To enhance the development of massive open online course (MOOC) teaching and learning, prevalent methods and characteristics employed by college students in MOOC learning strategies must be investigated. This study employed the grounded theory approach to systematically encode and construct a learning strategy model for the use of MOOCs among college students in China. This study used data obtained from 57 college students and applied qualitative research coding procedures and NVivo software (version 12.0). The results revealed that the core learning strategies used by college students in MOOC learning could be categorized into cognitive, resource management, and metacognitive types. Five specific learning strategies were most frequently used: elaboration, interactive and cooperation, help-seeking, effort management, and information selection strategies. However, association and questioning strategies are infrequently employed by college students in their learning. Therefore, educators must actively guide students to expand their innovative thinking abilities by implementing these strategies.

Microexpressions are very transitory expressions lasting about 1/25∼1/2 s, which can reveal people’s true emotions they try to hide or suppress. The PREMERT (pseudorandom ecological microexpression recognition test) could test the individual’s microexpression recognition ability with six microexpression Ms (the mean of accuracy rates of a microexpression type under six expression backgrounds), and six microexpression SDs (the standard deviation of accuracy rates of this microexpression type under six expression backgrounds), but it and other studies did not explore the general microexpression recognition ability (the GMERA) or could not test the GMERA effectively. Therefore, the current study put forward and established the GMERA with the behavioral data of the PREMERT. The spontaneous brain activity in the resting state is a stable index to measure individual cognitive characteristics. Therefore, the current study explored the relevant resting-state brain activity of the GMERA indicators to prove that GMERA is an individual cognitive characteristic from brain mechanisms with the neuroimaging data of the PREMERT. The results showed that (1) there was a three-layer hierarchical structure in human microexpression recognition ability: The GMERA (the highest layer); recognition of a type of microexpression under different expression backgrounds (the second layer); and recognition of a certain microexpression under a certain expression background (the third layer). A common factor GMERA was extracted from the six microexpression types recognition in PREMERT. Four indicators of the GMERA were calculated from six microexpression Ms and six microexpression SDs, such as GMERAL (level of GMERA), GMERAF (fluctuation of GMERA), GMERAB (background effect of GMERA), and GMERABF (fluctuation of GMERAB), which had good parallel-forms reliability, calibration validity, and ecological validity. The GMERA provided a concise and comprehensive overview of the individual’s microexpression recognition ability. The PREMERT was proved as a good test to measure the GMERA. (2) ALFFs (the amplitude of low-frequency fluctuations) in both eyes-closed and eyes-opened resting-states and ALFFs-difference could predict the four indicators of the GMERA. The relevant resting-state brain areas were some areas of the expression recognition network, the microexpression consciousness and attention network, and the motor network for the change from expression backgrounds to microexpression. (3) The relevant brain areas of the GMERA and different types of microexpression recognition belonged to the three cognitive processes, but the relevant brain areas of the GMERA were the “higher-order” areas to be more concise and critical than those of different types of microexpression recognition.

Both tai chi and walking can improve the physical health of the elderly, but the effect on the emotional cognitive function of the elderly is unclear. To investigate the effect of long-term walking and tai chi exercise on the emotional cognitive function of the elderly, 63 subjects were recruited in this study according to age and exercise habits, including 16 in the youth control group, 15 in the elderly non-exercise control group, 17 in the elderly walking group, and 18 in the elderly tai chi group. The “learning–test paradigm” of emotional faces was used to measure the subjects’ ability to recognize and remember emotional (negative and neutral) faces. Behavioral and EEG data were recorded during the learning and testing phases. The results showed that there is aging in emotional cognition in older adults compared with younger adults. Long-term walking and tai chi exercise can delay the deterioration of emotional cognitive function in older adults to some extent. Both walking and tai chi exercise can delay the decline in aging-related emotional face recognition function to some extent. Walking exercise can delay the decline in aging-related emotional face memory function to some extent.

In third‐generation synchrotron radiation sources, such as the Advanced Photon Source (APS), the sensitivity and reliability requirements for the x‐ray beam position monitors (XBPMs) are much higher than for earlier systems. Noise and contamination signals caused by radiation emitted from the bending magnet become a major problem. The regular XBPM calibration process can only provide signal correction for one set of conditions for the insertion devices (ID). During normal operation, parameters affecting the ID‐emitted beam, such as the gap of the ID magnets and the beam current, are the variables. A new smart x‐ray beam position monitor system (SBPM) has been conceived and designed for the APS. It has a built in self‐learning structure with EEPROM memory that is large enough to ‘‘remember’’ a complete set of calibration data covering all the possible operating conditions. During the self‐learning mode, the monitor system initializes a series of automatic scan motions with information for different ID setups and records them into the database array. During normal operation, the SBPM corrects the normalized output according to the ID setup information and the calibration database. So that, with this novel system, the SBPM is always calibrating itself with the changing ID set up conditions.

To examine the impact of music as a cross-modal prime on facial emotion recognition ability in autistic children, this study compares the priming effect of music with that of faces as an intramodal prime and nonverbal sounds as the same cross-modal prime. The response time and accuracy of facial emotion recognition (happy and sad) were compared among 21 neurotypical children and 17 autistic children under various priming stimuli. A data analysis revealed that autistic children exhibited worse recognition of facial emotional expressions and demonstrated longer reaction time than neurotypical children. Unlike the other two stimuli (facial expressions and nonverbal sounds), music as a cross-modal prime resulted in slightly higher response accuracy for emotionally congruent conditions compared with emotionally incongruent conditions among autistic children. Furthermore, reaction time was significantly prolonged under emotionally congruent conditions than under emotionally incongruent conditions. This suggests that autistic children demonstrate a greater propensity to allocate additional time to improve the accuracy of their judgments under congruent conditions compared with incongruent conditions. Therefore, music intervention has significant potential for supporting autistic children to empathize with faces by playing emotionally congruent music and improving their facial emotion recognition abilities.