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Visual tracking is an important area in computer vision. How to deal with illumination and occlusion problems is a challenging issue. This paper presents a novel and efficient tracking algorithm to handle such problems. On one hand, a target’s initial appearance always has clear contour, which is light-invariant and robust to illumination change. On the other hand, features play an important role in tracking, among which convolutional features have shown favorable performance. Therefore, we adopt convolved contour features to represent the target appearance. Generally speaking, first-order derivative edge gradient operators are efficient in detecting contours by convolving them with images. Especially, the Prewitt operator is more sensitive to horizontal and vertical edges, while the Sobel operator is more sensitive to diagonal edges. Inherently, Prewitt and Sobel are complementary with each other. Technically speaking, this paper designs two groups of Prewitt and Sobel edge detectors to extract a set of complete convolutional features, which include horizontal, vertical and diagonal edges features. In the first frame, contour features are extracted from the target to construct the initial appearance model. After the analysis of experimental image with these contour features, it can be found that the bright parts often provide more useful information to describe target characteristics. Therefore, we propose a method to compare the similarity between candidate sample and our trained model only using bright pixels, which makes our tracker able to deal with partial occlusion problem. After getting the new target, in order to adapt appearance change, we propose a corresponding online strategy to incrementally update our model. Experiments show that convolutional features extracted by well-integrated Prewitt and Sobel edge detectors can be efficient enough to learn robust appearance model. Numerous experimental results on nine challenging sequences show that our proposed approach is very effective and robust in comparison with the state-of-the-art trackers.

To assess the effect of telemedicine on stoma‐related complications in adults with enterostomy, we conducted a meta‐analysis to evaluate the effects of the telemedicine group compared to the usual group. Literature searches were performed in PubMed, Embase, Web of Science, The Cochrane Library, China Biology Medicine (CBM), China National Knowledge Infrastructure (CNKI), WanFang and VIP databases from their inception up to October 2023. Two authors independently screened and extracted data from the included and excluded literature according to predetermined criteria. Data collected were subjected to meta‐analysis using Review Manager 5.3 software. The final analysis included a total of 22 articles, encompassing 2237 patients (telemedicine group: 1125 patients, usual group: 1112 patients). The meta‐analysis results demonstrated that, compared to the usual group, the telemedicine group significantly reduced the overall occurrence of stoma‐related complications, with an odds ratio (OR) of 0.22 (95% CI = 0.15–0.32, p < 0.00001). Furthermore, it resulted in a decrease in stoma complications (OR = 0.27, 95% CI = 0.15–0.47, p < 0.00001) and peristomal complications (OR = 0.25, 95% CI = 0.19–0.34, p < 0.00001). Therefore, the existing evidence suggests that the application of telemedicine can reduce the incidence of stoma and peristomal complications, making it a valuable clinical recommendation.

Aim To examine the status quo and influencing factors of sleep quality and work engagement of nurses participating in COVID‐19 during the post‐epidemic era and to study the relationship between them. Design We conducted a cross‐sectional survey and correlational and predictive logic to determine the association between sleep quality and work engagement among nurses in Shanghai during the post‐epidemic era. Methods This design involved 1060 frontline nurses in Shanghai. The Pittsburgh Sleep Quality Index questionnaire and the Utrecht Work Engagement Scale‐9 scales were used for data collection. Results This study found that the sleep quality of frontline nurses was impaired and the nurses with poor sleep accounted for 48.20% during the post‐epidemic era. The work engagement of frontline nurses was at the medium level. Factors affecting nurses' sleep quality were the number of nurse night shifts, family support and nurse health. The factors affecting the nurse work engagement were monthly income, profession title, family support and self‐health status. There was a positive correlation between nurses' sleep quality and work engagement.

