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Yong Zhou - One of the best experts on this subject based on the ideXlab platform.
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stainless steel mesh supported three dimensional hierarchical sno2 zn2sno4 composite for the applications in solar cell gas sensor and photocatalysis
Applied Surface Science, 2020Co-Authors: Hao Yang, Lianfeng Zhang, Ruiheng Liu, Yong ZhouAbstract:Abstract Three dimensional (3D) hierarchical micro/nanostructures are an interesting class of materials with various tunable physicochemical properties and have good application foreground in environmental protection. However, it is hard for the micrometer-sized material to directly grow on high Curvature Surface of metal wire mesh for assembling flexible devices. In this paper, the two SnO2/Zn2SnO4 composites with different hierarchical morphologies prepared using solvothermal method were deposited onto stainless steel mesh by the multiple electrophoretic deposition, and used as photoanodes of flexible dye-sensitized solar cell (FDSSCs), flexible gas sensors to detect poisonous formaldehyde (HCHO) and immobilized photocatalysts for the degradation of two different types of organic dyes. Due to larger specific Surface areas, the higher light harvesting efficiency and mesoporous property, the performances of the flexible devices based SnO2/Zn2SnO4 microspheres consisting of nanoparticle-based nanosheets were superior to those based on SnO2/Zn2SnO4 microspheres consisting of nanobelts. This work extends the development of SnO2/Zn2SnO4 hierarchical micro/nanostructures on the flexible substrate and provides a strategy for constructing other 3D hierarchical material on the flexible wire mesh with the high Curvature Surface to enhance performance of corresponding flexible devices.
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porous nanosheet based hierarchical zinc oxide aggregations grown on compacted stainless steel meshes enhanced flexible dye sensitized solar cells and photocatalytic activity
Materials Research Bulletin, 2016Co-Authors: Guangyin Liu, Yong Zhou, Yezhen Zhang, Yuhan YangAbstract:Abstract Porous nanosheet-based hierarchical ZnO aggregations are prepared on compacted stainless steel meshes (CSSMs) using a facile precursor template method. The coverage density and morphology of the film are controlled by the concentration of alkaline in the precursor solution. Relative to the traditional planar substrate, CSSMs with multi-layered structure have larger Surface area and higher porosity to capture more light from all directions. The three-dimensional (3D) hierarchical ZnO aggregations assembled by porous nanosheets could take both the advantages of nanosheets and micrometer-sized assemblies. As expected, combining the virtues of ZnO aggregations and CSSMs, the ZnO aggregations/CSSMs structure exhibit the significantly improved conversion efficiency as photoelectrode of flexible dye-sensitized solar cells and photocatalytic performance in the photodegradation of pollutants with a better recyclability. Furthermore, our work would offer idea for the growth of complex 3D hierarchical micro-/nanoarchitecture on the high Curvature Surface of the substrates.
Jung Hyun Lee - One of the best experts on this subject based on the ideXlab platform.
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a novel ensemble bivariate statistical evidential belief function with knowledge based analytical hierarchy process and multivariate statistical logistic regression for landslide susceptibility mapping
Catena, 2014Co-Authors: Omar F Althuwaynee, Biswajeet Pradhan, Hyuckjin Park, Jung Hyun LeeAbstract:This study compares the landslide susceptibility maps from four application models, namely, (1) the bivariate model of the Dempster–Shafer based evidential belief function (EBF); (2) integration of the EBF in the knowledge-based analytical hierarchy process (AHP) as a pairwise comparison model processed by using all available causative factors; (3) integration of the EBF in the knowledge-based AHP as a pairwise comparison model by using high nominated causative factor weights only; and (4) integrated EBF in the logistic regression (LR) as a multivariate model by using nominated causative factor weights only. These models were tested in Pohang and Gyeongju Cities (South Korea) by using the geographic information system GIS platform. In the first step, a landslide inventory map consisting of 296 landslide locations were prepared from various data sources. Then, a total of 15 landslide causative factors (slope angle, slope aspect, Curvature, Surface roughness, altitude, distance from drainages, stream power index, topographic wetness index, wood age, wood diameter, wood type, forest density, soil thickness, soil texture, and soil drainage) were extracted from the database and then converted into a raster. Final susceptibility maps exhibit close results from the two models. Models 1 and 3 predicted 82.3% and 80% of testing data during the analysis, respectively. Thus, Models 1 and 3 show better performance than LR. These resultant maps can be used to extend the capability of bivariate statistical based model, by finding the relationship between each single conditioning factor and landslide locations, moreover, the proposed ensemble model can be used to show the inter-relationships importance between each conditioning factors, without the need to refer to the multivariate statistic. The research outcome may provide powerful tools for natural hazard assessment and land use planning.
