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Online since: December 2012
Authors: Dong Rui Yi, Rong Ge Xiao, Pei Fen Yao, Jia Quan Zhou
Intermittent transmission is mainly based on specific technical parameters of heating pipelines, crude oil rheology and environmental data such as temperature change, taking into account the running experience of existing pipelines and field test result building mathematical model and making cumbersome thermal and hydraulic calculation, to strike a pipeline safe shutdown time and safe low-flowrate range of intermittent transportation.
With the reduction in transmission capacity, the laminar flow segments growth.
Fig.3 The diagram of the change of the temperature of the oil along the pipeline length of the laminar flow with distance and flowrate With the reduction in transmission capacity, the laminar flow segments growth.
With the reduction in transmission capacity, the oil temperature is reduced.
With the reduction in transmission capacity, the laminar flow segments growth.
Fig.3 The diagram of the change of the temperature of the oil along the pipeline length of the laminar flow with distance and flowrate With the reduction in transmission capacity, the laminar flow segments growth.
With the reduction in transmission capacity, the oil temperature is reduced.
Online since: August 2013
Authors: Xue Li, Qiong Jia Yuan, Lu Wang
Conduct the analysis with the help of Quantity One gel imaging analysis system-measure the gray value of NF-κ B P50’s gene and protein expression, with SPSS17.0 statistical data.
With exercise time of 60 minutes (Group E2) exercise training on NF - κ B P50 protein is the most significant reduction.
Protein expression of Group E2 is significantly lower than Group E1 (p<0.01); protein expression of Group E3 is apparently weaker than Group E1 (p>0.05). protein expression of Group E3 is significantly higher than Group E2 (p<0.01).With exercise time of 60 minutes (Group E2) exercise training on NF - κ B P50 protein is the most significant reduction.
Repeated bouts of aerobic exercise lead to reductions in skeletal muscle free radical generation and unclear factor kappa B activation [J].J Physiol,2008.586(16):3979-90
With exercise time of 60 minutes (Group E2) exercise training on NF - κ B P50 protein is the most significant reduction.
Protein expression of Group E2 is significantly lower than Group E1 (p<0.01); protein expression of Group E3 is apparently weaker than Group E1 (p>0.05). protein expression of Group E3 is significantly higher than Group E2 (p<0.01).With exercise time of 60 minutes (Group E2) exercise training on NF - κ B P50 protein is the most significant reduction.
Repeated bouts of aerobic exercise lead to reductions in skeletal muscle free radical generation and unclear factor kappa B activation [J].J Physiol,2008.586(16):3979-90
Online since: August 2007
Authors: Richard P. Gangloff, Sang Shik Kim, Jenifer S. Warner
Vacuum data were reported by [11].
The orientation is LT unless noted and data reported by Gasem for chromate inhibition are included for comparison [2,3].
From the polarization data in Figure 1, the solution with lowest Cl and highest MoO42- contents creates the most stable passive film.
The upper bound fcrit suggested by these data is equal (15 Hz) for the low and high MoO42 levels examined.
The decreasing ∆K experiment at 10 Hz (Figure 2) did not show molybdate inhibition, certainly at ∆K higher then 3 MPa√m, consistent with the data in Figure 4 showing that this frequency was near fcrit upper bound for ∆K of 6 MPa√m and R of 0.65.
The orientation is LT unless noted and data reported by Gasem for chromate inhibition are included for comparison [2,3].
From the polarization data in Figure 1, the solution with lowest Cl and highest MoO42- contents creates the most stable passive film.
The upper bound fcrit suggested by these data is equal (15 Hz) for the low and high MoO42 levels examined.
The decreasing ∆K experiment at 10 Hz (Figure 2) did not show molybdate inhibition, certainly at ∆K higher then 3 MPa√m, consistent with the data in Figure 4 showing that this frequency was near fcrit upper bound for ∆K of 6 MPa√m and R of 0.65.
Online since: October 2016
Authors: Bruno Buchmayr, Gernot Eggbauer
For the forging industry this means a desire in reduction of forging heat and heat treatment steps, but still achieving desirably mechanical properties.
Studies concerning a further reduction of the carbon content with equal performance by adapting this alloying concept have been performed recently [13, 14].
