The amino acid contents of five floral sources Chinese honeys(jujube, rape, chaste, acacia, and lungan) were measured using reversed phase high-performance liquid chromatography(RP-HPLC). The results showed that proli...The amino acid contents of five floral sources Chinese honeys(jujube, rape, chaste, acacia, and lungan) were measured using reversed phase high-performance liquid chromatography(RP-HPLC). The results showed that proline was the main amino acid in most of the analyzed samples. Phenylalanine presents at the highest content in chaste honey samples, and the total amino acid contents of chaste honeys were also significantly higher than those of other honey samples. Based on the amino acid contents, honey samples were classified using chemometric methods(cluster analysis(CA), principal component analysis(PCA), and discriminant analysis(DA)). According to the CA results, chaste honeys could be separated from other honeys, while the remaining samples were correctly grouped together when the chaste honey data were excluded. By using DA, the overall correct classification rate reached 100%. The results revealed that amino acid contents could potentially be used as indicators to identify the botanical origin of unifloral honeys.展开更多
Polymer-blend geocell sheets(PBGS)have been developed as substitute materials for manufacturing geocells.Various attempts have been made to test and predict the behaviors of commonly used geogrids,geotextiles,geomembr...Polymer-blend geocell sheets(PBGS)have been developed as substitute materials for manufacturing geocells.Various attempts have been made to test and predict the behaviors of commonly used geogrids,geotextiles,geomembranes,and geocells.However,the elastic-viscoplastic behaviors of novel-developed geocell sheets are still poorly understood.Therefore,this paper investigates the elastic-viscoplastic behaviors of PBGS to gain a comprehensive understanding of their mechanical properties.Furthermore,the tensile load-strain history under various loading conditions is simulated by numerical calculation for widespread utilization.To achieve this goal,monotonic loading tests,short-term creep and stress relaxation tests,and multi-load-path tests(also known as arbitrary loading history tests)are performed using a universal testing machine.The results are simulated using the nonlinear three-component(NLTC)model,which consists of three nonlinear components,i.e.a hypo-elastic component,a nonlinear inviscid component,and a nonlinear viscid component.The experimental and numerical results demonstrate that PBGS exhibit significant elastic-viscoplastic behavior that can be accurately predicted by the NLTC model.Moreover,the tensile strain rates significantly influence the tensile load,with higher strain rates resulting in increased tensile loads and more linear load-strain curves.Also,parametric analysis of the rheological characteristics reveals that the initial tensile strain rates have negligible impact on the results.The rate-sensitivity coefficient of PBGS is approximately 0.163,which falls within the typical range observed in most geosynthetics.展开更多
Imaging objects hidden behind turbid media is of great scientific importance and practical value, which has been drawing a lot of attention recently. However, most of the scattering imaging methods rely on a narrow li...Imaging objects hidden behind turbid media is of great scientific importance and practical value, which has been drawing a lot of attention recently. However, most of the scattering imaging methods rely on a narrow linewidth of light, limiting their application. A mixture of the scattering light from various spectra blurs the detected speckle pattern, bringing difficulty in phase retrieval. Image reconstruction becomes much worse for dynamic objects due to short exposure times. We here investigate non-invasively recovering images of dynamic objects under white-light irradiation with the multi-frame OTF retrieval engine (MORE). By exploiting redundant information from multiple measurements, MORE recovers the phases of the optical-transfer-function (OTF) instead of recovering a single image of an object. Furthermore, we introduce the number of non-zero pixels (NNP) into MORE, which brings improvement on recovered images. An experimental proof is performed for dynamic objects at a frame rate of 20 Hz under white-light irradiation of more than 300 nm bandwidth.展开更多
For flow-related design optimization problems,e.g.,aircraft and automobile aerodynamic design,computational fluid dynamics(CFD)simulations are commonly used to predict flow fields and analyze performance.While importa...For flow-related design optimization problems,e.g.,aircraft and automobile aerodynamic design,computational fluid dynamics(CFD)simulations are commonly used to predict flow fields and analyze performance.While important,CFD simulations are a resource-demanding and time-consuming iterative process.The expensive simulation overhead limits the opportunities for large design space exploration and prevents interactive design.In this paper,we propose Flow DNN,a novel deep neural network(DNN)to efficiently learn flow representations from CFD results.Flow DNN saves computational time by directly predicting the expected flow fields based on given flow conditions and geometry shapes.Flow DNN is the first DNN that incorporates the underlying physical conservation laws of fluid dynamics with a carefully designed attention mechanism for steady flow prediction.This approach not only improves the prediction accuracy,but also preserves the physical consistency of the predicted flow fields,which is essential for CFD.Various metrics are derived to evaluate Flow DNN with respect to the whole flow fields or regions of interest(RoIs)(e.g.,boundary layers where flow quantities change rapidly).Experiments show that Flow DNN significantly outperforms alternative methods with faster inference and more accurate results.It speeds up a graphics processing unit(GPU)accelerated CFD solver by more than 14000×,while keeping the prediction error under 5%.展开更多
A general and facile approach was developed for the synthesis of almost monodisperse fluorescent silica nanoparticles (NPs) doped with inert dyes, which are organic fluorophores that are strongly fluorescent but are h...A general and facile approach was developed for the synthesis of almost monodisperse fluorescent silica nanoparticles (NPs) doped with inert dyes, which are organic fluorophores that are strongly fluorescent but are hydrophobic or lack a covalent binding group. The prepared NPs were mesoporous and the dye molecules were encapsulated in the pores via hydrophobic interaction with the CTAB template. The NPs were stable and highly fluorescent in aqueous solution, and have potential applications in bioanalysis and fluorescence encoding.展开更多
基金financially supported by the National Natural Science Foundation of China (no. 31272510)the Science Foundation of Xi'an (no. NC1405(1))the Innovation Fund of Graduate Student of Northwest University (no. YZZ13034)
文摘The amino acid contents of five floral sources Chinese honeys(jujube, rape, chaste, acacia, and lungan) were measured using reversed phase high-performance liquid chromatography(RP-HPLC). The results showed that proline was the main amino acid in most of the analyzed samples. Phenylalanine presents at the highest content in chaste honey samples, and the total amino acid contents of chaste honeys were also significantly higher than those of other honey samples. Based on the amino acid contents, honey samples were classified using chemometric methods(cluster analysis(CA), principal component analysis(PCA), and discriminant analysis(DA)). According to the CA results, chaste honeys could be separated from other honeys, while the remaining samples were correctly grouped together when the chaste honey data were excluded. By using DA, the overall correct classification rate reached 100%. The results revealed that amino acid contents could potentially be used as indicators to identify the botanical origin of unifloral honeys.
