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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]

How to analyze and predict the consumption behavior of Chinese residents is of great significance based on the existing consumption theory research and the actual situation in China. This study studies the factors influencing the consumption of urban and rural residents in China through factor analysis and builds a model of urban and rural residents' consumption expenditure based on BP neural network. At the same time, through the data collection, the consumption data of urban and rural residents in China from 1986 to 2017 were collected. The BP neural network prediction model was used to predict the consumption expenditure of urban and rural residents, and the absolute error and relative error were obtained. In addition, this study establishes a combined forecasting model based on Lasso regression analysis and BP neural network to predict the consumption expenditure of urban and rural residents and compares its prediction results with BP neural network prediction model. The research shows that the combined prediction model based on Lasso regression analysis and BP neural network comprehensively surpasses BP neural network prediction model in prediction accuracy, and it has certain effectiveness and puts forward practical suggestions, which provides theoretical reference for subsequent related research. [ABSTRACT FROM AUTHOR]

This paper examines the dynamical impact of market wide liquidity on the volatility by using the Markov switching-regime approach on the Heterogeneous Autoregressive model of the realized range-based volatility (MS-HAR-RRV-LIQ). First, based on the simple partition of the liquidity measures by their historical means, we find the impact of the market liquidity on the realized range-based volatility (RRV) is non-linear with significantly stronger when the market is fluctuating. Second, by utilizing the method of Markov regimes switching, we find that in sample results, the impact of market liquidity on RRV is dynamics with much stronger when the market is fluctuating. In addition, the impact of liquidity on RRV are persistent for both the fluctuating state and the stable state, which indicate that the impact of liquidity on volatility would remain in its original state unless there are major economic events. Third, in order to eliminate the hidden information of volatility in liquidity measures, we further decompose the liquidity measures to obtain the volatility-adjusted liquidity, and find that the impact of liquidity driven by the component of liquidity uncorrelated with volatility on market volatility is also dynamics with much stronger when the market is fluctuating. Finally, out-of-sample results suggest that the models of HAR-RRV-LIQ and their corresponding volatility-adjusted liquidity models with regimes switching can increase the forecasting ability significantly than those without regime switching. • This paper examines the dynamical impact of market wide liquidity on volatility. • The impact of the market wide liquidity on RRV is non-linear. • The impact of market wide liquidity on RRV is dynamics. • Our proposed models can increase the forecasting ability significantly. [ABSTRACT FROM AUTHOR]

Non-point source (NPS) pollution has become a major source of water pollution. A combination of models would provide the necessary direction and approaches designed to control NPS pollution through land use planning. In this study, NPS pollution load was simulated in urban planning, historic trends and ecological protection land use scenarios based on the Conversion of Land Use and its Effect at Small regional extent (CLUE-S) and Soil and Water Assessment Tool (SWAT) models applied to Hunhe-Taizi River Watershed, Liaoning Province, China. Total nitrogen (TN) and total phosphorus (TP) were chosen as NPS pollution indices. The results of models validation showed that CLUE-S and SWAT models were suitable in the study area. NPS pollution mainly came from dry farmland, paddy, rural and urban areas. The spatial distribution of TN and TP exhibited the same trend in 57 sub-catchments. The TN and TP had the highest NPS pollution load in the western and central plains, which concentrated the urban area and farm land. The NPS pollution load would increase in the urban planning and historic trends scenarios, and would be even higher in the urban planning scenario. However, the NPS pollution load decreased in the ecological protection scenario. The differences observed in the three scenarios indicated that land use had a degree of impact on NPS pollution, which showed that scientific and ecologically sound construction could effectively reduce the NPS pollution load in a watershed. This study provides a scientific method for conducting NPS pollution research at the watershed scale, a scientific basis for non-point source pollution control, and a reference for related policy making. [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.)

The spatial and temporal variability of soil CO 2 emissions from agricultural soils is inherently high. While tillage and crop residue practices play vital roles in governing soil CO 2 emission, their effects on the variability of soil CO 2 fluxes across depths and seasons are still poorly understood. To address this, an experiment consisting of four treatments, namely conventional tillage with (CT+) and without crop residue application (CT-), as well as no tillage with (NT+) and without crop residue application (NT-), was conducted to investigate soil CO 2 fluxes at top 40 cm soils with 10-cm depth intervals in a winter wheat-summer maize rotation system in the North China Plain. Our results showed soil CO 2 fluxes increased with depth in both the wheat- and maize-growing seasons. However, the dominant factors in regulating soil CO 2 fluxes changed with soil depth and seasons. In the wheat-growing season, increase in soil CO 2 fluxes with depth was attributed to the increase of dissolved organic carbon-to-nitrogen ratio (DOC/DON) and a decline in soil DON concentration along the soil profile. These factors explained about 55-96% of the total variation in soil CO 2 fluxes at different soil depths. In the maize-growing season, the dominant factors were soil DOC/DON ratio, soil DON concentrations, and soil moisture. These factors explained approximately 79-96% of the total variation in soil CO 2 fluxes along the soil depth. Greater soil CO 2 fluxes (except at 30-40 cm depth) were observed in NT- than CT- treatments. Furthermore, crop residue application enhanced soil CO 2 fluxes across different depths, but the enhancement was more prominent in CT+ than NT+. Moreover, soil CO 2 fluxes in the maize-growing season were greater than those in the wheat-growing season. Our results demonstrate that the effects of tillage regimes and crop residue management practices on soil CO 2 emissions are not confined only to the plough layer but can extend to soils of over 30 cm depths. We also need to revisit the general conventional view that no tillage can significantly reduce soil CO 2 emissions compared with conventional tillage for better climate change mitigation.

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.

As urbanization increases, urban runoff is an increasingly important component of total urban non-point source pollution. In this study, the properties of urban runoff were examined in Shenyang, in northeastern China. Runoff samples from a tiled roof, a concrete roof and a main road were analyzed for key pollutants (total suspended solids (TSS), total nitrogen (TN), total phosphorus (TP), chemical oxygen demand (COD), Pb, Cd, Cr, Cu, Ni, and Zn). The event mean concentration, site mean concentration, M(V) curves (dimensionless cumulative curve of pollutant load with runoff volume), and mass first flush ratio (MFF30) were used to analyze the characteristics of pollutant discharge and first flush (FF) effect. For all events, the pollutant concentration peaks occurred in the first half-hour after the runoff appeared and preceded the flow peaks. TN is the main pollutant in roof runoff. TSS, TN, TP, Pb, and Cr are the main pollutants in road runoff in Shenyang. There was a significant correlation between TSS and other pollutants except TN in runoff, which illustrated that TSS was an important carrier of organic matter and heavy metals. TN had strong positive correlations with total rainfall (Pearson's r = 0.927), average rainfall (Pearson's r = 0.995), and maximum rainfall intensity (Pearson's r = 0.991). TP had a strong correlation with rainfall intensity (Pearson's r = 0.940). A significant positive correlation between COD and rainfall duration (Pearson's r = 0.902, significance level = 0.05) was found. The order of FF intensity in different surfaces was concrete roof > tile roof > road. Rainfall duration and the length of the antecedent dry period were positively correlated with the FF. TN tended to exhibit strong flush for some events. Heavy metals showed a substantially stronger FF than other pollutant.