Aim To provide a rapid and reliable method for identifying the fork medicine Stellaria media （Linn. ） Cyr. （Herba Stellariae mediae） （Caryophyllaceae） from its adulterant Myosoton aquaticure （L.） Fries （Herba...Aim To provide a rapid and reliable method for identifying the fork medicine Stellaria media （Linn. ） Cyr. （Herba Stellariae mediae） （Caryophyllaceae） from its adulterant Myosoton aquaticure （L.） Fries （Herba Myosoti aquatici） （Caryophyllaceae） by polymerase chain reaction （PCR） technology. Methods A molecular genetic approach has been developed to identify S. media for the first time. 5S-rRNA spacer domain was amplified by PCR from the isolated genomic DNA, and the PCR products were then sequenced. Results The nucleotide sequences of S. media and M. aquaticum were measured to determine their identity. Furthermore, the nucleotide sequences of three Stellaria species, S. vestita, S. longifolia and S. radians, were also measured for the sake of providing the evidence of the biological phylogeny of SteUaria. Diversity between DNA sequence and restriction enzyme mapping among a variety of the species was found in their 5S-rRNA spacer domains. Conclusion The 5S-rRNA spacer domains can be used as a molecular marker for differentiating S. media from M. aquaticum and in phylogenetie studies of Stellaria.展开更多
The tight oil formation develops with microfractures and matrix pores,it is important to study the influence of matrix physical properties on flow characteristics.At first,the representative fracture and matrix sample...The tight oil formation develops with microfractures and matrix pores,it is important to study the influence of matrix physical properties on flow characteristics.At first,the representative fracture and matrix samples are selected respectively in the dual media,the fracture and matrix digital rocks are constructed with micro-CT scanning at different resolutions,and the corresponding fracture and matrix pore networks are extracted,respectively.Then,the modified integration method is proposed to build the dual network model containing both fracture and matrix pore-throat elements,while the geometric-topological structure equivalent matrix pores are generated to fill in the skeleton domain of fracture network,the constructed dual network could describe the geometric-topological structure characteristics of fracture and matrix pore-throat simultaneously.At last,by adjusting the matrix pore density and the matrix filling domain factor,a series of dual network models are obtained to analyze the influence of matrix physical properties on flow characteristics in dual-media.It can be seen that the matrix system contributes more to the porosity of the dual media and less to the permeability.With the decrease in matrix pore density,the porosity/permeability contributions of matrix system to dual media keep decreasing,but the decrease is not significant,the oil-water co-flow zone decreases and the irreducible water saturation increases,and the saturation interval dominated by the fluid flow in the fracture keeps increasing.With the decrease in matrix filling domain factor,the porosity/permeability contributions of matrix system to dual media decreases,the oil-water co-flow zone increases and the irreducible water saturation decreases,and the saturation interval dominated by the fluid flow in the fracture keeps increasing.The results can be used to explain the dual-media flow pattern under different matrix types and different fracture control volumes during tight oil production.展开更多
In this paper we study some nonoverlapping domain decomposition methods for solving a class of elliptic problems arising from composite materials and flows in porous media which contain many spatial scales. Our precon...In this paper we study some nonoverlapping domain decomposition methods for solving a class of elliptic problems arising from composite materials and flows in porous media which contain many spatial scales. Our preconditioner differs from traditional domain decomposition preconditioners by using a coarse solver which is adaptive to small scale heterogeneous features. While the convergence rate of traditional domain decomposition algorithms using coarse solvers based on linear or polynomial interpolations may deteriorate in the presence of rapid small scale oscillations or high aspect ratios, our preconditioner is applicable to multiple-scale problems without restrictive assumptions and seems to have a convergence rate nearly independent of the aspect ratio within the substructures. A rigorous convergence analysis based on the Schwarz framework is carried out, and we demonstrate the efficiency and robustness of the proposed preconditioner through numerical experiments which include problems with multiple-scale coefficients, as well problems with continuous scales.展开更多
Fake news has recently leveraged the power and scale of online social media to effectively spread misinformation which not only erodes the trust of people on traditional presses and journalisms, but also manipulates t...Fake news has recently leveraged the power and scale of online social media to effectively spread misinformation which not only erodes the trust of people on traditional presses and journalisms, but also manipulates the opinions and sentiments of the public. Detecting fake news is a daunting challenge due to subtle difference between real and fake news. As a first step of fighting with fake news, this paper characterizes hundreds of popular fake and real news measured by shares, reactions, and comments on Facebook from two perspectives:domain reputations and content understanding. Our domain reputation analysis reveals that the Web sites of the fake and real news publishers exhibit diverse registration behaviors, registration timing, domain rankings, and domain popularity. In addition, fake news tends to disappear from the Web after a certain amount of time. The content characterizations on the fake and real news corpus suggest that simply applying term frequency-inverse document frequency(tf-idf) and Latent Dirichlet Allocation(LDA) topic modeling is inefficient in detecting fake news,while exploring document similarity with the term and word vectors is a very promising direction for predicting fake and real news. To the best of our knowledge, this is the first effort to systematically study domain reputations and content characteristics of fake and real news, which will provide key insights for effectively detecting fake news on social media.展开更多
文摘Aim To provide a rapid and reliable method for identifying the fork medicine Stellaria media （Linn. ） Cyr. （Herba Stellariae mediae） （Caryophyllaceae） from its adulterant Myosoton aquaticure （L.） Fries （Herba Myosoti aquatici） （Caryophyllaceae） by polymerase chain reaction （PCR） technology. Methods A molecular genetic approach has been developed to identify S. media for the first time. 5S-rRNA spacer domain was amplified by PCR from the isolated genomic DNA, and the PCR products were then sequenced. Results The nucleotide sequences of S. media and M. aquaticum were measured to determine their identity. Furthermore, the nucleotide sequences of three Stellaria species, S. vestita, S. longifolia and S. radians, were also measured for the sake of providing the evidence of the biological phylogeny of SteUaria. Diversity between DNA sequence and restriction enzyme mapping among a variety of the species was found in their 5S-rRNA spacer domains. Conclusion The 5S-rRNA spacer domains can be used as a molecular marker for differentiating S. media from M. aquaticum and in phylogenetie studies of Stellaria.
