A population of a hybrid between Tragopogon porrifolius and T. pratensis (T. ×mirabilis), which occurs in SW part of the town of Roudnice nad Labem, N part of Central Bohemia, was analysed with respect to its morphology, fertility, life history, ploidy level and DNA content. Both parental species vary relatively little morphologically; they are biennials (monocarpic perennials) and diploids. T. pratensis is a native species in the Czech Republic, T. porrifolius was cultivated there in the past. The hybrid plants are extremely morphologically variable, with variation ranges of some characters overlapping those of the parental species (e.g. ligules are often longer than involucral bracts, peduncles are often lanate). Only diploids were found within the hybrid population; however, they have substantially lower DNA content than both parents (18% lower than T. pratensis, 42% lower than T. porrifolius). The plants of the Roudnice hybrid population are polycarpic perennials in contrast to the monocarpic perennial (mostly biennial) parents. The distribution is described in detail; it shows that the hybrid plants are spreading and at present even occur outside the town. The long-persisting population of fertile diploid hybrid plants in Roudnice nad Labem is an alternative evolutionary pathway to that of the allotetraploid Tragopogon species known from North America.
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.
Efficient and systematic survey methods are essential for wildlife researchers and conservationists to collect accurate ecological data that can be used to make informed conservation decisions. For endangered and elusive species, that are not easily detected by conventional methods, reliable, time- and cost-efficient methodologies become increasingly important. Across a growing spectrum of conservation research projects, survey outcomes are benefitting from scent detection dogs that assist with locating elusive species. This paper describes the training methodology used to investigate the ability of a scent detection dog to locate live riverine rabbits (Bunolagus monticularis) in their natural habitat, and to determine how species-specific the dog was towards the target scent in a controlled environment. The dog was trained using operant conditioning and a non-visual methodology, with only limited scent from roadkill specimens available. The dog achieved a 98% specificity rate towards the target scent, indicating that the dog was able to distinguish the scent of riverine rabbits from the scent of other lagomorph species. The dog has already been able to locate ten of these elusive individuals in the wild. The training method proved successful in the detection of this critically endangered species, where scent for training was only available from deceased specimens.
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.