Matching, odd [1,b][1,b]-factor and distance spectral radius of graphs with given some parameters

Let $\mu(G)$ denote the the distance spectral radius of $G$. A matching in a graph $G$ is a set of disjoint edges of $G$. The maximum size of a matching in $G$ is called the matching number of $G$, denoted by $\alpha(G)$. In this paper, we give a sharp upper bound in terms of the distance spectral radius to guarantee $\alpha(G)>\frac{n-k}{2}$ in a graph $G$ with given connectivity. Let $a$ and $b$ be two positive integers with $a\le b$. An $[a, b]$-factor of a graph $G$ is a spanning subgraph $G_0$ such that for every vertex $v\in V (G)$, the degree $d_{G_0}(v)$ of $v$ in $H$ satisfies $a\le d_{G_0}(v)\le b$. In this paper, we present a sharp upper bound in terms of distance spectral radius for the existence of an odd $[1,b]$-factor in a graph with given minimum degree $\delta$

Exploring the Key Influencing Factors on Teachers’ Reflective Practice Skill for Sustainable Learning: A Mixed Methods Study

In 2019, the United Nations released “Education for Sustainable Development for 2030”, emphasizing that sustainable learning is an important component of education for sustainable development, as it can enable learners to master the knowledge and skills required to keep learning in a variety of circumstances. To better understand teachers’ sustainable learning within the context of education, this study used a comprehensive method combining quantitative analysis and qualitative analysis to examine the key factors that influence teachers’ reflective practice skill through educational practice for sustainable learning. A total of 349 teachers responded to the survey. Based on the quantitative results, 10 teachers were chosen for qualitative analysis. Results showed that teaching support service, peer feedback, teacher–student interaction, and personal goal orientation were found to have a significant impact on teachers’ reflective practice skill, which is beneficial for promoting sustainable learning. Interestingly, the direct impact of pedagogical self-efficacy on reflective practice skill was not observed. The following qualitative research yielded five topics on teaching support service, peer feedback, teacher–student interaction, pedagogical self-efficacy, and personal goal orientation. These topics helped to explain the results of the quantitative analysis. The findings of the proposed model were conducive to understanding the mechanism that affects teachers’ reflective practice skill as well as providing practical implications for teachers’ sustainable learning in educational practice

Palaeogeography of China

Palaeogeography is a science that studies the features and evolution of physical geography in geological history and human history. The palaeogeography of China has multiple disciplines and multiple types of palaeogeographic map. These perfectly reflect “a hundred flowers blossom and a hundred schools of thought content”. The palaeogeography of China has three characteristics. The first is multiple disciplines flourishing simultaneously. The second is that lithofacies palaeogeography is in a leading position. The third is that palaeogeography of China is closely connected with industrial practice. It is why palaeogeography, especially lithofacies palaeogeography of China, can flourish continually. We have two journals of palaeogeography, i. e. Journal of Palaeogeography (Chinese Edition) and Journal of Palaeogeography (English Edition). The former primarily publishes articles of Chinese authors regarding Chinese palaeogeography and related disciplines and has a foothold in China. The latter will publish the articles of both Chinese and international authors and caters to foreign readers. These two journals will cooperate together and display their own expertise, which will effectively make a great contribution to the development and innovation of Chinese palaeogeography and international palaeogeography

Investigating the Influencing Factors of Teachers’ Information and Communications Technology-Integrated Teaching Behaviors toward “Learner-Centered” Reform Using Structural Equation Modeling

In the context of information-driven Education transformation, this study investigates factors that influence the continuous transformation of teacher information and communications technology (ICT) teaching methods. Although some studies have found that teacher psychological cognition exerts different effects on different types of teacher ICT-integrated teaching behaviors, the current literature on influencing factors lacks the classification of behaviors. Based on the learner-centered transformation, this study divides teacher ICT-integrated teaching behaviors into teacher-centered teaching behavior and student-centered teaching behavior, and constructs a hypothesis model of influencing factors on teacher ICT-integrated teaching behavior. We collected questionnaire data from 795 primary and secondary school teachers, then validated and adjusted the model through structural equation modeling (SEM). The social environment exerted a significant indirect impact on teacher technology application behaviors via mediation of teacher efficacy and outcome expectations. The two types of self-efficacy directly affected the student-centered ICT application behavior more than the teacher-centered ICT application behavior. The student-centered ICT application behavior exerted a significant impact on the teacher-centered ICT application behavior. This study confirms the significance of classifying teacher ICT-integrated teaching behavior and supports the transformation of learner-centered ICT-integrated teaching by improving the social environment to realize equitable and sustainable Education development

APTrans: Transformer-Based Multilayer Semantic and Locational Feature Integration for Efficient Text Classification

Text classification is not only a prerequisite for natural language processing work, such as sentiment analysis and natural language reasoning, but is also of great significance for screening massive amounts of information in daily life. However, the performance of classification algorithms is always affected due to the diversity of language expressions, inaccurate semantic information, colloquial information, and many other problems. We identify three clues in this study, namely, core relevance information, semantic location associations, and the mining characteristics of deep and shallow networks for different information, to cope with these challenges. Two key insights about the text are revealed based on these three clues: key information relationship and word group inline relationship. We propose a novel attention feature fusion network, Attention Pyramid Transformer (APTrans), which is capable of learning the core semantic and location information from sentences using the above-mentioned two key insights. Specially, a hierarchical feature fusion module, Feature Fusion Connection (FFCon), is proposed to merge the semantic features of higher layers with positional features of lower layers. Thereafter, a Transformer-based XLNet network is used as the backbone to initially extract the long dependencies from statements. Comprehensive experiments show that APTrans can achieve leading results on the THUCNews Chinese dataset, AG News, and TREC-QA English dataset, outperforming most excellent pre-trained models. Furthermore, extended experiments are carried out on a self-built Chinese dataset theme analysis of teachers’ classroom corpus. We also provide visualization work, further proving that APTrans has good potential in text classification work

ST-TGR: Spatio-Temporal Representation Learning for Skeleton-Based Teaching Gesture Recognition

Teaching gesture recognition is a technique used to recognize the hand movements of teachers in classroom teaching scenarios. This technology is widely used in education, including for classroom teaching evaluation, enhancing online teaching, and assisting special education. However, current research on gesture recognition in teaching mainly focuses on detecting the static gestures of individual students and analyzing their classroom behavior. To analyze the teacher’s gestures and mitigate the difficulty of single-target dynamic gesture recognition in multi-person teaching scenarios, this paper proposes skeleton-based teaching gesture recognition (ST-TGR), which learns through spatio-temporal representation. This method mainly uses the human pose estimation technique RTMPose to extract the coordinates of the keypoints of the teacher’s skeleton and then inputs the recognized sequence of the teacher’s skeleton into the MoGRU action recognition network for classifying gesture actions. The MoGRU action recognition module mainly learns the spatio-temporal representation of target actions by stacking a multi-scale bidirectional gated recurrent unit (BiGRU) and using improved attention mechanism modules. To validate the generalization of the action recognition network model, we conducted comparative experiments on datasets including NTU RGB+D 60, UT-Kinect Action3D, SBU Kinect Interaction, and Florence 3D. The results indicate that, compared with most existing baseline models, the model proposed in this article exhibits better performance in recognition accuracy and speed

RQ-OSPTrans: A Semantic Classification Method Based on Transformer That Combines Overall Semantic Perception and “Repeated Questioning” Learning Mechanism

The pre-trained language model based on Transformers possesses exceptional general text-understanding capabilities, empowering it to adeptly manage a variety of tasks. However, the topic classification ability of the pre-trained language model will be seriously affected in the face of long colloquial texts, expressions with similar semantics but completely different expressions, and text errors caused by partial speech recognition. We propose a long-text topic classification method called RQ-OSPTrans to effectively address these challenges. To this end, two parallel learning modules are proposed to learn long texts, namely, the repeat question module and the overall semantic perception module. The overall semantic perception module will conduct average pooling on the semantic embeddings produced by BERT, in addition to multi-layer perceptron learning. The repeat question module will learn the text-embedding matrix, extracting detailed clues for classification based on words as fundamental elements. Comprehensive experiments demonstrate that RQ-OSPTrans can achieve a generalization performance of 98.5% on the Chinese dataset THUCNews. Moreover, RQ-OSPTrans can achieve state-of-the-art performance on the arXiv-10 dataset (84.4%) and has a comparable performance with other state-of-the-art pre-trained models on the AG’s News dataset. Finally, the results indicate that our method exhibits a superior performance compared with the baseline methods on small-scale domain-specific datasets by validating RQ-OSPTrans on a specific task scenario by using our custom-built dataset CCIPC

An expression recognition algorithm based on convolution neural network and RGB-D Images

Aiming at the problem of recognition effect is not stable when 2D facial expression recognition in the complex illumination and posture changes. A facial expression recognition algorithm based on RGB-D dynamic sequence analysis is proposed. The algorithm uses LBP features which are robust to illumination, and adds depth information to study the facial expression recognition. The algorithm firstly extracts 3D texture features of preprocessed RGB-D facial expression sequence, and then uses the CNN to train the dataset. At the same time, in order to verify the performance of the algorithm, a comprehensive facial expression library including 2D image, video and 3D depth information is constructed with the help of Intel RealSense technology. The experimental results show that the proposed algorithm has some advantages over other RGB-D facial expression recognition algorithms in training time and recognition rate, and has certain reference value for future research in facial expression recognition

Psychological characteristics in high-risk MSM in China

<p>Abstract</p> <p>Background</p> <p>Men who have sex with men (MSM) have become a high-risk group of HIV infection in China. To date, little is known regarding the behavioral, social and psychological characteristics in Chinese MSM, which makes the implementation of preventive and therapeutic strategies for this high-risk subpopulation of people extremely difficult.</p> <p>Methods</p> <p>A total of 714 questionnaires were retrieved from the database of a Chinese government-sponsored National Key Research Project titled "Risk Analysis and Strategic Prevention of HIV Transmission from MSM to the General Population in China". The respondents were categorized into a high-risk group and a control group. Their behavioral, social and psychological characteristics were comparatively analyzed.</p> <p>Results</p> <p>Of the 714 MSM analyzed, 59 (8.26%) had high-risk homosexual behaviors. This sub-group of MSM had a higher in-marriage rate, a higher monthly income, heavier alcohol consumption and more serious problems with sexual abuse in childhood, intentional suicide attempts and mistaken assumption on condom's role in protecting HIV infection, as compared with the control group (<it>P </it>< 0.05). In contrast, the two groups did not differ significantly the sexual orientation, level of education, types of profession, drug use, condom use and experience of social stigma and discrimination (<it>P </it>> 0.05). A vast majority of the individuals in both behavior categories expressed support of legally protected gay clubs as well as gay marriage legislation in China. There was a strong correlation between high-risk behaviors and sexual abuse in childhood, alcohol drinking, income level and a mistaken belief in perfect HIV protection through the use of condoms.</p> <p>Conclusions</p> <p>MSM with and without high-risk homosexual behaviors have different social and psychological characteristics, which should be taken into account when implementing behavioral and therapeutic interventions aimed at preventing HIV/AIDS transmission among MSM as well as from MSM to the general population in China.</p