A Cooperative Security Monitoring Mechanism Aided by Optimal Multiple Slave Cluster Heads for UASNs

As each cluster head(CH)sensor node is used to aggregate,fuse,and forward data from different sensor nodes in an underwater acoustic sensor network(UASN),guaranteeing the data security in a CH is very critical.In this paper,a cooperative security monitoring mechanism aided by multiple slave cluster heads(SCHs)is proposed to keep track of the data security of a CH.By designing a low complexity“equilateral triangle algorithm(ETA)”,the optimal SCHs(named as ETA-based multiple SCHs)are selected from the candidate SCHs so as to improve the dispersion and coverage of SCHs and achieve largescale data security monitoring.In addition,by analyzing the entire monitoring process,the close form expression of the probability of the failure attack identification for the SCHs with respect to the probability of attack launched by ordinary nodes is deduced.The simulation results show that the proposed optimal ETA-based multiple SCH cooperation scheme has lower probability of the failure attack identification than that of the existing schemes.In addition,the numerical simulation results are consistent with the theoretical analysis results,thus verifying the effectiveness of the proposed scheme.

Behavior of reinforced concrete structural walls with various opening locations: experiments and macro model

Two experimental tests of three-storied reinforced concrete structural walls having large openings were performed.Based on an original macro model,a multiple modified macro-model was proposed to develop a simple method to design a reinforced concrete structural wall with large openings and various opening locations.The interaction between reinforcement ties and concrete struts formed along the perimeter of openings was neglected in the original model.However,the strut-and-tie node was proposed to take account of such interaction in the proposed model.The predicted behavior of two specimens using such a proposed model was compared with the experimental results.It is shown that the behavior of structural walls with large openings could be modeled well using the proposed model.Moreover,the study indicates that the proposed model is applicable even in cases of multi-story structural walls having large openings and various opening locations.

Genotype comparisons of strains of Candida albicans from patients with cutaneous candidiasis and vaginal candidiasis

Background It is uncertain whether genotypes of Candida albicans （C. albicans） are associated with colonizing body locations or variant conditions of infection. The aim of this study was to investigate whether there are significant associations between strain genotypes and body sites of infection and to determine the potential pathogenesis of cutaneous candidiasis at multiple locations. Methods A total of 151 strains of C. albicans were isolated from 74 infant patients with cutaneous candidiasis and 61 female patients with vaginal candidiasis. Patients were grouped according to the body sites and underlying conditions of infection. Genotypes were identified by polymerase chain reaction （PCR） of the 25S rDNA and PCR-restriction fragment length polymorphism （RFLP） of ALT repeats digested with EcoRI and Clal. Results Ten genotypes were detected. There were significant differences in genotype frequencies between the two groups. However, we found no clear association between genotypes and the sites of cutaneous infection or the underlying conditions of vaginal candidiasis （VVC）. In addition, strains of C. albicans from multiple cutaneous locations of the same patient had identical genotypes. Conclusions Populations of C. albicans from patients with cutaneous and vaginal candidiasis were genetically different. However, the lack of genetic difference between strains from different body sites with cutaneous infections or from different underlying conditions for VVC suggests no evidence of genotype selection for different skin surfaces or patients with different underlying conditions for VVC.

Nonlinear Equations Solving with Intelligent Optimization Algorithms: A Survey

Nonlinear Equations(NEs),which may usually have multiple roots,are ubiquitous in diverse fields.One of the main purposes of solving NEs is to locate as many roots as possible simultaneously in a single run,however,it is a difficult and challenging task in numerical computation.In recent years,Intelligent Optimization Algorithms(IOAs)have shown to be particularly effective in solving NEs.This paper provides a comprehensive survey on IOAs that have been exploited to locate multiple roots of NEs.This paper first revisits the fundamental definition of NEs and reviews the most recent development of the transformation techniques.Then,solving NEs with IOAs is reviewed,followed by the benchmark functions and the performance comparison of several state-of-the-art algorithms.Finally,this paper points out the challenges and some possible open issues for solving NEs.

Solving Nonlinear Equations Systems with an Enhanced Reinforcement Learning Based Differential Evolution

Nonlinear equations systems(NESs)arise in a wide range of domains.Solving NESs requires the algorithm to locate multiple roots simultaneously.To deal with NESs efficiently,this study presents an enhanced reinforcement learning based differential evolution with the following major characteristics:(1)the design of state function uses the information on the fitness alternation action;(2)different neighborhood sizes and mutation strategies are combined as optional actions;and(3)the unbalanced assignment method is adopted to change the reward value to select the optimal actions.To evaluate the performance of our approach,30 NESs test problems and 18 test instances with different features are selected as the test suite.The experimental results indicate that the proposed approach can improve the performance in solving NESs,and outperform several state-of-the-art methods.

A GLOBALLY CONVERGENT ALGORITHM FOR THE EUCLIDEAN MULTIPLICITY LOCATION PROBLEM

The Euclidean single facility location problem (ESFL) and the Euclidean multiplicity lo-cation problem (EMFL) are two special nonsmooth convex programming problems which haveattracted a largr literature. For the ESFL problem. there are algorithms which converge bothglobally and quadratically For the EMFL problem, there are some quadratically convergentalgorithms. but for global convergencel they all need nontrivial assumptions on the problem.In this paper, we present an algorithm for EMFL. With no assumption on the problem, it isproved that from any initial point, this algorithm generates a sequence of points which convergesto the closed convex set of optimal solutions of EMFL.