This paper tries to characterize volcanic rocks through the development and application of an empirical geomechanical system. Geotechnical information was collected from the samples from several Atlantic Ocean islands...This paper tries to characterize volcanic rocks through the development and application of an empirical geomechanical system. Geotechnical information was collected from the samples from several Atlantic Ocean islands including Madeira, Azores and Canarias archipelagos. An empirical rock classification system termed as the volcanic rock system(VRS) is developed and presented in detail. Results using the VRS are compared with those obtained using the traditional rock mass rating(RMR) system. Data mining(DM) techniques are applied to a database of volcanic rock geomechanical information from the islands.Different algorithms were developed and consequently approaches were followed for predicting rock mass classes using the VRS and RMR classification systems. Finally, some conclusions are drawn with emphasis on the fact that a better performance was achieved using attributes from VRS.展开更多
Big data analytics is emerging as one kind of the most important workloads in modern data centers. Hence,it is of great interest to identify the method of achieving the best performance for big data analytics workload...Big data analytics is emerging as one kind of the most important workloads in modern data centers. Hence,it is of great interest to identify the method of achieving the best performance for big data analytics workloads running on state-of-the-art SMT( simultaneous multithreading) processors,which needs comprehensive understanding to workload characteristics. This paper chooses the Spark workloads as the representative big data analytics workloads and performs comprehensive measurements on the POWER8 platform,which supports a wide range of multithreading. The research finds that the thread assignment policy and cache contention have significant impacts on application performance. In order to identify the potential optimization method from the experiment results,this study performs micro-architecture level characterizations by means of hardware performance counters and gives implications accordingly.展开更多
Rebaudioside D3, a novel steviol glycoside, is produced by specific UDP-glycosyltransferase of rebaudioside E, a minor steviol glycoside of Stevia rebaudiana Bertoni. The complete proton and carbon NMR spectral assign...Rebaudioside D3, a novel steviol glycoside, is produced by specific UDP-glycosyltransferase of rebaudioside E, a minor steviol glycoside of Stevia rebaudiana Bertoni. The complete proton and carbon NMR spectral assignments of rebaudioside D3, 13-[(2-O-β-D-glucopyranosyl-6-O-β-D-glucopyranosyl-β-D-glucopyranosyl) oxy] ent-kaur-16-en-19-oic acid-(2-O-β-D-glucopyranosyl-β-D-glucopyranosyl) ester, was achieved by the extensive 1D and 2D NMR (1H and 13C, TOCSY, HMQC, HMBC) as well as mass spectral data. Further, hydrolysis studies were performed on rebaudioside D3 using acid and enzymatic studies to identify aglycone and sugar residues in its structure. Rebaudioside D3 is detected in the commercial extract of the leaves of Stevia rebaudiana by LC-MS analysis, suggesting rebaudioside D3 is a natural steviol glycoside.展开更多
From the commercial extract of the leaves of Stevia rebaudiana Bertoni, a new minor ent-kaurane diterpene glycoside having five β-D-glucopyranosyl units has been isolated. The chemical structure of the new compound w...From the commercial extract of the leaves of Stevia rebaudiana Bertoni, a new minor ent-kaurane diterpene glycoside having five β-D-glucopyranosyl units has been isolated. The chemical structure of the new compound was characterized as 13-[(2-O-β-D-glucopyranosyl-β-D-glucopyranosyl)oxy] ent-kaur-16-en-19-oic acid-(2-O-β-D-glucopyranosyl-6-O-β-D-glucopyranosyl-β-D-glucopyranosyl) ester (1) on the basis of extensive 1D (1H & 13C) and 2D NMR (TOCSY, HMQC, and HMBC), and High Resolution (HR) mass spectroscopic data as well as hydrolysis studies.展开更多
In the paper, firstly, based on new non-tensor-product-typed partially inverse divided differences algorithms in a recursive form, scattered data interpolating schemes are constructed via bivariate continued fractions...In the paper, firstly, based on new non-tensor-product-typed partially inverse divided differences algorithms in a recursive form, scattered data interpolating schemes are constructed via bivariate continued fractions with odd and even nodes, respectively. And equivalent identities are also obtained between interpolated functions and bivariate continued fractions. Secondly, by means of three-term recurrence relations for continued fractions, the characterization theorem is presented to study on the degrees of the numerators and denominators of the interpolating continued fractions. Thirdly, some numerical examples show it feasible for the novel recursive schemes. Meanwhile, compared with the degrees of the numera- tors and denominators of bivariate Thiele-typed interpolating continued fractions, those of the new bivariate interpolating continued fractions are much low, respectively, due to the reduc- tion of redundant interpolating nodes. Finally, the operation count for the rational function interpolation is smaller than that for radial basis function interpolation.展开更多
