Internal combustion engine sensor network analysis using graph modeling

In recent years there has been a rapid development in technologies for smart monitoring applied to many different areas (e.g. building automation, photovoltaic systems, etc.). An intelligent monitoring system employs multiple sensors distributed within a network to extract useful information for decision-making. The management and the analysis of the raw data derived from the sensor network includes a number of specific challenges still unresolved, related to the different communication standards, the heterogeneous structure and the huge volume of data. In this paper we propose to apply a method based on complex network theory, to evaluate the performance of an Internal Combustion Engine. Data are gathered from the OBD sensor subset and from the emission analyzer. The method provides for the graph modeling of the sensor network, where the nodes are represented by the sensors and the edge are evaluated with non-linear statistical correlation functions applied to the time series pairs. The resulting functional graph is then analyzed with the topological metrics of the network, to define characteristic proprieties representing useful indicator for the maintenance and diagnosis

Predictive response-relevant clustering of expression data provides insights into disease processes

This article describes and illustrates a novel method of microarray data analysis that couples model-based clustering and binary classification to form clusters of ;response-relevant' genes; that is, genes that are informative when discriminating between the different values of the response. Predictions are subsequently made using an appropriate statistical summary of each gene cluster, which we call the ;meta-covariate' representation of the cluster, in a probit regression model. We first illustrate this method by analysing a leukaemia expression dataset, before focusing closely on the meta-covariate analysis of a renal gene expression dataset in a rat model of salt-sensitive hypertension. We explore the biological insights provided by our analysis of these data. In particular, we identify a highly influential cluster of 13 genes-including three transcription factors (Arntl, Bhlhe41 and Npas2)-that is implicated as being protective against hypertension in response to increased dietary sodium. Functional and canonical pathway analysis of this cluster using Ingenuity Pathway Analysis implicated transcriptional activation and circadian rhythm signalling, respectively. Although we illustrate our method using only expression data, the method is applicable to any high-dimensional datasets

Software systems through complex networks science: Review, analysis and applications

Complex software systems are among most sophisticated human-made systems, yet only little is known about the actual structure of 'good' software. We here study different software systems developed in Java from the perspective of network science. The study reveals that network theory can provide a prominent set of techniques for the exploratory analysis of large complex software system. We further identify several applications in software engineering, and propose different network-based quality indicators that address software design, efficiency, reusability, vulnerability, controllability and other. We also highlight various interesting findings, e.g., software systems are highly vulnerable to processes like bug propagation, however, they are not easily controllable

Who has influence in multistakeholder governance systems?

As multistakeholder governance has emerged as an important feature in development, new governance structures that foster the participation of multiple stakeholders from the public sector, civil society, and the private sector have emerged in various fields, ranging from the management of natural resources to the provision of public services. To make such governance structures work, it is essential to understand how different stakeholders influence decisionmaking and what determines their influence. This paper uses Net-Map, an innovative participatory method, to analyze how networking influences decisionmaking in multistakeholder governance structures, using the case of the governance board of the White Volta River Basin in northern Ghana as an example. The method visualizes both the relations between all stakeholders in watershed management as perceived by the 17 members on the board and their influence on development outcomes. The study suggests that significant effects of social networking are at play beyond the formal lines of command and funding as stakeholders in watershed management make decisions. Stakeholders are more influential if they participate more prominently in information exchange and provide more advice to others. This counterbalances the overrepresentation of government actors on the board. Meanwhile some government organizations have a low level of influence, even though they are central in giving funding and command. These findings may be interesting for program leaders and policymakers in watershed management: when designing governance structures they need to take into account the importance of social networking to attain main objectives of watershed development; it is important to provide space that allows the exchange of information and advice among stakeholders. Meanwhile, policymakers and program leaders as well must consider overrepresentation of social network champions in multistakeholder governance structures and the limited capacity of government bodies in social networking. The paper serves to introduce not only the specific findings concerning this case study but also the participatory research method (Net-Map) that was used.decisionmaking, multistakeholder governance, Natural resource management, Social networks,

Degree Ranking Using Local Information

Most real world dynamic networks are evolved very fast with time. It is not feasible to collect the entire network at any given time to study its characteristics. This creates the need to propose local algorithms to study various properties of the network. In the present work, we estimate degree rank of a node without having the entire network. The proposed methods are based on the power law degree distribution characteristic or sampling techniques. The proposed methods are simulated on synthetic networks, as well as on real world social networks. The efficiency of the proposed methods is evaluated using absolute and weighted error functions. Results show that the degree rank of a node can be estimated with high accuracy using only $1\%$ samples of the network size. The accuracy of the estimation decreases from high ranked to low ranked nodes. We further extend the proposed methods for random networks and validate their efficiency on synthetic random networks, that are generated using Erd\H{o}s-R\'{e}nyi model. Results show that the proposed methods can be efficiently used for random networks as well