This was a non‐blinded, single‐centre, randomized, controlled clinical trial that compared the effectiveness of direct observation of procedural skills (DOPSs)with traditional assessment methods in pressure injury (PI) care skills. The study population included 82 nursing professionals randomly assigned to the study group (n = 41) and the control group (n = 41). Both groups of nurses underwent a 6‐month training in PI care skills and were subsequently evaluated. The main outcome variables were the PI skill operation scores and theoretical scores. Secondary outcome variables included satisfaction and critical thinking abilities. Independent sample t‐tests and chi‐square tests were used to assess differences between the two groups of nurses. The results showed no statistically significant difference in PI skill operation scores between the two groups of nurses (p > 0.05). When comparing the PI theoretical scores, the study group scored higher than the control group, and this difference was statistically significant (p < 0.05). In terms of satisfaction assessment, the study group and the control group showed differences in improving self‐directed learning, enhancing communication skills with patients, improving learning outcomes and increasing flexibility in clinical application (p < 0.05). When comparing critical thinking abilities between the two groups of nurses, there was no statistically significant difference at the beginning of the training, but after 3 months following the training, there was a statistically significant difference between the two groups (p < 0.01).The results indicated that the DOPS was effective in improving PI theoretical scores, increasing nurse satisfaction with the training and enhancing critical thinking abilities among nurses.

The mangrove ecosystem is a largely unexplored source for actinomycetes with the potential to produce biologically active secondary metabolites. Consequently, we set out to isolate, characterize and screen actinomycetes from soil and plant material collected from eight mangrove sites in China. Over 2,000 actinomycetes were isolated and of these approximately 20%, 5%, and 10% inhibited the growth of Human Colon Tumor 116 cells, Candida albicans and Staphylococcus aureus, respectively, while 3% inhibited protein tyrosine phosphatase 1B (PTP1B), a protein related to diabetes. In addition, nine isolates inhibited aurora kinase A, an anti-cancer related protein, and three inhibited caspase 3, a protein related to neurodegenerative diseases. Representative bioactive isolates were characterized using genotypic and phenotypic procedures and classified to thirteen genera, notably to the genera Micromonospora and Streptomyces. Actinomycetes showing cytotoxic activity were assigned to seven genera whereas only Micromonospora and Streptomyces strains showed anti-PTP1B activity. We conclude that actinomycetes isolated from mangrove habitats are a potentially rich source for the discovery of anti-infection and anti-tumor compounds, and of agents for treating neurodegenerative diseases and diabetes.

NOABSTRACTThis paper presents an analysis of the concept of patient outcomes.The present study conducted searches on various databases, including Wanfang, Sinomed, CNKI, PubMed, Cochrane Library, Embase, Web of Science, and Ovid. The paper followed the Walker and Avant concept-analysis approach.Initially, 899 pieces of literature were identified through the search process, and after screening, 41 of them were ultimately included in the analysis. The identified attributes of the concept included (1) capability, (2) decision making, and (3) action. These antecedents were shaped by factors such as illness perception, self-efficacy, and family and social. The consequences included (1) physiological effects, (2) psychological effects, and (3) social influence.The concept analysis of self-management in patients with diabetic foot ulcers (DFUs) not only aids in clinical practice and supports interventions, but also contributes to the development of self-management theory. The common goal of clinical medical staff is to assist DFU patients in improving cognitive ability, making correct self-management decisions, and enhancing self-management behavior.

Target tracking is one of the most important issues in computer vision and has been applied in many fields of science, engineering and industry. Because of the occlusion during tracking, typical approaches with single classifier learn much of occluding background information which results in the decrease of tracking performance, and eventually lead to the failure of the tracking algorithm. This paper presents a new correlative classifiers approach to address the above problem. Our idea is to derive a group of correlative classifiers based on sample set method. Then we propose strategy to establish the classifiers and to query the suitable classifiers for the next frame tracking. In order to deal with nonlinear problem, particle filter is adopted and integrated with sample set method. For choosing the target from candidate particles, we define a similarity measurement between particles and sample set. The proposed sample set method includes the following steps. First, we cropped positive samples set around the target and negative samples set far away from the target. Second, we extracted average Haar-like feature from these samples and calculate their statistical characteristic which represents the target model. Third, we define the similarity measurement based on the statistical characteristic of these two sets to judge the similarity between candidate particles and target model. Finally, we choose the largest similarity score particle as the target in the new frame. A number of experiments show the robustness and efficiency of the proposed approach when compared with other state-of-the-art trackers.

