Classical biological control is an important means of managing the increasing threat of invasive plants. It constitutes the introduction of natural enemies from the native range of the target plant into the invaded area. This method may be the only cost-effective solution to control the rapidly expanding common ragweed, Ambrosia artemisiifolia, in non-crop habitats in Europe. Therefore, candidate biocontrol agents urgently need to be assessed for their suitability for ragweed control in Europe. A previous literature review prioritized the host-specific leaf beetle Ophraella slobodkini as a candidate agent for ragweed control in Europe, whereas it rejected its oligophagous congener O. communa. Meanwhile, O. communa was accidentally introduced and became established south of the European Alps, and we show here that it is expanding its European range. We then present a short version of the traditional pre-release risk-benefit assessment for these two candidate agents to facilitate fast decision-making about further research efforts. We selected two complementary tests that can be conducted relatively rapidly and inform about essential risks and benefits. We conducted a comparative no-choice juvenile performance assay using leaves of ragweed and sunflower, the most important non-target plant, in Petri dishes in climatic conditions similar to that in the current European range of O. communa. This informs on the fundamental host range and potential for increasing abundance on these host plants. The results confirm that O. slobodkini does not survive on, and is hence unlikely to cause severe damage to sunflower, while O. communa can survive but develops more slowly on sunflower than on ragweed. In parallel, our species distribution models predict no suitable area for the establishment of O. slobodkini in Europe, while O. communa is likely to expand its current range to include a maximum of 18% of the European ragweed distribution. Based on this early assessment, the prioritization and further assessment of O. slobodkini seem unwarranted whereas the results urgently advocate further risk-benefit analysis of O. communa. Having revealed that most of the European area colonized by ragweed is unlikely to be suitable for these species of Ophraella we suggest the use of such relatively short and cheap preliminary assessment to prioritise other candidate agents or strains for these areas., Suzanne T. E. Lommen, Emilien F. Jolidon, Yan Sun, José I. Bustamante Eduardo, Heinz Müller-Schärer., and Obsahuje bibliografii
An effective and novel roller bearing fault diagnosis technique based on empirical mode decomposition (EMD) energy entropy and support vector machine (SVM) is put forward in this article. The vibration signal of roller bearing is decomposed by EMD and the first 5 intrinsic mode function (IMF) components are obtained. SVM served as a fault diagnosis classifier and the extracted energy features of the first 5 IMFs are taken as network input vectors, and then the fault bearing and the normal bearing can be distinguished. An technique for fault of roller bearing by SVM is evaluated against a series of fault diagnosis methods that are widely used in machinery, with particular regard to the effect of training set size on fault diagnosis accuracy. We trained the SVM using RBF kernel function. We compare our experimental results with the existing results given by SMO and SVM-light algorithms. It can be seen that the fault diagnosis method based on SVM-light is superior to that based on SMO in diagnosis accuracy of roller bearing. In addition to the SVM, the same datasets were classified using RBF NN and Hopfield NN. The experimental results show that the technique of support vector machine based on EMD energy entropy has higher fault diagnosis ability.
Designing a wavelet neural network (WNN) needs to be done judiciously in attaining the optimal generalization performance. Its prediction competence relies highly on the initial value of translation vectors. However, there is no established solution in determining the appropriate initial value for the translation vectors at this moment. In this paper, we propose a novel enhanced fuzzy c-means clustering algorithm - specifically, the modified point symmetry-based fuzzy c-means (MPSDFCM) algorithm - in initializing the translation vectors of the WNNs. The effectiveness of embedding different activation functions in WNNs will be investigated as well. The categorization effectiveness of the proposed WNNs model was then evaluated in classifying the type 2 diabetics, and was compared with the multilayer perceptrons (MLPs) and radial basis function neural networks (RBFNNs) models. Performance assessment shows that our proposed model outperforms the rest, since a 100% superior classification rate was achieved.
Color quantization is an important process for image processing and various applications. Up to now, many color quantization methods have been proposed. The self-organizing maps (SOM) method is one of the most effective color quantization methods, which gives excellent color quantization results. However, it is slow, so it is not suitable for real-time applications. In this paper, we present a color importance{based SOM color quantization method. The proposed method dynamically adjusts the learning rate and the radius of the neighborhood using color importance. This makes the proposed method faster than the conventional SOM-based color quantization method. We compare the proposed method to 10 well-known color quantization methods to evaluate performance. The methods are compared by measuring mean absolute error (MAE), mean square error (MSE), and processing time. The experimental results show that the proposed method is effective and excellent for color quantization. Not only does the proposed method provide the best results compared to the other methods, but it uses only 67.18% of the processing time of the conventional SOM method.