Scheduled transportation networks give rise to very complex and large-scale networkoptimization problems requiring innovative solution techniques and ideas from mathematical optimization and theoretical computer science. Examples of scheduled transportation include bus, ferry, airline, and railway networks, with the latter being a prime application domain that provides a fair amount of the most complex and largest instances of such optimization problems. Scheduled transport optimization deals with planning and scheduling problems over several time horizons, and substantial progress has been made for strategic planning and scheduling problems in all transportation domains. This state-of-the-art survey presents the outcome of an open call for contributions asking for either research papers or state-of-the-art survey articles. We received 24 submissions that underwent two rounds of the standard peer-review process, out of which 18 were finally accepted for publication. The volume is organized in four parts: Robustness and Recoverability, Robust Timetabling and Route Planning, Robust Planning Under Scarce Resources, and Online Planning: Delay and Disruption Management.

This highly accessible book presents robustness testing as the methodology for conducting quantitative analyses in the presence of model uncertainty.

Now more than ever, biology has the potential to contribute practical solutions to many of the major challenges confronting the United States and the world. A New Biology for the 21st Century recommends that a "New Biology" approach-one that depends on greater integration within biology, and closer collaboration with physical, computational, and earth scientists, mathematicians and engineers-be used to find solutions to four key societal needs: sustainable food production, ecosystem restoration, optimized biofuel production, and improvement in human health. The approach calls for a coordinated effort to leverage resources across the federal, private, and academic sectors to help meet challenges and improve the return on life science research in general.

An important branch in insurance mathematics is the pricing of possible large claims that are either the results of many small claims occuring at once or that are caused by single events. A premium calculation principle that emphasises the structure of an insurance portfolio is the so called credibility premium.The credibility premium is a convex combination of the class mean, representing the insurance portfolio's general behaviour and the individual mean. The latter takes into account the individual claim history of the risks subsumed in the portfolio. The insurer calculating the premium does not necessarily need to know the claim amount distribution, even though she has to make some assumptions. In this thesis an insurance portfolio of $N$ risks -- then called risk classes -- is considered. It is assumed that each of the risks typically causesa small claim amount during an insurance period. But once in a while, the risks may produce large claim amounts due to a contamination of the small claim amount distribution function. For such models to calculate an insurance premium, the credibility approach can be applied combined with methods from robust statistics. In that case, both the claim amounts and the insurance premiums are separated into ordinary and extreme parts. The premium for the ordinary part is determined by applying the credibility principle. We assume the claim amount distribution function of risk $i, \, i=1, \ldots, N$ to be $\Gamma(\alpha, \theta_i)$ with risk parameter $\theta_i$, being a random variable itself. The distribution function of the independent risk parameters $\theta_i$ is known. The rare, large claim amounts originate from a contamination of the claim amount distribution function $\Gamma(\alpha, \theta_i)$. Thus, we will introduce robust estimators. Determining the premium of the extreme part, the mean excess function is going to be used. After a brief introduction of conecpts in robust statistics, such as robust M-estimators and influence functions, we will define two robust scale M-estimators with respect to our data model, both of them depending on parameters $a$ and $b$. We also discuss the question of choosing optimal values for $a$ and $b$. Besides we are going to compute the influence functions, gross errors and finite sample breakdown points for these estimators. It is also proved that the two estimators are asymptotically normally distributed. The thesis is completed by a simulation study. We analyse the sensitivity of the robust scale M-estimators towards different choices of $a$ and $b$, as well as changing sample sizes and possible occurings of large claims. The simulation will show that for reasonable choices of $a$ and $b$, the robust estimators can bear the comparison with the median, which is known as the most robust estimator. As well, we will estimate the credibility premiums for an insurance portfolio consisting of 25 risk classes and discuss the circumstances, when an actuary should apply the robust credibility approach.

Network Robustness under Large-Scale Attacks provides the analysis of network robustness under attacks, with a focus on large-scale correlated physical attacks. The book begins with a thorough overview of the latest research and techniques to analyze the network responses to different types of attacks over various network topologies and connection models. It then introduces a new large-scale physical attack model coined as area attack, under which a new network robustness measure is introduced and applied to study the network responses. With this book, readers will learn the necessary tools to evaluate how a complex network responds to random and possibly correlated attacks.

Data preparation involves transforming raw data in to a form that can be modeled using machine learning algorithms. Cut through the equations, Greek letters, and confusion, and discover the specialized data preparation techniques that you need to know to get the most out of your data on your next project. Using clear explanations, standard Python libraries, and step-by-step tutorial lessons, you will discover how to confidently and effectively prepare your data for predictive modeling with machine learning.