Uptake pathways: the potential of Bayesian belief networks to assist the management, monitoring and evaluation of development-orientated research

The effectiveness of development assistance has come under renewed scrutiny in recent years. In an era of growing economic liberalisation, research organisations are increasingly being asked to account for the use of public funds by demonstrating achievements. However, in the natural resources (NR) research field, conventional economic assessment techniques have focused on quantifying the impact achieved rather understanding the process that delivered it. As a result, they provide limited guidance for planners and researchers charged with selecting and implementing future research. In response, “pathways” or logic models have attracted increased interest in recent years as a remedy to this shortcoming. However, as commonly applied these suffer from two key limitations in their ability to incorporate risk and assess variance from plan. The paper reports the results of a case study that used a Bayesian belief network approach to address these limitations and outlines its potential value as a tool to assist the planning, monitoring and evaluation of development-orientated research.

Integration of remote sensing and GIS for modelling flash floods in Wadi Hudain catchment, Egypt

This paper presents a new approach to modelling flash floods in dryland catchments by integrating remote sensing and digital elevation model (DEM) data in a geographical information system (GIS). The spectral reflectance of channels affected by recent flash floods exhibit a marked increase, due to the deposition of fine sediments in these channels as the flood recedes. This allows the parts of a catchment that have been affected by a recent flood event to be discriminated from unaffected parts, using a time series of Landsat images. Using images of the Wadi Hudain catchment in southern Egypt, the hillslope areas contributing flow were inferred for different flood events. The SRTM3 DEM was used to derive flow direction, flow length, active channel cross-sectional areas and slope. The Manning Equation was used to estimate the channel flow velocities, and hence the time-area zones of the catchment. A channel reach that was active during a 1985 runoff event, that does not receive any tributary flow, was used to estimate a transmission loss rate of 7·5 mm h−1, given the maximum peak discharge estimate. Runoff patterns resulting from different flood events are quite variable; however the southern part of the catchment appears to have experienced more floods during the period of study (1984–2000), perhaps because the bedrock hillslopes in this area are more effective at runoff production than other parts of the catchment which are underlain by unconsolidated Quaternary sands and gravels. Due to high transmission loss, runoff generated within the upper reaches is rarely delivered to the alluvial fan and Shalateen city situated at the catchment outlet. The synthetic GIS-based time area zones, on their own, cannot be relied on to model the hydrographs reliably; physical parameters, such as rainfall intensity, distribution, and transmission loss, must also be considered.

Bayesian parameter estimation for latent Markov random fields and social networks

Undirected graphical models are widely used in statistics, physics and machine vision. However Bayesian parameter estimation for undirected models is extremely challenging, since evaluation of the posterior typically involves the calculation of an intractable normalising constant. This problem has received much attention, but very little of this has focussed on the important practical case where the data consists of noisy or incomplete observations of the underlying hidden structure. This paper specifically addresses this problem, comparing two alternative methodologies. In the first of these approaches particle Markov chain Monte Carlo (Andrieu et al., 2010) is used to efficiently explore the parameter space, combined with the exchange algorithm (Murray et al., 2006) for avoiding the calculation of the intractable normalising constant (a proof showing that this combination targets the correct distribution in found in a supplementary appendix online). This approach is compared with approximate Bayesian computation (Pritchard et al., 1999). Applications to estimating the parameters of Ising models and exponential random graphs from noisy data are presented. Each algorithm used in the paper targets an approximation to the true posterior due to the use of MCMC to simulate from the latent graphical model, in lieu of being able to do this exactly in general. The supplementary appendix also describes the nature of the resulting approximation.

Elicitation of multivariate prior distributions: A nonparametric Bayesian approach

