Only rational homology spheres admit Ω(f)\Omega(f) to be union of DE attractors

If there exists a diffeomorphism $f$ on a closed, orientable $n$-manifold $M$ such that the non-wandering set $\Omega(f)$ consists of finitely many orientable $(\pm)$ attractors derived from expanding maps, then $M$ must be a rational homology sphere; moreover all those attractors are of topological dimension $n-2$. Expanding maps are expanding on (co)homologies.Comment: 23 pages, 2 figure

Editorial: Polymer Solar Cells: Molecular Design and Microstructure Control

[No abstract available

Building socialist broadcasting with Chinese characteristics : the substance and contradictions of China's broadcasting policy in the Reform Era (1978-1994)

The denouement of the democratic movement in Tiananmen Square in 1989 shocked the whole world. Complex social, political, economic reasons precipitated this tragedy. This thesis attempts to explore the tension between economic liberalization and political totalitarianism, and how it caused increasing contradictions in China's broadcasting system. 'Building socialist broadcasting with Chinese characteristics' was created as a creed of faith to stifle broadcasting reform. The content, the ideological and theoretical bases of this concept will be disclosed. By using the integrative model of media and culture, broadcasting reform from 1978-1994 will be analyzed within the context of political and economic integration as a whole. The critique is mainly based on the libertarian theory of the press. Much attention is paid to the influence and determination of political power on broadcasting policy making. The main points of this thesis are as follows: The Chinese Communist Party's monopoly of and autocracy in broadcasting has become an obstacle to broadcasting reform; has been shaken by the tremendous economic decentralization, and should be replaced by libertarianism so as to meet the people's demand for information and to regain its credibilit

Wake redirection: Comparison of analytical, numerical and experimental models

This paper focuses on wake redirection techniques for wind farm control. Two control strategies are investigated: yaw misalignment and cyclic pitch control. First, analytical formulas are derived for both techniques, with the goal of providing a simple physical interpretation of the behavior of the two methods. Next, more realistic results are obtained by numerical simulations performed with CFD and by experiments conducted with scaled wind turbine models operating in a boundary layer wind tunnel. Comparing the analytical, numerical and experimental models allows for a cross-validation of the results and a better understanding of the two wake redirection techniques. Results indicate that yaw misalignment is more effective than cyclic pitch control in displacing the wake laterally, although the latter may have positive effects on wake recovery

Влияние докритических деформаций на сдвижение земной поверхности над очистной выработкой

Рассмотрены результаты маркшейдерских измерений горизонтальных смещений реперов наблюдательных станций над очистными выработками шахт Западного Донбасса. Показано, что на значительном удалении от границ мульды имеют место малые деформации, которые в сумме приводят к значительным сдвижениям наблюдаемых точек поверхности. Эти сдвижения достигают 20-30% от максимальных сдвижений в мульде

Eye-Tracking Signals Based Affective Classification Employing Deep Gradient Convolutional Neural Networks

Utilizing biomedical signals as a basis to calculate the human affective states is an essential issue of affective computing (AC). With the in-depth research on affective signals, the combination of multi-model cognition and physiological indicators, the establishment of a dynamic and complete database, and the addition of high-tech innovative products become recent trends in AC. This research aims to develop a deep gradient convolutional neural network (DGCNN) for classifying affection by using an eye-tracking signals. General signal process tools and pre-processing methods were applied firstly, such as Kalman filter, windowing with hamming, short-time Fourier transform (SIFT), and fast Fourier transform (FTT). Secondly, the eye-moving and tracking signals were converted into images. A convolutional neural networks-based training structure was subsequently applied; the experimental dataset was acquired by an eye-tracking device by assigning four affective stimuli (nervous, calm, happy, and sad) of 16 participants. Finally, the performance of DGCNN was compared with a decision tree (DT), Bayesian Gaussian model (BGM), and k-nearest neighbor (KNN) by using indices of true positive rate (TPR) and false negative rate (FPR). Customizing mini-batch, loss, learning rate, and gradients definition for the training structure of the deep neural network was also deployed finally. The predictive classification matrix showed the effectiveness of the proposed method for eye moving and tracking signals, which performs more than 87.2% inaccuracy. This research provided a feasible way to find more natural human-computer interaction through eye moving and tracking signals and has potential application on the affective production design process