Drainage network extraction plays an important role in geomorphologic analyses, hydrologic modeling, and non-point source pollutant simulation, among others. Flow enforcement, by imposing information ...of known river maps to digital elevation models (DEMs), leads to improved drainage network extraction. However, the existing flow enforcement methods (e.g., the elevation-based stream-burning method) have certain limitations, as they may cause unreal longitudinal profiles, lead to unintended topological errors, and even misinterpret the overall drainage patterns. The present study proposes an enhanced flow enforcement method without elevation modification towards an accurate and efficient drainage network extraction. In addition to preserving the Boolean-value information as to whether a DEM pixel belongs to a stream, the proposed method can also well preserve and fully utilize the topological relations among mapped streamlines and morphological information of each mapped streamline. The method involves two important steps: (1) proposal of an improved rasterization algorithm of mapped streamlines to yield continuous, unambiguous, and collision-free raster equivalent of stream vectors for flow enforcement; and (2) realization of the enhanced flow enforcement in a modified Priority-Flood procedure –– in this way, flows are enforced to completely follow the mapped streamlines, and hence, channel short-circuits and spurious confluences of adjacent streams are avoided. An efficient implementation of the method is made based on a size-balanced binary search tree. The method is also tested over the Rogue River Basin in the United States, using DEMs with various resolutions. Visual and statistical analyses of the results indicate three major advantages of the proposed method: (1) significant reduction in the misinterpretation of drainage patterns; (2) maximum channel displacement of one pixel to the river map at various resolutions; and (3) high computational efficiency.
•An improved method to extract drainage networks from DEMs and river maps is proposed.•An improved rasterization process to avoid misinterpretation of vector streams is attempted.•Mapped streams are incorporated in the Priority-Flood procedure without modifying a DEM.•Topological and morphological information of mapped streams are well preserved.
The long and the short of it. The first amido–digermyne to possess a short Ge–Ge multiple bond, LGeGeL, has been prepared and shown to activate H2 below 0 °C, thereby yielding the hydrido–digermene, ...L(H)GeGe(H)L. This possesses a very long GeGe bond. Spectroscopic and theoretical data indicate that the dimer dissociates in solution to give the two‐coordinate hydrido–germylene, :Ge(H)(L). L=N(Ar)(SiiPr3), Ar=2,6‐C(H)Ph22‐4‐iPrC6H2.
With the increasing resolution of digital elevation models (DEMs), computational efficiency problems have been encountered when extracting the drainage network of a large river basin at billion-pixel ...scales. The efficiency of the most time-consuming depression-filling pretreatment has been improved by using the O(NlogN) complexity least-cost path search method, but the complete extraction steps following this method have not been proposed and tested. In this paper, an improved O(NlogN) algorithm was proposed by introducing a size-balanced binary search tree (BST) to improve the efficiency of the depression-filling pretreatment further. The following extraction steps, including the flow direction determination and the upslope area accumulation, were also redesigned to benefit from this improvement. Therefore, an efficient and comprehensive method was developed. The method was tested to extract drainage networks of 31 river basins with areas greater than 500,000km2 from the 30-m-resolution ASTER GDEM and two sub-basins with areas of approximately 1000km2 from the 1-m-resolution airborne LiDAR DEM. Complete drainage networks with both vector features and topographic parameters were obtained with time consumptions in O(NlogN) complexity. The results indicate that the developed method can be used to extract entire drainage networks from DEMs with billions of pixels with high efficiency.
•We present a drainage network extraction method with O(NlogN) complexity.•We use a size-balanced binary search tree to speed up the depression-filling.•The vectors and parameters of channels and hillslopes are obtained after six steps.•This method can process both global and LiDAR DEMs at billion-pixel scales.•Extracting the whole Amazon River basin from 30-m ASTER GDEM requires only 14h.
