In Hungary, during the 2000s, pesticide poisoning became the most important threat for raptors, especially for the globally threatened Eastern imperial eagle (Aquila heliaca). In September 2013, with a focus on carbofuran and phorate, the first poison and carcass detection dog (PCDD) unit was formed in Hungary with a specifically trained detection dog and handler. Two more dogs were subsequently trained and joined the unit in 2017 and 2020 respectively. Between its inception until August 2020, the PCDD unit conducted 1,083 searches in five countries, which revealed 329 poisoned animals of 15 bird and nine mammal species, 120 poisoned baits and five pesticide products. Globally threatened species, including eight Eastern imperial eagles and four saker falcons (Falco cherrug), were among the detected victims. Present at 66.45% of wildlife poisoning events, the unit revealed 37.87% of the victims and 79.70% of the poisoned baits known in Hungary during the period 2013-2020. Compared to human surveys, the PCDD unit demonstrated a significantly higher find rate for poisoned baits. At 22 poisoning events (14.38% of all cases) only the PCDD unit revealed victims or poisoned baits; cases that would probably have gone undetected without the PCCD unit. Of the two focal pesticides, carbofuran was more frequently detected – in 88.56% of the positive samples. The unit played a significant role in detecting and combating wildlife poisoning incidents by deterring potential offenders and facilitating police investigations through retrieval of evidence otherwise difficult to obtain.
This article provides a critique of the use of Esping-Andersen and Kemeny’s typologies of welfare and housing regimes, both of which are often used as starting points for country selections in comparative housing research. We find that it is conceivable that housing systems may reflect the wider welfare system or diverge from it, so it is not possible to “read across” a housing system from Esping-Andersen’s welfare regimes. Moreover, both are dated and require revisiting to establish whether they still reflect reality. Of the two frameworks, Esping-Andersen’s use of the state-market-family triangle is more geographically mobile. Ultimately, housing systems are likely to be judged on the “housing outcomes” that they produce. However, it is suggested that current use of variables within EU-SILC in order to establish “housing outcomes” may be misleading since they do not reflect acceptable standards between countries with greatly differing general living standards and cultural norms.
Relations between two Boolean attributes derived from data can be
quantified by truth functions defined on four-fold tables corresponding to pairs of the attributes. Several classes of such quantifiers (implicational, double implicational, equivalence ones) with truth values in the unit interval were investigated in the frame of the theory of data mining methods. In the fuzzy logic theory, there are well-defined classes of fuzzy operators, namely t-norms representing various types of evaluations of fuzzy conjunction (and t-conorms representing fuzzy disjunction), and operators of fuzzy implications.
In the contribution, several types of constructions of quantifiers using fuzzy operators are described. Definitions and theorems presented by the author in previous contributions to WUPES workshops are summarized and illustrated by examples of well-known quantifiers and operators.
There is growing interest to analyze electroencephalogram (EEG) signals with the objective of classifying schizophrenic patients from the control subjects. In this study, EEG signals of 15 schizophrenic patients and 19 age-matched control subjects are recorded using twenty surface electrodes. After the preprocessing phase, several features including autoregressive (AR) model coefficients, band power and fractal dimension were extracted from their recorded signals. Three classifiers including Linear Discriminant Analysis (LDA), Multi-LDA (MLDA) and Adaptive Boosting (Adaboost) were implemented to classify the EEG features of schizophrenic and normal subjects. Leave-one (participant)-out cross validation is performed in the training phase and finally in the test phase; the results of applying the LDA, MLDA and Adaboost respectively provided 78%, 81% and 82% classification accuracies between the two groups. For further improvement, Genetic Programming (GP) is employed to select more informative features and remove the redundant ones. After applying GP on the feature vectors, the results are remarkably improved so that the classification rate of the two groups with LDA, MLDA and Adaboost classifiers yielded 82%, 84% and 93% accuracies, respectively.