This software allows the calculation of thermodynamic data, thermophysical properties and the phase transformation of steel directly from the current chemical composition using the well-known Kirkaldy approach [16, 18].
The reduction of area for a holding time of 15 min and several temperatures shows an approximately constant behavior of 30 to 35 %.
All those factors leading to favorable mechanical properties are beneficial for a reduction of processing time and energy.
Studies concerning a further reduction of the carbon content with equal performance by adapting this alloying concept have been performed recently [13, 14].
This software allows the calculation of thermodynamic data, thermophysical properties and the phase transformation of steel directly from the current chemical composition using the well-known Kirkaldy approach [16, 18].
The reduction of area for a holding time of 15 min and several temperatures shows an approximately constant behavior of 30 to 35 %.
All those factors leading to favorable mechanical properties are beneficial for a reduction of processing time and energy.
Online since: November 2020
Authors: Kenta Dejima, Hirokazu Ishitobi, Nobuyoshi Nakagawa
The chemical reduction using NaBH4 was conducted to this solution as follows.
The chemical reduction using NaBH4.
After the reduction, the mixed solution was then dried in an oven at 120ºCfor 7 h.
The chemical reduction using NaBH4 was conducted to this solution in the similar manner mentioned above.
An XPS analysis (data not shown) revealed no clear difference in the spectra measured by wide scan analysis and also that by narrow scan analysis for the appeared elements suggesting that the pulverization by the high power ultrasonication did not affect the chemical properties of the RGO surface.
The chemical reduction using NaBH4.
After the reduction, the mixed solution was then dried in an oven at 120ºCfor 7 h.
The chemical reduction using NaBH4 was conducted to this solution in the similar manner mentioned above.
An XPS analysis (data not shown) revealed no clear difference in the spectra measured by wide scan analysis and also that by narrow scan analysis for the appeared elements suggesting that the pulverization by the high power ultrasonication did not affect the chemical properties of the RGO surface.
Online since: February 2012
Authors: Qian Wang, Zhi Xia He, Zhao Chen Jiang, Ju Yan Liu, Li Li Tian
In order to analyze the influence of cavitaing flow in the nozzle hole on spray, a coupling spray simulation was carried out with the output data of cavitaing flow in nozzles using FIRE v2010.
Facing the energy crisis and environmental pollution, energy saving and emission reduction have been a strategic point of the development of internal-combustion engine.
A transient nozzle flow simulation was carried out and the simulating data was written to a file with a predefined temporal resolution.
Geometrical, temporal and flow data allow the mapping of nozzle orifice field to spray inlet, adopting a primary break-up model to initialize the droplet phases.
The data of spray tip penetration and SMD are shown in fig.9.
Facing the energy crisis and environmental pollution, energy saving and emission reduction have been a strategic point of the development of internal-combustion engine.
A transient nozzle flow simulation was carried out and the simulating data was written to a file with a predefined temporal resolution.
Geometrical, temporal and flow data allow the mapping of nozzle orifice field to spray inlet, adopting a primary break-up model to initialize the droplet phases.
The data of spray tip penetration and SMD are shown in fig.9.
Online since: November 2014
Authors: Peng Fang
This method combines the rating similarity and the user context similarity in the electronic commerce recommendation process to improve the prediction accuracy by efficiently managing the problem of data sparsity.
Introduction With the continuous development of all kinds of information processing technology, more and more people can access the digital resources, therefore, how to from a mass of data is convenient, quick access to the required information would come very naturally become very concerned about the problem.
Input Calculating user rating similarity Calculating user context similarity Prediction and Recommendation Selecting neighbors Combining the two similarity Figure 1 The algorithm steps 1) similarity calculation of similarity1 users based on user characteristic data; 2) the use of collaborative filtering algorithm, based on the user's rating data, calculation between the user similarity based on similarity2 score; 3) calculate the user the final similarity: similarity = percent* similarity1 + (1 - percent) * similarity2, where percent is the characteristics of the user similarity in the final similarity calculation based on the proportion of; 4) by using similarity numerical prediction score combined with traditional numerical algorithm.
This method combines the rating similarity and the user context similarity in the electronic commerce recommendation process to improve the prediction accuracy by efficiently managing the problem of data sparsity.
