Many topology control methods for autonomous mobile vehicles assume exact knowledge of the locations of neighboring nodes to make meaningful movement decisions. We present our node-spreading Voronoi algorithm (nsva) and node-spreading Voronoi-based genetic algorithm (nsvga), for self-positioning autonomous nodes in noisy environments. The performance of nsva and nsvga were evaluated in simulation experiments by measuring the network area coverage, average distance traveled and number of disconnected nodes. Experimental results show that both nsva and nsvga can adequately cover the deployment area despite errors in neighbor location information. nsvga can tolerate location errors and maintain network connectivity better than nsva at the cost of increased movement.

No abstract

No abstract

No abstract

Our Genomic Relevance Parameterization (GReP) model aims to explore a possible relationship between gene expression values from breast cancer patients and mathematical tumor growth modeling parameters calculated using data from clinical and preclinical measurements. We introduce two methods to relate genomic information and the tumor growth measurements. One method explores the impact of exponentiation of gene expression values, whereas the other utilizes the correlation between co-regulated genes and the growth parameters. As inputs to our GReP model, we used patient tumor volume measurements and genomic information for 74 breast cancer related genes from the I-SPY 1 TRIAL. We performed a preliminary validation of GReP model using experimental data from literature including MDA-MB-231 cell line, MDA-MB-231 cell line with CXCL12 gene over-expressed, and the MDA-MB-231 sub-cell lines 1834 and 4175. Tumor growth curves generated by GReP model, for the initial exponential phase of tumor growth closely match the pre-clinical data reported in the literature. These promising results show that it may be possible to build tools combining clinical information and genomic data to model cancerous tumor growth.