PROGENY: A GRASSHOPPER PLUG-IN THAT AUGMENTS CELLULAR AUTOMATA ALGORITHMS FOR 3D FORM EXPLORATIONS

Cellular automata (CA) is a well-known computation method introduced by John von Neumann and Stanislaw Ulam in the 1940s. Since then, it has been studied in various fields such as computer science, biology, physics, chemistry, and art. The Classic CA algorithm is a calculation of a grid of cells\u27 binary states based on neighboring cells and a set of rules. With the variation of these parameters, the CA algorithm has evolved into alternative versions such as 3D CA, Multiple neighborhood CA, Multiple rules CA, and Stochastic CA (Url-1). As a rule-based generative algorithm, CA has been used as a bottom-up design approach in the architectural design process in the search for form (Frazer,1995; Dinçer et al., 2014), in simulating the displacement of individuals in space, and in revealing complex relations at the urban scale (Güzelci, 2013). There are implementations of CA tools in 3D design software for designers as additional scripts or plug-ins. However, these often have limited ability to create customized CA algorithms by the designer. This study aims to create a customizable framework for 3D CA algorithms to be used in 3D form explorations by designers. Grasshopper3D, which is a visual scripting environment in Rhinoceros 3D, is used to implement the framework. The main difference between this work and the current Grasshopper3D plug-ins for CA simulation is the customizability and the real-time control of the framework. The parameters that allow the CA algorithm to be customized are; the initial state of the 3D grid, neighborhood conditions, cell states and rules. CA algorithms are created for each customizable parameter using the framework. Those algorithms are evaluated based on the ability to generate form. A voxel-based approach is used to generate geometry from the points created by the 3D cellular automata. In future, forms generated using this framework can be used as a form generating tool for digital environments

Mortality predictors of Staphylococcus aureus bacteremia: A prospective multicenter study

Background: Staphylococcus aureus is one of the causes of both community and healthcare-associated bacteremia. The attributable mortality of S. aureus bacteremia (SAB) is still higher and predictors for mortality and clinical outcomes of this condition are need to be clarified. In this prospective observational study, we aimed to examine the predictive factors for mortality in patients with SAB in eight Turkish tertiary care hospitals. Methods: Adult patients with signs and symptoms of bacteremia with positive blood cultures for S. aureus were included. All data for episodes of SAB including demographics, clinical and laboratory findings, antibiotics, and outcome were recorded for a 3-year (2010-2012) period. Cox proportional hazard model with forward selection was used to assess the independent effect of risk factors on mortality. A 28-day mortality was the dependent variable in the Cox regression analysis. Results: A total of 255 episodes of SAB were enrolled. The median age of the patients was 59years. Fifty-five percent of the episodes were considered as primary SAB and vascular catheter was the source of 42.1%. Healthcare associated SAB was defined in 55.7%. Blood cultures yielded methicillin-resistant S. aureus (MRSA) as a cause of SAB in 39.2%. Initial empirical therapy was inappropriate in 28.2%. Although overall mortality was observed in 52 (20.4%), 28-day mortality rate was 15.3%. Both the numbers of initial inappropriate empirical antibiotic treatment and the median hours to start an appropriate antibiotic between the cases of fatal outcome and survivors after fever onset were found to be similar (12/39 vs 60/216 and 6 vs 12h, respectively; p>0.05). High Charlson comorbidity index (CCI) score (p=0.002), MRSA (p=0.017), intensive care unit (ICU) admission (p<0.001) and prior exposure to antibiotics (p=0.002) all were significantly associated with mortality. The Cox analysis defined age [Hazard Ratio (HR) 1.03; p=0.023], ICU admission (HR 6.9; p=0.002), and high CCI score (HR 1.32; p=0.002) as the independent predictive factors mortality. Conclusions: The results of this prospective study showed that age, ICU stay and high CCI score of a patient were the independent predictors of mortality and MRSA was also significantly associated with mortality in SAB