Geophysical techniques can help to bridge the inherent gap that exists with regard to spatial resolution and coverage for classical hydrological methods. This has led to the emergence of a new and rapidly growing rese...Geophysical techniques can help to bridge the inherent gap that exists with regard to spatial resolution and coverage for classical hydrological methods. This has led to the emergence of a new and rapidly growing research domain generally referred to as hydrogeophysics. Given the differing sensitivities of various geophysical techniques to hydrologically relevant parameters, their inherent trade-off between resolution and range, as well as the notoriously site-specific nature of petrophysical parameter relations, the fundamental usefulness of multi-method surveys for reducing uncertainties in data analysis and interpretation is widely accepted. A major challenge arising from such endeavors is the quantitative integration of the resulting vast and diverse database into a unified model of the probed subsurface region that is consistent with all available measurements. To this end, we present a novel approach toward hydrogeophysical data integration based on a Monte-Carlo-type conditional stochastic simulation method that we consider to be particularly suitable for high-resolution local-scale studies. Monte Carlo techniques are flexible and versatile, allowing for accounting for a wide variety of data and constraints of differing resolution and hardness, and thus have the potential of providing, in a geostatistical sense, realistic models of the pertinent target parameter distributions. Compared to more conventional approaches, such as co-kriging or cluster analysis, our approach provides significant ad- vancements in the way that larger-scale structural information eontained in the hydrogeophysieal data can be accounted for. After outlining the methodological background of our algorithm, we present the results of its application to the integration of porosity log and tomographic crosshole georadar data to generate stochastic realizations of the detailed local-scale porosity structure. Our procedure is first tested on pertinent synthetic data and then applied to a field dataset collected at the Boise Hydrogeophysical Research Site. Finally, we compare the performance of our data integration approach to that of more conventional methods with regard to the prediction of flow and transport phenomena in highly heterogeneous media and discuss the implications arising.展开更多
为实现对无线网络异常数据流的快速辨识,文章基于弱监督学习,设计了一种新的智能辨识方法。基于网络节点连接强度,构建计算机无线网络多状态观测矩阵;引进弱监督学习领域的弱监督数据增强主动学习(Weakly Supervised Data Augmentation ...为实现对无线网络异常数据流的快速辨识,文章基于弱监督学习,设计了一种新的智能辨识方法。基于网络节点连接强度,构建计算机无线网络多状态观测矩阵;引进弱监督学习领域的弱监督数据增强主动学习(Weakly Supervised Data Augmentation Active Learning,WIDS-APL)模型,通过将转换样本映射到超球体空间中进行弱监督学习,实现对观测矩阵中数据的表征处理;将不同状态的数据导入改进后的长短期记忆人工神经网络模型,实现对异常数据流进行检测与辨识。实验表明,该方法不仅可以提高零日威胁响应时效,还能在优化辨识方法吞吐量的基础上,实现对数据流异常幅值的判定。展开更多
文摘This paper tries to characterize volcanic rocks through the development and application of an empirical geomechanical system. Geotechnical information was collected from the samples from several Atlantic Ocean islands including Madeira, Azores and Canarias archipelagos. An empirical rock classification system termed as the volcanic rock system(VRS) is developed and presented in detail. Results using the VRS are compared with those obtained using the traditional rock mass rating(RMR) system. Data mining(DM) techniques are applied to a database of volcanic rock geomechanical information from the islands.Different algorithms were developed and consequently approaches were followed for predicting rock mass classes using the VRS and RMR classification systems. Finally, some conclusions are drawn with emphasis on the fact that a better performance was achieved using attributes from VRS.
基金Supported by the National High Technology Research and Development Program of China(No.2015AA015308)the State Key Development Program for Basic Research of China(No.2014CB340402)
文摘Big data analytics is emerging as one kind of the most important workloads in modern data centers. Hence,it is of great interest to identify the method of achieving the best performance for big data analytics workloads running on state-of-the-art SMT( simultaneous multithreading) processors,which needs comprehensive understanding to workload characteristics. This paper chooses the Spark workloads as the representative big data analytics workloads and performs comprehensive measurements on the POWER8 platform,which supports a wide range of multithreading. The research finds that the thread assignment policy and cache contention have significant impacts on application performance. In order to identify the potential optimization method from the experiment results,this study performs micro-architecture level characterizations by means of hardware performance counters and gives implications accordingly.