In the context of Bayesian statistical analysis, elicitation is the process of formulating a prior density f(.) about one or more uncertain quantities to represent a person's knowledge and beliefs. Several different methods of eliciting prior distributions for one unknown parameter have been proposed. However, there are relatively few methods for specifying a multivariate prior distribution and most are just applicable to specific classes of problems and/or based on restrictive conditions, such as independence of variables. Besides, many of these procedures require the elicitation of variances and correlations, and sometimes elicitation of hyperparameters which are difficult for experts to specify in practice. Garthwaite et al. (2005) discuss the different methods proposed in the literature and the difficulties of eliciting multivariate prior distributions. We describe a flexible method of eliciting multivariate prior distributions applicable to a wide class of practical problems. Our approach does not assume a parametric form for the unknown prior density f(.), instead we use nonparametric Bayesian inference, modelling f(.) by a Gaussian process prior distribution. The expert is then asked to specify certain summaries of his/her distribution, such as the mean, mode, marginal quantiles and a small number of joint probabilities. The analyst receives that information, treating it as a data set D with which to update his/her prior beliefs to obtain the posterior distribution for f(.). Theoretical properties of joint and marginal priors are derived and numerical illustrations to demonstrate our approach are given. (C) 2010 Elsevier B.V. All rights reserved.

Experience acquired in Europe in the integration and co-ordinated operations of national electric power networks

Includes bibliography

Integration of wireless sensor networks to the internet of things using a 6LoWPAN Gateway

The Internet of Things is a new paradigm where smart embedded devices and systems are connected to the Internet. In this context, Wireless Sensor Networks (WSN) are becoming an important alternative for sensing and actuating critical applications like industrial automation, remote patient monitoring and domotics. The IEEE 802.15.4 protocol has been adopted as a standard for WSN and the 6LoWPAN protocol has been proposed to overcome the challenges of integrating WSN and Internet protocols. In this paper, the mechanisms of header compression and fragmentation of IPv6 datagrams proposed in the 6LoWPAN standard were evaluated through field experiments using a gateway prototype and IEEE 802.15.4 nodes.

Assessing the gain of biological data integration in gene networks inference

Background: A current challenge in gene annotation is to define the gene function in the context of the network of relationships instead of using single genes. The inference of gene networks (GNs) has emerged as an approach to better understand the biology of the system and to study how several components of this network interact with each other and keep their functions stable. However, in general there is no sufficient data to accurately recover the GNs from their expression levels leading to the curse of dimensionality, in which the number of variables is higher than samples. One way to mitigate this problem is to integrate biological data instead of using only the expression profiles in the inference process. Nowadays, the use of several biological information in inference methods had a significant increase in order to better recover the connections between genes and reduce the false positives. What makes this strategy so interesting is the possibility of confirming the known connections through the included biological data, and the possibility of discovering new relationships between genes when observed the expression data. Although several works in data integration have increased the performance of the network inference methods, the real contribution of adding each type of biological information in the obtained improvement is not clear. Methods: We propose a methodology to include biological information into an inference algorithm in order to assess its prediction gain by using biological information and expression profile together. We also evaluated and compared the gain of adding four types of biological information: (a) protein-protein interaction, (b) Rosetta stone fusion proteins, (c) KEGG and (d) KEGG+GO. Results and conclusions: This work presents a first comparison of the gain in the use of prior biological information in the inference of GNs by considering the eukaryote (P. falciparum) organism. Our results indicates that information based on direct interaction can produce a higher improvement in the gain than data about a less specific relationship as GO or KEGG. Also, as expected, the results show that the use of biological information is a very important approach for the improvement of the inference. We also compared the gain in the inference of the global network and only the hubs. The results indicates that the use of biological information can improve the identification of the most connected proteins.

