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Abstract. Between 1998 and 2001, a total of 1,062 human cases of visceral leishmaniasis were reported from the rural district of Meshkin-Shahr in the mountainous, north-western Iranian province of Ardabil. In the summer of 2008, a cross-sectional study of dogs was conducted in this endemic area by randomly selecting 384 animals from 21 villages and testing them serologically for leishmaniasis. Villages, in which more than 10% of investigated dogs showed antiLeishmania titres ≥1/320, were considered to be high-risk environments. Regression analysis showed no statistically significant correlation between topographic conditions and the prevalence of positive cases. However, when the results were compared with past meteorological records, a statistically significant positive correlation (P = 0.007) was found between the number of infected dogs with anti-Leishmania titres ≥1/640 and the number of days in a year with temperatures below 0 °C. While humidity showed an inverse correlation (P = 0.009) with the anti-Leishmania titres, a positive correlation (P <0.001) was found in relation to the amount of rainfall. Mapping of the areas at risk for kala-azar in the Meshkin-Shahr district supports the impression that the low temperatures prevalent in the Ardebil province constitute an important factor influencing the distribution of leishmaniasis there.

Abstract-Cloud computing is being welcomed as a new basis to manage and provide services on the internet. One of the reasons for increased efficiency of this environment is the appropriate structure of the tasks scheduler. Since the tasks scheduling in the cloud computing environment and distributed systems is an NP-hard problem, in most cases to optimize the scheduling issues, the meta-heuristic methods inspired by nature are used rather than traditional or greedy methods. One of the most powerful meta-heuristic methods of optimization in the complex problems is an Imperialist Competitive Algorithm (ICA). Thus, in this paper, a meta-heuristic method based on ICA is provided to optimize the scheduling issue in the cloud environment. Simulation results in MATLAB environment show the amount of 0.7 percent improvement in execution time compared with a Genetic Algorithm(GA).

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In this paper, we propose a new trust-region method for solving nonlinear systems of equalities and inequalities. The algorithm combines both standard and adaptive trust-region frameworks to construct the steps of the algorithm. The trust-region subproblem is solved in the first iteration using a given initial radius. Then, in each iteration, the standard trust-region method is followed whenever the current trial step is accepted, otherwise, the subproblem is resolved using an adaptive scheme. The convergence results for the new proposed algorithm are established under some mild and standard assumptions. Numerical results on some leastsquares test problems show the efficiency and effectiveness of the proposed algorithm in practice too.