In this paper we present a comparison between the performance of Multilayer Perceptrons (MLPs) and Support Vector Machines (SVMs) in a problem of wind speed prediction. Specifically, we analyze the behavior of both algorithms within a larger system of wind speed prediction, formed by global and mesoscale weather forecasting models, and with a final statistical down-scaling process where the MLPs and the SVM are used. The final objective is to forecast the mean hourly wind speed prediction at wind turbines in a wind farm. This is an important parameter used to predict the total power production of the wind farm. The specific model for the short-term wind speed forecast we use integrates two different meteorological prediction global models, observations at the surface level and in different heights using atmospheric soundings. Also, it includes a mesoscale prediction model producing the inputs used in the MLP or the SVM, which will forecast the final wind speed at each turbine of the wind farm. In the experiments carried out we compare the results obtained using the MLP or SVM as final steps of the prediction system. Interesting differences of performance can be found when using MLPs or SVMs, which we analyze in this paper. The results obtained are encouraging anyway, and good short-term predictions of wind speed at specific points are obtained with both techniques.
Scheduling is one of the core steps to efficiently exploit the capabilities of heterogeneous distributed computing systems and represents an NP-complete problem. Therefore, using meta-heuristic algorithms is a suitable approach in order to cope with its difficulty. In many meta-heuristic algorithms, generating individuals in the initial step has an important effect on the convergence behavior of the algorithm and final solutions. Using some pure heuristics for generating one or more near-optimal individuals in the initial step can improve the final solutions obtained by meta-heuristic algorithms. Pure heuristics may be used solitary for generating schedules in many real-world situations in which using the meta-heuristic methods are too difficult or inappropriate. Different criteria can be used for evaluating the efficiency of scheduling algorithms, the most important of which are makespan and flowtime. In this paper, we propose an efficient pure heuristic method and then we compare the performance with five popular heuristics for minimizing makespan and flowtime in heterogeneous distributed computing systems. We investigate the effect of these pure heuristics for initializing simulated annealing meta-heuristic approach for scheduling tasks on heterogeneous environments.
Qualitative and quantitative differences in prey are known to affect the life histories of predators. A laboratory study was used to evaluate the suitability of three aphid prey, Aphis gossypii, Aphis craccivora and Lipaphis erysimi, for the ladybird beetle, Anegleis cardoni (Weise). Development was fastest on A. gossypii followed by A. craccivora and L. erysimi. Percentage pupation, immature survival, adult weight and the growth index were all highest when reared on A. gossypii and lowest on L. erysimi. Similarly, oviposition period, lifetime fecundity and egg viability were all highest on a diet of A. gossypii, lowest on L. erysimi and intermediate on A. craccivora. Age-specific fecundity functions were parabolic. Adult longevity, reproductive rate and intrinsic rate of increase were all highest on A. gossypii and lowest on L. erysimi. Life table parameters reflected the good performance on A. gossypii and poor performance on L. erysimi. Estimates of individual fitness values for the adults reared on A. gossypii and A. craccivora were similar and higher than that of adults reared on L. erysimi. Thus, the three species of aphid can all be considered essential prey for A. cardoni.
Classifier combining is a popular technique for improving classification quality. Common methods for classifier combining can be further improved by using dynamic classification confidence measures which adapt to the currently classified pattern. However, in the case of dynamic classifier systems, the classification confidence measures need to be studied in a broader context as we show in this paper, the degree of consensus of the whole classifier team plays a key role in the process. We discuss the properties which should hold for a good confidence measure, and we define two methods for predicting the feasibility of a given classification confidence measure to a given classifier team and given data. Experimental results on 6 artificial and 20 real-world benchmark datasets show that for both methods, there is a statistically significant correlation between the feasibility of the measure, and the actual improvement in classification accuracy of the whole classifier system; therefore, both feasibility measures can be used in practical applications to choose an optimal classification confidence measure.
Balance control is a critical task of daily life, the ability to maintain upright posture becomes of particular concern during aging when the sensory and motor system becomes deteriorated. Falls contribute to the most deaths caused by injury within the aged population, and the mortality rate following a fall is drastically elevated. Longitudinal and reliable assessment of balance control abilities is a critical point in the prediction of increased risk of falling in an elderly population. The primary aim of the study was to evaluate the efficiency of the Homebalance test in the identification of persons being at higher risk of falling. 135 subjects (82 women and 53 men) with geriatric syndrome have been recruited and the Homebalance and the Tinetti Balance test were performed. Results of both tests strongly correlated proving the good performance of the Homebalance test. Standing balance declines with increasing body mass index in both genders. Analysis of fluctuations of the center of pressure (COP) revealed higher frequency and magnitude in mediolateral direction COP movements when compared women to men. A strong negative correlation has been found between Tinetti static balance score and the total length of the COP trajectory during the examination on Homebalance (r = -0.6, p<0.001). Although both methods revealed good performance in detecting balance impairment, Homebalance test possesses higher precision due to the continuous nature of COP-derived parameters. In conclusion, our data proved that the Homebalance test is capable to identify persons with impaired balance control and thus are at higher risk of falling.