基金supported by the National Natural Science Foundation of China(Grant Nos.42077262 and 42077261)the Research Fund Project of Xinjiang Transportation Planning Survey and Design Institute Co.,Ltd.(Grant No.KY2022042504).
文摘Polymer-blend geocell sheets(PBGS)have been developed as substitute materials for manufacturing geocells.Various attempts have been made to test and predict the behaviors of commonly used geogrids,geotextiles,geomembranes,and geocells.However,the elastic-viscoplastic behaviors of novel-developed geocell sheets are still poorly understood.Therefore,this paper investigates the elastic-viscoplastic behaviors of PBGS to gain a comprehensive understanding of their mechanical properties.Furthermore,the tensile load-strain history under various loading conditions is simulated by numerical calculation for widespread utilization.To achieve this goal,monotonic loading tests,short-term creep and stress relaxation tests,and multi-load-path tests(also known as arbitrary loading history tests)are performed using a universal testing machine.The results are simulated using the nonlinear three-component(NLTC)model,which consists of three nonlinear components,i.e.a hypo-elastic component,a nonlinear inviscid component,and a nonlinear viscid component.The experimental and numerical results demonstrate that PBGS exhibit significant elastic-viscoplastic behavior that can be accurately predicted by the NLTC model.Moreover,the tensile strain rates significantly influence the tensile load,with higher strain rates resulting in increased tensile loads and more linear load-strain curves.Also,parametric analysis of the rheological characteristics reveals that the initial tensile strain rates have negligible impact on the results.The rate-sensitivity coefficient of PBGS is approximately 0.163,which falls within the typical range observed in most geosynthetics.
基金supported by the National Natural Science Foundation of China (No.62375215)。
文摘Imaging objects hidden behind turbid media is of great scientific importance and practical value, which has been drawing a lot of attention recently. However, most of the scattering imaging methods rely on a narrow linewidth of light, limiting their application. A mixture of the scattering light from various spectra blurs the detected speckle pattern, bringing difficulty in phase retrieval. Image reconstruction becomes much worse for dynamic objects due to short exposure times. We here investigate non-invasively recovering images of dynamic objects under white-light irradiation with the multi-frame OTF retrieval engine (MORE). By exploiting redundant information from multiple measurements, MORE recovers the phases of the optical-transfer-function (OTF) instead of recovering a single image of an object. Furthermore, we introduce the number of non-zero pixels (NNP) into MORE, which brings improvement on recovered images. An experimental proof is performed for dynamic objects at a frame rate of 20 Hz under white-light irradiation of more than 300 nm bandwidth.
基金supported by the National Natural Science Foundation of China(Nos.61772542,61972408,and 12102467)the Foundation of the State Key Laboratory of High Performance Computing,China(Nos.201901-11 and 202001-03)。
文摘For flow-related design optimization problems,e.g.,aircraft and automobile aerodynamic design,computational fluid dynamics(CFD)simulations are commonly used to predict flow fields and analyze performance.While important,CFD simulations are a resource-demanding and time-consuming iterative process.The expensive simulation overhead limits the opportunities for large design space exploration and prevents interactive design.In this paper,we propose Flow DNN,a novel deep neural network(DNN)to efficiently learn flow representations from CFD results.Flow DNN saves computational time by directly predicting the expected flow fields based on given flow conditions and geometry shapes.Flow DNN is the first DNN that incorporates the underlying physical conservation laws of fluid dynamics with a carefully designed attention mechanism for steady flow prediction.This approach not only improves the prediction accuracy,but also preserves the physical consistency of the predicted flow fields,which is essential for CFD.Various metrics are derived to evaluate Flow DNN with respect to the whole flow fields or regions of interest(RoIs)(e.g.,boundary layers where flow quantities change rapidly).Experiments show that Flow DNN significantly outperforms alternative methods with faster inference and more accurate results.It speeds up a graphics processing unit(GPU)accelerated CFD solver by more than 14000×,while keeping the prediction error under 5%.
基金supported by the National Natural Science Foundation of China (20875079 and 20835005)the Planned Science and Technology Project of Xiamen, China (3502z20080011)the Specialized Research Fund for the Doctoral Program of Higher Education of China (200803840007)
文摘A general and facile approach was developed for the synthesis of almost monodisperse fluorescent silica nanoparticles (NPs) doped with inert dyes, which are organic fluorophores that are strongly fluorescent but are hydrophobic or lack a covalent binding group. The prepared NPs were mesoporous and the dye molecules were encapsulated in the pores via hydrophobic interaction with the CTAB template. The NPs were stable and highly fluorescent in aqueous solution, and have potential applications in bioanalysis and fluorescence encoding.
