In the model selection phase, we applied 2-fold cross validation, and selected parameters that gave the highest auROC.
In the model-building phase, we implemented the AR method in a server equipped with 12 CPUs (4 cores, 3 packages) and 128 GB of memory, and the server used for the ERP method was equipped with 2 CPUs (2 cores, 1 package) and 8 GB of memory.
To model the kinematic phase, we combined and modified 1-D spreading models from the literature which solve the kinetic energy balance equation for the first milliseconds of spreading.
Using an excised canine abdominal wall as a model phase screen, we experimentally observed an effective arrival time error ranging from 18.3 ns to 58 ns (root-mean-square error) across the swept positions.
After investigating the performance of the proposed algorithms (GA-PSO and iGA-PSO) running in a sequential manner for gene network modeling, in the second phase, we conducted two suites of experiments to examine how our iGA-PSO method can be performed in a parallel computing environment to speed up the computation in practice.
Modeling phase involves the selection of the modeling technique and the estimation of model's parameters.
During an internship at Neopost Inc., of 14 weeks, we developed the server component of a software bus, called the XBus, using formal methods during the design, validation and testing phase: we modeled our design of the XBus in the process algebra mCRL2, validated the design using the mCRL2-simulator, and fully automatically tested our implementation with the model-based test tool JTorX.
To model the exuberant connection phase, we allow neurons to make all-to-all synaptic contacts.
14 15 25 During the short-term phase, we modelled five key health states in line with DRL categories as defined by the WHO 16: very high, high, medium and low risk of drinking and abstinence (figure 1 and table 1).
