Graph coloring is a way of coloring the vertices of a graph such that no two adjacent vertices have the same color. Graph coloring problem (GCP) is about finding the smallest number of colors needed to color a given graph. The smallest number of colors needed to color a graph G, is called its chromatic number. GCP is a well-known NP-hard problems and, therefore, heuristic algorithms are usually used to solve it. GCP has many applications such as: bandwidth allocation, register allocation, VLSI design, scheduling, Sudoku, map coloring and so on. We try genetic algorithm (GA) and chaos theory to solve GCP. We proposed a heuristic algorithm called CMHn to implement multi-POINT CROSSOVER operation in GA. To generate initial population, a fast greedy algorithm is used. In this algorithm, the degree of each node and the number colors in its neighbor is used to assign a color to each node. Mutation operation in GA is used to explore the search space and scape from the local optima. In this study, a chaotic mutation operation is presented to select some vertices and change their color. The CROSSOVER and mutation parameters in the proposed algorithm is tuned based on some experiment. To evaluate the proposed algorithm, some experiment is conducted on DIMACS data set. Among DIMACS sample graphs, DSJ, Queen, Le450, Wap are well-known challenging samples for graph coloring. The proposed algorithm is executed 10 times on each sample and the best, worst and mean results are reported. Results show that the proposed algorithm can effectively solve GCP and have comparable outcome with the recent studies in this field. The proposed method outperforms other algorithms on very large graphs (Wap graphs).

The case-CROSSOVER design was developed in the early 1990s to study the effects of transient, short–term exposures on the risk of acute events such as myocardial infarction. To estimate relative risk, the exposure frequency during a period just before outcome onset (hazard period) is compared with exposure frequency during control time(s) in that person rather than in a control. One or more "control times" are supplied by each of the cases themselves to control for confounding by constant characteristics and self-confounding between the trigger’s acute and chronic effects.In the analysis of case-CROSSOVER studies, exposure frequency in the hazard period is compared with the control period or the individual's usual frequency of exposure. The design has been used frequently for heart diseases, injuries and air pollution epidemiology. This review article looks at published case-CROSSOVER studies and is intended to help the reader gain a better understanding of the strengths and limitations of the case-CROSSOVER design in studying the epidemiology of injuries and air pollution.

Background and Objective: Road traffic injuries are among the most important causes of death and disability in Iran, and the country has one of the highest prevalence of opioid drug use, especially among drivers. The effect of different situations related to opioid use needs great attention. The purpose of this study was to estimate the effect of driving in the withdrawal phase on the occurrence of traffic accidents leading to injury.Materials and Methods: This is a Case-CROSSOVER study on injured drivers of crashed motor vehicles in Kerman. Drivers having skipped one habitual drug dose within one hour of the driving session were considered as being in withdrawal. We compared the drivers' situation at the time of accident with their regular driving habits.Results: Among 75 drivers who had history of regular use of opium, 15 were in withdrawal phase -at the time of accident. The relative rate of occurrence of traffic injuries while driving in these circumstances was 2:67 (95% confidence interval: 1.52 - 4.68). Conclusion: According to these findings we can conclude that habitual opioid users are at greater risk of traffic accidents while driving in withdrawal status; this risk is more than two-fold, relative to not being in withdrawal status.

Non-cooperative intelligent control agents (ICAs) with dedicated cost functions, can lead the system to poor performance and in some cases, closed-loop instability. A robust solution to this challenge is to place the ICAs at the feedback Nash equilibrium POINT (FNEP) of the differential game between them. This paper introduces the designation of a robust decentralized infinite horizon LQR control system based on the FNEP for a linear time-invariant system. For this purpose, two control strategies are defined. The first one is a centralized infinite horizon LQR (CIHLQR) problem (i.e. a supervisory problem), and the second one is a decentralized control problem (i.e. an infinite horizon linear-quadratic differential game). Then, while examining the optimal solution of each of the above strategies on the performance of the other, the necessary and sufficient conditions for the equivalence of the two problems are presented. In the absence of the conditions, by using the least-squares error criterion, an approximated CIHLQR controller is presented. It is shown that the theorems could be extended from a two-agent control system to a multi-agent system. Finally, the results are evaluated using the simulation results of a Two-Area non-reheat power system.

Background: Finding a reliable diagnostic method for brucellosis is the most challengeable problem. In this study we determined the optimal diagnostic cut-off POINT for ELISA test.Methods: We gathered 56 confirmed cases of brucellosis. Furthermore blood samples from 126 controls including 73 healthy controls and 53 without brucellosis febrile patients were collected. In all of the cases and controls ELISA Ig G and ELISA Ig M levels were measured and compared with each other by Box plot graph and the Receiver Operating Characteristic (ROC) curve. The sensitivity and specificity of ELISA Ig G and Ig M were fixed in different cut-off values and Ig G and Ig M levels yielding maximal sensitivity plus specificity were selected for determination of optimal cut-off POINT.Results: The nineteen patients had positive blood cultures for Brucella melitensis. The standard agglutination test results were 1/160 or more in 54 patients. The Box plot graph indicated a high degree of dispersion for Ig G and Ig M data in patients with brucellosis compared with febrile patients without brucellosis and healthy controls. We observed partial overlap for Ig M data (not for Ig G) between cases and controls. The area under ROC curve for discrimination of cases and healthy controls was more for Ig G than Ig M.Conclusions: The ELISA Ig G is more reliable test than ELISA Ig M in diagnosis of brucellosis. Using cut-off of 10 IU/ml and 50 IU/ml have the most sensitivity (92.9%) and specificity (100%) for ELISA Ig G test, respectively. 

Statistical Shape Models are used to interpret shapes. They include mean and variance of corresponding POINTs of training shapes. One of the most important challenges in building statistical shape models is to establish correct correspondences among landmarks in a training set. In this paper, the non-rigid CPD (Coherent POINT Drift) method is used to find correct correspondences among POINTs. This method uses both Deterministic Annealing and a non-rigid scheme to register two shapes simultaneously. Then, the statistical shape model is built using a rigid transformation. The proposed method is evaluated using Compactness, Generalization ability and Specificity measures. The built model is compared to models created using the ICP (Iterative Closest POINT), TPS-RPM (Thin Plate Spline – Robust POINT Matching) and MDL (Minimum Descreption Length) methods by these metrics. The results show that the proposed method performs like the MDL regarding Specificity measure (0.21±0.06). The Compactness and Generalization ability measures of the proposed method are very similar to those for the MDL method. The run-time of our proposed method is about 68 seconds which is faster than non-rigid TPS-RPM and MDL approaches (390 and 3600 seconds respectively). Our results are superior to the ICP and TPS-RPM algorithms.