COVID-19 (Corona Virus Disease 2019) has significantly resulted in a large number of psychological consequences. The aim of this study is to explore the impacts of COVID-19 on people's mental health, ...to assist policy makers to develop actionable policies, and help clinical practitioners (e.g., social workers, psychiatrists, and psychologists) provide timely services to affected populations. We sample and analyze the Weibo posts from 17,865 active Weibo users using the approach of Online Ecological Recognition (OER) based on several machine-learning predictive models. We calculated word frequency, scores of emotional indicators (e.g., anxiety, depression, indignation, and Oxford happiness) and cognitive indicators (e.g., social risk judgment and life satisfaction) from the collected data. The sentiment analysis and the paired sample t-test were performed to examine the differences in the same group before and after the declaration of COVID-19 on 20 January, 2020. The results showed that negative emotions (e.g., anxiety, depression and indignation) and sensitivity to social risks increased, while the scores of positive emotions (e.g., Oxford happiness) and life satisfaction decreased. People were concerned more about their health and family, while less about leisure and friends. The results contribute to the knowledge gaps of short-term individual changes in psychological conditions after the outbreak. It may provide references for policy makers to plan and fight against COVID-19 effectively by improving stability of popular feelings and urgently prepare clinical practitioners to deliver corresponding therapy foundations for the risk groups and affected people.
The study aims to understand Twitter users' discourse and psychological reactions to COVID-19. We use machine learning techniques to analyze about 1.9 million Tweets (written in English) related to ...coronavirus collected from January 23 to March 7, 2020. A total of salient 11 topics are identified and then categorized into ten themes, including "updates about confirmed cases," "COVID-19 related death," "cases outside China (worldwide)," "COVID-19 outbreak in South Korea," "early signs of the outbreak in New York," "Diamond Princess cruise," "economic impact," "Preventive measures," "authorities," and "supply chain." Results do not reveal treatments and symptoms related messages as prevalent topics on Twitter. Sentiment analysis shows that fear for the unknown nature of the coronavirus is dominant in all topics. Implications and limitations of the study are also discussed.
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•GeoSOM is used to solve the spatial heterogeneity problem in landslide modelling.•Stacking ensemble technique improves the performance of landslide models.•The hybrid model ...outperforms three traditional landslide methods.
Landslides are a type of serious geologic disaster causing great damage to the human environment. Landslide susceptibility mapping is an effective means to reduce landslide risk. However, previous studies have not considered spatial heterogeneity. In this study, a hybrid model considering spatial heterogeneity is designed by integrating GeoSOM and Stacking ensemble methods and is applied to map the landslide susceptibility of Zhejiang Province, China. The GeoSOM method was used to cluster the study area into several homogeneous regions to solve the heterogeneity problem, and each region was assigned a cluster attribute as one of the landslide model inputs. The Stacking ensemble technique was utilized to design a high-performance landslide model by combining three traditional machine learning methods (support vector machine (SVM), artificial neural network (ANN), and gradient-boosting decision tree (GBDT)). We collected 1051 landslide samples and fourteen affecting factors after feature selection. For landslide modelling, the landslides were randomly split into two subsets: 70% samples for training and the rest for validation. Landslide models were assessed by the receiver operating characteristic (ROC) curve and statistical measures. The results indicated that the hybrid model was 0.11–0.135 higher than those of traditional machine learning methods in term of the area under the ROC curve (AUC). In general, this hybrid model can generate high-quality landslide susceptibility maps and help to develop policies that reduce the burden of landslides.
Introduction
Sleeping disorders is a high prevalent disorder, and although previous research has suggested a link between smoking and sleep disorders, there is a lack of large-scale, nationally ...representative studies examining this association across multiple sleep outcomes and exploring dose-response relationships.
