Hydraulic correction method (HCM) to enhance the efficiency of SRTM DEM in flood modeling

Digital Elevation Model (DEM) is one of the most important controlling factors determining the simulation accuracy of hydraulic models. However, the currently available global topographic data is confronted with limitations for application in 2-D hydraulic modeling, mainly due to the existence of vegetation bias, random errors and insufficient spatial resolution. A hydraulic correction method (HCM) for the SRTM DEM is proposed in this study to improve modeling accuracy. Firstly, we employ the global vegetation corrected DEM (i.e. Bare-Earth DEM), developed from the SRTM DEM to include both vegetation height and SRTM vegetation signal. Then, a newly released DEM, removing both vegetation bias and random errors (i.e. Multi-Error Removed DEM), is employed to overcome the limitation of height errors. Last, an approach to correct the Multi-Error Removed DEM is presented to account for the insufficiency of spatial resolution, ensuring flow connectivity of the river networks. The approach involves: (a) extracting river networks from the Multi-Error Removed DEM using an automated algorithm in ArcGIS; (b) correcting the location and layout of extracted streams with the aid of Google Earth platform and Remote Sensing imagery; and (c) removing the positive biases of the raised segment in the river networks based on bed slope to generate the hydraulically corrected DEM. The proposed HCM utilizes easily available data and tools to improve the flow connectivity of river networks without manual adjustment. To demonstrate the advantages of HCM, an extreme flood event in Huifa River Basin (China) is simulated on the original DEM, Bare-Earth DEM, Multi-Error removed DEM, and hydraulically corrected DEM using an integrated hydrologic-hydraulic model. A comparative analysis is subsequently performed to assess the simulation accuracy and performance of four different DEMs and favorable results have been obtained on the corrected DEM

Integrated remote sensing imagery and two-dimensional hydraulic modeling approach for impact evaluation of flood on crop yields

The projected frequent occurrences of extreme flood events will cause significant losses to crops and will threaten food security. To reduce the potential risk and provide support for agricultural flood management, prevention, and mitigation, it is important to account for flood damage to crop production and to understand the relationship between flood characteristics and crop losses. A quantitative and effective evaluation tool is therefore essential to explore what and how flood characteristics will affect the associated crop loss, based on accurately understanding the spatiotemporal dynamics of flood evolution and crop growth. Current evaluation methods are generally integrally or qualitatively based on statistic data or ex-post survey with less diagnosis into the process and dynamics of historical flood events. Therefore, a quantitative and spatial evaluation framework is presented in this study that integrates remote sensing imagery and hydraulic model simulation to facilitate the identification of historical flood characteristics that influence crop losses. Remote sensing imagery can capture the spatial variation of crop yields and yield losses from floods on a grid scale over large areas; however, it is incapable of providing spatial information regarding flood progress. Two-dimensional hydraulic model can simulate the dynamics of surface runoff and accomplish spatial and temporal quantification of flood characteristics on a grid scale over watersheds, i.e., flow velocity and flood duration. The methodological framework developed herein includes the following: (a) Vegetation indices for the critical period of crop growth from mid-high temporal and spatial remote sensing imagery in association with agricultural statistics data were used to develop empirical models to monitor the crop yield and evaluate yield losses from flood; (b) The two-dimensional hydraulic model coupled with the SCS-CN hydrologic model was employed to simulate the flood evolution process, with the SCS-CN model as a rainfall-runoff generator and the two-dimensional hydraulic model implementing the routing scheme for surface runoff; and (c) The spatial combination between crop yield losses and flood dynamics on a grid scale can be used to investigate the relationship between the intensity of flood characteristics and associated loss extent. The modeling framework was applied for a 50-year return period flood that occurred in Jilin province, Northeast China, which caused large agricultural losses in August, 2013. The modeling results indicated that (a) the flow velocity was the most influential factor that caused spring corn, rice and soybean yield losses from extreme storm event in the mountainous regions; (b) the power function archived the best results that fit the velocity-loss relationship for mountainous areas; and (c) integrated remote sensing imagery and two-dimensional hydraulic modeling approach are helpful for evaluating the influence of historical flood event on crop production and investigating the relationship between flood characteristics and crop yield losses

The Other Press, February 19, 1987

<p>Energy profile (in kcal.mol^-1) of face-on path for Erlotinib bioactivation by the Cpd I model of CYP3A4 and 1A2 in the gas and solvent phases.</p

Graphene Quantum Dot Hybrids as Efficient Metal-Free Electrocatalyst for the Oxygen Reduction Reaction

The doping of heteroatoms into graphene quantum dot nanostructures provides an efficient way to tune the electronic structures and make more active sites for electro-catalysis, photovoltaic, or light emitting applications. Other than the modification of chemical composition, novel architecture is very desirable to enrich the research area and provides a wide range of choices for the diverse applications. Herein, we show a novel lotus seedpod surface-like pattern of zero-dimension (0D) seed-like N-GODs of ca.3 nm embedded on the surface of a two-dimension (2D) N-GQD sheet of ca.35 nm. It is demonstrated that different photoluminescence (PL) could be tuned easily, and the novel multidimensional structure displays excellent performance toward oxygen reduction reaction in alkaline solutions. Thus, the fabricated N-GQD hybrids show bright perspective in biomedical imaging, biosensors, and conversion and storage of energy