Aurea Quintino - One of the best experts on this subject based on the ideXlab platform.
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darboux transforms and simple factor dressing of constant mean Curvature Surfaces
Manuscripta Mathematica, 2013Co-Authors: Francis E Burstall, Katrin Leschke, J F Dorfmeister, Aurea QuintinoAbstract:We define a transformation on harmonic maps $${N:\,M \to S^2}$$ from a Riemann Surface M into the 2-sphere which depends on a parameter $${\mu \in \mathbb{C}_*}$$ , the so-called μ-Darboux transformation. In the case when the harmonic map N is the Gauss map of a constant mean Curvature Surface $${f:\,M \to \mathbb{R}^3}$$ and μ is real, the Darboux transformation of −N is the Gauss map of a classical Darboux transform of f. More generally, for all parameter $${\mu \in \mathbb{C}_*}$$ the transformation on the harmonic Gauss map of f is induced by a (generalized) Darboux transformation on f. We show that this operation on harmonic maps coincides with simple factor dressing, and thus generalize results on classical Darboux transforms of constant mean Curvature Surfaces (Hertrich-Jeromin and Pedit Doc Math J DMV 2:313–333, 1997; Burstall Integrable systems, geometry, and topology, 2006; Inoguchi and Kobayashi Int J Math 16(2):101–110, 2005): every μ-Darboux transform is a simple factor dressing, and vice versa.
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darboux transforms and simple factor dressing of constant mean Curvature Surfaces
arXiv: Differential Geometry, 2010Co-Authors: Francis E Burstall, Katrin Leschke, J F Dorfmeister, Aurea QuintinoAbstract:We define a transformation on harmonic maps from a Riemann Surface into the 2-sphere which depends on a complex parameter, the so-called mu-Darboux transformation. In the case when the harmonic map N is the Gauss map of a constant mean Curvature Surface f and the parameter is real, the mu-Darboux transformation of -N is the Gauss map of a classical Darboux transform f. More generally, for all complex parameter the transformation on the harmonic Gauss map of f is induced by a (generalized) Darboux transformation on f. We show that this operation on harmonic maps coincides with simple factor dressing, and thus generalize results on classical Darboux transforms of constant mean Curvature Surfaces: every mu-Darboux transform is a simple factor dressing, and vice versa.
Biswajeet Pradhan - One of the best experts on this subject based on the ideXlab platform.
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a novel ensemble bivariate statistical evidential belief function with knowledge based analytical hierarchy process and multivariate statistical logistic regression for landslide susceptibility mapping
Catena, 2014Co-Authors: Omar F Althuwaynee, Biswajeet Pradhan, Hyuckjin Park, Jung Hyun LeeAbstract:This study compares the landslide susceptibility maps from four application models, namely, (1) the bivariate model of the Dempster–Shafer based evidential belief function (EBF); (2) integration of the EBF in the knowledge-based analytical hierarchy process (AHP) as a pairwise comparison model processed by using all available causative factors; (3) integration of the EBF in the knowledge-based AHP as a pairwise comparison model by using high nominated causative factor weights only; and (4) integrated EBF in the logistic regression (LR) as a multivariate model by using nominated causative factor weights only. These models were tested in Pohang and Gyeongju Cities (South Korea) by using the geographic information system GIS platform. In the first step, a landslide inventory map consisting of 296 landslide locations were prepared from various data sources. Then, a total of 15 landslide causative factors (slope angle, slope aspect, Curvature, Surface roughness, altitude, distance from drainages, stream power index, topographic wetness index, wood age, wood diameter, wood type, forest density, soil thickness, soil texture, and soil drainage) were extracted from the database and then converted into a raster. Final susceptibility maps exhibit close results from the two models. Models 1 and 3 predicted 82.3% and 80% of testing data during the analysis, respectively. Thus, Models 1 and 3 show better performance than LR. These resultant maps can be used to extend the capability of bivariate statistical based model, by finding the relationship between each single conditioning factor and landslide locations, moreover, the proposed ensemble model can be used to show the inter-relationships importance between each conditioning factors, without the need to refer to the multivariate statistic. The research outcome may provide powerful tools for natural hazard assessment and land use planning.