Methods
This study used data from 30,269 participants from the NHANES database (2007–2020). Weighted logistic regression models were used to assess the associations between smoking status (non-smoker, light smoker, moderate smoker, and heavy smoker) and various sleep outcomes, including insufficient sleep duration, reported sleep problems, snoring, snorting, or stopping breathing during sleep, and daytime sleepiness. Dose-response relationships were explored using restricted cubic splines.
Results
Compared to non-smokers, heavy smokers had significantly higher odds of experiencing insufficient sleep duration with OR 1.732 (95% CI 1.528–1.963, P <0.001), reported sleep problems with OR 1.990 (95% CI 1.766–2.243, P <0.001), occasional or frequent snoring with OR 1.908 (95% CI 1.164–3.128, P = 0.03), and occasional or frequent snorting or stopping breathing during sleep with OR 1.863 (95% CI 1.183–2.936, P = 0.022), while results for sometimes, often or almost always being overly sleepy during the day with OR 1.257 (95% CI 0.872–1.810, P = 0.115) are not significant. A trend of positive correlation was observed between smoking and all sleep disorder outcomes (P for trend < 0.05). Dose-response analyses revealed that the odds of these sleep outcomes increased with higher smoking levels.
Conclusion
Smoking is significantly associated with various sleep disorders, and a dose-response relationship exists between smoking levels and the odds of experiencing these sleep problems. These findings underscore the importance of addressing smoking as a modifiable risk factor for poor sleep health and suggest that reducing smoking, even if complete cessation is not achieved, may have positive effects on sleep outcomes.
In this letter, a coding diffuse metasurface for radar cross section (RCS) reduction is designed, simulated, and measured. First, two kinds of artificial magnetic conductor (AMC) unit cell are ...analyzed, and 5 × 5 AMC unit cells construct metamaterials block. A linear array factor is optimized by the ergodic algorithm, and the sequence is expanded from one-dimension code to two-dimension code. Then, the diffuse metasurface is constructed through the metamaterials blocks arrangement. Simulation results show that the bandwidth of RCS reduction is expanded compared to the classical chessboard arrangement. The diffuse characteristic of the metasurface is achieved under normal and oblique incidence wave. Experiment results verify the validity of the theoretical design and simulation. This letter provides a rapid design method to obtain diffuse metasurface, and this metasurface may achieve potential applications on low-scattering vehicle.
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•Changes in landscape pattern in opencast coal mine area were analyzed.•Complex network was used to analyze the interaction of landscape pattern index.•Mining affected landscape ...pattern change and land reclamation improved it.•Landscape shape index has a greater impact on other landscape indexes.
Coal is the main resource in China, and opencast mining plays an important role. The impact of coal mining on landscape ecology is huge. Based on the interpreted six-phase land use status map, the study analyzed the land use evolution process in Pingshuo opencast mining area of China using the land use degree index and land use fractal characteristics. Landscape level index and patch type level index were used to further analyze the spatial and temporal evolution of landscape pattern. The Gephi software was used to construct the complex network of landscape level index and patch type level index, and calculate the degree value, centrality degree, clustering coefficient, and number of triangles. The study area was in the period of land use development and socio-economic conditions increased steadily. The fractal dimensions of the land use types in the coal mining area were significantly different, and the shape boundary of the regions in each land use type developed in a complicated direction. From 1986 to 2015, the landscape heterogeneity of the mining area was unstable, the degree of landscape fragmentation increased, ecological functions were affected, and the correlation between the nodes inside a complex network became larger, the network became tighter, and the stability became stronger. The dominance of cultivated land landscape declined, the mining activities in opencast mining area reached the strongest, and the industrial and mining land was the most fragmented in 2013. The nodes of landscape shape index were most closely connected with other nodes and played the most important role in the process of network information transmission.
•Multiple linear regression model was used to qualify Chl-a concentrations in lakes on the Tibetan Plateau (QTP).•The less errors were achieved by Chl-a model.•The number of lakes with reduced Chl-a ...concentrations has increased over the past 30 years.•Contributions of temperature(23.4%) and rainfall(26.6%) to annual chl-a concentration in lakes are equal to lake area (50%).
