High-strength, flexible, and multifunctional characteristics are highly desirable for electromagnetic interference (EMI) shielding materials in the field of electric devices. In this work, inspired by natural nacre, we fabricated large-scale, layered MXene/amarid nanofiber (ANF) nanocomposite papers by blade-coating process plus sol–gel conversion step. The as-synthesized papers possess excellent mechanical performance, that is, exceptional tensile strength (198.80 ± 5.35 MPa), large strain (15.30 ± 1.01%), and good flexibility (folded into various models without fracture), which are ascribed to synergetic interactions of the interconnected three-dimensional network frame and hydrogen bonds between MXene and ANF. More importantly, the papers with extensive continuous conductive paths formed by MXene nanosheets present a high EMI shielding effectiveness of 13188.2 dB cm2 g–1 in the frequency range of 8.2–12.4 GHz. More interestingly, the papers show excellent Joule heating performance with a fast thermal response (<10 s) and a low driving voltage (≤4 V). As such, the large-scale MXene/ANF papers are considered as promising alternatives in a wide range of applications in electromagnetic shielding and thermal management.

Background. Due to the redundant information contained in multichannel electroencephalogram (EEG) signals, the classification accuracy of brain-computer interface (BCI) systems may deteriorate to a large extent. Channel selection methods can help to remove task-independent electroencephalogram (EEG) signals and hence improve the performance of BCI systems. However, in different frequency bands, brain areas associated with motor imagery are not exactly the same, which will result in the inability of traditional channel selection methods to extract effective EEG features. New Method. To address the above problem, this paper proposes a novel method based on common spatial pattern- (CSP-) rank channel selection for multifrequency band EEG (CSP-R-MF). It combines the multiband signal decomposition filtering and the CSP-rank channel selection methods to select significant channels, and then linear discriminant analysis (LDA) was used to calculate the classification accuracy. Results. The results showed that our proposed CSP-R-MF method could significantly improve the average classification accuracy compared with the CSP-rank channel selection method.

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The adaptability to wide salinities remains a big challenge for artificial nanofluidic systems, which plays a vital role in water–energy nexus science. Here, inspired by euryhaline fish, sandwich‐structured nanochannel systems are constructed to realize salinity self‐adaptive nanofluidic diodes, which lead to high‐performance salinity‐gradient power generators with low internal resistance. Adaptive to changing salinity, the pore morphology of one side of the nanochannel system switches from a 1D straight nanochannel (45 nm) to 3D network pores (1.9 nm pore size and ≈1013 pore density), along with three orders of magnitude change for charge density. Thus, the abundant surface charges and narrow pores render the membrane‐based osmotic power generator with power density up to 26.22 Wm−2. The salinity‐adaptive membrane solves the surface charge‐shielding problem caused by abundant mobile ions in high salinity and increases the overlapping degree of the electric double layer. The dynamic adaption process of the membrane to the hypersaline environment endows it with good salt endurance and stability. New routes for designing nanofluidic devices functionally adaptable to different salinities and building power generators with excellent salt endurance are demonstrated.

To prevent short-circuits between the upper and lower switches of power converters from over-current protection, the dead time is mandatory in the switching gating signal for voltage source converters. However, this results in many negative effects on system operations, such as output voltage and current distortions (e.g., increased level of fifth and seventh harmonics), zero-current-clamping phenomenon, and output fundamental-frequency voltage reduction. Many solutions have been presented to cope with this problem. First, the dead-time effect is analyzed by taking into account factors such as the zero-clamping phenomenon, voltage drops on diodes and transistors, and the parameters of inverter loads, as well as the parasitic nature of semiconductor switches. Second, the state-of-the-art dead-time compensation algorithms are presented in this paper. Third, the advantages and disadvantages of existing algorithms are discussed, together with the future trends of dead-time compensation algorithms. This article provides a complete scenario of dead-time compensation with control strategies for voltage source converters for researchers to identify suitable solutions based on demand and application.

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Few-shot video classification aims to learn new video categories with only a few labeled examples, alleviating the burden of costly annotation in real-world applications. However, it is particularly challenging to learn a class-invariant spatial-temporal representation in such a setting. To address this, we propose a novel matching-based few-shot learning strategy for video sequences in this work. Our main idea is to introduce an implicit temporal alignment for a video pair, capable of estimating the similarity between them in an accurate and robust manner. Moreover, we design an effective context encoding module to incorporate spatial and feature channel context, resulting in better modeling of intra-class variations. To train our model, we develop a multi-task loss for learning video matching, leading to video features with better generalization. Extensive experimental results on two challenging benchmarks, show that our method outperforms the prior arts with a sizable margin on Something-Something-V2 and competitive results on Kinetics.

The nanopore technique employs a nanoscale cavity to electrochemically confine individual molecules, achieving ultrasensitive single-molecule analysis based on evaluating the amplitude and duration of the ionic current. However, each nanopore sensing interface has its own intrinsic sensing ability, which does not always efficiently generate distinctive blockade currents for multiple analytes. Therefore, analytes that differ at only a single site often exhibit similar blockade currents or durations in nanopore experiments, which often produces serious overlap in the resulting statistical graphs. To improve the sensing ability of nanopores, herein we propose a novel shapelet-based machine learning approach to discriminate mixed analytes that exhibit nearly identical blockade current amplitudes and durations. DNA oligomers with a single-nucleotide difference, 5′-AAAA-3′ and 5′-GAAA-3′, are employed as model analytes that are difficult to identify in aerolysin nanopores at 100 mV. First, a set of the most informative and discriminative segments are learned from the time-series data set of blockade current signals using the learning time-series shapelets (LTS) algorithm. Then, the shapelet-transformed representation of the signals is obtained by calculating the minimum distance between the shapelets and the original signals. A simple logistic classifier is used to identify the two types of DNA oligomers in accordance with the corresponding shapelet-transformed representation. Finally, an evaluation is performed on the validation data set to show that our approach can achieve a high F 1 score of 0.933. In comparison with the conventional statistical methods for the analysis of duration and residual current, the shapelet-transformed representation provides clearly discriminated distributions for multiple analytes. Taking advantage of the robust LTS algorithm, one could anticipate the real-time analysis of nanopore events for the direct identification and quantification of multiple biomolecules in a complex real sample (e.g., serum) without labels and time-consuming mutagenesis.