Creativity is a specific higher brain function and integral for the evolution of human society. Creative inspirations generated during creative activities often solve complex problems. The human prefrontal cortex (PFC), related to creativity, is an important target to elucidate and control higher brain function. The present study examined the PFC activities when creative inspiration happened and analyzed the influence of brain activities on the quality of creation using the simplified music composition task and near-infrared spectroscopy (NIRS). A deep understanding of these brain functions may help build a foundation for developing methods to stimulate brain activation for creativity. We established a new simplified method of music composition to analyze the subjects’ brain activity and compared their brain activities before and after receiving creative inspirations. The correlation between brain activity and self-evaluation measured by visual analog scale (VAS) was investigated. We found that the right and ventral PFC were significantly activated by creative inspiration (p < 0.05). The activation area differed between the individuals with high creativity and those with low creativity. We did not observe a correlation between brain activity and self-evaluation (p > 0.05). In conclusion, the right and ventral PFC activation indicated an important mechanism of creativity.

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An accurate prediction time to the occurrence of calving events is important in the dairy cow reproduction process. In this aspect, the monitoring and automatic detecting of cow behaviors play a crucial role. Recently, there have been used various types of sensors to monitor the behaviors of cows. However, the sensor devices connected to cows are inhibited for the movement and emotion of cows. In this paper, the techniques of computer vision and image processing are proposed to detect the behaviors of cow motions under the 360-degree views of overhead cameras. To analyze cow motion behaviors, the first thing is to detect change point areas from frame sequences as a time series analysis. In addition, we utilized principle component analysis (PCA) to extract features having rotation invariant properties. We then perform the classification process to classify cow behaviors to be used for calving time prediction. Specifically, the beta features are used to detect transition changes of cows. In order to confirm the validity of the proposed method, we collected our own dataset and tested it. As the result of our experiment, the total average accuracy is nearly 93% in the case of detecting and classifying cow motions.

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Although quantitative metrics (e.g., walking distance and time) are undeniably significant for walking training, researchers often neglect the quality of walking (stride length, walking speed, and floor reaction force), which is greatly influenced by gait and trunk posture. This explains the absence of clear walking instructions and an appropriate quantitative analysis method for walking quality in current medical practice. Therefore, we propose an omnidirectional walking training robot that improves gait and corrects trunk posture for a better quantity and quality of walking. The robot features a specially designed traction device and an omnidirectional movement mechanism. The combination of these two elements allows the robot to move in any directions and adjust spine alignment. The robot is also clinically proven to be useful in enhancing the users’ balance functions, particularly for those of elderly people aged above 70 years old. The findings demonstrate the promising potential of our robot for practical applications in healthcare facilities.

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Although the Internet of Things (IoT) and cloud computing can help farmers deal with the efficiency evaluation problem in agriculture, there is little empirical analysis to verify its effectiveness. This paper formulates an IoT framework embedded with DEA to evaluate the greenhouse cucumber production efficiency. We use super efficiency data envelopment analysis (SEDEA) and the Malmquist-DEA index model to evaluate the greenhouse cucumber production efficiencies of 21 provinces (autonomous regions, municipalities) located in the eastern, northeastern, central and western regions of China. The results show that there are significant differences among these provinces (autonomous regions, municipalities). The highest efficiency is Hubei (1.94) and the lowest is Shanxi (0.64). There are regional differences, but the gap is narrow. Greenhouse cucumber average production efficiency is highest in the central region of China (1.068), and lowest in the northeast region of China (0.934). From 2011 to 2018, greenhouse cucumber production efficiency showed a decreasing trend. The most important reason for the low efficiency is technical efficiency. According to the SEDEA model and the Malmquist-DEA index model, decision-makers can find the causes of low efficiency and guide farmers to adjust production modes to realize efficient production.

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