Sveprisutne Arhimedove kružnice kolinearnog arbelosa

We generalize the arbelos and its Archimedean circles, and show the existence of the generalized Archimedean circles which cover the plane.Generaliziramo arbelos i njegove Arhimedove kružnice te pokazujemo postojanje generaliziranih Arhimedovih kružnica koje pokrivaju ravninu

Polukružnice u arbelosima s produžecima i dijeljenje s nulom

We consider special semicircles, whose endpoints lie on a circle, for a generalized arbelos called the arbelos with overhang considered in [4] with division by zero.U radu proučavamo posebne polukružnice, one čije krajnje točke leže na jednoj kružnici, u poopćenim arbelosima s produžecima kao u [4] uz korištenje dijeljenja s nulom

Arbelos s privjeskom

We consider a generalized arbelos consisting of three semicircles with collinear centers, in which only two of the three semicircles touch. Many Archimedean circles of the ordinary arbelos are generalized to our generalized arbelos.Promatra se poopćeni arbelos koji se sastoji od tri polukružnice s kolinearnim središtima, pri čemu se dvije od njih dodiruju. Mnoge Arhimedove kružnice običnog arbelosa su poopćene za poopćeni arbelos

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

A Semi-Lagrange Galerkin Method for Shallow Water Equations

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv