Resumen: La esencia del desarrollo del ecoturismo moderno reside en la coexistencia armónica y armoniosa de la relación entre el hombre y la naturaleza, que es la práctica ecológica del turismo bajo el trasfondo del desarrollo sostenible moderno. El desarrollo del turismo tradicional se centra en los intereses económicos y descuida la protección sostenible de la ecología turística, y el ecoturismo se ha convertido gradualmente en la dirección de desarrollo y reforma del turismo contemporáneo. El taoísmo hace hincapié en seguir (...) a la naturaleza, lo que tiene evidentes tendencias naturalistas, proporciona diferentes perspectivas filosóficas clásicas tradicionales para comprender la relación entre la naturaleza y el desarrollo humano, y tiene un importante significado esclarecedor para promover el desarrollo común del hombre y la naturaleza. El taoísmo aboga por respetar e imitar las leyes del desarrollo natural, persiguiendo la armonía general entre el hombre y la naturaleza, y es coherente con la naturaleza intrínseca del ecoturismo moderno. Por lo tanto, basándose en el pensamiento ecológico taoísta, la investigación explora la vía de desarrollo de la cultura ecoturística sostenible contemporánea y proporciona orientación teórica para el desarrollo armonioso y la unidad del hombre y la naturaleza. (shrink)

Previous studies have demonstrated positive correlations between children’s interpersonal trust and social adjustment. However, the psychological mechanism underlying this effect is still unclear. The current study tested the indirect roles of teacher–student relationships from both students’ and teachers’ perspectives in a Chinese context. In total, 709 pupils from grade three to grade five, and their 17 head teachers from a Chinese public primary school participated in this study. The Children’s Generalized Trust Beliefs Scale, Social Adjustment Scale for Children and Adolescents, (...) and Teacher–Student Relationship Questionnaire were used in this study. All these variables were correlated with each other. Structural equation models showed that the interpersonal trust indirectly influenced social adjustment through the teacher–student relationship from students’ perspectives, while the teacher–student relationship from teachers’ perspectives did not play an indirect role. These findings suggest that the teacher–student relationship perceived by students is more important for children’s social adjustment than that perceived by teachers. Both parents and teachers should pay more attention to developing children’s interpersonal trust, build better teacher–student relationships, and focus more on how children feel about the relationship. (shrink)

Classification and gradation system adopts different security protection schemes for different types of data by implementing classification and gradation management of data, which is an important pretechnical means for data security protection and prevention of data leakage. This paper introduces artificial intelligence classification, machine learning, and other means to learn and train enterprise documents according to the characteristics of enterprise sensitive data. The generated training model can intelligently identify and classify file streams, improving work efficiency and accuracy of classification and (...) gradation. At the same time, the differences, advantages, and disadvantages of K-NN, DT, and LinearSVC algorithms are compared. The experimental data shows that LinearSVC algorithm is applicable to high-dimensional data, with discrete, sparse data features and large number of features, which is more suitable for classification of sensitive data of enterprises. (shrink)

In a congested large-scale subway network, the distribution of passenger flow in space-time dimension is very complex. Accurate estimation of passenger path choice is very important to understand the passenger flow distribution and even improve the operation service level. The availability of automated fare collection data, timetable, and network topology data opens up a new opportunity to study this topic based on multisource data. A probability model is proposed in this study to calculate the individual passenger’s path choice with multisource (...) data, in which the impact of the network time-varying state on passenger path choice is fully considered. First, according to the number and characteristics of OD candidate paths, the AFC data among special kinds of OD are selected to estimate the distribution of passengers’ walking time and waiting time of each platform. Then, based on the composition of path travel time, its real-time probability distribution is formulated with the distribution of walking time, waiting time, and in-vehicle time as parameters. Finally, a membership function is introduced to evaluate the dependence between passenger’s travel time and the real-time travel time distribution of each candidate path and take the path with the largest membership degree as passenger’s choice. Finally, a case study with Beijing Subway data is applied to verify the effectiveness of the model presented in this study. We have compared and analysed the path calculation results in which the time-varying characteristics of network state are considered or not. The results indicate that a passenger’s path choice behavior is affected by the network time-varying state, and our model can quantify the time-varying state and its impact on passenger path choice. (shrink)

Previous studies have shown that the motion intention recognition for lower limb prosthesis mainly focused on the identification of performed gait. However, the bionic prosthesis needs to know the next movement at the beginning of a new gait, especially in complex operation environments. In this paper, an upcoming locomotion prediction scheme via multilevel classifier fusion was proposed for the complex operation. At first, two motion states, including steady state and transient state, were defined. Steady-state recognition was backtracking of a completed (...) gait, which would be used as prior knowledge of motion prediction. In steady-state recognition, surface electromyographic and inertial sensors were fused to improve recognition accuracy; five typical locomotion modes were recognized by random forest classifier with over 97.8% accuracy. The transient state was defined as an observation period at the initial stage of upcoming movement, in which only the sEMG signal was recorded due to the limitation of sliding window length. LightGBM classifier was validated to outperform other methods in the accuracy and prediction time of transient-state recognition. Finally, a simplified HMM model based on prior knowledge and observation result was constructed to predict upcoming locomotion. The results indicated that the locomotion prediction was over 91% accuracy. The proposed scheme implements the locomotion prediction at the initial stage of each gait and provides critical information for the gait control of lower limb prosthesis. (shrink)