We propose a probability routing strategy that leverages sine function-based gravitational centrality to improve the traffic capacity of communication networks. Previous routing strategies based on gravitational centrality have demonstrated effectiveness in enhancing network traffic capacity by distinguishing node importance through gravitational centrality. Our proposed strategy introduces an adjustment to the gravitational centrality of nodes by incorporating the sine function, enabling a more uniform distribution of node importance across the network. This adjustment facilitates the efficient utilization of high-importance nodes for packet transmission, leading to a more balanced load distribution among nodes. Numerical experiments confirm that our strategy achieves superior network traffic capacity compared to both the effective gravitational path routing strategy and the previously proposed routing strategies.

Traffic congestion in the transportation networks, which increases vehicle travel times, energy consumption, and environmental pollution, is one of the major social problems occurring in most urban areas. One of the ways to reduce traffic congestion is to use effective routing methods to guide vehicles to their destinations that avoid or reduce traffic congestion. However, routing for vehicles, especially in urban areas, is difficult due to the dynamic and unpredictable nature of the transportation networks. To deal with these characteristics, the use of connected vehicles, which successfully obtain real-time data by using Internet communication, is attracting as the vehicles in the transportation networks. The ability to communicate over the Internet allows connected vehicles to communicate with other connected vehicles and infrastructure to monitor road congestion. In this work, we propose a routing method using chaotic neurodynamics for the transportation networks in which connected vehicle move and evaluate its effectiveness. In addition, we use Simulation of Urban MObility (SUMO) traffic simulator to construct realistic traffic environments. Numerical experiments using SUMO show that the routing method using chaotic neuro-dynamics shows effective routing performance compared to other routing methods in both the number of arriving vehicles and the average travel time.

BACKGROUND: Label-free quantitation of mass spectrometric data is one of the simplest and least expensive methods for differential expression profiling of proteins and metabolites. The need for high accuracy and performance computational label-free quantitation methods is still high in the biomarker and drug discovery research field. However, recent most advanced types of LC-MS generate huge amounts of analytical data with high scan speed, high accuracy and resolution, which is often impossible to interpret manually. Moreover, there are still issues to be improved for recent label-free methods, such as how to reduce false positive/negatives of the candidate peaks, how to expand scalability and how to enhance and automate data processing. AB3D (A simple label-free quantitation algorithm for Biomarker Discovery in Diagnostics and Drug discovery using LC-MS) has addressed these issues and has the capability to perform label-free quantitation using MS1 for proteomics study. RESULTS: We developed an algorithm called AB3D, a label free peak detection and quantitative algorithm using MS1 spectral data. To test our algorithm, practical applications of AB3D for LC-MS data sets were evaluated using 3 datasets. Comparisons were then carried out between widely used software tools such as MZmine 2, MSight, SuperHirn, OpenMS and our algorithm AB3D, using the same LC-MS datasets. All quantitative results were confirmed manually, and we found that AB3D could properly identify and quantify known peptides with fewer false positives and false negatives compared to four other existing software tools using either the standard peptide mixture or the real complex biological samples of Bartonella quintana (strain JK31). Moreover, AB3D showed the best reliability by comparing the variability between two technical replicates using a complex peptide mixture of HeLa and BSA samples. For performance, the AB3D algorithm is about 1.2 - 15 times faster than the four other existing software tools. CONCLUSIONS: AB3D is a simple and fast algorithm for label-free quantitation using MS1 mass spectrometry data for large scale LC-MS data analysis with higher true positive and reasonable false positive rates. Furthermore, AB3D demonstrated the best reproducibility and is about 1.2- 15 times faster than those of existing 4 software tools.

Particle swarm optimization (abbr. PSO) is one of the most effective optimization algorithms. The PSO contains many control parameters, therefore, the performance of the searching ability of the PSO is significantly alternated. In order to analyze the dynamics of such PSO system rigorously, we have analyzed a deterministic PSO (abbr. D-PSO) systems which does not contain any stochastic factors, and its coordinate of the phase space is normalized. The found global best information influences the dynamics. This situation can be regarded as the full-connection state. On the other hand, there is the case where the best information in a limited population. Such information is called as lbest. How to get the lbest information from any population is equivalent to a network structure. Such network structure influences the performance of searching ability. In order to clarify a relationship between network structures of the PSO and its performance, we pay attention to the degree and the average distance used in graph theory. We consider the two cases where the D-PSO has an extended cycle structure and a Small World network structure. Our numerical simulation results indicates the searching performance of the D-PSO is depended on the average distance of the node. Especially, the long average distance exerts the search performance on the D-PSO. We confirm that the search performance properties of the D-PSO and the conventional stochastic PSO are completely different to the average distance. The search performance of the D-PSO is improved according to the average distance. On the other hand, the search performance of the conventional stochastic PSO is deteriorated according to the average distance. We consider that the slow transmission of the beneficial information leads to the diversification of the particles of the D-PSO. Also, we clarify the small perturbation of the random range of the stochastic PSO is important.

To establish reliable communicate between end users, alleviation of the congestion of packets in the communication networks is the most important problem. Many approaches have been attempting to resolve such a problem. As one of the effective routing strategies for reliable communication, we have also proposed a routing strategy with chaotic neurodynamics. By a refractory effect which is the most important effect of chaotic neuron, the routing strategy shows high performance for communication networks as compared to the shortest path approach. In addition, we improved the routing strategy by combining information of the shortest paths and waiting times at adjacent nodes. As a result, we confirmed that the routing strategy using chaotic neurodynamics is the most effective policy to alleviate the congestion of the packets in the communication network. However, in the previous works, the chaotic routing strategy was evaluated for ideal communication networks; each node has same transmission capability for routing the packets and same size of buffer for storing the packets. From a view point of realistic application of the chaotic routing strategy, it is important to evaluate the performance of the routing strategy under realistic conditions. In 2007, M.Hu et al. proposed a realistic communication network in which the largest storage capacity and processing capability are introduced[5]. Thus, in this paper, we evaluate the chaotic routing strategy for the realistic communication networks[5]. Results show that the chaotic routing strategy keeps the highest arrival rate of the packets as compared to the conventional routing strategies by avoiding the congestion of the packets effectively. Also, we confirmed that the chaotic routing strategy has much possibility for application in the real communication networks.

We propose a new algorithm for controling packet routing using chaotic dynamics. First, we construct a packet routing method using a neural network based on information of shortest distances from a node to the other nodes, which works as the Dijkstra algorithm. We discovered that when the network structure is regular, the algorithm works well, however, when the network structure becomes irregular, the algorithm does not work well; almost all packets cannot be sent to their destinations because of congestion of packets at the nodes. To avoid this problem, we extended this model to employ chaotic dynamics. We confirm that our routing algorithm is very effective for many irregular and scale-free networks comparing with the Dijkstra algorithm.

An experimental teaching method for an introductory programming course is presented and evaluated. The method is based on the observation that program reading skills should preceed program writing skills, and that such reading capability can be self-learned by a careful study of a large number of example programs.

It is shown that a mesh-connected n×n multiprocessor system can compute the inverse of a n×n matrix in linear time to n. The algorithm is based on a theorem known to Sylvester in 1851. It computes the cofactor matrix in n steps, each of which involves 4 unit distance message routing and 4 arithmetic operations for every processor. A coding and memory requirement for each processor is the same and is independent of n.It is also shown that the same algorithm solves systems of n linear equations in linear time of n with n × (n+l) processors.

The importance of recursive programming in an introductory computer science course is discussed from the viewpoint that the notions of 'process', 'program' and 'processor' are fundamental in computer programming. Several examples demonstrate how the concept of recursive programming and proving program correctness can be taught to freshman business majors before they are exposed to any programming language.

An extension of regular expressions is introduced to represent the activities of synchronizing concurrent processes. A communication link is defined as an automaton (infinite) with one-to-one partial transformations, and communication protocol as an assignment of events to actions of a communication link. A synchronization problem is defined as a problem of concurrent decomposition of a process into a deadlock-free family of processes sharing a communication protocol.