Named entity recognition is a subtask in natural language processing, and its accuracy greatly affects the effectiveness of downstream tasks. Aiming at the problem of insufficient expression of potential Chinese features in named entity recognition tasks, this paper proposes a multifeature adaptive fusion Chinese named entity recognition model. The model uses bidirectional long short-term memory neural network to extract stroke and radical features and adopts a weighted concatenation method to fuse two sets of features adaptively. This method can better integrate (...) the two sets of features, thereby improving the model entity recognition ability. In order to fully test the entity recognition performance of this model, we compared the basic model and other mainstream models on Microsoft Research Asia and “China People’s Daily” dataset from January to June 1998. Experimental results show that this model is better than other models, with F1 values of 97.01% and 96.78%, respectively. (shrink)

BackgroundThe research objective was to investigate social and cultural factors affecting breast cancer survivors’ experiences in Lebanese.MethodsA snowball sampling of 20 breast cancer survivors participated in the study. Semi-structured open-ended interviews were used to collect data.ResultsThe results showed that family support and religious beliefs were the primary supporting sources for breast cancer survivors. On the other hand, their body image and children were the major concerns. Thus, family and religious beliefs were needed to overcome breast cancer’s daily burden.ConclusionWomen with breast (...) cancer perceived their cancer experience through their social roles, reflecting a concern for image and role preservation. (shrink)

To better reflect the precise clustering results of the data samples with different shapes and densities for affinity propagation clustering algorithm, an improved integrated clustering learning strategy based on three-stage affinity propagation algorithm with density peak optimization theory was proposed in this paper. DPKT-AP combined the ideology of integrated clustering with the AP algorithm, by introducing the density peak theory and k-means algorithm to carry on the three-stage clustering process. In the first stage, the clustering center point was selected by (...) density peak clustering. Because the clustering center was surrounded by the nearest neighbor point with lower local density and had a relatively large distance from other points with higher density, it could help the k-means algorithm in the second stage avoiding the local optimal situation. In the second stage, the k-means algorithm was used to cluster the data samples to form several relatively small spherical subgroups, and each of subgroups had a local density maximum point, which is called the center point of the subgroup. In the third stage, DPKT-AP used the AP algorithm to merge and cluster the spherical subgroups. Experiments on UCI data sets and synthetic data sets showed that DPKT-AP improved the clustering performance and accuracy for the algorithm. (shrink)

Some studies show that athlete students are more likely to engage in health-risk behaviors with negative health consequences, while others suggest that they lead a healthier life than their non-athlete peers. Given these inconsistent results, this study aims to compare health behaviors, depression, and perceived health status between athlete and non-athlete students, and explore the associations between health behaviors and health outcomes. An online questionnaire survey including Heath Habits Scale for five health-risk behaviors and five health-promoting behaviors, Patient Health Questionnaire-9, (...) and 5-point scale for perceived health status was conducted in Beijing Sports University in March 2021. Data from 372 athlete students and 252 non-athlete students aging from 18 to 22 were included in this study. Chi-squared tests and t-tests were used to determine differences between athlete and non-athlete samples, and logistic regression analyses were conducted to examine the associations of health behaviors with depression and perceived health status. The significance level was p < 0.05. The results show that compared with non-athlete students, athlete students perform better in health habits, report lower proportion of depression and higher proportion of good health. Health behaviors, such as getting adequate sleeping, participating in vigorous physical activity, overeating, and smoking, were significantly associated with health outcomes of athlete students. The findings may contribute to the better understanding of health behaviors in athlete students and warrant continued attention on mental health and health habits in this population. (shrink)