. Since the network performance is directly linked to the functional states of its physical elements, most efforts are devoted to the derivation of fragility functions for bridges exposed to potential earthquake, flood and ground failure events. Thus, a harmonization effort is required in order to reconcile fragility models and damage scales from different hazard types. The proposed framework starts with the inventory of the various hazard-specific damaging mechanisms or failure modes that may affect each bridge component (e.g. piers, deck, bearings). Component fragility curves are then derived for each of these component failure modes, while corresponding functional consequences are proposed in a component-level damage-functionality matrix, thanks to an expert-based survey. Functionality-consistent failure modes at the bridge level are then assembled for specific configurations of component damage states. Finally, the development of a Bayesian Network approach enables the robust and efficient derivation of system fragility functions that (i) directly provide probabilities of reaching functionality losses and (ii) account for multiple types of hazard loadings and multi-risk interactions. At the network scale, a fully probabilistic approach is adopted in order to integrate multi-risk interactions at both hazard and fragility levels. A temporal dimension is integrated to account for joint independent hazard events, while the hazard-harmonized fragility models are able to capture cascading failures. The quantification of extreme events cannot be achieved by conventional sampling methods, and therefore the inference ability of Bayesian Networks is investigated as an alternative. Elaborate Bayesian Network formulations based on the identification of link sets are benchmarked, thus demonstrating the current computational difficulties to treat large and complex systems

Agent-based model on resilience-oriented rapid responses of road networks under seismic hazard

This paper explores a new pathway towards seismic resilience of Road Networks (RNs) under earthquake hazards, by leveraging post-shock rapid responses as the key to minimize the functionality losses of RNs, especially in the immediate aftermath of earthquakes. Accordingly, an agent-based modelling (ABM) framework is developed to enable the nuanced examination on resilience of earthquake-damaged RNs, when different system repair approaches are considered. In this framework, those different approaches are predicated on the damage level of individual bridges and on the system recovery timeline, i.e. the response to rehabilitation need is considered as a function of the time elapsed from the event. Each approach is represented by a different agent, whose behaviour is shaped by a set of pre-defined behavioural attributes, while the interplay among those agents is also accounted for, during the entirety of post-shock recovery campaigns. To demonstrate its applicability, the ABM framework is applied to a real-world RN across Luchon, France. As shown by the case-study, post-shock rapid responses are found to be a viable strategy to increase the recovery rate of RNs’ functionality in the immediate-, and mid-term aftermath of damaging earthquakes, and ultimately, to improve the seismic resilience thereof

Analyzing and predicting the spatial penetration of Airbnb in U.S. cities

In the hospitality industry, the room and apartment sharing platform of Airbnb has been accused of unfair competition. Detractors have pointed out the chronic lack of proper legislation. Unfortunately, there is little quantitative evidence about Airbnb's spatial penetration upon which to base such a legislation. In this study, we analyze Airbnb's spatial distribution in eight U.S. urban areas, in relation to both geographic, socio-demographic, and economic information. We find that, despite being very different in terms of population composition, size, and wealth, all eight cities exhibit the same pattern: that is, areas of high Airbnb presence are those occupied by the \newpart{``talented and creative''} classes, and those that are close to city centers. This result is consistent so much so that the accuracy of predicting Airbnb's spatial penetration is as high as 0.725

Exploration of two methods for quantitative Mitomycin C measurement in tumor tissue in vitro and in vivo

Two methods of quantifying Mitomycin C in tumor tissue are explored. A method of ultraviolet-visible absorption microscopy is developed and applied to measure the concentration of Mitomycin C in preserved mouse tumor tissue, as well as in gelatin samples. Concentrations as low as 60 μM can be resolved using this technique in samples that do not strongly scatter light. A novel method for monitoring the Mitomycin C concentrations inside a tumor is developed, based on microdialysis and ultraviolet-visible spectroscopy. A pump is used to perfuse a microdialysis probe with Ringer’s solution, which is fed to a flow cell to determine intratumor concentrations in real time to within a few μM. The success and limitations of these techniques are identified, and suggestions are made as to further development. To the authors’ knowledge these are the first attempts made to quantify Mitomycin C concentrations in tumor tissue

Compact groups with a dense free abelian subgroup

The compact groups having a dense infinite cyclic subgroup (known as monothetic compact groups) have been studied by many authors for their relevance and nice applications. In this paper we describe in full details the compact groups $K$ with a dense free abelian subgroup $F$ and we describe the minimum rank $r_t(K)$ of such a subgroup $F$ of $K$. Surprisingly, it is either finite or coincides with the density character $d(K)$ of $K$.

Rapid earthquake loss updating of spatially distributed systems via sampling-based bayesian inference

Within moments following an earthquake event, observations collected from the affected area can be used to define a picture of expected losses and to provide emergency services with accurate information. A Bayesian Network framework could be used to update the prior loss estimates based on ground-motion prediction equations and fragility curves, considering various field observations (i.e., evidence). While very appealing in theory, Bayesian Networks pose many challenges when applied to real-world infrastructure systems, especially in terms of scalability. The present study explores the applicability of approximate Bayesian inference, based on Monte-Carlo Markov-Chain sampling algorithms, to a real-world network of roads and built areas where expected loss metrics pertain to the accessibility between damaged areas and hospitals in the region. Observations are gathered either from free-field stations (for updating the ground-motion field) or from structure-mounted stations (for the updating of the damage states of infrastructure components). It is found that the proposed Bayesian approach is able to process a system comprising hundreds of components with reasonable accuracy, time and computation cost. Emergency managers may readily use the updated loss distributions to make informed decisions

Ethnographic understandings of ethnically diverse neighbourhoods to inform urban design practice

The aim of this paper is to inform urban design practice through deeper understanding and analysis of the social dynamics of public outdoor space in ethnically diverse neighbourhoods. We hypothesise that findings from ethnographic research can provide a resource that improves cultural literacy and supports social justice in professional practice. The primary method is a meta-synthesis literature review of 24 ethnographic research papers, all of which explore some dimensions of public open space use and values in UK urban contexts characterised by ethnic and racial diversity. We summarise thematic understandings and significance of neighbourhood places of shared activity, parks, spaces of passing-by and of retreat. We evaluate the implications for intercultural social dynamics, exploring the spatial and temporal dimensions of conviviality and racism in public open space. We then argue that it is possible to develop principles for urban design practice informed by this work, and propose four for discussion: maximising straightforward participation, legitimising diversity of activity, designing in micro-retreats of nearby quietness and addressing structural inequalities of open space provision. We conclude that ethnographic research can provide detailed insights into the use of the public realm and also inform a more nuanced understanding of outdoor sociality relevant for an increasingly diverse society. The challenge is two-fold: for ethnographers to become less cautious in engaging with decisions and priorities regarding how cities change, and for urban designers to explicitly embed informed understandings of difference into their broad desire for inclusive public space