Stroke not only impacts patients physically but also economically. Post-stroke depression (PSD), as a common complication of stroke, always obstructs the process of stroke rehabilitation. ...Accordingly, defining the risk factors associated with PSD has extraordinary importance. Although there have been many studies investigating the risk factors for PSD, the results are inconsistent.
The objectives of this study were to identify the risk factors for PSD by evidence-based medicine.
A systematic and comprehensive database search was performed of PubMed, Medline, CENTRAL, EMBASE.com, the Cochrane library and Web of Science for Literature, covering publications from January 1, 1998 to November 19, 2016.
Studies on risk factors for PSD were identified, according to inclusion and exclusion criteria. The risk of bias tool, described in the Cochrane Handbook version 5.1.0, was used to assess the quality of each study. Meta-analysis was performed using RevMan 5.3 software.
Thirty-six studies were included for review. A history of mental illness was the highest ranking modifiable risk factor; other risk factors for PSD were female gender, age (<70 years), neuroticism, family history, severity of stroke, and level of handicap. Social support was a protective factor for PSD.
There are many factors that have effects on PSD. The severity of stroke is an important factor in the occurrence of PSD. Mental history is a possible predictor of PSD. Prevention of PSD requires social and family participation.
•Both models have applicability in prediction of group-occurring shallow landslides.•Combining Scoops3D and TRIGRS shows more effective landslide prediction result.•TRIGRS has the problem of ...over-prediction in the simulation of unstable area.•Fitting curve of rainfall threshold supports landslide warning in basin lacking data.
The stability evaluation of rainfall-induced landslides using a physical determination model supports disaster prevention, but it is mostly applied to the area with few landslides, and there is a lack of quantitative study on rainfall and landslide stability. This paper combined the Scoops3D model with the TRIGRS model (3D) to predict the shallow landslide spatiotemporal distribution and compared the simulation results with those of the TRIGRS model alone (1D), aiming to obtain more accurate assessment results. At the same time, the relationship between landslide stability and accumulative rainfall was quantitatively fitted to improve the real-time landslide prediction system. We applied the 1D and 3D models to the July 21, 2013 group-occurring landslide event (976 shallow landslides) in the Niangniangba basin, China. The required geotechnical parameters of both models were acquired by field and laboratory tests. We calculated the pressure head over time using the TRIGRS model based on practical rainfall data and predicted the shallow landslide stability using the Scoops3D model according to the resulting piezometric surface. We compared the landslide stability spatial distributions of the two models under initial and saturated conditions with the landslide catalogue. The success rate of landslides predicted by 3D model is 4.20% higher than 1D model. A composite index to quantitatively evaluate both models’ performances indicates over-prediction by the 1D model in the stable region, while the 3D model more effectively predicts shallow landslides with a smaller unstable region. The relationship between instability proportion and accumulative rainfall in the 1D and 3D model can be represented by y=24.57x0.18 and y=11.59x0.33, respectively. The 3D model shows more conservative result, and the rainfall threshold analysis proposed in this paper can provide reference for the time of most landslides in the case of insufficient data, which is an important indicator for disaster early warning and prediction.
The reactivation of landslides has always been a prominent problem that has endangered town construction and people’s safety worldwide. At about 8 a.m. on July 12, 2018, on a mountain near the ...Bailong River in Nanyu Township, Zhouqu County, Gansu Province, China, a landslide collapse event occurred. About 10,000 m
3
of sloped material slid into the Bailong River, with the largest stone reaching 3 m
3
. As a result, a large number of houses were flooded. Highways and bridges were destroyed. Using field investigations, unmanned aerial vehicle (UAV) photography, InSAR traces, historical records, and multiple remote sensing images, we extracted the landslide’s geometry and geomorphic parameters to quantify the characteristics of the Jiangdingya landslide. Based on high-resolution topographic data collected before and after the landslide, the change in the geomorphological factors, geomorphologic stability, and detection of the precursory motion before the landslide failure were analyzed to fully investigate the temporal geomorphological changes. Synthesizing the above research, we discuss the causes of landslide reactivation. The Jiangdingya landslide is a typical ancient landslide formed by the coupling of internal and external dynamics. Rainfall, seismic fault zone activity, human activities, and river erosion were the main causes of this reactivation event.
•Subsidence caused by coal mining increases the likelihood of landslide occurrences.•Landslides occur in response to the spread of slow subsidence.•Subsidence curves can be adequately fitted with ...logistic regression.•The frequency ratio of landslides and fissures increases with subsidence.
