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Abstract Wearable electronics presage a future in which healthcare monitoring and rehabilitation are enabled beyond the limitation of hospitals, and self‐powered sensors and energy generators are key prerequisites for a self‐sustainable wearable system. A triboelectric nanogenerator (TENG) based on textiles can be an optimal option for scavenging low‐frequency and irregular waste energy from body motions as a power source for self‐sustainable systems. However, the low output of most textile‐based TENGs (T‐TENGs) has hindered its way toward practical applications. In this work, a facile and universal strategy to enhance the triboelectric output is proposed by integration of a narrow‐gap TENG textile with a high‐voltage diode and a textile‐based switch. The closed‐loop current of the diode‐enhanced textile‐based TENG (D‐T‐TENG) can be increased by 25 times. The soft, flexible, and thin characteristics of the D‐T‐TENG enable a moderate output even as it is randomly scrunched. Furthermore, the enhanced current can directly stimulate rat muscle and nerve. In addition, the capability of the D‐T‐TENG as a practical power source for wearable sensors is demonstrated by powering Bluetooth sensors embedded to clothes for humidity and temperature sensing. Looking forward, the D‐T‐TENG renders an effective approach toward a self‐sustainable wearable textile nano‐energy nano‐system for next‐generation healthcare applications.

A rapid and nondestructive method is greatly important for the classification of aflatoxin B1 (AFB1) concentration of single maize kernel to satisfy the ever-growing needs of consumers for food safety. A novel method for classification of AFB1 concentration of single maize kernel was developed on the basis of the near-infrared (NIR) hyperspectral imaging (1100–2000 nm). Four groups of AFB1 samples with different concentrations (10, 20, 50, and 100 ppb) and one group of control samples were prepared, which were preprocessed with Savitzky–Golay (SG) smoothing and first derivative (FD) algorithms for their raw NIR spectra. A key wavelength selection method, combining the variance and order of average spectral intensity, was proposed on the basis of pretreated spectra. Moreover, principal component analysis (PCA) was conducted to reduce the dimensionality of hyperspectral data. Finally, a classification model for AFB1 concentrations was developed through linear discriminant analysis (LDA), combined with five key wavelengths and the first three PCs. The results show that the proposed method achieved an ideal performance for classifying AFB1 concentrations in a single maize kernel with overall accuracy, with an F1-score and Kappa values of 95.56%, 0.9554, and 0.9444, respectively, as well as the test accuracy yield of 88.67% for independent validation samples. The combinations of variance and order of average spectral intensity can be used for key wavelength selection which, combined with PCA, can achieve an ideal dimensionality reduction effect for model development. The findings of this study have positive significance for the classification of AFB1 concentration of maize kernels.

In order to analyze the potential vulnerabilities in the firmware systematically and effectively,a formal verification method based on TLA,in a collaborated form of software and hardware was proposed.With this method,the interaction mechanism of software and hardware in the computer boot process was modeled and analyzed.By adjusting the attack model,a secure vulnerability in the update process of the firmware was found,and its existence by an experiment,which proved the reliability of formal verification was demonstrated.

We demonstrate control of wireless power transfer to an implanted bioelectronic device using harmonic feedback from the backscattered field. The feedback mechanism relies only on inherent nonlinearities in the receiver circuit and can provide a direct measurement of the extracted power without any modification of the device. We use the method to control an implanted light-emitting diode while varying the coupling between the source and receiver.

Here, we report the extraordinary electrochemical energy storage capability of NiMoO4@NiMoO4 homogeneous hierarchical nanosheet-on-nanowire arrays (SOWAs), synthesized on nickel substrate by a two-stage hydrothermal process. Comparatively speaking, the SOWAs electrode displays superior electrochemical performances over the pure NiMoO4 nanowire arrays. Such improvements can be ascribed to the characteristic homogeneous hierarchical structure, which not only effectively increases the active surface areas for fast charge transfer, but also reduces the electrode resistance significantly by eliminating the potential barrier at the nanowire/nanosheet junction, an issue usually seen in other reported heterogeneous architectures. We further evaluate the performances of the SOWAs by constructing an asymmetric hybrid supercapacitor (ASC) with the SOWAs and activated carbon (AC). The optimized ASC shows excellent electrochemical performances with 47.2 Wh/kg in energy density of 1.38 kW/kg at 0−1.2 V. Moreover, the specific capacity retention can be as high as 91.4% after 4000 cycles, illustrating the remarkable cycling stability of the NiMoO4@NiMoO4//AC ASC device. Our results show that this unique NiMoO4@NiMoO4 SOWA has great prospects for future energy storage applications.

The COVID-19 pandemic has been a source of conflict between liberals and conservatives in the U.S., with many politicized debates focusing on college students and universities. To understand this partisan conflict and how it might be mitigated, one useful approach is to examine how collective sensemaking about the virus and virus response, as reflected in language use, has differed between different political groups. Using semantic network analysis of a corpus of college students' descriptions of their worries about COVID-19, we found that there were many similarities in sensemaking across the political spectrum, but also important differences between ideological groups. In particular, collective sensemaking for conservative students (more so than for liberal and moderate students) was organized around words related to anxiety and close personal relationships. These results have implications for addressing partisan intergroup conflict about COVID-19. [ABSTRACT FROM AUTHOR]