Engineering rock mass classification,based on empirical relations between rock mass parameters and engineering applications,is commonly used in rock engineering and forms the basis for designing rock structures.The ba...Engineering rock mass classification,based on empirical relations between rock mass parameters and engineering applications,is commonly used in rock engineering and forms the basis for designing rock structures.The basic data required may be obtained from visual observation and laboratory or field tests.However,owing to the discontinuous and variable nature of rock masses,it is difficult for rock engineers to directly obtain the specific design parameters needed.As an alternative,the use of geophysical methods in geomechanics such as seismography may largely address this problem.In this study,25 seismic profiles with the total length of 543 m have been scanned to determine the geomechanical properties of the rock mass in blocks Ⅰ,Ⅲ and Ⅳ-2 of the Choghart iron mine.Moreover,rock joint measurements and sampling for laboratory tests were conducted.The results show that the rock mass rating（RMR） and Q values have a close relation with P-wave velocity parameters,including P-wave velocity in field(V;).P-wave velocity in the laboratory(V;) and the ratio of V;V;(i.e.K;= V;/V;.However,Q value,totally,has greater correlation coefficient and less error than the RMR,In addition,rock mass parameters including rock quality designation（RQD）,uniaxial compressive strength（UCS）,joint roughness coefficient（JRC） and Schmidt number（RN） show close relationship with P-wave velocity.An equation based on these parameters was obtained to estimate the P-wave velocity in the rock mass with a correlation coefficient of 91%.The velocities in two orthogonal directions and the results of joint study show that the wave velocity anisotropy in rock mass may be used as an efficient tool to assess the strong and weak directions in rock mass.展开更多
CC’s(Cloud Computing)networks are distributed and dynamic as signals appear/disappear or lose significance.MLTs(Machine learning Techniques)train datasets which sometime are inadequate in terms of sample for inferrin...CC’s(Cloud Computing)networks are distributed and dynamic as signals appear/disappear or lose significance.MLTs(Machine learning Techniques)train datasets which sometime are inadequate in terms of sample for inferring information.A dynamic strategy,DevMLOps(Development Machine Learning Operations)used in automatic selections and tunings of MLTs result in significant performance differences.But,the scheme has many disadvantages including continuity in training,more samples and training time in feature selections and increased classification execution times.RFEs(Recursive Feature Eliminations)are computationally very expensive in its operations as it traverses through each feature without considering correlations between them.This problem can be overcome by the use of Wrappers as they select better features by accounting for test and train datasets.The aim of this paper is to use DevQLMLOps for automated tuning and selections based on orchestrations and messaging between containers.The proposed AKFA(Adaptive Kernel Firefly Algorithm)is for selecting features for CNM(Cloud Network Monitoring)operations.AKFA methodology is demonstrated using CNSD(Cloud Network Security Dataset)with satisfactory results in the performance metrics like precision,recall,F-measure and accuracy used.展开更多
本研究利用最新的Quinacrine Mustard and 33258 Hoechst(Q—H)复合荧光染色技术对10个品系的近交系小鼠的核型进行分析。在同一细胞内,按各号染色体着丝粒带大小排列、分组,建立该10个品系近交系小鼠特有的染色体着丝粒带核型,作为各...本研究利用最新的Quinacrine Mustard and 33258 Hoechst(Q—H)复合荧光染色技术对10个品系的近交系小鼠的核型进行分析。在同一细胞内,按各号染色体着丝粒带大小排列、分组,建立该10个品系近交系小鼠特有的染色体着丝粒带核型,作为各品系小鼠遗传质量监测的染色体标记指标。本研究还对615小鼠品系的生化标汜检测与染色体标记检测的结果进行比较,同时比较了不同来源615小鼠的染色体标记,从而进一步阐明了该方法作为实验动物遗传监测方法之一与其他方法间的互补性及其自身特点。展开更多
文摘Engineering rock mass classification,based on empirical relations between rock mass parameters and engineering applications,is commonly used in rock engineering and forms the basis for designing rock structures.The basic data required may be obtained from visual observation and laboratory or field tests.However,owing to the discontinuous and variable nature of rock masses,it is difficult for rock engineers to directly obtain the specific design parameters needed.As an alternative,the use of geophysical methods in geomechanics such as seismography may largely address this problem.In this study,25 seismic profiles with the total length of 543 m have been scanned to determine the geomechanical properties of the rock mass in blocks Ⅰ,Ⅲ and Ⅳ-2 of the Choghart iron mine.Moreover,rock joint measurements and sampling for laboratory tests were conducted.The results show that the rock mass rating（RMR） and Q values have a close relation with P-wave velocity parameters,including P-wave velocity in field(V;).P-wave velocity in the laboratory(V;) and the ratio of V;V;(i.e.K;= V;/V;.However,Q value,totally,has greater correlation coefficient and less error than the RMR,In addition,rock mass parameters including rock quality designation（RQD）,uniaxial compressive strength（UCS）,joint roughness coefficient（JRC） and Schmidt number（RN） show close relationship with P-wave velocity.An equation based on these parameters was obtained to estimate the P-wave velocity in the rock mass with a correlation coefficient of 91%.The velocities in two orthogonal directions and the results of joint study show that the wave velocity anisotropy in rock mass may be used as an efficient tool to assess the strong and weak directions in rock mass.
文摘CC’s(Cloud Computing)networks are distributed and dynamic as signals appear/disappear or lose significance.MLTs(Machine learning Techniques)train datasets which sometime are inadequate in terms of sample for inferring information.A dynamic strategy,DevMLOps(Development Machine Learning Operations)used in automatic selections and tunings of MLTs result in significant performance differences.But,the scheme has many disadvantages including continuity in training,more samples and training time in feature selections and increased classification execution times.RFEs(Recursive Feature Eliminations)are computationally very expensive in its operations as it traverses through each feature without considering correlations between them.This problem can be overcome by the use of Wrappers as they select better features by accounting for test and train datasets.The aim of this paper is to use DevQLMLOps for automated tuning and selections based on orchestrations and messaging between containers.The proposed AKFA(Adaptive Kernel Firefly Algorithm)is for selecting features for CNM(Cloud Network Monitoring)operations.AKFA methodology is demonstrated using CNSD(Cloud Network Security Dataset)with satisfactory results in the performance metrics like precision,recall,F-measure and accuracy used.
文摘本研究利用最新的Quinacrine Mustard and 33258 Hoechst(Q—H)复合荧光染色技术对10个品系的近交系小鼠的核型进行分析。在同一细胞内,按各号染色体着丝粒带大小排列、分组,建立该10个品系近交系小鼠特有的染色体着丝粒带核型,作为各品系小鼠遗传质量监测的染色体标记指标。本研究还对615小鼠品系的生化标汜检测与染色体标记检测的结果进行比较,同时比较了不同来源615小鼠的染色体标记,从而进一步阐明了该方法作为实验动物遗传监测方法之一与其他方法间的互补性及其自身特点。
