Search

With the rapid growth of the global population and the increasing urbanization, the urban landscape in China is gradually enriched, and the scale of the landscape that plays a healing role is expanding. However, curing the problem of landscape ecological security is an important part of Homeland security, economic and social sustainable development. We must deal with the relationship between high-quality social development and ecological environment protection on the basis of scientific evaluation. To address this issue, research has provided better data support for feature extraction through image preprocessing. Then the Convolutional neural network in deep learning is trained through a large number of collected measured data. Finally, the pressure state response model is used to evaluate the ecological security of the healing landscape. The results show that the average error of the ground class in 2010 was 13.65%, and the fitting accuracy reached 86.35%, indicating that this method has high accuracy and can be effectively applied in evaluation. Meanwhile, in 2010 and 2019, the average landscape ecological security levels of City A were 7.27 and 6.65, both at a "safe" level, but the overall security level showed a downward trend. It is recommended to optimize the land use pattern in future urban planning and construction, improve the urban landscape ecological security index value, and maintain consistency with the actual situation of the city. This can provide reference for the evaluation model of urban landscape ecological security, and further provide scientific basis and guidance for the ecological civilization construction of urban agglomerations. In subsequent research, the evolution trend of urban landscape ecological security can be taken as the research goal, and finally, guidance on optimizing urban landscape ecological security can be provided. [ABSTRACT FROM AUTHOR]

Medical students in China and other non-English speaking countries are susceptible to English as a Foreign Language (EFL) writing anxiety. English writing is not only a vital component tested for admission to postgraduate and doctoral programs, but it is also critical for the publication of academic papers. Although there is mounting evidence indicating relationships between anxiety, self-esteem and mobile phone addiction, pathways between these three constructs within a structural equation model have not yet been examined. Furthermore, there has been a dearth of studies exploring EFL writing anxiety, from which medical students in China as well as other non-English speaking countries are prone to suffer. The study was to assess EFL writing anxiety among Chinese medical students and to examine the relationships between EFL writing anxiety, self-esteem, and mobile phone addiction, with an aim to offer empirical evidence for effective preventive or intervention measures to alleviate EFL writing anxiety. Cross-sectional data were obtained from 1,238 medical students in China, with the administration of a self-administered questionnaire comprising the Second Language Writing Anxiety Inventory (SLWAI), the Rosenberg Self Esteem Scale (RSES) and the Mobile Phone Addiction Tendency Scale (MPATS). The results indicated that both self-esteem and mobile phone addiction exerted significant direct effects on EFL writing anxiety. Self-esteem also had a significant indirect effect on EFL writing anxiety via the mediating role of mobile phone addiction. The path coefficients of self-esteem on EFL writing anxiety were significantly reduced when mobile phone addiction was modeled as a mediator. Efforts to alleviate EFL writing anxiety among medical students may benefit from enhancing self-esteem and establishing a healthy relationship with mobile phones. [ABSTRACT FROM AUTHOR]

The market for urban distributed photovoltaics (DPV) is expected to take off in the next decade. However, these systems are often subject to complex urban contexts and sub-optimal conditions, requiring scalable and comprehensive solutions to detect their underperformances. In recent years, deep generative models (DGMs) have exhibited outstanding performance in the anomaly detection domain, dealing with generic high-dimensional time series data. Nevertheless, the existing applications of DGMs in the photovoltaic (PV) sector are still unable to account for environmental information, limiting their performance under various environmental conditions. This study proposes the Sequential Conditional Variational Autoencoder (SCVAE), which can cope with the sequential impacts of the environment on PV power generation. Using real-world data collected from 30 rooftop PV sites located across China, a data processing pipeline is developed to construct the training datasets which contain mostly normal samples for unsupervised SCVAE model training. This work also constructs a synthetic dataset with a wide variety of artificial anomalies in reference to the domain insights and engineering practice of DPV systems. After checking and refining by experts, the synthetic dataset can finally be used to validate the anomaly detection models. The results demonstrate that the SCVAE model outperforms existing state-of-the-art unsupervised anomaly detection models and can be effectively generalized to unseen PV sites. Moreover, the latent variables of SCVAE could be used to identify the type of DPV failure, thereby enabling more targeted diagnostics of anomaly mechanisms. • An anomaly detection method is proposed for DPV in various environmental scenarios. • A novel unsupervised model is proposed for time series anomaly detection. • A real-world data processing pipeline and a labeled synthetic dataset are proposed. [ABSTRACT FROM AUTHOR]

Purpose: Chronic obstructive pulmonary disease (COPD) is a significant disease impacting health and quality of life. Yunnan Province, a major tobacco producer, lacks comprehensive COPD studies. The purpose of this study is to describe the epidemic situation of COPD in Yunnan province and explore its influencing factors., Methods: This study is a cross-sectional research conducted in a representative sample of adults aged 20 and older from 13 prefectures and cities in Yunnan Province, China. COPD was diagnosed using post-bronchodilator pulmonary function tests. Demographics were analyzed with descriptive statistics. The influencing factors of COPD were examined by using the multivariate logistic regression models., Results: Our study found that high-risk individuals for COPD accounted for 20.30% of the screened population aged 20 and above, with a COPD prevalence of 27.18% among this high-risk group. Male had a higher prevalence (33.01%) than did female (16.35%; p <0.001 for sex difference). Additionally, the proportion of severe and extremely severe COPD cases in Yunnan Province was higher than the national average and other provinces. After considering the potential confounding variables, male (OR=2.291, 95% CI: 1.584-3.313), age (OR=1.501, 95% CI: 1.338-1.685), underweight (OR=1.747, 95% CI: 1.225-2.491), previous smoking (OR=1.712, 95% CI: 1.182-2.478), passive smoking (OR=1.444, 95% CI: 1.159-1.800), and a history of respiratory system diseases in childhood (OR=2.010, 95% CI: 1.346-3.001) were significantly associated with an increased risk of COPD. Conversely, being overweight (OR=0.636, 95% CI: 0.489-0.828), and residing in high-altitude counties (OR=0.445, 95% CI: 0.263-0.754) were negatively correlated with the risk of COPD., Conclusion: There is significant prevalence of COPD (27.18%) among high-risk population aged 20 and above in Yunnan Province, China. Apart from male, smoking, BMI and other known risk factors for COPD. We found that high-altitude residence had a lower prevalence of COPD. There is no significant difference in COPD prevalence between Han and ethnic minority populations., Competing Interests: The authors declare that they have no conflicts of interest in this work., (© 2024 Wen et al.)