Essays on Bayesian Inference for Social Networks

This thesis presents Bayesian solutions to inference problems for three types of social network data structures: a single observation of a social network, repeated observations on the same social network, and repeated observations on a social network developing through time. A social network is conceived as being a structure consisting of actors and their social interaction with each other. A common conceptualisation of social networks is to let the actors be represented by nodes in a graph with edges between pairs of nodes that are relationally tied to each other according to some definition. Statistical analysis of social networks is to a large extent concerned with modelling of these relational ties, which lends itself to empirical evaluation. The first paper deals with a family of statistical models for social networks called exponential random graphs that takes various structural features of the network into account. In general, the likelihood functions of exponential random graphs are only known up to a constant of proportionality. A procedure for performing Bayesian inference using Markov chain Monte Carlo (MCMC) methods is presented. The algorithm consists of two basic steps, one in which an ordinary Metropolis-Hastings up-dating step is used, and another in which an importance sampling scheme is used to calculate the acceptance probability of the Metropolis-Hastings step. In paper number two a method for modelling reports given by actors (or other informants) on their social interaction with others is investigated in a Bayesian framework. The model contains two basic ingredients: the unknown network structure and functions that link this unknown network structure to the reports given by the actors. These functions take the form of probit link functions. An intrinsic problem is that the model is not identified, meaning that there are combinations of values on the unknown structure and the parameters in the probit link functions that are observationally equivalent. Instead of using restrictions for achieving identification, it is proposed that the different observationally equivalent combinations of parameters and unknown structure be investigated a posteriori. Estimation of parameters is carried out using Gibbs sampling with a switching devise that enables transitions between posterior modal regions. The main goal of the procedures is to provide tools for comparisons of different model specifications. Papers 3 and 4, propose Bayesian methods for longitudinal social networks. The premise of the models investigated is that overall change in social networks occurs as a consequence of sequences of incremental changes. Models for the evolution of social networks using continuos-time Markov chains are meant to capture these dynamics. Paper 3 presents an MCMC algorithm for exploring the posteriors of parameters for such Markov chains. More specifically, the unobserved evolution of the network in-between observations is explicitly modelled thereby avoiding the need to deal with explicit formulas for the transition probabilities. This enables likelihood based parameter inference in a wider class of network evolution models than has been available before. Paper 4 builds on the proposed inference procedure of Paper 3 and demonstrates how to perform model selection for a class of network evolution models.

Sustainable improvement of water networks : to achieve best decisions for rehabilitation plans using advanced GIS and statistical techniques

Many of developing countries are facing crisis in water management due to increasing of population, water scarcity, water contaminations and effects of world economic crisis. Water distribution systems in developing countries are facing many challenges of efficient repair and rehabilitation since the information of water network is very limited, which makes the rehabilitation assessment plans very difficult. Sufficient information with high technology in developed countries makes the assessment for rehabilitation easy. Developing countries have many difficulties to assess the water network causing system failure, deterioration of mains and bad water quality in the network due to pipe corrosion and deterioration. The limited information brought into focus the urgent need to develop economical assessment for rehabilitation of water distribution systems adapted to water utilities. Gaza Strip is subject to a first case study, suffering from severe shortage in the water supply and environmental problems and contamination of underground water resources. This research focuses on improvement of water supply network to reduce the water losses in water network based on limited database using techniques of ArcGIS and commercial water network software (WaterCAD). A new approach for rehabilitation water pipes has been presented in Gaza city case study. Integrated rehabilitation assessment model has been developed for rehabilitation water pipes including three components; hydraulic assessment model, Physical assessment model and Structural assessment model. WaterCAD model has been developed with integrated in ArcGIS to produce the hydraulic assessment model for water network. The model have been designed based on pipe condition assessment with 100 score points as a maximum points for pipe condition. As results from this model, we can indicate that 40% of water pipeline have score points less than 50 points and about 10% of total pipes length have less than 30 score points. By using this model, the rehabilitation plans for each region in Gaza city can be achieved based on available budget and condition of pipes. The second case study is Kuala Lumpur Case from semi-developed countries, which has been used to develop an approach to improve the water network under crucial conditions using, advanced statistical and GIS techniques. Kuala Lumpur (KL) has water losses about 40% and high failure rate, which make severe problem. This case can represent cases in South Asia countries. Kuala Lumpur faced big challenges to reduce the water losses in water network during last 5 years. One of these challenges is high deterioration of asbestos cement (AC) pipes. They need to replace more than 6500 km of AC pipes, which need a huge budget to be achieved. Asbestos cement is subject to deterioration due to various chemical processes that either leach out the cement material or penetrate the concrete to form products that weaken the cement matrix. This case presents an approach for geo-statistical model for modelling pipe failures in a water distribution network. Database of Syabas Company (Kuala Lumpur water company) has been used in developing the model. The statistical models have been calibrated, verified and used to predict failures for both networks and individual pipes. The mathematical formulation developed for failure frequency in Kuala Lumpur was based on different pipeline characteristics, reflecting several factors such as pipe diameter, length, pressure and failure history. Generalized linear model have been applied to predict pipe failures based on District Meter Zone (DMZ) and individual pipe levels. Based on Kuala Lumpur case study, several outputs and implications have been achieved. Correlations between spatial and temporal intervals of pipe failures also have been done using ArcGIS software. Water Pipe Assessment Model (WPAM) has been developed using the analysis of historical pipe failure in Kuala Lumpur which prioritizing the pipe rehabilitation candidates based on ranking system. Frankfurt Water Network in Germany is the third main case study. This case makes an overview for Survival analysis and neural network methods used in water network. Rehabilitation strategies of water pipes have been developed for Frankfurt water network in cooperation with Mainova (Frankfurt Water Company). This thesis also presents a methodology of technical condition assessment of plastic pipes based on simple analysis. This thesis aims to make contribution to improve the prediction of pipe failures in water networks using Geographic Information System (GIS) and Decision Support System (DSS). The output from the technical condition assessment model can be used to estimate future budget needs for rehabilitation and to define pipes with high priority for replacement based on poor condition. rn