文摘Rebaudioside D3, a novel steviol glycoside, is produced by specific UDP-glycosyltransferase of rebaudioside E, a minor steviol glycoside of Stevia rebaudiana Bertoni. The complete proton and carbon NMR spectral assignments of rebaudioside D3, 13-[(2-O-β-D-glucopyranosyl-6-O-β-D-glucopyranosyl-β-D-glucopyranosyl) oxy] ent-kaur-16-en-19-oic acid-(2-O-β-D-glucopyranosyl-β-D-glucopyranosyl) ester, was achieved by the extensive 1D and 2D NMR (1H and 13C, TOCSY, HMQC, HMBC) as well as mass spectral data. Further, hydrolysis studies were performed on rebaudioside D3 using acid and enzymatic studies to identify aglycone and sugar residues in its structure. Rebaudioside D3 is detected in the commercial extract of the leaves of Stevia rebaudiana by LC-MS analysis, suggesting rebaudioside D3 is a natural steviol glycoside.
文摘From the commercial extract of the leaves of Stevia rebaudiana Bertoni, a new minor ent-kaurane diterpene glycoside having five β-D-glucopyranosyl units has been isolated. The chemical structure of the new compound was characterized as 13-[(2-O-β-D-glucopyranosyl-β-D-glucopyranosyl)oxy] ent-kaur-16-en-19-oic acid-(2-O-β-D-glucopyranosyl-6-O-β-D-glucopyranosyl-β-D-glucopyranosyl) ester (1) on the basis of extensive 1D (1H & 13C) and 2D NMR (TOCSY, HMQC, and HMBC), and High Resolution (HR) mass spectroscopic data as well as hydrolysis studies.
基金Supported by the Special Funds Tianyuan for the National Natural Science Foundation of China(Grant No.11426086)the Fundamental Research Funds for the Central Universities(Grant No.2016B08714)the Natural Science Foundation of Jiangsu Province for the Youth(Grant No.BK20160853)
文摘In the paper, firstly, based on new non-tensor-product-typed partially inverse divided differences algorithms in a recursive form, scattered data interpolating schemes are constructed via bivariate continued fractions with odd and even nodes, respectively. And equivalent identities are also obtained between interpolated functions and bivariate continued fractions. Secondly, by means of three-term recurrence relations for continued fractions, the characterization theorem is presented to study on the degrees of the numerators and denominators of the interpolating continued fractions. Thirdly, some numerical examples show it feasible for the novel recursive schemes. Meanwhile, compared with the degrees of the numera- tors and denominators of bivariate Thiele-typed interpolating continued fractions, those of the new bivariate interpolating continued fractions are much low, respectively, due to the reduc- tion of redundant interpolating nodes. Finally, the operation count for the rational function interpolation is smaller than that for radial basis function interpolation.
基金supported by the Swiss National Science Foundation
文摘Geophysical techniques can help to bridge the inherent gap that exists with regard to spatial resolution and coverage for classical hydrological methods. This has led to the emergence of a new and rapidly growing research domain generally referred to as hydrogeophysics. Given the differing sensitivities of various geophysical techniques to hydrologically relevant parameters, their inherent trade-off between resolution and range, as well as the notoriously site-specific nature of petrophysical parameter relations, the fundamental usefulness of multi-method surveys for reducing uncertainties in data analysis and interpretation is widely accepted. A major challenge arising from such endeavors is the quantitative integration of the resulting vast and diverse database into a unified model of the probed subsurface region that is consistent with all available measurements. To this end, we present a novel approach toward hydrogeophysical data integration based on a Monte-Carlo-type conditional stochastic simulation method that we consider to be particularly suitable for high-resolution local-scale studies. Monte Carlo techniques are flexible and versatile, allowing for accounting for a wide variety of data and constraints of differing resolution and hardness, and thus have the potential of providing, in a geostatistical sense, realistic models of the pertinent target parameter distributions. Compared to more conventional approaches, such as co-kriging or cluster analysis, our approach provides significant ad- vancements in the way that larger-scale structural information eontained in the hydrogeophysieal data can be accounted for. After outlining the methodological background of our algorithm, we present the results of its application to the integration of porosity log and tomographic crosshole georadar data to generate stochastic realizations of the detailed local-scale porosity structure. Our procedure is first tested on pertinent synthetic data and then applied to a field dataset collected at the Boise Hydrogeophysical Research Site. Finally, we compare the performance of our data integration approach to that of more conventional methods with regard to the prediction of flow and transport phenomena in highly heterogeneous media and discuss the implications arising.
文摘为实现对无线网络异常数据流的快速辨识,文章基于弱监督学习,设计了一种新的智能辨识方法。基于网络节点连接强度,构建计算机无线网络多状态观测矩阵;引进弱监督学习领域的弱监督数据增强主动学习(Weakly Supervised Data Augmentation Active Learning,WIDS-APL)模型,通过将转换样本映射到超球体空间中进行弱监督学习,实现对观测矩阵中数据的表征处理;将不同状态的数据导入改进后的长短期记忆人工神经网络模型,实现对异常数据流进行检测与辨识。实验表明,该方法不仅可以提高零日威胁响应时效,还能在优化辨识方法吞吐量的基础上,实现对数据流异常幅值的判定。
