Yanyan Li

It is interesting to explore the effects of second language acquisition on anatomical change in brain at different stages for the neural structural adaptations are dynamic. Short-term Chinese training effects on brain anatomical structures in alphabetic language speakers have been already studied. However, little is known about the adaptations of the gray matter induced by acquiring Chinese language for a relatively long learning period in adult alphabetic language speakers. To explore this issue, we recruited 38 Indian overseas students in China as our subjects. The learned group included 17 participants who had learned Mandarin Chinese for an average of 3.24 years and achieved intermediate Chinese language proficiency. The control group included 21 subjects who had no knowledge about Chinese. None of the participants had any experience in learning logographic and tonal language before Chinese learning. We found that the learned group had significantly greater gray matter volume in the left lingual gyrus compared with the control group; the Chinese characters’ reading accuracy was significantly and positively correlated to the GMV in the left LG and fusiform gyrus across the two groups; and in the learned group, the duration of Chinese learning was significantly and positively correlated with the GMV in the left inferior frontal gyrus after correction for multiple comparisons with small volume corrections. Our structural imaging findings are in line with the functional imaging studies reporting increased brain activation induced by Chinese acquisition in alphabetic language speakers. The regional gray matter changes reflected the additional requirements imposed by the more difficult processing of Chinese characters and tones. The present study also show that the biological bases of the adaptations induced by a relatively long period of Chinese learning were limited in the common areas for first and foreign language processing.

Working memory is a limited capacity memory system that involves the short-term storage and processing of information. Neuroscientific studies of working memory have mostly focused on the essential roles of neural oscillations during item encoding from single sensory modalities (e.g., visual and auditory). However, the characteristics of neural oscillations during multisensory encoding in working memory are rarely studied. Our study investigated the oscillation characteristics of neural signals in scalp electrodes and mapped functional brain connectivity while participants encoded complex audiovisual objects in a working memory task. Experimental results showed that theta oscillations (4–8 Hz) were prominent and topographically distributed across multiple cortical regions, including prefrontal (e.g., superior frontal gyrus), parietal (e.g., precuneus), temporal (e.g., inferior temporal gyrus), and occipital (e.g., cuneus) cortices. Furthermore, neural connectivity at the theta oscillation frequency was significant in these cortical regions during audiovisual object encoding compared with single modality object encoding. These results suggest that local oscillations and interregional connectivityviatheta activity play an important role during audiovisual object encoding and may contribute to the formation of working memory traces from multisensory items.

The diversities in the natural, social, and human environment of various regions often lead to the differences in their regional economic development level and industrial structure and layout; moderate regional differences can mobilize economic vitality and improve development efficiency, but excessive differences may lead to social instability or even turbulence. Intelligent algorithms or their improved and hybrid algorithms can recently achieve more suitable solutions to practical problems of nonlinear, discrete, nondifferentiable, and multiple constraints. Therefore, this paper's main point is on the analysis of regional economic development differences based on intelligent hybrid algorithms. On the basis of summarizing and analyzing previous research works, this paper expounded the research status and significance of regional economic development differences analysis, elaborated the development background, current status, and future challenges of intelligent hybrid algorithms, introduced the methods and principles of principal component distance weighting algorithm and sequential quadratic programming algorithm, constructed a basic model for regional economic development differences based on intelligent hybrid algorithm, conducted the construction of analysis index system and the hybrid of intelligence algorithm, analyzed the regional economic development differences based on intelligent hybrid algorithm, performed the principal component analysis and time-space change analysis of regional economic development, and finally carried out a simulation experiment and its result analysis. The study results show that the intelligent hybrid algorithm can simulate the intelligent behavior of certain groups in nature when analyzing the differences in regional economic development, so that it has essentially parallelism, high accuracy, and convergence speed. The study results of this paper provide a reference for further researches on the regional economic development differences analysis based on intelligent hybrid algorithm.