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Smart and wearable electromagnetic (EM) functional materials that can reasonably use EM energy and prevent EM pollution are of great significance to the future information society, but their design and preparation are still challenging. Here, a multifunctional EM response film is prepared based on a magnetic liquid metal droplet-filled elastomer with magnetic field-induced anisotropic structure, which can reversibly construct conductive network and tune heterogeneous interface during cyclic stretching. This film can withstand up to 600% tensile strain and shows stretching-enhanced electrical conductivity. Its EM interference shielding effectiveness (SE) significantly increases with stretching, and the relative SE change (ΔSE/SE0) shows a linear response to the tensile strain. The specific shielding effectiveness (an ultrahigh value of 404 dB mm−1 at 400% strain) and strain sensitivity of the film outperform other stretchable EM functional materials. More interestingly, this film exhibits rapid switching between EM wave transmission and shielding through continuous stretching, which enables an off/on switchable EM interference shielding function. Furthermore, it can be used in a wearable EM sensor to wirelessly monitor human movements by recording changes of EM wave intensity.

Attribution of climate change and human activities have been extensively discussed over the past few decades, particularly in urbanization area. However, the relationships among different factors may not be well explained by traditional statistical methods. In this study, we took one of the highly urbanized regions, Central Taihu Basin, as an example. Linear regression (LR), random forest (RF) and support vector machine (SVM) were used for regression and the attributions of climate change and human activities on water level alterations at different scales from 1961 to 2018 were quantified by residual analysis. The regression results indicated that SVM performed best. Water level at each scale showed an increasing trend and human activities were the dominant influence. The altered period was further divided into three sub-periods and human activities contributed the most in the sub-period II (2000–2009). Finally, the importance of thirty-eight factors were quantified by RF based on daily data series from 2008 to 2018. The results showed that cumulative antecedent precipitation (CAP) of five days was one of the important climate factors and daily maximum discharge of sluices were the important human activities factors. The methods and results of this study can help to provide support in flood control.

We present VideoLSTM for end-to-end sequence learning of actions in video. Rather than adapting the video to the peculiarities of established recurrent or convolutional architectures, we adapt the architecture to fit the requirements of the video medium. Starting from the soft-Attention LSTM, VideoLSTM makes three novel contributions. First, video has a spatial layout. To exploit the spatial correlation we hardwire convolutions in the soft-Attention LSTM architecture. Second, motion not only informs us about the action content, but also guides better the attention towards the relevant spatio-temporal locations. We introduce motion-based attention. And finally, we demonstrate how the attention from VideoLSTM can be exploited for action localization by relying on the action class label and temporal attention smoothing. Experiments on UCF101, HMDB51 and THUMOS13 reveal the benefit of the video-specific adaptations of VideoLSTM in isolation as well as when integrated in a combined architecture. It compares favorably against other LSTM architectures for action classification and especially action localization.

Background Amnestic mild cognitive impairment (aMCI) is an essential stage of early detection and potential intervention for Alzheimer’s disease (AD). Patients with aMCI exhibit partially abnormal functional brain connectivity and it is suggested that these features may represent a new diagnostic marker of early AD. New method In this paper, we constructed two brain network models, a phase synchronization index (PSI) undirected network and a directed transfer function (DTF) directed network, to evaluate the cognitive function in patients with aMCI. We then built SVM classification models using the network clustering coefficient, global efficiency and average node degree as features to distinguish between aMCI patients and controls. Results Our results reveal a classification accuracy and AUC of 66.6 ± 1.7% and 0.7475 and 80.0 ± 2.2% and 0.7825, respectively, for the two network models (PSI and DTF). As the directed network model performed better than the undirected model, we introduced an improved graph theory feature, efficiency density, which resulted in an increased classification accuracy and AUC value 86.6 ± 2.6% and 0.8295, respectively. Comparison with existing methods The analysis of network models and the directionality of information flow is suitable for analysis of nonlinear EEG signals for assessment of the functional state of the brain. Compared with traditional network features, our proposed improved features more comprehensively evaluate transmission efficiency and density of the brain. Conclusion In this study, we demonstrate that an improved efficiency density feature is helpful for enhancing classification the accuracy of aMCI. Moreover, directed brain network models exhibit better classification for aMCI diagnosis than undirected networks.

Objective To achieve the early diagnosis of amnestic mild cognitive impairment (aMCI), this paper proposes a multi-dimensional index, which combines the advantages of the multiscale fuzzy entropy (FuzzyEn) and phase locking value (PLV) based on electroencephalography (EEG). Methods The complexity and synchronization of the EEG were characterized using FuzzyEn and PLV in five frequency bands, respectively. By combining the two methods, the changes in the health of brain function were comprehensively analyzed. The extreme learning machine (ELM) method was used to classify aMCI patients based on a multi-dimensional index. Results Compared with aMCI patients, the multiscale FuzzyEn and PLV of normal controls (NC) were higher and statistically significant (P Concludes The multi-dimensional index based on prefrontal lobe could diagnosis cognitive decline of aMCI patients. Significance The results showed that features integrated multiscale FuzzyEn and PLV could be used as a biomarker of cognitive decline and help realize the early diagnosis of aMCI patients.

For families of piecewise expanding maps which converge to a map with a fixed or periodic turning point touching a branch with slope of modulus equal to or less than 2, the standard Lasota–Yorke argument fails to prove stability. It is the goal of this paper to use instead the harmonic average of slopes condition for a large class of maps satisfying the summable oscillation condition for the reciprocal of the derivative. Using Rychlik’s Theorem for a family of perturbations we prove weak compactness in L1 of the density functions associated with them. From this it follows that we have stability of absolutely continuous invariant measures of the limit map.

In this paper, the integral bifurcation method is used to study a nonlinearly dispersive wave equation of Camassa–Holm equation type. Loop soliton solution and periodic loop soliton solution, solitary wave solution and solitary cusp wave solution, smooth periodic wave solution and non-smooth periodic wave solution of this equation are obtained, their dynamic characters are discussed. Some solutions have an interesting phenomenon that one solution admits multi-waves when parameters vary.

In this paper, the Dullin–Gottwald–Holm equation is studied using semi-inverse method and integral bifurcation method. New periodic waves such as peakon-like periodic wave, compacton-like periodic wave and singular periodic wave are found and their dynamical behaviors and certain strange phenomena are explained using the proposed criterion. The exact parametric representations of these waves are also presented.