The goal of this study is to improve the quality and diversity of text paraphrase generation, a critical task in Natural Language Generation (NLG) that requires producing semantically equivalent sentences with varied structures and expressions. Existing approaches often fail to generate paraphrases that are both high-quality and diverse, limiting their applicability in tasks such as machine translation, dialogue systems, and automated content rewriting. To address this gap, we introduce two self-contrastive learning models designed to enhance paraphrase generation: the Contrastive Generative Adversarial Network (ContraGAN) for supervised learning and the Contrastive Model with Metrics (ContraMetrics) for unsupervised learning. ContraGAN leverages a learnable discriminator within an adversarial framework to refine the quality of generated paraphrases, while ContraMetrics incorporates multi-metric filtering and keyword-guided prompts to improve unsupervised generation diversity. Experiments on benchmark datasets demonstrate that both models achieve significant improvements over state-of-the-art methods. ContraGAN enhances semantic fidelity with a 0.46 gain in BERTScore and improves fluency with a 1.57 reduction in perplexity. In addition, ContraMetrics achieves gains of 0.37 and 3.34 in iBLEU and P-BLEU, respectively, reflecting greater diversity and lexical richness. These results validate the effectiveness of our models in addressing key challenges in paraphrase generation, offering practical solutions for diverse NLG applications.

Effective teamwork can provide safe and effective care in various medical systems. Thus, there is increasing recognition of the value of interprofessional collaborative practice. The Attitudes Toward Interprofessional Health Care Teams Scale (ATIHCTS) has been applied to a wide variety of health professions for evaluating attitudes toward health care teams. The ATIHCTS has been widely used internationally, but no Chinese version has been developed. The aim of this study was to adapt a Chinese version of the ATIHCTS among Chinese health care professionals and to test its validity. The English version of the ATIHCTS was translated into Chinese, back-translated, and modified for cultural adaptation according to Brislin's guideline. A total of 306 health professionals in a Shanghai tertiary hospital were investigated using the Chinese version of the ATIHCTS to test its validity. The Chinese version of the ATIHCTS was adjusted based on expert review and pilot testing. According to expert opinions, the text that did not conform to the Chinese language habits and the Chinese medical environment was adjusted. A total of five adjustments were made. After the pilot testing, minor corrections were made to improve the sentence structure of the scale instructions to make it easier to understand. Factor analysis was subsequently conducted with 306 respondents. The Chinese version of the ATIHCTS had 14 items. Exploratory factor analysis extracted two common factors, quality of care and time constraints, with the cumulative variance contribution rate reaching 70.011% and the load value of each entry on its common factor > 0.4. In addition, for scale confirmatory factor analysis (CFA), the chi-square/degrees of freedom ratio (X /df) was 1.46, the normed fit index (NFI) was 0.97, the Tucker-Lewis index (TLI) was 0.99, the incremental fit index (IFI) was 0.99, the comparative fit index (CFI) was 0.99, and the root mean square error of approximation (RMSEA) was 0.04. The fitting values all met the judgment criteria, and the scale had good structural validity. Cronbach's α of the Chinese version of the ATIHCTS was 0.861, and the Cronbach's α values of each factor were 0.949 and 0.838, respectively. The split-half reliability was 0.644, and the Guttman split-half coefficients of each factor were 0.904 and 0.779, respectively. The Chinese version of the ATIHCTS has good validity. It is a valuable tool for evaluating attitudes toward interprofessional health care teams among the health care professionals in China.