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This work presents a hybrid tissue-anchoring mechanism that combines a pre-compressed microspring for autonomous actuation and a barbed microneedle (MN) for robust tissue anchoring with minimized preloading. The spring-microneedle unit (SMU) allows facile integration with potential ingestible resident systems to enable prolonged operation in the gastrointestinal (GI) tract (e.g. biomarker monitoring and therapeutic drug delivery). Utilizing high-precision direct laser writing (DLW), the SMU is directly fabricated on a flexible polyimide substrate, which that can be conveniently attached onto the exterior packages of ingestible devices to reduce the form factor and therefore to save room for multifunctional system components. The 3-D printed microspring can be compressed and embedded within a thin layer of dissolvable polymer (polyethylene glycol) and autonomously actuated with a ~140- $\mu \text{m}$ displacement upon polymer dissolution in aqueous media at 37°C. Mechanical calibration of the SMU demonstrated an average release force of 8 mN for a 100- $\mu \text{m}$ actuation. According to our previous research into barbed MNs, this actuation force provides a large enough preload for a MN to achieve robust tissue-anchoring. Above all, the novel and effective tissue-anchoring mechanism has a great potential for enabling long-term operation of miniature biosensing and drug delivery systems. [2020-0169] [ABSTRACT FROM AUTHOR]

Soil discharge occurs when it is subjected to high impulse current, and it is closely associated with the impulse performance of grounding systems. In this paper, a new method is put forward to visualize the inner impulse discharge region of simulate soil (fine sand) directly, using X-ray imaging technology. According to the three views of the discharge region, the boundary surface of the discharge region can be obtained based on image filtering and edge extraction by 3-D reconstruction. On this basis, the ionization parameters (i.e., the residual resistivity of soil discharge region \rhores and the critical breakdown electric-field strength E_c ) are calculated by a finite-element method. The results show that discharge region volume enlarges with water content increasing, and \rho_{0} . E_c$ increases from 100 to 150 kV/m, as soil water content decreases from 5% to 2%. [ABSTRACT FROM AUTHOR]

A penetrating X-ray imaging technology is applied to study the soil ionization under surge current, by which grayscale images are obtained. Based on soil breakdown mechanism, there are series of discharge channels generating in the soil. By comparing the discharge time and imaging time, the filamentary paths, which can be directly observed in these grayscale images, are determined to be discharge air channels. The principle and process of imaging are introduced in detail in this paper. According to the Beer–Lambert law, the relationship between grayscale value and equivalent air thickness of any points in the discharge channel area has been proposed. Image processing technology is used to dispose the grayscale images to make them more distinct for analysis without losing the accurate information of the original images by maintaining their grayscale value percentage. Analyses of experimental imaging results are also presented, and effects of factors, such as configuration of grounding electrode and amplitude of surge current, are considered on the distribution of discharge channels as well. In addition, soil residual resistivity of discharge area was calculated by a finite-element method, according to experiment parameters and 3-D diagram of discharge area. [ABSTRACT FROM AUTHOR]

This work presents novel solid microneedles (MNs) integrated with a complementary capillary system (CCS) that enables autonomous localization of therapeutic loading. The CCS-MN, realized by 3-D direct-laser-writing (DLW), displays a double-stacked cone structure with a unique top cone consisting of opposing capillary actions: 1) external/internal capillary channels for rapid wicking/loading of liquid contents, respectively, and 2) a hydrophobic doubly-reentrant structure located at the bottom circumference for autonomous liquid confinement at the MN tip. A $2\times2$ CCS-MN array achieved robust skin penetration, withstanding 90 mN force while penetrating $300~\mu \text{m}$ into porcine skin. Significantly enhanced localized liquid loading was confirmed with the integrated CCS-MN when compared to a conventional conical MN, showing a 3.3-fold increase in loaded liquid volume (10 nL vs. 3 nL) - in good agreement with the qualitative staining test on the porcine skin from the delivered dye. Combined, this work provides a unique MN design solution realized by the DLW technology that will significantly advance minimally invasive transdermal delivery platforms. [2020-0166] [ABSTRACT FROM AUTHOR]

This work presents a miniaturized electrochemical sensor-integrated bioprocess monitoring pod (bPod) that wirelessly monitors local dissolved oxygen (DO) saturation within bioreactors in real-time. The system comprises a compact printed circuit board (PCB) that integrates a potentiostat analog-front-end (AFE) and a Bluetooth Low Energy (BLE) microcontroller with an electrochemical DO sensor. In situ detection of DO within the bioreactor is enabled via a 3-D printed ABS-M30i shell, representing a robust and biocompatible packaging solution for prolonged monitoring. The platform is evaluated in a bench-scale bioreactor filled with aqueous media, and data is extracted wirelessly via a custom-developed application. A linear response corresponding to DO saturation levels was achieved using chronoamperometry (CA), exhibiting a dynamic voltage range between 1.3 V ($-4.9\,\,\mu \text{A}$) and 0.87 V ($-11.1\,\,\mu \text{A}$), corresponding to 0% and 100% DO saturation respectively, with a sensitivity of 6.3 mV/DO%. This work highlights a unique sensor assembly and packaging approach to overcome challenges for localized bioprocess monitoring platforms in bioreactors. [2020-0149] [ABSTRACT FROM AUTHOR]

There are multiple discharges in one breakdown during a lightning incident. A lightning incident usually contains 3–5 return strokes. In this case, the lightning incident will not be well simulated by the first stroke. To date, a single impulse current waveform has been used to simulate the lightning impulse incident in research on the lightning impulse characteristics of soils, leading to some differences with observations of actual lightning discharges. In this paper, a time-delayed control technique and a controlled gaps-group isolation technique were used to produce the multiple high-impulse currents discharge to a soil sample, while nonintrusive X-ray imaging technology was used to obtain images of the discharge channel in the soil. The impulse characteristics of the soil under multiple impulse currents are studied. The differences between the discharge characteristics of the soil under multiple impulse currents and a single pulse are compared. It can be found that there has been a reduction in the effective soil resistivity under multiple impulse currents, and this reduction will be recovered over time. At the same time, a brief analysis of the discharge mechanism of the soil under multiple impulse currents is proposed, based on analysis of X-ray images of discharge channels. [ABSTRACT FROM AUTHOR]

• The double convolutional neural network model for fault data identification is proposed in this paper. The identification rate of distribution network fault types is improved. • A novel convolutional auto-encoding network is used to realize the auto-extraction of the features. This method improves the feature extraction ability and efficiency of the network. • The F 1 score and robustness of the DCNN model were analysed. The optimal structural parameters of DCNN model were determined by parameter sensitivity analysis in discussion. The rapid and accurate identification of different types of faults in the power grid is of great significance to the stable operation of the power grid. An identification model of transient fault recording data for the distribution network based on a double convolutional neural network is proposed in this study. The 1-dimension convolutional auto-encoder (1-D CAE) is used to learn features from the power grid transient data. The obtained low-dimensional fault features are imported into the 1-dimension convolutional neural network (1-D CNN) identification model. The identification accuracy of the proposed model is higher than that of the traditional methods by the verification of the measured transient fault data of the power distribution network. The robustness study implies that the DCNN model can be applied in practical situations prone to using contaminated samples. [ABSTRACT FROM AUTHOR]