本书简要地阐明了文学本文的创造过程,文学作品的构成及其实现过程,文学的审美价值系统及其实现过程,文学的历史发展过程。.

Background: Organizational climate refers to an individual's perception and experience of the climate of the work environment, and it is the most important environmental variable that affects individuals’ work performance. This study aims to classify characteristics of transformational leadership among kindergarten principals and examine their relationship to organizational climate. Methods: Convenience sampling yielded 498 kindergarten principals who completed the “Questionnaire on the Principal’s Transformational Leadership Behavior” and “Questionnaire on Organizational Climate.” Ethics approval was obtained from the Academic Ethics Committee of (...) the College of Psychology of Northeast Normal University prior to starting the study. Results: Three latent classes were identified, including the high-level (68.8%), care-virtues (35.7%), and virtues groups (5.3%). There were significant differences in support, directive, restrictive, colleague, intimate, and disengaged behavior scores between groups. In terms of support, colleague, and intimate behavior, the high-level group had the highest scores, followed by the care-virtues group and virtues group, respectively. Regarding restrictive and disengaged behavior, the highest scores were received by the virtues group, followed by the care-virtues and high-level group, respectively. Conclusion: The study suggested that principals’ transformational leadership could be classified into three latent classes that are related to organizational climate. (shrink)

The identification and analysis of the spatiotemporal dynamic traffic patterns in citywide road networks constitute a crucial process for complex traffic management and control. However, city-scale and synchronal traffic data pose challenges for such kind of quantification, especially during peak hours. Traditional studies rely on data from road-based detectors or multiple communication systems, which are limited in not only access but also coverage. To avoid these limitations, we introduce real-time, traffic condition digital maps as our input. The digital maps keep (...) spatiotemporal urban traffic information in nature and are open to access. Their pixel colors represent traffic conditions on corresponding road segments. We propose a stacked convolutional autoencoder-based method to extract a low-dimension feature vector for each input. We compute and analyze the distances between vectors. The statistical results show different traffic patterns during given periods. With the actual data of Chongqing city, we compare the feature extraction performance between our proposed method and histogram. The result shows our proposed method can extract spatiotemporal features better. For the same data set, there is little difference in the number distribution of red pixels found in the statistics result of the histogram, while differences do exist in the results of our proposed method. We find the most fluctuated morning is on Friday; the most fluctuated evening is on Tuesday; and the most stable evening is on Wednesday. The distance captured by our method can represent the evolution of different traffic conditions during the morning and evening peak hours. Our proposed method provides managers with assistance to sense the dynamics of citywide traffic conditions in quantity. (shrink)