Threshold switching in nickel-doped zinc oxide based memristor for artificial sensory applications.

Electronic devices featuring biomimetic behaviour as electronic synapses and neurons have motivated the emergence of a new era in information and humanoid robotics technologies. In the human body, a nociceptor is a unique sensory neuron receptor that is capable of detecting harmful signals, leading to the central nervous system initiating a motor response. Herein, a nickel-doped zinc oxide (NZO)/Au based memristor is fabricated for the first time and characterized for artificial nociceptor application. For this, the introduction of a nickel-doped zinc oxide (NZO) layer between P++-Si and Au electrodes is used to eliminate the surface effects of the NZO layer, resulting in improved volatile threshold switching performance. Depending on the intensity, duration, and repetition rate of the external stimuli, this newly created memristor exhibits various critical nociceptive functions, including threshold, relaxation, allodynia, and hyperalgesia. The electron trapping/detrapping to/from the traps in the NZO layer is responsible for these nociceptive properties. This kind of NZO-based device produces a multifunctional nociceptor performance that is essential for applications in artificial intelligence systems, such as neural integrated devices with nanometer-sized features.

An Analysis of Image Features Extracted by CNNs to Design Classification Models for COVID-19 and Non-COVID-19.

The SARS-CoV-2 virus causes a respiratory disease in humans, known as COVID-19. The confirmatory diagnostic of this disease occurs through the real-time reverse transcription and polymerase chain reaction test (RT-qPCR). However, the period of obtaining the results limits the application of the mass test. Thus, chest X-ray computed tomography (CT) images are analyzed to help diagnose the disease. However, during an outbreak of a disease that causes respiratory problems, radiologists may be overwhelmed with analyzing medical images. In the literature, some studies used feature extraction techniques based on CNNs, with classification models to identify COVID-19 and non-COVID-19. This work compare the performance of applying pre-trained CNNs in conjunction with classification methods based on machine learning algorithms. The main objective is to analyze the impact of the features extracted by CNNs, in the construction of models to classify COVID-19 and non-COVID-19. A SARS-CoV-2 CT data-set is used in experimental tests. The CNNs implemented are visual geometry group (VGG-16 and VGG-19), inception V3 (IV3), and EfficientNet-B0 (EB0). The classification methods were k-nearest neighbor (KNN), support vector machine (SVM), and explainable deep neural networks (xDNN). In the experiments, the best results were obtained by the EfficientNet model used to extract data and the SVM with an RBF kernel. This approach achieved an average performance of 0.9856 in the precision macro, 0.9853 in the sensitivity macro, 0.9853 in the specificity macro, and 0.9853 in the F1 score macro.

Understanding Variations in the Tracking and Erosion Performance of HTV-SR-Based Composites due to AC-Stressed Aging.

Among the polymeric family, high-temperature-vulcanized silicone rubber (HTV-SR) is the most deployed material for high voltage insulation applications. However, in an outdoor environment, due to contamination and wetting-induced dry band arcing, consequently SR experiences surface tracking and erosion. From a practical standpoint, the tracking and erosion performance under multi-stress aging is required to be known. It is in that context that the present study was undertaken to measure and analyze the effect of multi-stress aging on tracking and erosion performance. Composite samples of SR having different filler concentrations of silica and alumina trihydroxide (ATH) were aged in a multi-stress chamber for a period of 5000 h, and after that their electrical tracking performance was studied. Simultaneously, unaged samples were also exposed to tracking test for comparison. To conduct this test, the inclined plane testing technique was used in accordance with IEC-60587. All samples exposed to tracking test were analyzed using different diagnostic and measuring techniques involving surface leakage current measurement, Fourier transform infrared spectroscopy (FTIR), thermal stability and hydrophobicity classification. Experimental results shown that the tracking lifetime increased through incorporation of silica and ATH fillers in the SR. Amongst all test samples, two samples designated as filled with 2% nano silica and 20% micro silica/ATH exhibited greater resistance to tracking. This was attributed to the optimum loading as well as better dispersion of the fillers in the polymer matrix. The presence of nano-silica enhanced time-to-tracking failure, owing to both improved thermal stability and enhanced shielding effect on the surface of nanocomposite insulators.