Feasibility of Information-Centric Networking Integration into LTE Mobile Networks

With the current growth of mobile devices usage, mobile net- works struggle to deliver content with an acceptable Quality of Experience. In this paper, we propose the integration of Information Centric Networking into 3GPP Long Term Evolution mobile networks, allowing its inherent caching feature to be explored in close proximity to the end users by deploying components inside the evolved Node B. Apart from the advantages brought by Information-Centric Networking’s content requesting paradigm, its inherent caching features enable lower latencies to access content and reduce traffic at the core network. Results show that the impact on the evolved Node B performance is low and ad- vantages coming from Information-Centric Networking are considerable. Thus, mobile network operators reduce operational costs and users end up with a higher perceived network quality even in peak utilization periods.

Integration versus Outsourcing in Stable Industry Equilibrium with Communication Networks

For the selection of a firm's structure between vertical integration and arm's-length outsourcing, the importance of the thickness of the market had been emphasized in the previous literature. Here we take account of communication networks such as telephone, telex, fax, and the Internet. By doing so, we could illustrate the relationship between communication networks and the make-or-buy decision. With communication network technology differing in each type of firm, both vertically integrated firms and arm's-length outsourcing firms coexist, which was never indicated in the previous literature. However, when common network technology is introduced, such coexistence generically does not occur.

Integration versus segregation in functional brain networks

We propose a new methodology to evaluate the balance between segregation and integration in functional brain networks by using singular value decomposition techniques. By means of magnetoencephalography, we obtain the brain activity of a control group of 19 individuals during a memory task. Next, we project the node-to-node correlations into a complex network that is analyzed from the perspective of its modular structure encoded in the contribution matrix. In this way, we are able to study the role that nodes play I/O its community and to identify connector and local hubs. At the mesoscale level, the analysis of the contribution matrix allows us to measure the degree of overlapping between communities and quantify how far the functional networks are from the configuration that better balances the integrated and segregated activity

Integration of Timetable Planning and Rolling Stock in Rapid Transit Networks

The aim of this paper is to propose an integrated planning model to adequate the offered capacity and system frequencies to attend the increased passenger demand and traffic congestion around urban and suburban areas. The railway capacity is studied in line planning, however, these planned frequencies were obtained without accounting for rolling stock flows through the rapid transit network. In order to provide the problem more freedom to decide rolling stock flows and therefore better adjusting these flows to passenger demand, a new integrated model is proposed, where frequencies are readjusted. Then, the railway timetable and rolling stock assignment are also calculated, where shunting operations are taken into account. These operations may sometimes malfunction, causing localized incidents that could propagate throughout the entire network due to cascading effects. This type of operations will be penalized with the goal of selectively avoiding them and ameliorating their high malfunction probabilities. Swapping operations will also be ensured using homogeneous rolling stock material and ensuring parkings in strategic stations. We illustrate our model using computational experiments drawn from RENFE (the main Spanish operator of suburban passenger trains) in Madrid, Spain. The results show that through this integrated approach a greater robustness degree can be obtained

Contribution to integration and coordination mechanisms for mobile distributed services in publish/subscribe networks