Giant pandas (Ailuropoda melanoleuca) are now confined to fragmented habitats in western China, with more than 60 % of individuals inhabiting 63 protected areas. Knowledge of the environmental features required by giant pandas is critically important for protected area spatial arrangement and subsequent assessments. Here we developed a distribution model for giant pandas in the Tangjiahe Nature Reserve using Ecological Niche Factor Analysis (ENFA) model. We found that less than 40 % of this key reserve is of high suitability for giant pandas, highly suitable habitat being primarily characterized as coniferous forests away from roads within the reserve. Although there was a clear core zone occupied by giant pandas, which included the vast majority of known giant panda locations, only about 45 % of this zone was classified as highly suitable habitat (suitable and optimal). Therefore, the spatial arrangement within the reserve may need to be modified to effectively manage the remaining population of giant pandas. Of particular concern are several tourism proposals being considered by local government, which, if implemented, will increase the isolation of the local population from those in the surrounding area. Our analysis identifies Caijiaba and Baixiongping as areas that should become conservation priorities. Our approach provides valuable data to advise conservation policy and could be easily replicated across other protected areas.
Integrated Population Modelling (IPMs) is a computational method for estimating population and demographic parameters that can improve precision relative to traditional methods. Here we compare the precision of IPM to traditional mark-recapture analysis to estimate population parameters in the common dormouse (Muscardinus avellanarius). This species is relatively rare across its European range and field estimation of demographic parameters can be challenging, as several parts of the life history are difficult to observe in the field. We develop an IPM model incorporating dormouse nest counts and offspring counts, which is data often recorded as a standard part of dormouse nest box monitoring. We found a significant improvement in precision in the estimation of demographic parameters using IPM compared to standard mark-recapture estimation. We discuss our results in the context of common dormouse conservation monitoring.
Mass releases of Trichogramma confusum Viggiani and T. maidis Pintureau & Voegele are widely used to control lepidopterous pests. They have long been considered to be the subspecies of T. chilonis Ishii and T. brassicae Bezdenko, respectively. To re-examine the taxonomic status of these closely related Trichogramma species, the internally transcribed spacer 2 (ITS2) of ribosomal DNA was used as a molecular marker to detect between-species differences. The ITS2 regions of 7 different Trichogramma species collected from China, Germany and France were sequenced and the inter-species distances were calculated. To quantify within-species sequence variation, the ITS2 regions of 6 geographical populations of T. dendrolimi Matsumura collected from across China were sequenced and compared. The results show that the ITS2 sequences of T. confusum and T. maidis are sufficiently different from those of T. chilonis and T. brassicae, respectively, that it is difficult to group them as cryptic species, whereas there are only minor differences between the T. dendrolimi populations. The ITS2 sequences identified in this study, coupled with 67 ITS2 sequences from a wide geographical distribution retrieved from GenBank, were then used for phylogenetic analyses. The results support previous records of minor within-species ITS2 sequence divergence and distinct interspecies differences. The cladograms show the T. maidis sequence clustered within T. evanescens Westwood, while the ITS2 sequences of T. confusum and T. chilonis are clustered in different branches. Taken together, these data suggest that T. maidis is not T. brassicae, but a cryptic or sibling species of T. evanescens; T. confusum and T. chilonis are not cryptic species but two closely related sister species.
Several algorithms have been developed for time series forecasting. In this paper, we develop a type of algorithm that makes use of the numerical methods for optimizing on objective function that is the Kullbak-Leibler divergence between the joint probability density function of a time series xi, X2, Xn and the product of their marginal distributions. The Grani-charlier expansion is ušed for estimating these distributions.
Using the weights that have been obtained by the neural network, and adding to them the Kullback-Leibler divergence of these weights, we obtain new weights that are ušed for forecasting the new value of Xn+k.
The paper deals with application of MF-ARTMAP neural network on
financial fraud data. The focus was on classification of data into 5 types of fraud based on expert knowledge with the aim to achieve the tool with highest classification accuracy. The fraud was characterized by 22 features and the verbal features were encoded into numerical values to be able to use them in the classification proceduře. The results show that in the čase of sufficient data (fraud) representation neural networks could be used with success; in case there are rather small examples, expert generated rules are preferred.