This article explored the effect of physical activity time and that of the heart rate during physical activity on mental fatigue intervention. First, we acquired electrocardiogram from 104 subjects, calculated the inter-beat intervals (IBI), removed the abnormal values in the IBI series, segmented and divided the IBI series into training and validation datasets and application datasets according to the inclusion criteria of mental fatigued and non-fatigued states. Second, we extracted 39 linear and non-linear RR parameters as fatigue physiological features, and applied Leave-One-Subject-Out cross test and Sequential Backward Selection algorithm for feature selection while training some traditional classifiers. Third, we applied the best trained classifier to detect mental fatigue before and after physical activity. Finally, we analyzed the change of mental fatigue status before and after physical activity and obtained the following three findings: (1) physical activity did not have intervention effect on mental fatigue for 62% observed cases; (2) for 38% observed cases, physical activity could aggravate or relieve mental fatigue; (3) for the observed cases that the physical activity time was in the range of 5-15 minutes, the average heart rate in 100-140 beat per minute during physical activity was more likely to relieve mental fatigue
Face recognition algorithm is a very promising technique in biometric authentication. However, the recognition precision can be affected by many factors, such as feature extraction method and classifier selection. In this paper, a novel algorithm for face recognition is presented according to the advances of the wavelet decomposition technique and the Support Vector Machines (SVM) model. The extracted features from human images by wavelet decomposition are less sensitive to facial expression variation. As a classifier, SVM provides high generation performance without transcendental knowledge. First, we detect the face region using an improved AdaBoost algorithm. Second, we extract the appropriate features of the face by wavelet decomposition, and compose the face feature vectors as input to SVM. Third, we train the SVM model by the face feature vectors, and then use the trained SVM model to classify the human face. In the training process, three different kernel functions are adopted: Radial basis function, Polynomial and Linear kernel function. Finally, we present a face recognition system that can achieve high recognition precision and fast recognition speed in practice. Experimental results indicate that the proposed method can achieve recognition precision of 96.78 percent based on 96 persons in Ren-FEdb database that is higher than other approaches.
Electrochemical nitrate reduction (NO3−RR) has been recognized as a promising strategy for sustainable ammonia (NH3) production due to its environmental friendliness and economical nature. However, the NO3−RR reaction involves an eight-electron coupled proton transfer process with many by-products and low Faraday efficiency. In this work, a molybdenum oxide (MoOx)-decorated titanium dioxide nanotube on Ti foil (Mo/TiO2) was prepared by means of an electrodeposition and calcination process. The structure of MoOx can be controlled by regulating the concentration of molybdate during the electrodeposition process, which can further influence the electron transfer from Ti to Mo atoms, and enhance the binding energy of intermediate species in NO3−RR. The optimized Mo/TiO2-M with more Mo(IV) sites exhibited a better activity for NO3−RR. The Mo/TiO2-M electrode delivered a NH3 yield of 5.18 mg h−1 cm−2 at −1.7 V vs. Ag/AgCl, and exhibited a Faraday efficiency of 88.05% at −1.4 V vs. Ag/AgCl. In addition, the cycling test demonstrated that the Mo/TiO2-M electrode possessed a good stability. This work not only provides an attractive electrode material, but also offers new insights into the rational design of catalysts for NO3−RR.
