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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]

Analyzing spatiotemporal characteristics of the historical urbanization process is essential in understanding the dynamics of urbanization and scientifically planned urban development. Based on historical urban area maps and remote sensing images, this study examined the urban expansion of Shenyang from 1910 to 2010 using area statistics, typology identification, and landscape metrics approaches. The population and gross domestic product were analyzed as driving factors. The results showed that the urban area of Shenyang increased 43.39-fold during the study period and that the growth rate has accelerated since the 1980s. Three urban growth types were distinguished: infilling, edge-expansion, and spontaneous growth. Edge-expansion was the primary growth type. Infilling growth became the main growth type in the periods 1946–70, 1988–97, and 2004–10. Spontaneous growth was concentrated in the period of 1997 to 2000. The results of landscape metrics indicate that the urban landscape of Shenyang originally was highly aggregated, but has become increasingly fragmented. The urban fringe area was the traditional hot zone of urbanization. Shenyang was mainly located north of the Hun River before 1980; however, the south side of the river has been the hot zone of urbanization since the 1980s. The increase of urban area strongly correlated with the growth of GDP and population. Over a long time scale, the urbanization process has been affected by major historical events. [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.)

Genotyping epidermal growth factor receptor ( EGFR ) gene in patients with advanced non-small cell lung cancers (NSCLC) is essential for identifying those patients who may benefit from targeted therapies. Systemically evaluating EGFR mutation detection rates of different methods currently used in clinical setting will provide valuable information to clinicians and laboratory scientists who take care of NSCLC patients. This study retrospectively reviewed the EGFR data obtained in our laboratory in last 10 years. A total of 21,324 NSCLC cases successfully underwent EGFR genotyping for clinical therapeutic purpose, including 5,244 cases tested by Sanger sequencing, 13,329 cases tested by real-time PCR, and 2,751 tested by next-generation sequencing (NGS). The average EGFR mutation rate was 45.1%, with 40.3% identified by Sanger sequencing, 46.5% by real-time PCR and 47.5% by NGS. Of these cases with EGFR mutations identified, 93.3% of them harbored a single EGFR mutation (92.1% with 19del or L858R, and 7.9% with uncommon mutations) and 6.7% harbored complex EGFR mutations. Of the 72 distinct EGFR variants identified in this study, 15 of them (single or complex EGFR mutations) were newly identified in NSCLC. For these cases with EGFR mutations tested by NGS, 65.3% of them also carried tumor-related variants in some non- EGFR genes and about one third of them were considered candidates of targeted drugs. NGS method showed advantages over Sanger sequencing and real-time PCR not only by providing the highest mutation detection rate of EGFR but also by identifying actionable non- EGFR mutations with targeted drugs in clinical setting., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Mao, Zhao, Li, Zhang, Zhou, Zhou, Ou, Xu, Tang, Ou, Hu, Ding, Luo and Yu.)

Background: Whether cigarette smoking and moderate drinking are associated with non-alcoholic fatty liver disease (NAFLD)has not been fully described. This study investigated the separate and joint effects of smoking and moderate drinking on Chinese men with NAFLD., Methods: Across-sectional assay from DFTJ Cohort study was performed with a size of 9432 elderly Chinese men excluding excessive alcohol consumption (<210g/week). Fatty liver was diagnosed by standardized ultrasonographic inspection. The odds ratio (OR) of alcohol consumption and smoking for the prevalence of NAFLD were analyzed using multiple logistic regression with multiple adjustments., Results: The prevalence of NAFLD in current smokers (pack-year≥40) and drinkers (80~210g/week or drinking duration≥35years) was significantly higher than that in non-smokers and non-drinkers, respectively. The combination of current smoking (pack-year≥40) and drinking (80~210g/week) was associated with the highest risk of NAFLD (OR 1.85; 95% confidence interval [CI] 1.28-2.68;P<0.01). The similar combined effect was found in participants with pack-year≥40 and drinking duration≥35 years (OR 1.72; 95% CI 1.26-2.34;P<0.01). Moreover, an interaction was observed between current smoking and moderate drinking in NAFLD., Conclusions: In elderly Chinese men, cigarette smoking and moderate alcohol consumption exerts an evident joint effect and interaction on the prevalence of NAFLD, although both are significantly and independently associated with NAFLD prevalence. Such findings highlight particular significance of avoidance of cigarette and alcohol on NAFLD prevention.