From November 1997 to June 1998, 3,118 specimens of Echinogammarus stammeri (Karaman, 1931) (Amphipoda) were collected from the River Brenta (Northern Italy) and examined for larval helminths. Larvae of Polymorphus minuius (Goeze, 1782) singly infected the hemocoel of 23 (0.74%) crustaceans; all these larvae were cystacanth stages. This is the first record of Polymorphus minuius in E. stammeri. Some cystacanths had their forebody and hindbody fully inverted. Parasites were bright orange in colour and each was surrounded by a thin acellular envelope. This envelope likely protects the developing parasite larva from cellular responses of the amphipod. Hemocytes were seen adherent to the outer surface of the envelope. The sex ratio among the parasitised E. stammeri was almost 1:1. All Polymorphus minutus larvae were central in the amphipod body, made intimate contact with host internal organs, and frequently induced a marked displacement of them. None of the infected females of E. stammeri. carried eggs or juveniles in their brood pouch. In five hosts, Polymorphus minuius co-occurrcd with the cystacanth of another acanlhocephalan, Pomphorhynchus laevis (Millier, 1776), a parasite offish.
The object of the research was to investigate the spectral properties of Rayleigh-type surface waves, generated by shot-hole explosions during seismic refraction experiments which were carried out in the area of the Bohemian Massif and West Carpathians. The records of displacement amplitudes were spectrally analyzed and prevailing frequency fp, relative Δfr and absolute widths of the spectra Δfa were chosen as essential parameters. Whilst the prevailing frequencies were recorded within the interval f ÷ 0.80 - 3.70 Hz at the site of the observations, situated on the territory of the Bohemian Massif, the respecti ve frequency range f ÷ 0.80 - 2.6 Hz was found in the West Carpathians. Some functional dependences of the spectral amplitude parameters on epicentral distance were observed and regularities of their decrease were defined. Moreover, the influence of local seismogeological conditions at the shot point as well as at the site of observation occurred., Karel Holub., and Obsahuje bibliografické odkazy
This paper provides a method for indexing and retrieving Arabic texts, based on natural language processing. Our approach exploits the notion of template in word stemming and replaces the words by their stems. This technique has proven to be effective since it has returned significant relevant retrieval results by decreasing silence during the retrieval phase. Series of experiments have been conducted to test the performance of the proposed algorithm ESAIR (Enhanced Stemmer for Arabic Information Retrieval). The results obtained indicate that the algorithm extracts the exact root with an accuracy rate up to 96% and hence, improving information retrieval.
Automatic detection and classification of cardiac arrhythmias with high accuracy and by using as little information as possible is highly useful in Holter monitoring of the high risk patients and in telemedicine applications where the amount of information which must be transmitted is an important issue. To this end, we have used an adaptive-learning-rate neural network for automatic classification of four types of cardiac arrhythmia. In doing so, we have employed a mix of linear, nonlinear, and chaotic features of the R-R interval signal to significantly reduce the required information needed for analysis, and substantially improve the accuracy, as compared to existing systems (both ECG-based and R-R interval-based). For normal sinus rhythm (NSR), premature ventricular contraction (PVC), ventricular fibrillation (VF), and atrial fibrillation (AF), the discrimination accuracies of 99.59%, 99.32%, 99.73%, and 98.69% were obtained, respectively on the MIT-BIH database, which are superior to all existing classifiers.
It is well known that a large neighborhood interior point algorithm for linear optimization performs much better in implementation than its small neighborhood counterparts. One of the key elements of interior point algorithms is how to update the barrier parameter. The main goal of this paper is to introduce an "adaptive'' long step interior-point algorithm in a large neighborhood of central path using the classical logarithmic barrier function having O(nlog(x0)Ts0ϵ) iteration complexity analogous to the classical long step algorithms. Preliminary encouraging numerical results are reported.
As an improved algorithm of standard extreme learning machine, online sequential extreme learning machine achieves excellent classification and regression performance. However, online sequential extreme learning machine gives the same weight to the old and new training samples, and fails to highlight the importance of the new training samples. At the same time, the algorithm updates the network weights after obtaining the new training samples. This network weight updating mode lacks flexibility and increases unnecessary computation. This paper proposes an adaptive online sequential extreme learning machine with an effective sample updating mechanism. The new and old samples are given different weights. The effect of new training samples on the algorithm is further enhanced, which can further improve the regression prediction ability of extreme learning machine. At the same time, an improved artificial bee colony algorithm is proposed and used to optimize the parameters of the adaptive online sequential extreme learning machine. The stability and convergence property of proposed prediction method are proved. The actual collected short-term wind speed time series is used as the research object and verify the prediction performance of the proposed method. Multi step prediction simulation of short-term wind speed is performed out. Compared with other prediction methods, the simulation results show that the proposed approach has higher prediction accuracy and reliability performance, meanwhile improve the performance indicators.
The artificial Immune Recognition System (AIRS) algorithm inspired by a natural immune system makes use of the training data to generate memory cells (or prototypes). These memory cells are used in the test phase to classify unseen data using the K-nearest neighbor (K-NN) algorithm. The performance of the AIRS algorithm, similar to other distance-based classifiers, is highly dependent on the distance function used to classify a test instance. In this paper, we present a new version of the AIRS algorithm named Adaptive Distance AIRS (AD-AIRS) that uses an adaptive distance metric to improve the generalization accuracy of the basic AIRS algorithm. The adaptive distance metric is based on assigning weights to the evolved memory cells. The weights of memory cells are used in the test phase to classify test instances. Apart from this, the AD-AIRS algorithm uses the concept of clustering to modify the way that memory cells are generated. Each memory cell represents a group of similar instances (or antigens). A subset of the UCI datasets is used to evaluate the effectiveness of the proposed AD-AIRS algorithm in comparison with the basic AIRS. Experimental results show that the AD-AIRS achieves higher accuracy with a fewer number of memory cells when compared with the basic AIRS algorithm.