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进入21世纪以来，中国学术界越来越多的质疑与批判，已能说明王国维“戏曲者，谓以歌舞演故事也”这一戏曲定义确实是错的，是有实践上的危害性的。这种危害性的一个重要体现，就是中国戏曲被不适当地话剧化了。中国戏曲研究必须结束王国维时代，尽快开辟新时代。 同时，王国维的失误是有着世界文化背景的。2000多年前，亚里士多德在他的《诗学》中关于情节是悲剧之灵魂的论述，已经被扩展为全世界戏剧的标准。世界现在依然顽固地奉行着戏剧一定要以故事情节为灵魂之理念，但实际上，包括中国戏曲在内的世界戏剧的情况并非如此，世界上存在着大量的不以故事情节为灵魂的戏剧作品。 本文尝试给世界戏剧进行新的分类，依据故事情节是否占据整部戏剧的主导，根据故事情节是否是整部戏剧作品的灵魂，人类的戏剧应该可以大致上归纳为两大类：其一，是“以故事情节为主导的戏剧”，或称“以故事情节为灵魂的戏剧”，也即“亚里士多德式的戏剧”，在今天以话剧为代表；其二，是“不以故事情节为主导的戏剧”，或称“不以故事情节为灵魂的戏剧”，歌剧、舞剧、歌舞剧（包括中国戏曲）等剧种从体制上来说即属于此类。 本文分析了“不以故事情节为灵魂的戏剧”这一戏剧学概念之所以如此晩出的原因，并且认为，采用“四级细分法”来分析故事情节，有助于我们更为深刻地把握戏剧故事情节的本质特质。 本文还探讨了所谓“无情节”的戏剧作品在分类学上的归属问题。

he rapid development and application of Large Language Models (LLMs) have significantly enhanced their capabilities, influencing human-machine interactions in profound ways. As LLMs evolve, society is shifting from traditional interpersonal interactions to a multilayered structure integrating human-to-human, human-to-machine, and machine-to-machine interactions. In this context, understanding how humans perceive and evaluate LLMsand whether this follows the Big Two model of warmth and competence in interpersonal perceptionhas become critical. This study examines human perceptions of LLMs through three progressive empirical studies.Participants with prior LLM experience were recruited for the studies. Study 1 comprised two sub-studies: Study 1a (N = 207) used a free-response task, asking participants to describe their impressions of LLMs using at least three words, which were analyzed using the Semi-Automated Dictionary Creation for Analyzing Text to identify key dimensions of perception. Study 1b (N = 219) involved a lexical rating task, in which participants rated the applicability of selected evaluation words to LLMs. Study 2 (N = 178) used a questionnaire, in which participants rated a familiar LLM and provided feedback on their willingness to continue using it and their liking of it. Study 3 (N = 207) employed a questionnaire survey to assess participants ratings of warmth and competence for both humans and LLMs.Study 1 found that humans primarily perceive LLMs through warmth and competence, similar to how they perceive other humans. In general contexts, participants prioritized competence over warmth when evaluating LLMs, showing a significant priority effect (odds ratio = 2.88, z = 9.512, 95% CI [2.32, 3.59], p < 0.001). This contrasts with the typical warmth-priority effect in human-to-human perception. Study 2 investigated the relationship between perceptions of warmth and competence and human attitudes toward LLMs, specifically their emotional (e.g., liking) and functional (e.g., willingness to continue using) attitudes. Results showed that both dimensions positively predicted participants liking and willingness to continue using LLMs. Warmth had a stronger predictive effect on liking (warmth: = 0.41, p < 0.001; competence: = 0.27, p < 0.001), while competence had a stronger predictive effect on willingness to continue using (warmth: = 0.19, p = 0.005; competence: = 0.45, p < 0.001). This outcome suggests that the priority effect of warmth and competence shifts across attitude predictions. Study 3 examined specific LLMs ratings in terms of warmth and competence. Results showed no significant difference in warmth ratings between humans (M = 5.06, SD = 1.09) and LLMs (M = 5.11, SD = 1.23), t(206) = 0.60, p = 0.551. However, LLMs were rated significantly higher on competence (M = 5.16, SD = 1.20) than humans (M = 4.81, SD = 1.23), t(206) = 3.51, p < 0.001, Cohens d = 0.29.This study makes two significant contributions to the field. First, it establishes a preliminary theoretical framework for understanding human perception of LLMs. Second, it offers new insights into human-machine interaction by emphasizing the importance of warmth and competence in shaping user attitudes. The findings have practical implications for AI design and policymaking, providing a framework for improving user acceptance, optimizing LLM design, and promoting responsible human-AI coexistence.

