Predation of livestock ticks by chickens as a tick-control method in a resource-poor urban environment

The increasing prevalence of streptococci as causes of potentially fatal nosocomial bacteremia requires that antimicrobial agents used for empiric therapy in hospitalized patients include both pneumococci and viridans group streptococci as well as beta-hemolytic streptococci in their activity profile. In this study, the in vitro activity of cefepime, a new fourth-generation cephalosporin, was compared with other cephalosporins versus 197 nosocomial blood stream isolates of streptococci (20 Streptococcus pneumoniae, 104 viridans group, and 73 beta-hemolytic) isolated from patients at more than 30 medial centers from 1995 to 1997. Additional agents tested included penicillin, erythromycin, and vancomycin. Overall, cefepime inhibited 83% of the isolates at concentrations < or = 0.5 microgram/mL and 100% at < or = 8 micrograms/mL. By comparison, ceftazidime inhibited 35 and 88% of isolates at the same concentrations. Cefepime was approximately eightfold more potent than ceftazidime against S. pneumoniae, viridans group streptococci, and beta-hemolytic streptococci. Among the 42 isolates with penicillin MICs > 0.12 microgram/mL, 100% were inhibited by cefepime and only 48% by ceftazidime at < or = 8 micrograms/mL. The rank order of activity for all six agents against the 197 isolates was vancomycin > ceftriaxone > cefepime > penicillin > erythromycin > ceftazidime. Based on the results of the present study, cefepime and ceftriaxone were the superior cephalosporins in potency and spectrum for empiric coverage of patients at risk for streptococcal blood stream infections.     

Transformations for nonideal uniform circular arrays operating in correlated signal environments

The Davies transformation is a method to transform the steering vector of a uniform circular array (UCA) to one with Vandermonde form. As such, it allows techniques such as spatial smoothing for direction-of-arrival (DOA) estimation in a correlated signal environment, developed originally for uniform linear arrays, to be applied to UCAs. However, the Davies transformation can be highly sensitive to perturbations of the underlying array model. This paper presents a method for deriving a more robust transformation using optimization techniques. The effectiveness of the method is illustrated through a number of DOA estimation examples.     

Fast algorithm for computing discrete cosine transform

An efficient method for computing the discrete cosine transform (DCT) is proposed. Based on direct decomposition of the DCT, the recursive properties of the DCT for an even length input sequence is derived, which is a generalization of the radix 2 DCT algorithm. Based on the recursive property, a new DCT algorithm for an even length sequence is obtained. The proposed algorithm is very structural and requires fewer computations when compared with others. The regular structure of the proposed algorithm is suitable for fast parallel algorithm and VLSI implementation     

Fuzzy rule interpolation for multidimensional input spaces in determining d50c of hydrocyclones

Fuzzy rule-based systems have been very popular in many engineering applications. In mineral engineering, fuzzy rules are normally constructed using some fuzzy rule extraction techniques to establish the determination model in predicting the d50c of hydrocyclones. However, when generating fuzzy rules from the available information, it may result in a sparse fuzzy rule base. The use of more than one input variable is also common in hydrocyclone data analysis. This paper examines the application of fuzzy interpolation to resolve the problems using sparse fuzzy rule bases, and to perform analysis of fuzzy interpolation in multidimensional input spaces.     

Decentralized control design for large-scale systems with strong interconnections using neural networks

We propose a decentralized neural network (NN) controller for a class of large-scale nonlinear systems with the strong interconnections. The NNs are used to approximate the unknown subsystems and interconnections. Due to the functional approximation capabilities of NNs, the additional precautions are not required to be made for avoiding the possible control singularity problems. Semiglobal asymptotic stability results are obtained and the tracking error converges to zero. Furthermore, the issue of transient performance of the subsystems is also addressed under an analytical framework.     

Zero-quantised discrete cosine transform prediction technique for video encoding

A new analytical model to eliminate redundant discrete cosine transform (DCT) and quantisation (Q) computations in block-based video encoders is proposed. The dynamic ranges of the quantised DCT coefficients are analysed, then a threshold scheme is derived to determine whether the DCT and Q computations can be skipped without video quality degradation. In addition, fast DCT/inverse DCT (IDCT) algorithms are presented to implement the proposed analytical model. The proposed analytical model is compared with other comparable analytical models reported in the literature. Both the theoretical analysis and experimental results demonstrate that the proposed analytical model can greatly reduce the computational complexity of video encoding without any performance degradation and outperforms other analytical models     

Genetic algorithm hybridized with multilayer perceptron to have an economical slope stability design

The present work aimed to evaluate and optimize the design of an artificial neural network (ANN) combined with an optimization algorithm of genetic algorithm (GA) for the calculation of slope stability safety factors (SF) in a pure cohesive slope. To make datasets of training and testing for the developed predictive models, 630 finite element limit equilibrium (FELE) analyses were performed. Similar to many artificial intelligence-based solutions, the database was involved in 189 testing datasets (e.g., 30% of the entire database) and 441 training datasets; for example, a range of 70% of the total database. Moreover, variables of multilayer perceptron (MLP) algorithm (for example, number of nodes in any hidden layer) and the algorithm of GA like population size was optimized by utilizing a series of trial and error process. The parameters in input, which were used in the analysis, consist of slope angle (β), setback distance ratio (b/B), applied stresses on the slope (F_y) and undrained shear strength of the cohesive soil (C_u) where the output was taken SF. The obtained network outputs for both datasets from MLP and GA-MLP models are evaluated according to many statistical indices. A total of 72 MLP trial and error (e.g., parameter study) the optimal architecture of 4 × 8 × 1 were determined for the MLP structure. Both proposed techniques result in a proper performance; however, according to the statistical indices, the GA–MLP model can somewhat accomplish the least mean square error (MSE) when compared to MLP. In an optimized GA–MLP network, coefficient of determination (R²) and root mean square error (RMSE) values of (0.975, and 0.097) and (0.969, and 0.107) were found, respectively, to both of the normalized training and testing datasets.

     

Nonlinear evolutionary swarm intelligence of grasshopper optimization algorithm and gray wolf optimization for weight adjustment of neural network

Engineering with Computers - The advent of new data-mining techniques and, more recently, swarm-based optimization algorithms have antiquated traditional models in the field of energy performance...     

MicroPort: A general simulation platform for seaport container terminals

Seaport container terminals are essential nodes in sea cargo transportation networks. As such, the operational efficiency of container terminals in handling containers passing through them plays a critical role in a globalized world economy. Many models and algorithms have been developed to address various decision problems in container terminals to help improve operational efficiency. These decision support tools are usually used separately for specific purposes. However, the problems they are trying to tackle are often interrelated. Therefore, in this regard, an evaluation tool which can capture as many operational conditions as possible for different decision problems is necessary. This paper introduces a general simulation platform, named MicroPort, which aims to provide an integrated and flexible modeling system for evaluating the operational capability and efficiency of different designs of seaport container terminals. The software structure of MicroPort comprises three programming layers: (1) the Functions layer; (2) the Applications layer; and (3) the Extensions layer. Different layers are bound by Application Programming Interfaces (APIs). Basic functions built in the Functions layer support the Applications layer in which major operation processes can be modeled by an agent-based method. External modules and decision support tools in the Extensions layer then use APIs to adjust the system to produce suitable simulation models for specific purposes.