Description: This article discusses agent-based distance vector routing. Abstract: In spite of the ever-increasing availability of computation and communication resources in modern networks, the overhead associated with network management protocols, such as traffic control and routing, continues to be an important aspect in the design of new methodologies. Resource efficiency of such protocols has become even more prominent with the recent developments of wireless and ad-hoc networks, which are marked by much more severe resource constraints in terms of bandwidth, memory, and computational capabilities. This paper presents an Agent-Based approach to Distance Vector Routing that addresses these resources constraints. Agent-Based Distance Vector Routing (ADVR) is a resource efficient implementation of Distance Vector Routing that is fault tolerant and scales well for large networks. ADVR draws upon some basic biologically inspired principles to facilitate coordination among the mobile agents that implement the routing task. Specifically, simulated pheromones are used to control the movement of agents within the network and to dynamically adjust the number of agents in the population. The behavior of ADVR is analyzed and compared to that of traditional Distance Vector Routing.

Description: This article discusses classifying genes to the correct Gene Ontology Slim term in Saccharomyces cerevisiae using neighbouring genes with classification learning. Abstract: Background: There is increasing evidence that gene location and surrounding genes influence the functionality of genes in the eukaryotic genome. Knowing the Gene Ontology Slim terms associated with a gene gives the authors insight into a gene's functionality by informing the authors how its gene product behaves in a cellular context using three different ontologies: molecular function, biological process, and cellular component. In this study, the authors analyzed if they could classify a gene in Saccharomyces cerevisiae to its correct Gene Ontology Slim term using information about its location in the genome and information from its nearest-neighbouring genes using classification learning. Results: The authors performed experiments to establish that the MultiBoostAB algorithm using the J48 classifier could correctly classify Gene Ontology Slim terms of a gene given information regarding the gene's location and information from its nearest-neighbouring genes for training. Different neighbourhood sizes were examined to determine how many nearest neighbours should be included around each gene to provide better classification rules. The authors' results show that by just incorporating neighbour information from each gene's two-nearest neighbours, the ...

Description: This article discusses dynamic intimate contact social networks and epidemic interventions. Abstract: Sexually transmitted diseases and infections are, by definition, transferred among intimate social settings. Although the circumstances under which these social settings are established and maintained may vary, the common prerequisite remains an intimate level of social atmosphere. For this reason, the development of sexually transmitted disease mathematical and computational models must utilize dynamic and evolving social network simulation. This paper presents DynSNIC (Dynamic Social Network of Intimate Contacts), a computational simulator created to embody the intimate dynamic and evolving social networks related to the transmission of sexually transmitted diseases and infections. DynSNIC's utilization by health professionals will facilitate evaluation of targeted intervention strategies and public health policies.

Description: This article discusses the effects of interference on capacity in multi-cell CDMA networks. Abstract: An overwhelming number of models in the literature use average interference for calculation of capacity of a CDMA network. In this paper, we calculate the actual per-user interference and analyze the effect of user-distribution on the capacity of a CDMA network. We show that even though the capacity obtained using average interference is a good approximation to the capacity calculated using actual interference for a uniform user distributions, the deviation can be tremendously large for non-uniform user distributions. We also present an analytical model for approximating the user distributions using 2-dimensional Gaussian distributions by determining the means and the standard deviations of the distributions for every cell. This allows us to calculate the inter-cell interference and the reverse-link capacity of the network. We compare their model with simulation results and show that it is fast and accurate enough to be used efficiently in the planning process of large CDMA networks.

Description: This paper presents a novel approach for designing a call-admission control (CAC) algorithm for code-division multiple-access (CDMA) networks with arbitrary call-arrival rates. The design of the CAC algorithm uses global information; it incorporates the call-arrival rates and the user mobilities across the network and guarantees the users' quality of service (QoS) as well as prescribed blocking probabilities. On the other hand, its implementation in each cell uses local information; it only requires the number of calls currently active in that cell. The authors present several cases for a nontrivial network topology where their CAC algorithm guarantees QoS and blocking probabilities while achieving significantly higher throughput than that achieved by traditional techniques. The authors also calculate the network capacity, i.e., the maximum throughput for the entire network, for prespecified blocking probabilities and QoS requirements.