To improve the performance of geographically distributed cosimulation, we propose a concept called hierarchically grouped message. The concept improves cosimulation performance, preserving the cosimulation accuracy, by hierarchically grouping messages transferred between simulators in a short period of simulated time into a single physical message, thereby reducing the number of physical messag...

 Abstract Background: The aim of this study was to investigate the various aspects of illness perceptions about breast cancer in affected patients. Methods: A cross-sectional study enrolled 140 patients with breast cancer in their chemotherapy period within 14 months. The revised Illness Perception Questionnaire (revised IPQ) was used to assess the disease representations of breast cancer. The ...

Rough sets, introduced by Pawlak as a model to capture impreciseness in data have been a very useful tool in several applications. These basic rough sets are defined by taking equivalence relations over a universe. In order to enhance the modeling powers of rough sets, several extensions to the basic definition has been introduced over the past few years. Extending the single granular structure...

In this paper, we describe a distributed dynamic load balancing algorithm for parallel optimistic gate level simulation. Our optimistic simulator is based on Time Warp. The load balancing algorithm makes decisions based on the processing and communication loads at each processor. At the core of the algorithm is a genetic algorithm which is used to determine the values of the tuning parameters a...

− The fuzzy weighted average (FWA) is used in many engineering problems where aggregation of fuzzy information is dealt with. In this framework, many algorithms have been proposed to compute efficiently the FWA according to Zadeh's extension principle. However, due to fuzzy interval calculus, the exact solution presents a characteristic that may be viewed as an important drawback. Indeed, repla...

Yishay Mansourt Vie sho,v the convergence of tV/O deterministic variants of Qlearning. The first is the widely used optimistic Q-learning, which initializes the Q-values to large initial values and then follows a greedy policy with respect to the Q-values. We show that setting the initial value sufficiently large guarantees the converges to an Eoptimal policy. The second is a new and novel algo...