Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in RésuméIl y a peu d'information au sujet de la répartition des actifs et de la formation des prix sur les marchés de gré à gré. En 1999, Furfine a conçu un nouvel algorithme qui fournit des données relativement aux transactions effectuées sur le marché du financement interbancaire. La validité de ces données n'est cependant pas bien établie. En procédant à une permutation des données, j'estime que la limite supérieure du taux de faux positifs quotidien généré par cet algorithme est légèrement au-dessus de 10 %. Je propose des améliorations permettant de réduire cette limite sous 10 % tout en subissant une perte de puissance statistique négligeable. Les résultats donnent à penser que les quantités et les prix des prêts à un jour déduits grâce à cette méthode constituent des estimations valables de l'activité sur le marché du financement interbancaire.

Chatoyant is a tool for the simulation and the analysis of heterogeneous free-space optoelectronic architectures. It is capable of modeling digital and analog electronic and optical signal propagation with mechanical tolerancing at the system level. We present models for a variety of optoelectronic devices and results that demonstrate the system's ability to predict the effects of various component parameters, such as detector geometry, and system parameters, such as alignment tolerances, on system-performance measures, such as the bit-error rate.

New research in optoelectronic devices, which have made it practical to use optoelectronics in computing and communications systems, as well as the need.for these systems to support higher informatr'on capacities has brought about a growing need for dtaign and analysis tools for optoelectronic systems. While there are many research groups developing new and exciting optoelectronic devices, the integration of these devices into practical systems has been slow to follow. The reason for this lag is that researchers who design systems need to be able to evaluate how these new devices can be used to make components, and then how these components can be used to build systems. By having tools for effectively evaluating new designs based on new deviccs, system designers will be able to evaluate possible derigns, and give feedback to materials and devices researchers for improved components.

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The study of infectious disease models has become increasingly important during the COVID-19 pandemic. The forecasting of disease spread using mathematical models has become a common practice by public health authorities, assisting in creating policies to combat the spread of the virus. Common approaches to the modeling of infectious diseases include compartmental differential equations and cellular automata, both of which do not describe the spatial dynamics of disease spread over unique geographical regions. We introduce a new methodology for modeling disease spread within a pandemic using geographical models. We demonstrate how geography-based Cell-Discrete-Event Systems Specification (DEVS) and the Cadmium JavaScript Object Notation (JSON) library can be used to develop geographical cellular models. We exemplify the use of these methodologies by developing different versions of a compartmental model that considers geographical-level transmission dynamics (e.g. movement restriction or population disobedience to public health guidelines), the effect of asymptomatic population, and vaccination stages with a varying immunity rate. Our approach provides an easily adaptable framework that allows rapid prototyping and modifications. In addition, it offers deterministic predictions for any number of regions simulated simultaneously and can be easily adapted to unique geographical areas. While the baseline model has been calibrated using real data from Ontario, we can update and/or add different infection profiles as soon as new information about the spread of the disease become available.