In this paper, we modeled the progressive loss of declarative memory in Alzheimer's disease (AD) using an artificial neural network (ANN) framework. Declarative memory is associated with tasks involving recognition. Furthermore, we modeled the declarative memory in a healthy individual as a fully connected Hopfield neural network. Memory loss in the healthy memory models was implemented by resetting randomly selected synaptic weights to 0'. Unhealthy memory models were created by damaging the healthy Hopfield memory models by a percentage of the weights. Recognition of the memory models was evaluated using recall/response rates. In the neural model, the detection of optimal network architectures was crucial. The paradigm that we have identified has three critical phases. The detection of neural synaptic promoters in the neural network was extremely imperative in the first phase of the investigation. In order to determine this, the network was trained on datasets and corresponding recall/response rates were observed. The second facet of the experiment involved the evaluation of different ANN's by which the optimal network architecture could be identified. And finally, the third facet, involved the detection of areas where synaptic connectivity was affected and where it could be improved.

This experiment examined the overall times required to make messaging calls in parallel programs within a distributed computing environment. These messaging calls are defined in the Message Passing Interface (MPI) standards, which is a set of recommendations for designing communication calls for parallel programs. Different libraries based on the MPI standards exist, and this project focused on two commonly used implementations: MPICH and LAM-MPI. Both provide the same essential methods and functions, but their package and algorithm structures differ enough so that the administrative, processing, and messaging times of a program may be improved by selecting one package over the other. With adequate foreknowledge of the strengths and weaknesses of the MPI packages, programmers and administrators can better select a package based on their needs.

Fat has been termed the "new tobacco" by the Canadian Heart and Stroke Foundation, because of the high general risk of an individual eventually becoming obese. This review will discuss the cause and effect of childhood obesity as well as compile recommendations and initiatives currently in place to decrease childhood and adult obesity. For children of the 21st century, obesity is one of the most common metabolic and nutritional diseases. Healthcare professionals can measure the percent of body fat in children by using Body Mass Index (BMI). Specifically, for children and BMI that is age and gender specific can be used to take measurements of the percent of body fat. Researchers have identified three main causes of obesity and they include genetics, overeating and lack of exercise. The effects of obesity on children have a huge impact and can range from low self-esteem to increased risk of cardiovascular diseases. The most effective cure for childhood obesity is prevention. Parents and healthcare professionals can work together to make prevention more effective and one day perhaps abolishing this epidemic.