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The hydrogen generated via the water splitting method is restricted by the high level of theoretical potential exhibited by the anode. The work focuses on synthesizing a bifunctional catalyst with a high efficiency, that is, a nickel phosphide doped with the reduced graphene oxide nanosheets supported on the Ni foam (Ni2P/rGO/NF), via the hydrothermal approach together with the calcination approach specific to the hydrogen evolution reaction (HER) and the oxygen evolution reaction (OER). The Raman, X-Ray Diffraction (XRD), X-ray Photoelectron Spectroscopy (XPS), Transmission Electron Microscope (TEM), Scanning Electron Microscopy (SEM), High-Resolution Transmission Electron Microscopy (HRTEM), as well as elemental mapping, are adopted to study the composition and morphology possessed by Ni2P/rGO/NF. The electrochemical testing is performed by constructing a parallel two-electrode electrolyzer (Ni2P/rGO/NF||Ni2P/rGO/NF). Ni2P/rGO/NF||Ni2P/rGO/NF needs a voltage of only 1.676 V for driving 10 mA/cm2, which is extremely close to Pt/C/NF||IrO2/NF (1.502 V). It is possible to maintain the current density for no less than 30 hours. It can be demonstrated that Ni2P/rGO/NF||Ni2P/rGO/NF has commercial feasibility, relying on the strong activity and high stability.This study aimed to design an optimal emotion recognition method using multiple physiological signal parameters acquired by bio-signal sensors for improving the accuracy of classifying individual emotional responses. Multiple physiological signals such as respiration (RSP) and heart rate variability (HRV) were acquired in an experiment from 53 participants when six basic emotion states were induced. Two RSP parameters were acquired from a chest-band respiration sensor, and five HRV parameters were acquired from a finger-clip blood volume pulse (BVP) sensor. A newly designed deep-learning model based on a convolutional neural network (CNN) was adopted for detecting the identification accuracy of individual emotions. Additionally, the signal combination of the acquired parameters was proposed to obtain high classification accuracy. Furthermore, a dominant factor influencing the accuracy was found by comparing the relativeness of the parameters, providing a basis for supporting the results of emotion classification. The users of this proposed model will soon be able to improve the emotion recognition model further based on CNN using multimodal physiological signals and their sensors.Near-field acoustic holography (NAH) based on equivalent source method (ESM) is an effective method for identifying sound sources. Conventional ESM focuses on relatively low frequencies and cannot provide a satisfactory solution at high frequencies. So its improved method called wideband acoustic holography (WBH) has been proposed, which has high reconstruction accuracy at medium-to-high frequencies. However, it is less accurate for coherent sound sources at low frequencies. To improve the reconstruction accuracy of conventional ESM and WBH, a sound source identification algorithm based on Bayesian compressive sensing (BCS) and ESM is proposed. https://www.selleckchem.com/products/deferoxamine-mesylate.html This method uses a hierarchical Laplace sparse prior probability distribution, and adaptively adjusts the regularization parameter, so that the energy is concentrated near the correct equivalent source. Referring to the function beamforming idea, the original algorithm with order v can improve its dynamic range, and then more accurate position information is obtained. Based on the simulation of irregular microphone array, comparisons with conventional ESM and WBH show that the proposed method is more accurate, suitable for a wider range of frequencies, and has better reconstruction performance for coherent sources. By increasing the order v, the coherent sources can be located accurately. Finally, the stability and reliability of the proposed method are verified by experiments.Warm-season legumes have been receiving increased attention as forage resources in the southern United States and other countries. However, the near infrared spectroscopy (NIRS) technique has not been widely explored for predicting the forage quality of many of these legumes. The objective of this research was to assess the performance of NIRS in predicting the forage quality parameters of five warm-season legumes-guar (Cyamopsis tetragonoloba), tepary bean (Phaseolus acutifolius), pigeon pea (Cajanus cajan), soybean (Glycine max), and mothbean (Vigna aconitifolia)-using three machine learning techniques partial least square (PLS), support vector machine (SVM), and Gaussian processes (GP). Additionally, the efficacy of global models in predicting forage quality was investigated. A set of 70 forage samples was used to develop species-based models for concentrations of crude protein (CP), acid detergent fiber (ADF), neutral detergent fiber (NDF), and in vitro true digestibility (IVTD) of guar and tepary bean focuracy of predictions of in vitro true digestibility (IVTD) for the different legumes was variable (R2cv/R2v = 0.42-0.98). Machine learning algorithms like SVM could help develop robust NIRS-based models for predicting forage quality with a relatively small number of samples, and thus needs further attention in different NIRS based applications.Strenuous exercise (any activity that expends six metabolic equivalents per minute or more causing sensations of fatigue and exhaustion to occur, inducing deleterious effects, affecting negatively different cells), induces muscle damage and hematological changes associated with high production of pro-inflammatory mediators related to muscle damage and sports anemia. The objective of this study was to determine whether short-term oral ubiquinol supplementation can prevent accumulation of inflammatory mediators and hematological impairment associated to strenuous exercise. For this purpose, 100 healthy and well-trained firemen were classified in two groups Ubiquinol (experimental group), and placebo group (control). The protocol was two identical strenuous exercise tests with rest period between tests of 24 h. Blood samples were collected before supplementation (basal value) (T1), after supplementation (T2), after first physical exercise test (T3), after 24 h of rest (T4), and after second physical exercise test (T5).