Wei Liu

Time is a necessary element in understanding AI art. Firstly, time reveals the historical process by which art-theoretical predicates move from the unmarked to the marked, which can thus be utilized as a defense for arguing the legitimacy of AI art as art. Furthermore, AI art should be seen as a “new” art that is temporally ahead of the descriptive forms of art theory. Secondly, time provides a unique interpretation of AI artworks’ characteristics and aesthetic experience. The absence of experience, the de-depth of AI artworks, and the “short experience/short memory” aesthetic mode of the masses are closely linked to the scarcity of time in an age of acceleration. Finally, time reflects the loss of “aura” and the new end of art that AI art may bring about. Time provides a defense and explanation of AI art, as well as a perspective for reflecting on the development of art in contemporary times.

English grammar error correction algorithm refers to the use of computer programming technology to automatically recognize and correct the grammar errors contained in English text written by nonnative language learners. Classification model is the core of machine learning and data mining, which can be applied to extracting information from English text data and constructing a reliable grammar correction method. On the basis of summarizing and analyzing previous research works, this paper expounded the research status and significance of English grammar error correction algorithm, elaborated the development background, current status, and future challenges of the classification model, introduced the methods and principles of feature extraction method and dynamic residual structure, constructed a basic model for English grammar error correction based on the classification model, analyzed the classification model and translation model of English grammar error correction, proposed the English grammar error correction algorithm based on the classification model, performed the analyses of the model architecture and model optimizer of the grammar error correction algorithm, and finally conducted a simulation experiment and its result analysis. The study results show that, with the continuous increase of training samples and the continuous progress of learning process, the proposed English grammar error correction algorithm based on the classification model will continue to increase its classification accuracy, further refine its recognition rules, and gradually improve correction efficiency, thereby reducing processing time, saving storage space, and streamlining processing flow. The study results of this paper provide a certain reference for the further research on English grammar error correction algorithm based on the classification model.

Tang Dynasty in ancient China made ​​remonstrance the maturity period of institutional development, in particular, to become scholars admonish Emperor Zhen Guan Zheng phase interference in domestic affairs of the golden age. Appeal based on age, Tang Zheng scholars with a strong sense of remonstrance, in order to correct the error king, they boldly made ​​the bridge, and fear of punishment Wei, and sometimes even at the cost of life. The remonstrance Zheng Tang scribes not only the spirit and political lives, but also for literary realist tendency to produce a very profound impact. The remonstrant system in ancient China had been matured in Tang dynasty especially during the Zhenguan period. Therefore, the gentry were audacious to make remonstrance to correct the emperors' mistakes without fearing penalty even losing their lives, which was related to politics and had made heavy influence on literature in Tang dynasty

In this paper, we analyze the intelligent subdesign of the simulated marking system through an in-depth study of it. This paper proposes a correlation analysis-based quantification of N-element sense values and a rationality enhancement-based scoring fitting algorithm for English essays. This paper also extracts word features, sentence features, and chapter structure features in essays to fit English composition scores. Since not all students can complete the essays according to the topic requirements, a triage scoring model is used to separate the normal essays from the low-scoring essays. Statistically, it was found that the essay scores also showed a certain normal distribution. The standard support vector regression algorithm is prone to data skewing problems, so this paper addresses this problem by using a rationality enhancement method that gives a corresponding penalty factor according to the distribution of the dataset. The results show that the English essay scoring fitting algorithm proposed in this paper can well improve the prediction accuracy of some data and solve the problem of skewed data where the scores show a normal distribution. This paper designs and implements an online mock examination system that incorporates an intelligent scoring system for essays, enabling it to meet the needs of teachers and students for online examinations and intelligent scoring.

Patients with advanced Alzheimer's disease experience cognitive impairment and physical disabilities in daily life. Currently, there are no treatments available to slow down the course of the disease, and limited treatments exist only to treat symptoms. However, deep brain stimulation of the nucleus basalis of Meynert has been reported to improve cognitive function in individuals with AD. Here, we report the effects of NBM-DBS on cognitive function in a subject with severe AD. An 80-year-old male with severe AD underwent surgery for bilateral NBM-DBS electrode placement. After 10 weeks of stimulation, Mini-Mental State Examination assessment improved from a score of 5 to 9 points, and assessment using the Alzheimer's Disease Assessment Scale–Cognitive Subscale showed a marked reduction in total score from 43 to 33 points, suggesting cognitive benefits from NBM-DBS. The patient's postoperative course was complicated by a subdural effusion that occurred several days after surgery, with complete recovery. Interestingly, the subject also displayed abnormal thermoregulation with stimulation initiation and stimulation parameter modifications. NBM-DBS may serve as a potential therapy for severe AD patients.Clinical Trial Registration: ChiCTR1900022324.

Ritualistic consumption refers to integrating ritual elements into the process of product design and usage. By conducting three studies, we find that ritualistic consumption can offer new and interesting experiences and help consumers gain a sense of control. Both positive and negative emotions can promote ritualistic consumption tendencies. However, their underlying psychological mechanisms are different. Specifically, positive emotion can arouse consumers’ desire for interesting experience and thus promotes their preference for ritualistic consumption, while negative emotion can arouse consumers’ need for control and thus promote their preference for ritualistic consumption. Our research results offer a theoretical contribution and practical inspiration for emotional marketing.

The fault prediction and abductive fault diagnosis of three-phase induction motors are of great importance for improving their working safety, reliability, and economy; however, it is difficult to succeed in solving these issues. This paper proposes a fault analysis method of motors based on modified fuzzy reasoning spiking neural P systems with real numbers for fault prediction and abductive fault diagnosis. To achieve this goal, fault fuzzy production rules of three-phase induction motors are first proposed. Then, the rMFRSNPS is presented to model the rules, which provides an intuitive way for modelling the motors. Moreover, to realize the parallel data computing and information reasoning in the fault prediction and diagnosis process, three reasoning algorithms for the rMFRSNPS are proposed: the pulse value reasoning algorithm, the forward fault prediction reasoning algorithm, and the backward abductive fault diagnosis reasoning algorithm. Finally, some case studies are given, in order to verify the feasibility and effectiveness of the proposed method.