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Data for abatement curve for HFC, PFC and SF6 emission from industry process and other sources were used in IPAC model.
Figure 4 present the cost learning curve used in the model compared with data by 2010.
Figure 4 technology learning curve used in IPAC/AIM-technology model and data for 2010.
By 2010, cement output and steel output is 1.8billion ton and 630million ton, which is already higher or near to the data in table 2.
Recently IPAC modeling team made analysis again on the demand for cement and steel by using similar methodology with energy forecasting, it is again confirmed the data in table is the way for China to go.

Direct Damage-Based Seismic Design Method of RC Frame Structures Lanfang LUO, Jing XU (Southwest Jiaotong University, Cheng Du, Si Chuan,610031,China) Keywords: Reinforced Concrete Frame Structures; Direct Damage-Based Seismic Design; Damage-Based Inelastic Response Spectrum Abstract: Based on the existing research, this paper presents an innovative methodology to realize direct damage-based seismic design for RC frame structures by mobilizing ESDOF theory and the damage-based strength reduction factor(RD factor).
Basic data used see in [13] and main data for calculation are as follows: (1), , , ;; (2) , (3) Equivalent mass: , Elastic Base Shear Force: , Inelastic Base Shear Force for SDOF: ; (4) Inelastic Base Shear Force for MDOF: Shear force of each storey: , , , ; Then the design of section can be completed

And as to Peter Lory protocol, the reduction is profitable in situation where log2k is smaller than n.
Let us assume that the data of t corrupted players with the indices i1,i2,…,it are revealed to the adversary.
And as to the protocol in [7], the reduction is profitable in situations where log2k is smaller than n.
The new modification not only makes use of Newton’s scheme of divided differences and difference, Yanghui Triangle, and Fourier Fast Transform, but also adopts ideas of online algorithms and offline algorithms to obtain profitable reduction in situation where log2k is smaller than n.

● Data alarm function.
Create a data acquisition thread used for data collection and create a data processing thread for the analysis of monitoring data as well as alarm analysis and fault analysis.
Data retention thread dedicated to holding various data.
The data processing thread and data storage thread are triggered when the amount of data collected reaches a certain magnitude.
Queue data and overflow handling In the process of data transmission, if there appears too much data in the acquisition queue, which exceeds the capacity to process for the data process thread.

The representing problem is different from the approximation to data points; planar curve provided more information than data points.
Approximate the sampled data points using least square approximation in a given tolerance.
The planar curves supply more information than the discrete data points.
The sampling can be with any high density and accuracy, so we ignore the sampling error in the representing. 3.2 Interpolate the data points and tangent vectors Given the data points along with their tangent vectors, we can construct Bezier segments between each adjacent pair points interpolate these data and tangents.
The final curve along with its control polygon its control polygon In figure 3, the data points and their data points are interpolated by a quadratic B-spline curve.

The new procedure, entirely developed in the APDL programming language, has been tested with a holed plate subject to two different load conditions; the obtained results are promising in terms of mass reduction and structural performances.
Usually, common convergence constraints are related to the structure mass reduction, the tensile strength of the material or the maximum value of the displacement.
A further interesting development could be related to a different data processing of the elements strain energy tables of the single load steps.

The experimental data of 24 hr temperature cycle of the ambient air is used for the analysis of performance of thermal comfort inside the room with the incorporation of different thickness of the insulating materials.
With the basis experimental data further studies can be made with numerical modeling and simulation to optimize the performance.
The emphasis of the study is to understand the energy demand for the thermal comfort inside the room and its comparison with experimental data [9].
Further studies are needed to be performed for ventilated, ACPH conditions with insulated materials suspended at certain height below the ceiling will help to improve the better design of the buildings with detailed validation of experimental data.
Bhargava: Climatic Data Handbook for Building Design in India, Tata Mc Graw Hill Pvt.

Design and safety assessment of advanced ductile cast iron (DCI) components like wind turbines or transport and storage casks for radioactive materials require appropriate material data in terms of strength and fracture toughness.
A comprehensive compilation of investigations and results concerning mechanical properties, fracture mechanics test methods and material data can be found for instance in [1-3].
Primary goal of these investigations was to establish the experimental data base for development and validation of a key curve single specimen method for dynamic R-curve testing of DCI.
This has to be done with deliberation and profund experimental experience can be seen as a vital prerequisite to be able to provide valuable toughness data of DCI.
Baer, Advanced Fracture Mechanics Testing of DCI - a Key to Valuable Toughness Data, 2013 Keith Millis Symposium on Ductile Cast Iron, Nashville, TN, Oct 15-17, 2013.

There are adequate data and results in earlier inquiry reports to comprehend the influence of operational conditions on cylindrical roller bearings cage slip.
After reaching the shaft's self-weight of 135N, the radial load was progressively decreased to that value while a data logger recorded the velocities of the inner race, cage, and roller.
This results in a lower specific load (the load-bearing by each individual roller), as well as a corresponding reduction in the amount of deformation.
The contact breadth was found by applying the same data to Eq. 5 and Eq. 6.
Hollowness between 0% and 40% results in a linear reduction in contact pressure.

In this phase, more training data are generated to fit the CNN model by using data augmentation techniques.
Usually, deep learning models are trained with a huge amount of data and obtaining data to fit these models is very difficult.
This helps us to say whether the model is classifying well with new data(data that hasn't seen before), or the model is doing good only for trained data (memorizing the data fed before).
It is not recommended to use training data for evaluating a model because the model remembers all data samples which are fed during training, i.e. it predicts correctly for all the data points in the training but not for data which hasn't seen during the training.
The result that is obtained from the experiment using the VGG­16 pre­trained model is presented in Table 4 in terms of classification accuracy metrics in the form of percentage for the train data, val­idation data, and test data separately.