基金This work was supported by National Natural Science Foundation of China(No.51704033,No.51804038)PetroChina Innovation Foundation(No.2018D-5007-0210).
文摘The tight oil formation develops with microfractures and matrix pores,it is important to study the influence of matrix physical properties on flow characteristics.At first,the representative fracture and matrix samples are selected respectively in the dual media,the fracture and matrix digital rocks are constructed with micro-CT scanning at different resolutions,and the corresponding fracture and matrix pore networks are extracted,respectively.Then,the modified integration method is proposed to build the dual network model containing both fracture and matrix pore-throat elements,while the geometric-topological structure equivalent matrix pores are generated to fill in the skeleton domain of fracture network,the constructed dual network could describe the geometric-topological structure characteristics of fracture and matrix pore-throat simultaneously.At last,by adjusting the matrix pore density and the matrix filling domain factor,a series of dual network models are obtained to analyze the influence of matrix physical properties on flow characteristics in dual-media.It can be seen that the matrix system contributes more to the porosity of the dual media and less to the permeability.With the decrease in matrix pore density,the porosity/permeability contributions of matrix system to dual media keep decreasing,but the decrease is not significant,the oil-water co-flow zone decreases and the irreducible water saturation increases,and the saturation interval dominated by the fluid flow in the fracture keeps increasing.With the decrease in matrix filling domain factor,the porosity/permeability contributions of matrix system to dual media decreases,the oil-water co-flow zone increases and the irreducible water saturation decreases,and the saturation interval dominated by the fluid flow in the fracture keeps increasing.The results can be used to explain the dual-media flow pattern under different matrix types and different fracture control volumes during tight oil production.
基金Supported by STATOIL under the VISTA programSupported in part by a grant from National Science Foundation under the contract DMS-0073916by a grant from Army Research Office under the contract DAAD19-99-1-0141.
文摘In this paper we study some nonoverlapping domain decomposition methods for solving a class of elliptic problems arising from composite materials and flows in porous media which contain many spatial scales. Our preconditioner differs from traditional domain decomposition preconditioners by using a coarse solver which is adaptive to small scale heterogeneous features. While the convergence rate of traditional domain decomposition algorithms using coarse solvers based on linear or polynomial interpolations may deteriorate in the presence of rapid small scale oscillations or high aspect ratios, our preconditioner is applicable to multiple-scale problems without restrictive assumptions and seems to have a convergence rate nearly independent of the aspect ratio within the substructures. A rigorous convergence analysis based on the Schwarz framework is carried out, and we demonstrate the efficiency and robustness of the proposed preconditioner through numerical experiments which include problems with multiple-scale coefficients, as well problems with continuous scales.
基金supported in part by National Science Foundation (NSF) Algorithms for Threat Detection (ATD) Program (No. DMS #1737861)NSF Computer and Network Systems (CNS) Program (No. CNS #1816995)
文摘Fake news has recently leveraged the power and scale of online social media to effectively spread misinformation which not only erodes the trust of people on traditional presses and journalisms, but also manipulates the opinions and sentiments of the public. Detecting fake news is a daunting challenge due to subtle difference between real and fake news. As a first step of fighting with fake news, this paper characterizes hundreds of popular fake and real news measured by shares, reactions, and comments on Facebook from two perspectives:domain reputations and content understanding. Our domain reputation analysis reveals that the Web sites of the fake and real news publishers exhibit diverse registration behaviors, registration timing, domain rankings, and domain popularity. In addition, fake news tends to disappear from the Web after a certain amount of time. The content characterizations on the fake and real news corpus suggest that simply applying term frequency-inverse document frequency(tf-idf) and Latent Dirichlet Allocation(LDA) topic modeling is inefficient in detecting fake news,while exploring document similarity with the term and word vectors is a very promising direction for predicting fake and real news. To the best of our knowledge, this is the first effort to systematically study domain reputations and content characteristics of fake and real news, which will provide key insights for effectively detecting fake news on social media.