Text categorization is based on the idea of content-based texts clustering. An Artificial Neural Network (ANN) or simply Neural Network (NN) classifier for Arabic texts categorization is proposed. The Singular Value Decomposition (SVD) is used as preprocessor with the aim of further reducing data in terms of both size and dimensionality. Indeed, the use of SVD makes data more amenable to classification and the convergence training process faster. Specifically, the effectiveness of the Multilayer Perceptron (MLP) and the Radial Basis Function (RBF) classifiers are implemented. Experiments are conducted using an in-house corpus of Arabic texts. Precision, recall and F-measure are used to quantify categorization effectiveness. The results show that the proposed SVD-Supported MLP/RBF ANN classifier is able to achieve high effectiveness. Experimental results also show that the MLP classifier outperforms the RBF classifier and that the SVD-supported NN classifier is better than the basic NN, as far as Arabic text categorization is concerned.
Closely related species can be used for studying the ecological significance of their traits. The response in terms of survival, clonal growth and vegetative and generative characteristics of three related Myosotis species to competition and soil characteristics were studied in a three year pot experiment. Plants from four populations per species were cultivated in a factorial combination of substrate (nutrient-rich soil and mixtures with sand) and competition (with or without Holcus lanatus) treatments. Survival, clonal growth and the majority of the growth characteristics of all three Myosotis species were reduced by competition. The effect of substrate was less pronounced, and variable for various traits: the soil with sand mixture was more suitable for survival, clonal growth and seed germination whereas in the nutrient-rich soil plants were taller, but this effect was modified by competition. The differences among species corresponded well to expectation based on their known habitat preferences. Myosotis caespitosa, a species typical of short-term habitats such as emerged bottoms of ponds, exhibited the shortest life span and was also the most sensitive to competition: all plants of this species died in the competition treatment before the end of the second season. Nevertheless, the surviving plants (in the no-competition treatment) were able to form several daughter rosettes or stolons; some of them spread clonally till the third year. Myosotis palustris subsp. laxiflora, which inhabits the banks of rivers and brooks often disturbed by torrential floods, survived best and had the highest potential for clonal growth and spreading. Most plants of this species produced rhizomes and stolons and spread the furthest of all the three species. Myosotis nemorosa, which lives mostly in meadows, the most stable habitat of the studied congeners, but also a habitat with a strongly competitive matrix of species, was intermediate in terms of survival, and clonal growth, forming mainly short rhizomes. This species exhibited the highest among-population variability in all recorded characteristics, which might be due to its local adaptation to a wide spectrum of habitats. We argue that the details of prevailing disturbance regime, rather than some general disturbance intensity explain the clonal behaviour of the species compared.
The definition of the performance parameters, especially accuracy, reliability, dependability are presented. Their estimations are developed by using results from the theory of statistical tolerance intervals in the case of random sample from a normal distribution. The proposed approach is illustrated on two examples.
Cíl práce: Upozornit na problematiku apendicitidy v graviditě. Typ práce: kazuistika. Vlastní pozorování: Formou kazuistiky prezentovaný výskyt perforované apendicitidy s difuzní peritonitidou ve 27. týdnu gravidity. Následný porod císařským řezem ve 36. týdnu a komplikované pooperační období a infekční komplikace v šestinedělí. Závěr: Apendicitida v těhotenství je spojována se zvýšenou mateřskou a fetální morbiditou. Perforace apendixu přispívá ke zvýšenému riziku dalších komplikací, které zahrnují např. předčasný porod, abort a mateřské komplikace, zejména infekční. Včasná diagnóza s následnou operací může snížit mateřskou i fetální morbiditu., Objective: To draw attention to the issue of appendicitis in pregnancy. Design: Case report. Case report: We present a case study in the form of a case of occurrence of appendicitis with peritonitis in 27 weeks pregnancy. And describes the birth and childbed, which is complicated by infectious complications. Conslusion: Appendicitis in pregnancy is associated with increased maternal and fetal morbidity and even more in case of peritonitis. Delay surgical intervention correlates with increased risk of perforation, which contributes to the increase of other complications, including premature birth, abortion and maternal complications. Prompt diagnosis and early surgery can reduce maternal and fetal morbidity., and Renáta Hlistová