Data_Sheet_1_Study on the regional risk classification method for the prevention and control of emerging infectious diseases based on directed graph theory.PDF

BackgroundEmerging infectious diseases are a class of diseases that are spreading rapidly and are highly contagious. It seriously affects social stability and poses a significant threat to human health, requiring urgent measures to deal with them. Its outbreak will very easily lead to the large-scale spread of the virus, causing social problems such as work stoppages and traffic control, thereby causing social panic and psychological unrest, affecting human activities and social stability, and even endangering lives. It is essential to prevent and control the spread of infectious diseases effectively.PurposeWe aim to propose an effective method to classify the risk level of a new epidemic region by using graph theory and risk classification methods to provide a theoretical reference for the comprehensive evaluation and determination of epidemic prevention and control, as well as risk level classification.MethodsUsing the graph theory method, we first define the network structure of social groups and construct the risk transmission network of the new epidemic region. Then, combined with the risk classification method, the classification of high, medium, and low risk levels of the new epidemic region is discussed from two cases with common and looped graph nodes, respectively. Finally, the reasonableness of the classification method is verified by simulation data.ResultsThe directed weighted scale-free network can better describe the transmission law of an epidemic. Moreover, the proposed method of classifying the risk level of a region by using the correlation function between two regions and the risk value of the regional nodes can effectively evaluate the risk level of different regions in the new epidemic region. The experiments show that the number of medium and high risk nodes shows no increasing trend. The number of high-risk regions is relatively small compared to medium-risk regions, and the number of low-risk regions is the largest.ConclusionsIt is necessary to distinguish scientifically between the risk level of the epidemic area and the neighboring regions so that the constructed social network model of the epidemic region's spread risk can better describe the spread of the epidemic risk in the social network relations.</p

Climate and website visit data for multiple cities

The archive contains the following files: -analysis_government_sites.R: code for our analysis of visits to government websites -analysis_lashou.R: code for our analysis of the lash shopping dataset -analysis_transport_sites.R: code for our analysis of visits to public transport websites -data_government_sites (directory): contains number of daily visits to various government websites, as reported by Alexa. -data_transport_sites (directory): contains number of daily visits to various public transport websites, as reported by Alexa -data_weather.csv: daily weather data, analysed in conjunction with the shopping dataset -results_government_sites.csv: table summarising the results of analysing visits to government websites -results_transport_sites.csv: table summarising the results of analysing visits to public transport website

DataSheet1_Flour derived porous carbon as anode for highly robust potassium-ion batteries.doc

Potassium-ion batteries (PIBs) have attracted increasing research interest because of the natural abundance and low cost of potassium. Nevertheless, lacking of suitable anode materials that can deliver high reversible capacity and long cycle life highly hinder the further development of PIBs. Here, we report a flour chemistry strategy to establish a porous phosphorus-doped carbon (PPDC) as anode for high-performance PIBs. The as-prepared PPDC with high hierarchically porous structure and rich P-doping not only offers fast transport of K+ and electrons during continuous cycling, but also affords sufficient inner space to relieve volume expansion of active electrode. Therefore, the PPDC displayed high reversible capacity, excellent cyclic stability, outstanding rate performance. These results imply a great potential for applications in the field of high-energy storage devices.</p

Graphitic Carbon Nitride Sensitized with CdS Quantum Dots for Visible-Light-Driven Photoelectrochemical Aptasensing of Tetracycline

Graphitic carbon nitride (g-C₃N₄) is a new type of metal-free semiconducting material with promising applications in photocatalytic and photoelectrochemical (PEC) devices. In the present work, g-C₃N₄ coupled with CdS quantum dots (QDs) was synthesized and served as highly efficient photoactive species in a PEC sensor. The surface morphological analysis showed that CdS QDs with a size of ca. 4 nm were grafted on the surface of g-C₃N₄ with closely contacted interfaces. The UV–visible diffuse reflection spectra (DRS) indicated that the absorption of g-C₃N₄ in the visible region was enhanced by CdS QDs. As a result, g-C₃N₄–CdS nanocomposites demonstrated higher PEC activity as compared with either pristine g-C₃N₄ or CdS QDs. When g-C₃N₄–CdS nanocomposites were utilized as transducer and tetracycline (TET)-binding aptamer was immobilized as biorecognition element, a visible light-driven PEC aptasensing platform for TET determination was readily fabricated. The sensor showed a linear PEC response to TET in the concentration range from 10 to 250 nM with a detection limit (3S/N) of 5.3 nM. Thus, g-C₃N₄ sensitized with CdS QDs was successfully demonstrated as useful photoactive nanomaterials for developing a highly sensitive and selective PEC aptasensor

Correlation between average yearly temperature and traffic difference (between comfortable and cold months) for public transport websites.

<p>Each data point represents a city.</p

The effect of winter length (number of days) on planning activities across 28 cities.

<p>Each point represents a city in our dataset. For each city we indicate the length of winter defined as the number of days with DIK < = 60 (y-axis), the correlation strength as reported in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0126205#pone.0126205.s001" target="_blank">S1 Text</a> figure 1 (x-axis), and the absolute T-value of the correlation (color scale). Blue points represent the cities reporting no significant correlation. The grey area shows the 95% CIs.</p