Statistical modeling of ground motion relations for seismic hazard analysis

We introduce a new approach for ground motion relations (GMR) in the probabilistic seismic hazard analysis (PSHA), being influenced by the extreme value theory of mathematical statistics. Therein, we understand a GMR as a random function. We derive mathematically the principle of area-equivalence; wherein two alternative GMRs have an equivalent influence on the hazard if these GMRs have equivalent area functions. This includes local biases. An interpretation of the difference between these GMRs (an actual and a modeled one) as a random component leads to a general overestimation of residual variance and hazard. Beside this, we discuss important aspects of classical approaches and discover discrepancies with the state of the art of stochastics and statistics (model selection and significance, test of distribution assumptions, extreme value statistics). We criticize especially the assumption of logarithmic normally distributed residuals of maxima like the peak ground acceleration (PGA). The natural distribution of its individual random component (equivalent to exp(epsilon_0) of Joyner and Boore 1993) is the generalized extreme value. We show by numerical researches that the actual distribution can be hidden and a wrong distribution assumption can influence the PSHA negatively as the negligence of area equivalence does. Finally, we suggest an estimation concept for GMRs of PSHA with a regression-free variance estimation of the individual random component. We demonstrate the advantages of event-specific GMRs by analyzing data sets from the PEER strong motion database and estimate event-specific GMRs. Therein, the majority of the best models base on an anisotropic point source approach. The residual variance of logarithmized PGA is significantly smaller than in previous models. We validate the estimations for the event with the largest sample by empirical area functions. etc

Anonymization of Sensitive Quasi-Identifiers for l-diversity and t-closeness

A number of studies on privacy-preserving data mining have been proposed. Most of them assume that they can separate quasi-identifiers (QIDs) from sensitive attributes. For instance, they assume that address, job, and age are QIDs but are not sensitive attributes and that a disease name is a sensitive attribute but is not a QID. However, all of these attributes can have features that are both sensitive attributes and QIDs in practice. In this paper, we refer to these attributes as sensitive QIDs and we propose novel privacy models, namely, (l1, ..., lq)-diversity and (t1, ..., tq)-closeness, and a method that can treat sensitive QIDs. Our method is composed of two algorithms: an anonymization algorithm and a reconstruction algorithm. The anonymization algorithm, which is conducted by data holders, is simple but effective, whereas the reconstruction algorithm, which is conducted by data analyzers, can be conducted according to each data analyzer’s objective. Our proposed method was experimentally evaluated using real data sets

An experimental and modeling study on the reactivity of extremely fuel-rich methane/dimethyl ether mixtures

Chemical reactions in stoichiometric to fuel-rich methane/dimethyl ether/air mixtures (fuel air equiva- lence ratio φ=1–20) were investigated by experiment and simulation with the focus on the conversion of methane to chemically more valuable species through partial oxidation. Experimental data from dif- ferent facilities were measured and collected to provide a large database for developing and validating a reaction mechanism for extended equivalence ratio ranges. Rapid Compression Machine ignition delay times and species profiles were collected in the temperature range between 660 and 1052 K at 10 bar and equivalence ratios of φ= 1–15. Ignition delay times and product compositions were measured in a shock tube at temperatures of 630–1500 K, pressures of 20–30 bar and equivalence ratios of φ= 2 and 10. Ad- ditionally, species concentration profiles were measured in a flow reactor at temperatures between 473 and 973 K, a pressure of 6 bar and equivalence ratios of φ= 2, 10, and 20. The extended equivalence ratio range towards extremely fuel-rich mixtures as well as the reaction-enhancing effect of dimethyl ether were studied because of their usefulness for the conversion of methane into chemically valuable species through partial oxidation at these conditions. Since existing reaction models focus only on equivalence ratios in the range of φ= 0.3–2.5, an extended chemical kinetics mechanism was developed that also covers extremely fuel-rich conditions of methane/dimethyl ether mixtures. The measured ignition delay times and species concentration profiles were compared with the predictions of the new mechanism, which is shown to predict well the ignition delay time and species concentration evolution measure- ments presented in this work. Sensitivity and reaction pathway analyses were used to identify the key reactions governing the ignition and oxidation kinetics at extremely fuel-rich conditions

Functorial Data Migration

In this paper we present a simple database definition language: that of categories and functors. A database schema is a small category and an instance is a set-valued functor on it. We show that morphisms of schemas induce three "data migration functors", which translate instances from one schema to the other in canonical ways. These functors parameterize projections, unions, and joins over all tables simultaneously and can be used in place of conjunctive and disjunctive queries. We also show how to connect a database and a functional programming language by introducing a functorial connection between the schema and the category of types for that language. We begin the paper with a multitude of examples to motivate the definitions, and near the end we provide a dictionary whereby one can translate database concepts into category-theoretic concepts and vice-versa.Comment: 30 page