RESUMEN
3D printing, also known as additive manufacturing, is capable of fabricating 3D hierarchical micro/nanostructures by depositing a layer-upon-layer of precursor materials and solvent-based inks under the assistance of computer-aided design (CAD) files. 3D printing has been employed to construct 3D hierarchically micro/nanostructured electrodes for rechargeable batteries, endowing them with high specific surface areas, short ion transport lengths, and high mass loading. This review summarizes the advantages and limitations of various 3D printing methods and presents the recent developments of 3D-printed electrodes in rechargeable batteries, such as lithium-ion batteries, sodium-ion batteries, and lithium-sulfur batteries. Furthermore, the challenges and perspectives of the 3D printing technique for electrodes and rechargeable batteries are put forward. This review will provide new insight into the 3D printing of hierarchically micro/nanostructured electrodes in rechargeable batteries and promote the development of 3D printed electrodes and batteries in the future.
RESUMEN
An electrocardiogram (ECG) records the electrical activity of the heart; it contains rich pathological information on cardiovascular diseases, such as arrhythmia. However, it is difficult to visually analyze ECG signals due to their complexity and nonlinearity. The wavelet scattering transform can generate translation-invariant and deformation-stable representations of ECG signals through cascades of wavelet convolutions with nonlinear modulus and averaging operators. We proposed a novel approach using wavelet scattering transform to automatically classify four categories of arrhythmia ECG heartbeats, namely, nonectopic (N), supraventricular ectopic (S), ventricular ectopic (V), and fusion (F) beats. In this study, the wavelet scattering transform extracted 8 time windows from each ECG heartbeat. Two dimensionality reduction methods, principal component analysis (PCA) and time window selection, were applied on the 8 time windows. These processed features were fed to the neural network (NN), probabilistic neural network (PNN), and k-nearest neighbour (KNN) classifiers for classification. The 4th time window in combination with KNN (k = 4) has achieved the optimal performance with an averaged accuracy, positive predictive value, sensitivity, and specificity of 99.3%, 99.6%, 99.5%, and 98.8%, respectively, using tenfold cross-validation. Thus, our proposed model is capable of highly accurate arrhythmia classification and will provide assistance to physicians in ECG interpretation.