Tracing Lines of Conditional Influence: Matrix and Paths

As a crucial part of producer services, the logistics industry is highly dependent on the manufacturing industry. In general, the interactive development of the logistics and manufacturing industries is essential. Due to the existence of a certain degree of interdependence between any two factors, interaction between the two industries has produced a basis for measurement; identifying the key factors affecting the interaction between the manufacturing and logistics industries is a kind of decision problem in the field of multiple criteria decision making (MCDM). A hybrid MCDM method, DEMATEL-based ANP (DANP) is appropriate to solve this problem. However, DANP uses a direct influence matrix, which involves pairwise comparisons that may be more or less influenced by the respondents. Therefore, we propose a decision model, Grey DANP, which can automatically generate the direct influence matrix. Statistical data for the logistics and manufacturing industries in the China Statistical Yearbook (2006–2015) were used to identify the key factors for interaction between these two industries. The results showed that the key logistics criteria for interaction development are the total number of employees in the transport business, the volume of goods, and the total length of routes. The key manufacturing criteria for interaction development are the gross domestic product and the value added. Therefore, stakeholders should increase the number of employees in the transport industry and freight volumes. Also, the investment in infrastructure should be increased.

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Speed optimized influence matrix processing in inverse treatment planning tools

Physics in Medicine and Biology ◽

10.1088/0031-9155/53/9/n02 ◽

2008 ◽

Vol 53 (9) ◽

pp. N157-N164 ◽

Cited By ~ 10

Author(s):

Peter Ziegenhein ◽

Jan J Wilkens ◽

Simeon Nill ◽

Thomas Ludwig ◽

Uwe Oelfke

Keyword(s):

Treatment Planning ◽

Influence Matrix ◽

Inverse Treatment Planning ◽

Inverse Treatment ◽

Planning Tools

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Implementation of Neumann boundary condition with influence matrix method for viscous annular flow using pseudospectral collocation

Journal of Computational and Applied Mathematics ◽

10.1016/j.cam.2015.02.012 ◽

2015 ◽

Vol 285 ◽

pp. 100-115 ◽

Cited By ~ 3

Author(s):

B. Smith ◽

R. Laoulache ◽

A. Heryudono

Keyword(s):

Boundary Condition ◽

Neumann Boundary Condition ◽

Matrix Method ◽

Annular Flow ◽

Neumann Boundary ◽

Influence Matrix

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Influence of CO<sub>2</sub> observations on the optimized CO<sub>2</sub> flux in an ensemble Kalman filter

Atmospheric Chemistry and Physics ◽

10.5194/acp-14-13515-2014 ◽

2014 ◽

Vol 14 (24) ◽

pp. 13515-13530 ◽

Cited By ~ 5

Author(s):

J. Kim ◽

H. M. Kim ◽

C.-H. Cho

Keyword(s):

Kalman Filter ◽

North America ◽

Information Content ◽

Ensemble Kalman Filter ◽

Co2 Flux ◽

Experimental Period ◽

Diagonal Element ◽

Cumulative Impact ◽

Influence Matrix ◽

The Tropics

Abstract. In this study, the effect of CO2 observations on an analysis of surface CO2 flux was calculated using an influence matrix in the CarbonTracker, which is an inverse modeling system for estimating surface CO2 flux based on an ensemble Kalman filter. The influence matrix represents a sensitivity of the analysis to observations. The experimental period was from January 2000 to December 2009. The diagonal element of the influence matrix (i.e., analysis sensitivity) is globally 4.8% on average, which implies that the analysis extracts 4.8% of the information from the observations and 95.2% from the background each assimilation cycle. Because the surface CO2 flux in each week is optimized by 5 weeks of observations, the cumulative impact over 5 weeks is 19.1%, much greater than 4.8%. The analysis sensitivity is inversely proportional to the number of observations used in the assimilation, which is distinctly apparent in continuous observation categories with a sufficient number of observations. The time series of the globally averaged analysis sensitivities shows seasonal variations, with greater sensitivities in summer and lower sensitivities in winter, which is attributed to the surface CO2 flux uncertainty. The time-averaged analysis sensitivities in the Northern Hemisphere are greater than those in the tropics and the Southern Hemisphere. The trace of the influence matrix (i.e., information content) is a measure of the total information extracted from the observations. The information content indicates an imbalance between the observation coverage in North America and that in other regions. Approximately half of the total observational information is provided by continuous observations, mainly from North America, which indicates that continuous observations are the most informative and that comprehensive coverage of additional observations in other regions is necessary to estimate the surface CO2 flux in these areas as accurately as in North America.

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Application of Quadratic Programming Method to Cable Force Calculation of Cable Erection Arch Bridge

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.587-589.1364 ◽

2014 ◽

Vol 587-589 ◽

pp. 1364-1369

Author(s):

Cheng Wu ◽

Jin Yu Liu ◽

Shui Xing Zhou

Keyword(s):

Quadratic Programming ◽

High Precision ◽

Steel Tube ◽

Influence Matrix ◽

Calculation Process ◽

Cable Force ◽

Quadratic Programming Method ◽

Cable Forces ◽

Force Calculation ◽

Number Of Iterations

Taking the bare arch deformation under gravity as target alignment, the influence matrix that associates the cable forces with segment deformation is obtained via ANSYS program, and the cable force is quickly calculated by MATLAB quadratic programming toolbox. It is illustrated with an example of Guizhou Zong-xi River Bridge, which is a 360-meter concrete filled steel tube bridge in construction, and the calculation process is given. The results show that, this new method has the advantages of high precision and less number of iterations.

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