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landslide susceptibility mapping using support vector machine and gis at the golestan province iran
Journal of Earth System Science, 2013Co-Authors: Hamid Reza Pourghasemi, Abbas Goli Jirandeh, Biswajeet Pradhan, Chong Xu, C GokceogluAbstract:The main goal of this study is to produce landslide susceptibility map using GIS-based support vector machine (SVM) at Kalaleh Township area of the Golestan province, Iran. In this paper, six different types of kernel classifiers such as linear, polynomial degree of 2, polynomial degree of 3, polynomial degree of 4, radial basis function (RBF) and sigmoid were used for landslide susceptibility mapping. At the first stage of the study, landslide locations were identified by aerial photographs and field surveys, and a total of 82 landslide locations were extracted from various sources. Of this, 75% of the landslides (61 landslide locations) are used as training dataset and the rest was used as (21 landslide locations) the validation dataset. Fourteen input data layers were employed as landslide conditioning factors in the landslide susceptibility modelling. These factors are slope degree, slope aspect, altitude, plan Curvature, profile Curvature, tangential Curvature, Surface area ratio (SAR), lithology, land use, distance from faults, distance from rivers, distance from roads, topographic wetness index (TWI) and stream power index (SPI). Using these conditioning factors, landslide susceptibility indices were calculated using support vector machine by employing six types of kernel function classifiers. Subsequently, the results were plotted in ArcGIS and six landslide susceptibility maps were produced. Then, using the success rate and the prediction rate methods, the validation process was performed by comparing the existing landslide data with the six landslide susceptibility maps. The validation results showed that success rates for six types of kernel models varied from 79% to 87%. Similarly, results of prediction rates showed that RBF (85%) and polynomial degree of 3 (83%) models performed slightly better than other types of kernel (polynomial degree of 2 = 78%, sigmoid = 78%, polynomial degree of 4 = 78%, and linear = 77%) models. Based on our results, the differences in the rates (success and prediction) of the six models are not really significant. So, the produced susceptibility maps will be useful for general land-use planning.
Katrin Leschke - One of the best experts on this subject based on the ideXlab platform.
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darboux transforms and simple factor dressing of constant mean Curvature Surfaces
Manuscripta Mathematica, 2013Co-Authors: Francis E Burstall, Katrin Leschke, J F Dorfmeister, Aurea QuintinoAbstract:We define a transformation on harmonic maps $${N:\,M \to S^2}$$ from a Riemann Surface M into the 2-sphere which depends on a parameter $${\mu \in \mathbb{C}_*}$$ , the so-called μ-Darboux transformation. In the case when the harmonic map N is the Gauss map of a constant mean Curvature Surface $${f:\,M \to \mathbb{R}^3}$$ and μ is real, the Darboux transformation of −N is the Gauss map of a classical Darboux transform of f. More generally, for all parameter $${\mu \in \mathbb{C}_*}$$ the transformation on the harmonic Gauss map of f is induced by a (generalized) Darboux transformation on f. We show that this operation on harmonic maps coincides with simple factor dressing, and thus generalize results on classical Darboux transforms of constant mean Curvature Surfaces (Hertrich-Jeromin and Pedit Doc Math J DMV 2:313–333, 1997; Burstall Integrable systems, geometry, and topology, 2006; Inoguchi and Kobayashi Int J Math 16(2):101–110, 2005): every μ-Darboux transform is a simple factor dressing, and vice versa.
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darboux transforms and simple factor dressing of constant mean Curvature Surfaces
arXiv: Differential Geometry, 2010Co-Authors: Francis E Burstall, Katrin Leschke, J F Dorfmeister, Aurea QuintinoAbstract:We define a transformation on harmonic maps from a Riemann Surface into the 2-sphere which depends on a complex parameter, the so-called mu-Darboux transformation. In the case when the harmonic map N is the Gauss map of a constant mean Curvature Surface f and the parameter is real, the mu-Darboux transformation of -N is the Gauss map of a classical Darboux transform f. More generally, for all complex parameter the transformation on the harmonic Gauss map of f is induced by a (generalized) Darboux transformation on f. We show that this operation on harmonic maps coincides with simple factor dressing, and thus generalize results on classical Darboux transforms of constant mean Curvature Surfaces: every mu-Darboux transform is a simple factor dressing, and vice versa.
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harmonic map methods for willmore Surfaces
arXiv: Differential Geometry, 2010Co-Authors: Katrin LeschkeAbstract:In this note we demonstrate how the analogy between the harmonic Gauss map of a constant mean Curvature Surface and the harmonic conformal Gauss map of a Willmore Surface can be used to obtain results on Willmore Surfaces.
