Pritchard Beck (squidfarmer83)

Our results highlight that the application of a gate insulator that shows superior immunity to the γ-ray irradiation is a crucial factor for the improvement of the total ionizing dose effect in GaN-based MIS-HEMTs.Salmon is a highly perishable food due to temperature, pH, odor, and texture changes during cold storage. Intelligent monitoring and spoilage rapid detection are effective approaches to improve freshness. The aim of this work was an evaluation of IoT-enabled monitoring system (IoTMS) and electronic nose spoilage detection for quality parameters changes and freshness under cold storage conditions. The salmon samples were analyzed and divided into three groups in an incubator set at 0 °C, 4 °C, and 6 °C. The quality parameters, i.e., texture, color, sensory, and pH changes, were measured and evaluated at different temperatures after 0, 3, 6, 9, 12, and 14 days of cold storage. The principal component analysis (PCA) algorithm can be used to cluster electronic nose information. Furthermore, a Convolutional Neural Networks and Support Vector Machine (CNN-SVM) based algorithm is used to cluster the freshness level of salmon samples stored in a specific storage condition. In the tested samples, the results show that the training dataset of freshness is about 95.6%, and the accuracy rate of the test dataset is 93.8%. For the training dataset of corruption, the accuracy rate is about 91.4%, and the accuracy rate of the test dataset is 90.5%. The overall accuracy rate is more than 90%. This work could help to reduce quality loss during salmon cold storage.In recent years, maintenance work on public transport routes has drastically decreased in many countries due to difficult economic situations. The various studies that have been conducted by groups of drivers and groups related to road safety concluded that accidents are increasing due to the poor conditions of road surfaces, even affecting the condition of vehicles through costly breakdowns. click here Currently, the processes of detecting any type of damage to a road are carried out manually or are based on the use of a road vehicle, which incurs a high labor cost. To solve this problem, many research centers are investigating image processing techniques to identify poor-condition road areas using deep learning algorithms. The main objective of this work is to design of a distributed platform that allows the detection of damage to transport routes using drones and to provide the results of the most important classifiers. A case study is presented using a multi-agent system based on PANGEA that coordinates the different parts of the architecture using techniques based on ubiquitous computing. The results obtained by means of the customization of the You Only Look Once (YOLO) v4 classifier are promising, reaching an accuracy of more than 95%. The images used have been published in a dataset for use by the scientific community.The present study investigated the effects of reactive microglia/macrophages-derived interleukin-4 (IL-4) on hippocampal neurons in prothrombin kringle-2 (pKr-2)-lesioned rats. pKr-2 was unilaterally injected into hippocampus in the absence or presence of IL-4 neutralizing antibody (IL-4Nab). Immunohistochemical analysis showed a significant loss of Nissl+ and NeuN+ cells and activation of microglia/macrophages (increase in reactive OX-42+ and OX-6+ cells) in the hippocampus at 7 days after pKr-2 injection. The levels of IL-4 expression were upregulated in the reactive OX-42+ microglia/macrophages as early as 1 day, maximal at 3 days and maintained up to 7 days after pKr-2 injection. Treatment with IL-4Nab significantly increased neuronal survival in pKr-2-treated CA1 layer of hippocampus in vivo. Accompanying neuroprotection, IL-4 neutralization inhibited activation of microglia/macrophages, reactive oxygen species-derived oxidative damages, production of myeloperoxidase- and inducible nitric oxide synthase-derived reactive nitrogen species and nitr