Abstract
Airflow behavior and outdoor PM
2.5
dispersion depend significantly on the building-tree layouts and orientation towards the prevailing wind conditions. To investigate this issue, the ...present work evaluates the aerodynamic effect of different building-tree layouts on the outdoor PM
2.5
dispersions in the urban communities of Shijiazhuang City, China. The adopted numerical CFD technique was based on the standard k–ε model and the Disperse Phase Model (DPM). For this study, ten different building-tree arrangements were conceptualized and all these configurations were simulated by using Ansys Fluent software to quantify the implications on the outdoor PM
2.5
dispersion due to their presence. The results have shown that: (1) a wide building interval space could benefit the air ventilation and thus decrease PM
2.5
concentrations, however, this effectiveness is highly influenced by the presence of the trees; (2) the trees on the leeward side of a building tend to increase the local wind velocity and decrease the pedestrian-level PM
2.5
concentrations, while those on the windward side tend to decrease the wind velocity. The small distance with trees in the central space of the community forms a wind shelter, hindering the particle dispersion; and (3) the configuration of parallel type buildings with clustered tree layouts in the narrow central space is most unfavorable to the air ventilation, leading to larger areas affected by excessive PM
2.5
concentration.
L. (purslane) is a widely distributed plant with a long history of cultivation and consumption. Notably, polysaccharides obtained from purslane exhibit surprising and satisfactory biological ...activities, which explain the various benefits of purslane on human health, including anti-inflammatory, antidiabetic, antitumor, antifatigue, antiviral and immunomodulatory effects. This article systematically reviews the extraction and purification methods, chemical structure, chemical modification, biological activity and other aspects of polysaccharides from purslane collected in the Chinese Pharmacopoeia, Flora of China, Web of Science, PubMed, Baidu Scholar, Google Scholar and CNKI databases in the last 14 years, using the keywords "
L. polysaccharides" and "purslane polysaccharides". The application of purslane polysaccharides in different fields is also summarized, and its application prospects are also discussed. This paper provides an updated and deeper understanding of purslane polysaccharides, which will provide useful guidance for the further optimization of polysaccharide structures and the development of purslane polysaccharides as a novel functional material, as well as a theoretical basis for its further research and application in human health and manufacturing development.
Due to safety problems caused by the use of organic electrolytes in lithium-ion batteries and the high production cost brought by the limited lithium resources, water-based zinc-ion batteries have ...become a new research focus in the field of energy storage due to their low production cost, safety, efficiency, and environmental friendliness. This paper focused on vanadium dioxide and expanded graphite (EG) composite cathode materials. Given the cycling problem caused by the structural fragility of vanadium dioxide in zinc-ion batteries, the feasibility of preparing a new composite material is explored. The EG/VO2 composites were prepared by a simple hydrothermal method, and compared with the aqueous zinc-ion batteries assembled with a single type of VO2 under the same conditions, the electrode materials composited with high-purity sulfur-free expanded graphite showed more excellent capacity, cycling performance, and multiplicity performance, and the EG/VO2 composites possessed a high discharge ratio of 345 mAh g−1 at 0.1 A g−1, and the Coulombic efficiency was close to 100%. The EG/VO2 composite has a high specific discharge capacity of 345 mAh g−1 at 0.1 A g−1 with a Coulombic efficiency close to 100%, a capacity retention of 77% after 100 cycles, and 277.8 mAh g−1 with a capacity retention of 78% at a 20-fold increase in current density. The long cycle test data demonstrated that the composite with expanded graphite effectively improved the cycling performance of vanadium-based materials, and the composite maintained a stable Coulombic efficiency of 100% at a high current density of 2 A/g and still maintained a specific capacity of 108.9 mAh/g after 2000 cycles.
•Development of the framework of SOA for ensemble flood forecast from NWP.•Development of a method to automatically download and update the NWP.•Realization of implementing multiple scenarios of ...flood forecast at the same time.•Validation of the new method through simulating flood flow at two river basins.