Surface subsidence caused by underground coal mining affects the hillslope stability conditions. However, few studies have focused on the coupling relationship between slow surface subsidence and landslide occurrences. A detailed landslide and fissure inventory in a coal mining area in Shaanxi Province, China, was produced based on interpretation of multitemporal satellite images and unmanned aerial vehicle (UAV) surveys. We used the interferometric synthetic aperture radar (InSAR) technique and landslide and fissure spatiotemporal statistics to investigate the spreading process of the slow subsidence caused by underground mining and examined its impact on the occurrence of shallow landslides. The InSAR results indicate that the actual extent of the subsidence zone is larger than the range of underground mining, which formed a subsidence basin along the coal mining panels. The subsidence curves go through initial, accelerative, and slow subsidence stages and characterized by S-shaped, which can be adequately fitted with logistic regression. Moreover, subsidence does not cease after the end of coal exploitation. Logistic models predicted that the duration of residual subsidence reached about 2–3 years. Subsidence significantly increased the likelihood of landslide occurrences. The spatial pattern of landslides is associated with the actual coal mining. We also investigated the clustering phenomenon of landslides and fissures under the impacts of subsidence. The frequency ratio of landslides and fissures increased with the cumulative subsidence. Finally, we propose a schematic view for landslides caused by coal mining and precipitation. This study will be helpful for elucidating the spatial–temporal evolution of slow subsidence and its impact on loess landslides in coal mining area.
•A coupled model for predicting landslide-debris flow hazard chains was proposed.•The coupled model is capable of predicting the hazard chain by the hour.•The input of landslide area influences the ...prediction of debris flow.•The rainfall threshold curve supports landslide prediction in areas lacking data.
Landslides, debris flows, and other destructive natural hazards induced by heavy rainfall in mountainous regions are sometimes not independent but combined to form a disaster chain. Based on the integral link between the triggering of the landslide and the subsequent debris flow, we propose an approach that combines the Transient Rainfall Infiltration and Grid-Based Regional Slope Stability (TRIGRS) model and the Rapid Mass Movements Simulation (RAMMS) model to achieve hourly hazard prediction. The results indicate that the TRIGRS model performed well in predicting the spatial distribution of the shallow landslides, with a success rate of 81.86%. Thus, it is reasonable to use it as the initial input for debris flow simulations. The relationship between the landslide area and the accumulated rainfall obtained using the TRIGRS model is a power-law relationship, which provides a reference for regions that lack rainfall data to predict the material source of a debris flow. The coupled model was found to have a good accuracy of 76.77% in simulating the debris flow. This was close to the debris flow simulation based on the interpreted landslides, and it still produced reasonable results and a more practical value. Furthermore, the proposed coupled model can dynamically predict disasters by the hour based on actual rainfall events. Therefore, the results of this study help provide a more complete hazard prediction picture for rainfall-induced landslide-debris flow hazards in mountainous regions.
•UAV surveys can be used for evaluating long-term hillslope morphology evolution.•Successive landslides influence frequency distributions of topographic features.•Successive landslides gradually ...reduce slope gradient, roughness and local relief.•The slope gradient changes with elevation.
Landslides are recognized as dominant geomorphic events of morphological evolution in most mountainous and hilly landscapes. However, the lack of multitemporal high-resolution topographic data has resulted in a lack of quantitative estimates of topographic changes influenced by successive landslides. Taking a typical hillslope with successive loess landslides in the Heifangtai loess tableland, China, as an example, we conducted four unmanned aerial system (UAS) surveys and created corresponding high-resolution digital elevation models (HRDEMs) and orthophotos. We found that multitemporal UAS surveys have become a powerful new approach for addressing local topographic changes and evolution over a relatively long time series. Moreover, landslides can leave persistent geomorphic imprints on hillslope topography. The frequency distributions of topographic indexes are significantly influenced by successive landslides. The mean slope gradient, roughness and local relief decreased with successive landslide occurrences, whereas the mean topographic wetness index (TWI) increased. However, the mean slope aspect was almost unchanged by successive landslides. Furthermore, analysis of the coefficient of variation demonstrates that the frequency distribution of the slope gradient becomes more dispersed with landslide occurrences, while the slope aspect and TWI become more concentrated. The slope gradient changes with elevation. More broadly, this study provides new insights into the prediction of the local topographic feature changes and morphology evolution trends caused by successive landslides.
