This exploration aims to promote the organic integration of innovation and entrepreneurship education and art education, further promote the reform of college Students’ cultural and aesthetic education, improve college Students’ aesthetic perception ability, and help contemporary colleges establish a correct political morality. This thesis aims to further promote the reform of college Students’ cultural and aesthetic education, improve college Students’ aesthetic perception ability, and help contemporary colleges establish correct political and moral values. First, the connotation of college Students’ aesthetic education and the definition of cultural aesthetics are introduced, which is based on the characteristics of two-way interaction, multiple selectivity, timeliness and popularization of film and television media in the new media era; then, the way of questionnaire is adopted. With five universities as the research object, 250 questionnaires are distributed, and 235 valid questionnaires are collected, with a valid response rate of 94%. Finally, through the six questions, it is concluded that 68.9% of the students watch 3–5 h a day, and 4.3% of the students watch more than 7 h; 89.4% of the students hold that the same products as stars in film and television will exert an impact on consumption. Film and television culture and art have a positive and negative impact on college Students’ cultural aesthetic perception. The positive impact is that the film and television media not only provides a good way to cultivate the aesthetic perception ability of contemporary college students, but also helps them to establish the correct aesthetic values. The negative impact is mainly reflected in two levels, namely, the vulgarization of film and television media works and the consumption of aesthetic concepts. The advantage of this exploration is to put forward the reform measures of college Students’ cultural and artistic aesthetic education under the current educational background in China to help colleges better carry out college Students’ cultural and artistic aesthetic education. Based on this, the reform measures of college Students’ cultural aesthetic education under the current education in China were put forward, so as to help colleges and universities better carry out college Students’ cultural aesthetic education.
Music is a form of art in which the sounds are timed and organized. Music is a kind of entertainment that mixes sounds in a way that people like, find fascinating, or to which they desire to dance. Most music is created via one or more people's vocal or instrumental efforts. By the dictionary, music is defined as having at least one of the following three elements: rhythm, melody, and harmony. Music is utilized in therapy because of its apparent benefits on behavior. Various physiological circumstances have different metabolite expression patterns that can be studied using pattern recognition in multimedia information processing. Music therapy includes various activities, including singing, playing instruments, dancing, and listening to music. Music-making with artificial intelligence (AI) uses neural networks, which are massive collections of computer bits that aim to stimulate brain activity. The neural network (NN) may be bombarded with music to see if it picks up on patterns the way the human brain does when repeatedly exposed to new stimuli. It will get the hang of them eventually. Experts believed that AI would be unable to generate music unless it first mimics a human-created data collection. By providing a conceptual paradigm for multimedia information processing. The end effect will be entirely different depending on how many hours of music are placed into it. For AI to learn from patterns or features in data on its own, it needs big data (BD), fast, repeated processing, and complex algorithms. The use of technology makes the process of creating analytical models much faster. A new AI-BD tool is an opportunity, not a danger for people currently working as artists. People are beginning to ask what constitutes acceptable work as AI grows more prominent in the music and art industries to gain efficiency of 97.8%. Future music will be heavily impacted by listeners' bodies and emotions all the time. For example, wearable technology may detect a person's mood and play the music that matches it. It is the next step in personalization. The AI-BD methodology improves the efficiency, accuracy, etc., compared to other existing models by gaining 97.8%, performance analysis 97.2%, reliability ratio 95.6%, and survivability analysis 98.2%.
The authors address the problem of tensor completion from limited samplings. An improved generalized tubal Kronecker decomposition is first proposed to reveal the tensor structure of the targeted data, and the improved generalized tensor tubal-rank and multi-rank are also introduced. The tensor completion problem is then formulated as the improved multi-rank minimisation problem. Instead of using the singular value decomposition(SVD), this work proposes an alternating optimisation method to update all of the subspaces of the data tensor for the recovery process, and thus it is computationally inexpensive. A rank-decreasing method is derived to reveal the tensor rank in order to avoid the troublesome user defined hyper-parameter selection. Experiments are carried out on both the synthetic data and the real datasets, and it is shown that the proposed approach can get a better performance than the state-of-the-art approaches with moderate computational complexity.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Chlorella is a unicellular spherical green microalga with alternate colors from blue green to yellowish or red due to different components of innate pigments. Light and salinity are two important environmental factors in Chlorella culture. Light conditions directly affect the growth and biochemical composition of microalgae, while salinity change could influence the pigment composition of Chlorella. Therefore, it has crucial research significance to monitor the response of Chlorella to salinity stress under different light conditions. Recently, Fluorescence Lifetime Imaging Microscopy (FLIM) technology has been widely applied into biological fields, providing fluorescence lifetime values for quantitative analysis. Here, FLIM method was used to observe the autofluorescence of a freshwater microalga, Chlorella sp.. Chlorella cells were treated with a series of salinity concentrations (control sample in normal culture medium, 3S sample with an additional 3× salinity, 7S sample with an additional 7× salinity, respectively) under light (12 h/12 h light/dark cycles) or dark (0 h/24 h light/dark cycles) treatments. After one day, images of the microalgae cells from each group were obtained with FLIM system, followed by an analysis with SPCImage software. The results showed that 3× salinity condition had little effect on Chlorella in both light/dark conditions, suggesting the adaptive capacity of Chlorella to seawater salinity. By contrast, the mean fluorescence lifetime values in 7S samples under light conditions were significantly decreased compared to that of the control. Interestingly, similar lifetime values were observed in 7S samples and the control samples under dark conditions, which indicated a potential high salinity resistance induced by different light/dark conditions. In conclusion, FLIM could work as a fast evaluation method of the physiological status of living Chlorella sp. under different culture conditions in a quantitative way.
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