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Panoramic video provides immersive and interactive experience by enabling humans to control the field of view (FoV) through head movement (HM). Thus, HM plays a key role in modeling human attention on panoramic video. This paper establishes a database collecting subjects' HM in panoramic video sequences. From this database, we find that the HM data are highly consistent across subjects. Furthermore, we find that deep reinforcement learning (DRL) can be applied to predict HM positions, via maximizing the reward of imitating human HM scanpaths through the agent's actions. Based on our findings, we propose a DRL-based HM prediction (DHP) approach with offline and online versions, called offline-DHP and online-DHP. In offline-DHP, multiple DRL workflows are run to determine potential HM positions at each panoramic frame. Then, a heat map of the potential HM positions, named the HM map, is generated as the output of offline-DHP. In online-DHP, the next HM position of one subject is estimated given the currently observed HM position, which is achieved by developing a DRL algorithm upon the learned offline-DHP model. Finally, the experiments validate that our approach is effective in both offline and online prediction of HM positions for panoramic video, and that the learned offline-DHP model can improve the performance of online-DHP.

Eight heavy metals (Cu, Zn, Fe, Mn, Cd, Cr, Ni, and Pb) in 14 different wild-growing edible mushroom species (Coprinus comatus, Voluariella volvacea, Pleurotus nebrodensis, Hypsizigus marmoreus, Hericium erinaceus, Agrocybe aegerita, Lenfinus edodes, Collybia velutipes, Agaricus bisporus, Russula albida, Clitocybe conglobata, Pleurotus eryngii, Lepista sordida, and Pleurotus ostreatus) collected from Yunnan province, China, were determined by inductively coupled plasma-atomic emission spectrometry after microwave digestion. All element concentrations were determined on a dry weight basis. The ranges of element concentrations for copper, zinc, iron, manganese, cadmium, chromium, nickel, and lead were 6.8-31.9, 42.9-94.3, 67.5-843, 13.5-113, 0.06-0.58, 10.7-42.7, 0.76-5.1, and 0.67-12.9 mg/kg, respectively. In general, iron content was higher than other metals in all mushroom species. The levels of zinc, cadmium, and lead in some edible mushroom samples were found to be higher than legal limits. The relative standard deviations were found below 10%. The accuracy of procedure was confirmed by certified reference material.

To research the problems of the rolling bearing fault diagnosis under different noises and loads, a dual-input model based on a convolutional neural network (CNN) and long-short term memory (LSTM) neural network is proposed. The model uses both time domain and frequency domain features to achieve endto-end fault diagnosis. One-dimensional convolutional and pooling layers are utilized to extract the spatial features and retain the sequence features of the data. In addition, an LSTM layer is employed to extract the sequence features. Finally, a dense layer is applied for fault classification. To enhance recognition accuracy under different noises and loads, three techniques are applied to the proposed model, including taking timefrequency domain signals as input, using the CNN-LSTM model, and adopting the mini-batch and batch normalization methods. The Case Western Reserve University and Drivetrain Diagnostics Simulator data sets are used to construct experiments under different conditions, including varying loads and different noises. The proposed model can achieve a high fault recognition rate under variable load and noise conditions as well as satisfactory anti-noise and load adaptability.