Floods in mountainous river basins are generally highly destructive, usually causing enormous losses of lives and property. It is important and necessary to develop an effective flood forecast method to prevent people from suffering flood disasters. This paper proposed a general framework for a service-oriented architecture (SOA) for ensemble flood forecast based on numerical weather prediction (NWP), taking advantage of state-of-the-art technologies, e.g., high-accuracy NWP, high-capacity cloud computing, and an interactive web service. With the predicted rainfall data derived from the NWP, which are automatically downloaded, hydrological models will be driven to run on the cloud. Judging from the simulation results and flood control requirements offered by users, warning information about possible floods will be generated for potential sufferers and then sent to them as soon as possible if needed. Moreover, by using web service in a social network, users can also acquire such information on the clients and make decisions about whether to prepare for possible floods. Along with the real-time updates of the NWP, simulation results will be refreshed in a timely manner, and the latest warning information will always be available to users. From the sample demonstrations, it is concluded that the SOA is a feasible way to develop an effective ensemble flood forecast method. After being put into practice, it would be valuable for preventing or reducing the losses caused by floods in mountainous river basins.
This study was undertaken to evaluate the effects of mixing BDC-NO2 and BDC-NH2 linkers in the synthesis of Zr-based metal organic frameworks (Zr-MOFs) on their adsorption and separation of CO2 and ...CH4. UiO-66 with single or binary -NO2 and -NH2 samples were synthesized under solvothermal conditions and activated by solvent exchanging using methanol. Structural analyses of the materials were conducted using FTIR, XRD, TGA, SEM, 1HNMR and N2 adsorption/desorption techniques and adsorption of CO2 and CH4 at high pressures and different temperatures (273 and 298 K) was investigated. It was found that UiO-66-NH2 exhibited higher CO2 and CH4 adsorption capacities than those of UiO-66-NO2. Addition of -NH2 functional group in UiO-66-NO2 could enhance CO2 and CH4 adsorption due to the extra CO2 adsorption sites of -NH2 functional groups. Addition of -NO2 functional group to UiO-66-NH2 at a low loading could also increase CO2 and CH4 adsorption, however, a high loading of NO2 functional group to UiO-66-NH2 would result in decreased adsorption.
KNN classification is an improvisational learning mode, in which they are carried out only when a test data is predicted that set a suitable K value and search the K nearest neighbors from the whole ...training sample space, referred them to the lazy part of KNN classification. This lazy part has been the bottleneck problem of applying KNN classification due to the complete search of K nearest neighbors. In this paper, a one-step computation is proposed to replace the lazy part of KNN classification. The one-step computation actually transforms the lazy part to a matrix computation as follows. Given a test data, training samples are first applied to fit the test data with the least squares loss function. And then, a relationship matrix is generated by weighting all training samples according to their influence on the test data. Finally, a group lasso is employed to perform sparse learning of the relationship matrix. In this way, setting K value and searching K nearest neighbors are both integrated to a unified computation. In addition, a new classification rule is proposed for improving the performance of one-step KNN classification. The proposed approach is experimentally evaluated, and demonstrated that the one-step KNN classification is efficient and promising.
In Alzheimer's disease, the researchers found that if the patients were treated at the early stage of the disease, it could effectively delay the development of the disease. At present, multi-modal ...feature selection is widely used in the early diagnosis of Alzheimer's disease. However, existing multi-modal feature selection algorithms focus on learning the internal information of multiple modalities. They ignore the relationship between modalities, the importance of each modality and the local structure in the multi-modal data. In this paper, we propose a multi-modal feature selection algorithm with anchor graph for Alzheimer's disease. Specifically, we first use the least square loss and
l
2,1
−norm to obtain the weight of the feature under each modality. Then we embed a modal weight factor into the objective function to obtain the importance of each modality. Finally, we use anchor graph to quickly learn the local structure information in multi-modal data. In addition, we also verify the validity of the proposed algorithm on the published ADNI dataset.
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