In this work, an alternative solution to the tracking problem for a SISO nonlinear dynamical system exhibiting points of singularity is given. An inversion-based controller is synthesized using the Fliess generalized observability canonical form associated to the system. This form depends on the input and its derivatives. For this purpose, a robust exact differentiator is used for estimating the control derivatives signals with the aim of defining a control law depending on such control derivative estimates and on the system state variables. This control law is such that, when applied to the system, bounded tracking error near the singularities is guaranteed.
Globalization is a process. It is sweeping away old cultural norms and social orders and is bringing death to traditional beliefs and customs. This is a process of change, and many aspects of culture are changed forever. It is possible for this kind of transition to happen in many different ways and in many situations "the clash of civilizations" does not necessarily result in conflict. Cultural norms are voluntarily exchanged between various groups, but the differences in social standards are too difficult to accept, one culture begins to change the other. In such cases, one important question emerges . "Who has the right to decide which cultural norms and traditions are better?" This is a question about "cultural borders" and the universality of human rights and social norms. In this discussion, the issue of ritual slavery finds a place. Triokosi, and similar practices that can be found in some West African countries, are clear examples of the traditional bastions in the "war" against globalization. Those are the practices cosidered as being harmful by international society bud defended by traditional believers. The exploration of ritual bondage from both modern and historical perspectives is he main purpose of this article;; it also aims to shed some light on the discussion about the legitimization or suppression of cultural norms that are considered as being harmful.
Cardiovascular diseases are the most common cause of mortality and morbidity in most populations. As the traditional modifiable risk factors (smoking, hypertension, dyslipidemia, diabetes mellitus, and obesity) were defined decades ago, we decided to analyze recent data in patients who survived acute coronary syndrome (ACS). The Czech part of the study included data from 999 males, and compared them with the post-MONICA study (1,259 males, representing general population). The Lithuanian study included 479 male patients and 456 age-matched controls. The Kazakhstan part included 232 patients and 413 controls. In two countries, the most robust ACS risk factor was smoking (OR 3.85 in the Czech study and 5.76 in the Lithuanian study), followed by diabetes (OR 2.26 and 2.07) and hypertension (moderate risk elevation with OR 1.43 and 1.49). These factors did not influence the ACS risk in Kazakhstan. BMI had no significant effect on ACS and plasma cholesterol was surprisingly significantly lower (P<0.001) in patients than in controls in all countries (4.80 ±1.11 vs. 5.76 ±1.06 mmol /l in Czechs; 5.32 ±1.32 vs. 5.71 ±1.08 mmol /l in Lithuanians; 4.88 ±1.05 vs. 5.38±1.13 mmol /l in Kazakhs/Russians). Results from our study indicate substantial heterogeneity regarding major CVD risk factors in different populations with the exception of plasma total cholesterol which was inversely associated with ACS risk in all involved groups. These data reflect ethnical and geographical differences as well as changing pattern of cardiovascular risk profiles., J. A. Hubacek, V. Stanek, M. Gebauerova, V. Adamkova, V. Lesauskaite, D. Zaliaduonyte-Peksiene, A. Tamosiunas, A. Supiyev, A. Kossumov, A. Zhumadilova, J. Pitha., and Obsahuje bibliografii
With the gradual improvement of the telecommunication infrastructure in China, the Internet and other new technologies have been frequently used. The Internet technology also brings many network security threats, for example, botnet, while bringing convenience. Botnet is a network formed between hosts controlled by malicious code. One of the most serious threat to network security faced by the Internet is a variety of malicious network attacks on the carrier of botnet. Back propagation (BP) neural network is proposed to detect botnet threat transmission. In this study, a botnet detection model was established using BP neural network system. BP neural network classifier could identify the botnet traffic and normal traffic. Moreover a test was carried out to detect botnet traffic using BP neural network; the performance of the BP neural network classifier was evaluated by the detection rate and false positive rate. The results showed that it had high detection rate and low false positive rate, which indicated that the BP neural network had a good performance in detecting the traffic of botnet threat transmission.
Trafficking of the rhoptry chimeric protein RhopH2-GFP, which contains RhopH2 signal peptide plus the downstream five amino acids, was dissected by treating parasites with Brefeldin A at three different time points. Twenty eight hrs-stage trophozoites accumulated the chimera within the parasite endoplasmic reticulum. In 32 hrs-stage schizonts, the chimera was distributed in the parasite cytoplasm but not in the parasitophorous vacuole. In 36 hrs stage-schizonts, the chimera was detected in the individual parasitophorous vacuoles of the developing merozoites and, in contrary to non-treated parasites, no immature rhoptry vesicles could be detected in the cytoplasm of immature merozoites. These data show that this chimera is trafficked to the rhoptries via Brefeldin A-sensitive pathway indicating that this trafficking is similar to that of the endogenous rhoptry proteins, and that the five amino acids downstream of the signal peptide cleavage site may contain the sorting signal required for rhoptry targeting.
Train-induced vibration prediction in multi-story buildings can effectively provide the effect of vibrations on buildings. With the results of prediction, the corresponding measures can be used to reduce the influence of the vibrations. To accurately predict the vibrations induced by train in multi-story buildings, support vector machine (SVM) is used in this paper. Since the parameters in SVM are very vital for the prediction accuracy, shuffled frog-leaping algorithm (SFLA) is used to optimize the parameters for SVM. The proposed model is evaluated with the data from field experiments. The results show SFLA can effectively provide better parameter values for SVM and the SVM models outperform a better performance than artificial neural network (ANN) for train-induced vibration prediction
This paper presents a neural network (NN) approach to detect intrusions. Previous works used many KDD records to train NNs for detecting intrusions. That is why; our objective here is to show that in case of the KDD data sets, we can obtain good results by training some NNs with a small data subset. To prove that, this study compares the attacks detection and classification by using two training sets: a set of only 260 records and a set of 65536 records. The testing set is composed of 65536 records randomly chosen from the KDD testing set. Our study focused on two classification types of records: a single class (normal or attack), and a multi class where the category of the attack is detected by the NN. Four different types of NNs were tested: Multi-Layer Perceptron (MLP), Modular, Jordan/Elman and Principal Component Analysis (PCA) NN. Two NN structures were used: the first one contains only one hidden layer and the second contains ten hidden layers. Our simulations show that the small data subset (260 records) can be trained to detect and classify attacks more efficiently than the second data subset.
The purpose of this study is to analyze the performances of some neural networks (NNs) when all the KDD data set is used to train them, in order to classify and detect attacks. Five different types of NNs were tested: Multi-Layer Perceptron (MLP), Self Organization Feature Map (SOFM), Radial Basis Function/Generalized Regression/Probabilistic (RBF/GR/P), Jordan/Elman, and Recurrent NNs. The experiment study is done on the Knowledge Discovery and Data mining (KDD) data sets. We consider two levels of attack granularities depending on whether dealing with four main categories, or only focusing on the normal/attack connection types. Our simulations show that our results are competitive with some other artificial intelligence or data mining intrusion detection systems.
In the paper the existing results concerning a special kind of trajectories and the theory of first return continuous functions connected with them are used to examine some algebraic properties of classes of functions. To that end we define a new class of functions (denoted $Conn^*$) contained between the families (widely described in literature) of Darboux Baire 1 functions (${\rm DB}_1$) and connectivity functions ($Conn$). The solutions to our problems are based, among other, on the suitable construction of the ring, which turned out to be in some senses an “optimal construction“. These considerations concern mainly real functions defined on $[0,1]$ but in the last chapter we also extend them to the case of real valued iteratively $H$-connected functions defined on topological spaces.