Plateau lake chlorophyll-a concentration Chl-a is one of the key water quality parameters to characterize trophic state responding to climate change. A total of 106 samples were collected in situ lake samples from 16 typical lakes in Qinghai-Tibet Plateau (QTP) from 2015 and 2019 to match Landsate series satellite imagery data. We developed a new empirical model of Chl-a and then analyze their relationships with climate factors, for oligotrophic lakes. The results show that the Chl-a model performed well with good fitting performance (R2 = 0.72) and few errors (RMSE = 0.49 ug L-1, and MAE = 0.38 ug L-1). The results further revealed that the amount of lakes with reduced Chl-a has gradually increased over the past 30 years (1990–2000: 50%; 2000–2010: 57.8 %; 2010–2020: 70%). Although the biochemical parameters of QTP lakes have been studied in more detail, this study found thatChl-a showed equilibrium with climate factors, e.g., averaged temperature and precipitation, from satellite long-term observations, as well as the lake area changes.The model could use available bands on Landsat sensors to generate historicalChl-a data for evaluating the water qualities changes and decision-making related to the Plateau's oligotrophic lakes and global climate change.
The majority of existing deep learning pan-sharpening methods often use simulated degraded reference data due to the missing of real fusion labels which affects the fusion performance. The normally ...used convolutional neural network (CNN) can only extract the local detail information well which may cause the loss of important global contextual characteristics with long-range dependencies in fusion. To address these issues and to fuse spatial and spectral information with high quality information from the original panchromatic (PAN) and multispectral (MS) images, this paper presents a novel pan-sharpening method by designing the CNN+ pyramid Transformer network with no-reference loss (CPT-noRef). Specifically, the Transformer is used as the main architecture for fusion to supply the global features, the local features in shallow CNN are combined, and the multi-scale features from the pyramid structure adding to the Transformer encoder are learned simultaneously. Our loss function directly learns the spatial information extracted from the PAN image and the spectral information from the MS image which is suitable for the theory of pan-sharpening and makes the network control the spatial and spectral loss simultaneously. Both training and test processes are based on real data, so the simulated degraded reference data is no longer needed, which is quite different from most existing deep learning fusion methods. The proposed CPT-noRef network can effectively solve the huge amount of data required by the Transformer network and extract abundant image features for fusion. In order to assess the effectiveness and universality of the fusion model, we have trained and evaluated the model on the experimental data of WorldView-2(WV-2) and Gaofen-1(GF-1) and compared it with other typical deep learning pan-sharpening methods from both the subjective visual effect and the objective index evaluation. The results show that the proposed CPT-noRef network offers superior performance in both qualitative and quantitative evaluations compared with existing state-of-the-art methods. In addition, our method has the strongest generalization capability by testing the Pleiades and WV-2 images on the network trained by GF-1 data. The no-reference loss function proposed in this paper can greatly enhance the spatial and spectral information of the fusion image with good performance and robustness.
The leaf inclination angle (LIA), defined as the leaf or needle inclination angle to the horizontal plane, is vital in radiative transfer, precipitation interception, evapotranspiration, ...photosynthesis, and hydrological processes. This paper reviews the field and remote sensing methods to determine LIA. In the field, LIA is determined using direct and indirect methods. The direct methods include direct contact, photographic, and light detection and ranging (LiDAR) methods, while the indirect methods are composed of the gap fraction, four-component, and polarization measurement methods. The direct methods can obtain LIA accurately at individual leaves, crown, and plot scales, whereas the indirect methods work well for crops at the plot level. The remote sensing methods to estimate LIA are mainly based on the empirical, radiative transfer model, and gap fraction methods. More advanced inversion strategies and validation studies are necessary to improve the robustness of LIA remote sensing estimation. In future studies, automated observation systems can be developed and the LIA measurement can be incorporated into existing ground observation networks to enhance spatial coverage.
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