Esta tesis doctoral está enmarcada en dos diferentes pero complementarias áreas de investigación: las redes de Publicación/Subscripción y los servicios móviles distribuidos. Con el paso de los años las redes de Publicación/Subscripción han ido ofreciendo el soporte de comunicaciones desacopladas y ligeras que a su vez, han mejorado la distribución de la información en muchos escenarios de aplicación como lo son la ejecución de servicios distribuidos en entornos fijos. Los servicios móviles distribuidos han de ser desplegados en ambientes inalámbricos en donde los dispositivos móviles deben confiar en las mismas características que las redes de Publicación/Subscripción han estado ofreciendo a sus contrapartes fijos. En este contexto, una de las líneas de investigación pendientes consiste en cómo tomar ventaja de estas características, y cómo avanzar hacia nuevas soluciones no existentes con el fin de mejorar la integración entre los dispositivos fijos y móviles, y la ejecución de los servicios móviles distribuidos. En esta tesis doctoral se pretende avanzar en los mecanismos de integración y coordinación de los servicios móviles distribuidos en el contexto de las redes de Publicación/Subscripción. Los objetivos específicos de esta disertación están enfocados en lograr la integración de los sistemas de Publicación/Suscripción fijos y móviles, y la pro-visión de una versión de red de Publicación/Subscripción específica y uniforme que cuente con mecanismos de coordinación que mejoren la ejecución de los servicios móviles distribuidos. Los resultados de esta tesis doctoral están enmarcados en una versión específica de una red de Publicación/Subscripción que integra brokers fijos y móviles, y permite una coordinación totalmente desacoplada y mejorada entre dispositivos móviles que ejecutan fragmentos de servicios. Las contribuciones específicas son las siguientes: una nueva arquitectura de broker móvil que he llamado Rendezvous Mobile broker, un modelo abstracto de servicios móviles distribuidos coordinados sobre una red de Publicación/Subscripción, mejoras en los mecanismos de enrutamiento epidémicos para diseminar eventos de control producidos por fragmentos de servicios, una solución para soportar servicios altamente fragmentados y geográficamente dispersos, y finalmente una solución de interconexión entre dos dominios de red basados en Publicación/Subscripción: una red basada en el protocolo PubSubHubbub y otro en una red basada en el Publish/Subscribe Internet Routing Paradigm (PSIRP). Los experimentos llevados a cabo confirman que la versión específica de red de Pu-blicación/Subscripción propuesta incrementa el rendimiento de la red en términos de tiempo de espera entre nodos finales, permite una coordinación de los servicios móviles distribuidos más resistente a interrupciones y un mejor uso de los recursos de red, y finalmente logra exitosamente, con variaciones mínimas en el rendimiento de las comunicaciones, la interconexión entre estos dominios de Publicación/Subscripción diferentes. ABSTRACT This dissertation is made up of two different but complementary research areas: Publish/Subscribe networks and mobile distributed services. Over the years, Publish/Subscribe networks have been offering the lightweight and decoupled communication characteristics to improve the information distribution in several application domains such as the execution of distributed services. Mobile distributed services are set to be deployed in wireless environments where mobile devices must rely on the same features Publish/Subscribe networks can offer; so one of the pending research directions consists of how to take advantage of these features and further advance to-wards new un-existing solutions that enhance the integration between mobile and fixed systems and the execution of mobile distributed services. This dissertation seeks to advance the integration and coordination mechanisms of mobile distributed services in the context of Publish/Subscribe networks. The specific objectives aim to enable the integration of mobile and fixed Publish/Subscribe systems and provide a uniform and specific version of a Publish/Subscribe network with new coordination mechanisms that improve the execution of mobile distributed services. The results of this dissertation are enclosed in one specific version of a Publish/Subscribe network that integrates mobile and fixed brokers and coordinates the execution of mobile distributed services. These specific contributions are: a new architecture of a mobile broker I called Rendezvous Mobile Broker, an abstract model for coordinating mobile distributed services executions using a Publish/Subscribe net-work, new gossip routing solutions to disseminate events of services, mechanisms to support highly partitioned and geographically dispersed services and finally, an inter-networking solution between two Publish/Subscribe domains: a PubSubHubbub-based network and the Publish/Subscribe Internet Routing Paradigm (PSIRP)-based network. The experimental efforts confirm that the specific version of the Publish/Subscribe proposed in this dissertation improves the performance of the overall network in terms of end-to-end delay, enables a more resilience execution of mobile distributed services, a better usage of the existing network resources, and finally successfully achieves, with minor variations in the network performance, the internetworking between two different Publish/Subscribe domains.

Integration of quantum key distribution in metropolitan area networks

The deployment of Quantum Key Distribution forces the development of QKD-links to be operated in current and next-generation photonic metro-access networks. These highly heterogeneous architectures determine the conditions QKD-links need to be optimized for.