Kelleher, Experiments in sparsity reduction: Using clustering in collaborative recommenders, in Procs. of the Thirteenth Irish Conference on Artificial Intelligence and Cognitive Science, pp. 144–149.
Introduction With the continuous development of all kinds of information processing technology, more and more people can access the digital resources, therefore, how to from a mass of data is convenient, quick access to the required information would come very naturally become very concerned about the problem.
Input Calculating user rating similarity Calculating user context similarity Prediction and Recommendation Selecting neighbors Combining the two similarity Figure 1 The algorithm steps 1) similarity calculation of similarity1 users based on user characteristic data; 2) the use of collaborative filtering algorithm, based on the user's rating data, calculation between the user similarity based on similarity2 score; 3) calculate the user the final similarity: similarity = percent* similarity1 + (1 - percent) * similarity2, where percent is the characteristics of the user similarity in the final similarity calculation based on the proportion of; 4) by using similarity numerical prediction score combined with traditional numerical algorithm.
This method combines the rating similarity and the user context similarity in the electronic commerce recommendation process to improve the prediction accuracy by efficiently managing the problem of data sparsity.
Kelleher, Experiments in sparsity reduction: Using clustering in collaborative recommenders, in Procs. of the Thirteenth Irish Conference on Artificial Intelligence and Cognitive Science, pp. 144–149.
Online since: November 2024
Authors: Nagia Mohamed Jadalla
To ensure complete reduction, the solution was stored in complete darkness for a whole day.
Data analysis: Data are presented as mean values ± standard deviation (SD), and one-way analysis of variance (ANOVA) with P < .05 was used to evaluate the significance of differences.
From the data shown in Fig. 2, it was clear that L. leucocephala, M. aquatica leaves, and Z. officinale rhizomes extract lowered silver ions, allowing for the production of AgNPs.
According to FTIR data, neither silver ion interactions nor silver nanoparticle binding had any negative effects on the secondary structure of proteins [62, 63].
Yu, ''Formation of colloidal silver nanoparticles stabilized by Na ± poly (gamma-glutamic acid)-silver nitrate complex via chemical reduction process'' Colloids Surf.
Data analysis: Data are presented as mean values ± standard deviation (SD), and one-way analysis of variance (ANOVA) with P < .05 was used to evaluate the significance of differences.
From the data shown in Fig. 2, it was clear that L. leucocephala, M. aquatica leaves, and Z. officinale rhizomes extract lowered silver ions, allowing for the production of AgNPs.
According to FTIR data, neither silver ion interactions nor silver nanoparticle binding had any negative effects on the secondary structure of proteins [62, 63].
Yu, ''Formation of colloidal silver nanoparticles stabilized by Na ± poly (gamma-glutamic acid)-silver nitrate complex via chemical reduction process'' Colloids Surf.
Online since: November 2005
Authors: Joon Soo Park, Yi Hyun Park, Han Ki Yoon, Akira Kohyama
Therefore, reduction of sintering temperature and pressure is key requirements for the fabrication of
SiCf/SiC composites by hot pressing method.
Therefore, reduction of the process temperature and pressure is key requirement for the fabrication of SiCf/SiC composites by hot pressing method [11].
Each data point shows the average of four tests.
Therefore, reduction of the process temperature and pressure is key requirement for the fabrication of SiCf/SiC composites by hot pressing method [11].
Each data point shows the average of four tests.
Online since: February 2023
Authors: K. Karthigeiyan, M. Durga Devi
Data mining is a new area of study in crop production analysis.
All of these data properties will be examined, and the data will be processed using a variety of machine learning approaches to build a classification model.
Data accessibility at all times and in all places. 3.
Crop Data Predicted amount of Fertilizer needed Random Forest Clustering Data Pre-processing Fertilizer Data Fig. 1.
Once the data has been accessible, it must be examined.
All of these data properties will be examined, and the data will be processed using a variety of machine learning approaches to build a classification model.
Data accessibility at all times and in all places. 3.
Crop Data Predicted amount of Fertilizer needed Random Forest Clustering Data Pre-processing Fertilizer Data Fig. 1.
Once the data has been accessible, it must be examined.