Social Network Analysis

(1991). Applied Linear Regression Models. Journal of Quality Technology: Vol. 23, No. 1, pp. 76-77.

Applied Linear Regression Models

Many ``real-world'' networks are clearly defined while most ``social'' networks are to some extent subjective. Indeed, the accuracy of empirically-determined social networks is a question of some concern because individuals may have distinct perceptions of what constitutes a social link. One unambiguous type of connection is sexual contact. Here we analyze data on the sexual behavior of a random sample of individuals, and find that the cumulative distributions of the number of sexual partners during the twelve months prior to the survey decays as a power law with similar exponents $\alpha \approx 2.4$ for females and males. The scale-free nature of the web of human sexual contacts suggests that strategic interventions aimed at preventing the spread of sexually-transmitted diseases may be the most efficient approach.

The Web of Human Sexual Contacts

Thank you very much for reading input output analysis foundations and extensions. As you may know, people have search hundreds times for their chosen readings like this input output analysis foundations and extensions, but end up in infectious downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they are facing with some malicious virus inside their desktop computer.

Input Output Analysis Foundations And Extensions

This paper presents a polynomial dimensional decomposition (PDD) method for global sensitivity analysis of stochastic systems subject to independent random input following arbitrary probability distributions. The method involves Fourier-polynomial expansions of lower-variate component functions of a stochastic response by measure-consistent orthonormal polynomial bases, analytical formulae for calculating the global sensitivity indices in terms of the expansion coefficients, and dimension-reduction integration for estimating the expansion coefficients. Due to identical dimensional structures of PDD and analysis-of-variance decomposition, the proposed method facilitates simple and direct calculation of the global sensitivity indices. Numerical results of the global sensitivity indices computed for smooth systems reveal significantly higher convergence rates of the PDD approximation than those from existing methods, including polynomial chaos expansion, random balance design, state-dependent parameter, improved Sobol’s method, and sampling-based methods. However, for non-smooth functions, the convergence properties of the PDD solution deteriorate to a great extent, warranting further improvements. The computational complexity of the PDD method is polynomial, as opposed to exponential, thereby alleviating the curse of dimensionality to some extent. Mathematical modeling of complex systems often requires sensitivity analysis to determine how an output variable of interest is influenced by individual or subsets of input variables. A traditional local sensitivity analysis entails gradients or derivatives, often invoked in design optimization, describing changes in the model response due to the local variation of input. Depending on the model output, obtaining gradients or derivatives, if they exist, can be simple or difficult. In contrast, a global sensitivity analysis (GSA), increasingly becoming mainstream, characterizes how the global variation of input, due to its uncertainty, impacts the overall uncertain behavior of the model. In other words, GSA constitutes the study of how the output uncertainty from a mathematical model is divvied up, qualitatively or quantitatively, to distinct sources of input variation in the model [1].

Reliability Engineering and System Safety

An approach for modelling interdependent infrastructures in the context of vulnerability analysis

Reliability and vulnerability analyses of critical infrastructures: Comparing two approaches in the context of power systems

A new method for identifying and ranking critical components and sets of components in technical infrastructures is presented. The criticality of a component or a set of components is defined as the vulnerability of the system to failure in a specific component, or set of components. The identification of critical components is increasingly difficult when considering multiple simultaneous failures. This is especially difficult when dealing with failures of multiple components with synergistic consequences, i.e. consequences that cannot be calculated by adding the consequences of the individual failures. The proposed method addresses this problem. In exemplifying the method, an analysis of an electric power distribution system in a Swedish municipality is presented. It is concluded that the proposed method facilitates the identification of critical sets of components for large-scale technical infrastructures.

Identifying critical components in technical infrastructure networks

Information can be provided by studying and evaluating past emergencies and the response in connection to them. This information would then be useful in efforts directed at preventing, mitigating and/or preparing for future emergencies. However, the analysis and evaluation of emergency response operations is not an easy task, especially when the operation involves several cooperating actors (e.g. the fire and rescue services, the police, the emergency medical services, etc.). Here, we identify and discuss four aspects of this challenge: (1) issues related to the values governing the evaluation, (2) issues related to the complexity of the systems involved, (3) issues related to the validity of the information on which the analysis and evaluation is based and (4) issues related to the limiting conditions under which the emergency response system operated. An outline of a framework for such an analysis and evaluation, influenced by systems theory, accident investigation theories and programme evaluation theories dealing with the above aspects, is introduced, discussed and exemplified using empirical results from a case study. We conclude that the proposed framework may provide a better understanding of how an emergency response system functioned during a specific operation, and help to identify the potential events and/or circumstances that could significantly affect the performance of the emergency response system, either negatively or positively. The insights gained from using the framework may allow the actors involved in the response operation to gain a better understanding of how the emergency response system functioned as a whole, as well as how the actors performed as individual components of the system. Furthermore, the information can also be useful for actors preparing for future emergencies.

Towards a System‐Oriented Framework for Analysing and Evaluating Emergency Response

Critical infrastructure systems must be both robust and resilient in order to ensure the functioning of society. To improve the performance of such systems, we often use risk and vulnerability analysis to find and address system weaknesses. A critical component of such analyses is the ability to accurately determine the negative consequences of various types of failures in the system. Numerous mathematical and simulation models exist that can be used to this end. However, there are relatively few studies comparing the implications of using different modeling approaches in the context of comprehensive risk analysis of critical infrastructures. In this article, we suggest a classification of these models, which span from simple topologically-oriented models to advanced physical-flow-based models. Here, we focus on electric power systems and present a study aimed at understanding the tradeoffs between simplicity and fidelity in models used in the context of risk analysis. Specifically, the purpose of this article is to compare performance estimates achieved with a spectrum of approaches typically used for risk and vulnerability analysis of electric power systems and evaluate if more simplified topological measures can be combined using statistical methods to be used as a surrogate for physical flow models. The results of our work provide guidance as to appropriate models or combinations of models to use when analyzing large-scale critical infrastructure systems, where simulation times quickly become insurmountable when using more advanced models, severely limiting the extent of analyses that can be performed.

Henrik Hassel

Papers

An approach for modelling interdependent infrastructures in the context of vulnerability analysis

Reliability and vulnerability analyses of critical infrastructures: Comparing two approaches in the context of power systems

Identifying critical components in technical infrastructure networks

Towards a System‐Oriented Framework for Analysing and Evaluating Emergency Response

Topological Performance Measures as Surrogates for Physical Flow Models for Risk and Vulnerability Analysis for Electric Power Systems