Objective] To translate and adapt the Attitude Scale Towards the Use of Artificial Intelligence Technologies in Nursing (ASUAITIN) into Chinese, and to test its reliability and validity, so as to provide an evaluation tool for assessing the attitudes of clinical nurses towards the application of artificial intelligence technologies in their work. [Methods] Using the Brislin translation model, the scale was translated, back-translated, culturally adapted, and pre-investigated to form the Chinese version of ASUAITIN. By convenience sampling, a questionnaire survey was conducted among 396 nurses in a tertiary first-class hospital in Beijing in March 2025 to test the reliability and validity of the scale. [Results] The Chinese version of ASUAITIN consisted of 15 items in 2 dimensions. The Cronbachs coefficient of the scale was 0.939, the split-half reliability was 0.738, the Cronbachs coefficients of each dimension were 0.945 and 0.956 respectively, and the test-retest reliability was 0.935. The content validity index at the scale level was 0.981, and the content validity index at the item level ranged from 0.875 to 1.000. Through exploratory factor analysis, 2 common factors were extracted, with a cumulative variance contribution rate of 77.402%. The results of confirmatory factor analysis (CFA) showed that the chi-square to degrees of freedom ratio (/df) was 2.242, the Comparative Fit Index (CFI) was 0.966, the Goodness-of-Fit Index (GFI) was 0.882, the Adjusted Goodness-of-Fit Index (AGFI) was 0.941, the Incremental Fit Index (IFI) was 0.967, the Tucker-Lewis Index (TLI) was 0.959, the Relative Fit Index (RFI) was 0.929, and the Root Mean Square Error of Approximation (RMSEA) was 0.079. [Conclusion] The Chinese version of ASUAITIN has good reliability and validity, and can be used to evaluate the attitudes of Chinese clinical nursing staff towards the application of artificial intelligence technologies.

Based on the tensor-based large margin distribution and the nonparallel support tensor machine, we establish a novel classifier for binary classification problem in this paper, termed the Large Margin Distribution based NonParallel Support Tensor Machine (LDM-NPSTM).  The proposed classifier has the following advantages: First, it utilizes tensor data as training samples, which helps to comprehensively preserve the inherent structural information of high-dimensional data, thereby improving classification accuracy. Second, this classifier not only considers traditional empirical risk and structural risk but also incorporates the marginal distribution information of the samples, further enhancing its classification performance. To solve this classifier, we use alternative projection algorithm. Specifically, building on the formulation where in the proposed LDM-NPSTM, the parameters defining the separating hyperplane form a tensor (tensorplane) constrained to be the sum of rank-one tensors, the corresponding optimization problem is solved iteratively using alternative projection algorithm. In each iteration, the parameters related to the projections along a single tensor mode are estimated by solving a typical Support Vector Machine-type optimization problem. Finally, the efficiency and performance of the proposed model and algorithm are verified through theoretical analysis and some numerical examples.

Grouting serves as an effective method for mitigating geotechnical disasters in subsea tunnels. However, current theories and designs, primarily based on terrestrial tunnel contexts, seldom address the long-term effects of seawater ion erosion on reinforcement. An improved sand permeation grouting simulation test system was employed to examine the mechanical property evolution of sand layer grouting reinforcement under seawater erosion, utilizing various grout types. The mechanical properties of grouting reinforcement, under varying curing conditions, were analyzed using uniaxial compression test, permeability test, and scanning electron microscope (SEM) test. Test results indicate that seawater curing conditions initially enhance the strength and impermeability of grouting reinforcement; however, prolonged curing diminishes these mechanical benefits. The onset of this process occurs significantly sooner in cement-sodium silicate grout (28d to 56d) compared to cement grout (56d to 90d). For cement grouting reinforcement, the deformation modulus increases over time, albeit at a decreasing rate. The deformation modulus of cement-sodium silicate grouting reinforcement follows an increase-decrease-increase pattern, correlating with the volume ratio over time. The decline in mechanical properties of grouting reinforcement during the test's mid to late stages under seawater conditions results from the interplay between erosive ions, which inhibit mechanical growth and accelerate deterioration.