Computational Fluid Dynamic (CFD) has been widely used for the gas release and dispersion modeling, which however could not support real-time emergency response planning due to its high computation ...overhead. Surrogate models offer a potential alternative to rigorous computational approaches, however, as the point-estimation alternatives, the existing neural network-based surrogate models are not able to quantify the uncertainty of the released gas spatial concentration. This study aims to fill a gap by proposing an advanced hybrid probabilistic Convolutional-Variational Autoencoder-Variational Bayesian neural network (Conv-VAE-VBnn). Experimental study based on a benchmark simulation dataset was conducted. The results demonstrated the additional uncertainty information estimated by the proposed model contributes to reducing the harmful effect of too ‘confidence’ of the point-estimation models. In addition, the proposed model exhibits competitive accuracy with R2 = 0.94 compared and real-time capacity with inference time less than 1s. Latent size Nz = 2, noise σz=0.1 and Monte Carlo sampling number m = 500 to ensure the model’s real-time capacity, were also given. Overall, our proposed model could provide a reliable alternative for constructing a digital twin for emergency management during the exploration and exploitation of marine natural gas hydrate (NHG) in the near future.
•Advanced probabilistic hybrid Conv-VAE-VBnn model is proposed.•Model correlates points with distribution of high dimensional spatial features.•Model quantifies uncertainty of spatial concentration given scenario-related inputs.•Model exhibits competitive accuracy and superior real-time application capability.•Hyper-parameters influencing model real-time application capability are analyzed.
Landslides triggered by rainstorms and earthquakes are prominent geological hazards that exhibit distinctive spatial and morphological characteristics due to diverse instability mechanisms. However, ...studies on differences between the two types of landslides remain limited. In this study, we explored differences in location and geometric properties between rainstorm‐induced landslides in Qinzhou, Longchuan and Fukuoka and earthquake‐induced landslides in Lushan, Iburi and Kaikōura. We normalized the location of landslides across the slope and quantified the landslide polygons using four geometric properties. Findings revealed that both location and geometric properties are specific to landslide type and differ between them. Earthquake‐induced landslides are more common near the ridge of a slope, while rainstorm‐induced landslides are more frequent in the valley or near streams. The quantitative analysis of geometric properties showed that earthquake‐induced landslides are generally larger and have a more compact, rounded and less complex shape. The two landslide types present different hazards, particularly in their runout zones, where dispersion of materials occurs. Insights from our quantitative approach serve as a critical foundation for informed decision‐making in emergency scenarios and contribute to enhancing landslide hazard management.
This study found that both location and geometric properties are specific to landslide type and differ between rainstorm‐ and earthquake‐induced landslides. Compared with rainstorm‐induced landslides, earthquake‐induced landslides are more common near the ridge of a slope and have a larger, more compact, rounded and less complex shape.
In order to quantify air pollution effects on climate change, we investigated the climate response associated with anthropogenic particulate matters (PMs) by dividing fine PM (PM2.5, particle size ...≤2.5 μm) and coarse particulate matter (CPM, particle size >2.5 μm) in great detail in this work, with an aerosol‐climate coupled model. We find that the changes in PM2.5 and CPM are very different and thus result in different, even opposite effects on climate, especially on a regional scale. The column burden of PM2.5 increases globally from 1850 to the present, especially over Asia's southern and eastern parts, whereas the column concentration of CPM increases over high‐latitude regions and decreases over South Asia. The resulted global annual mean effective radiative forcing (ERF) values due to PM2.5 and CPM changes are −1.21 W·m−2 and −0.24 W·m−2, respectively. Increases in PM2.5 result in significant cooling effects on the climate, whereas changes in CPM produce small and even opposite effects. The global annual mean surface air temperature (SAT) decreases by 0.94 K due to PM2.5 increase. Coolings caused by increased PM2.5 are more apparent over Northern Hemisphere (NH) terrain and ocean at mid‐ and high latitudes. Increases in SATs caused by increased CPM are identified over high latitudes in the NH, whereas decreases are identified over mid‐latitude regions. Strong cooling due to increased PM2.5 causes a southward shift of the Intertropical Convergence Zone (ITCZ), whereas the Hadley circulation associated with CPM is enhanced slightly over both hemispheres, along with the weak movement of corresponding ITCZ. The global annual mean precipitation decreases by approximately 0.11 mm day−1 due to the increased PM2.5. Generally, PM2.5 concentration changes contribute more than 80% of the variation caused by all anthropogenic aerosols in ERF, SAT, cloud fraction, and precipitation.
Changes in PM2.5 account for about 95% of the total changes in total anthropogenic particulate matter from 1850 to the present.
The global annual mean effective radiative forcing values were −1.21 and −0.24 W·m−2 for the changes in PM2.5 and coarse particulate matter (CPM), respectively, from the year 1850 to the present.
Reducing PM2.5 would have a much higher impact on climate than reducing CPM.
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