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For the thermal building and system simulations an average climatic data set (over 10 years) is used (Tambient,average: 10.7 °C, heating degree days: 3102 Kd, global hor. radiation: 1206 kWh/m²a, diffuse hor. radiation: 616 kWh/m²a) [6].
The evaluation of the simulation data revealed that an efficient option is to use the surface temperature of the heat transfer surface (TABS or Floor) as the control variable.
For the LEB, this means a reduction of the total electricity consumption from 1458 kWh/a (9, System B) to 1100 kWh/a (11, System C) in case of the TABS system.
In the case of the LEB with TABS and System C compared to the underfloor heating system without solar direct charging (System B), this means a reduction of approx. 25 % of the total electricity consumption (approx. 400 kWh/a).
[6] Meteotest, Meteonorm 7.1.11.24422, Global Meteorological Database for Engineers, Planner and Educations, Software and Data on CD-Rom, Bern, Switzerland, 2015

The powder X-ray diffraction data were collected on a Siemens D5000 powder diffractometer with Cu Kα radiation at 40 kV, 30 mA, with a step size of 0.02º 2θ and a scanning time of 1s per step.
Profile refinement was then performed on the XRD data using the TOPAS software (Version 2.1, Bruker-AXS).
Fig. 1 XRD patterns of the 0.25LiMn2O3•0.75LiNi1/2Mn1/2O2 samples (left) obtained from precursor-prheating at 450°C for 4 h in air(a), for 10 h in air (b) and for 4 h in N2(c) and the expanded region between 20 and 25° 2θ (right) To gain insight into the microstructure, Rietveld refinement was performed on Bragg diffraction data initially using a model of Rm phase with Li and some Ni on the 3a sites (0, 0, 0), transition metal ions and some Li on the 3b sites (0, 0, 1/2), and oxygen on the 6c sites (0, 0, zox).
It has been recently confirmed by ab initio calculations that the very small reduction of the Li-O slab thickness from 2.64 to 2.62 Å results in a 20 to 30 meV increase in the activation barrier for Li+ migration [8].
Correspondingly, one peak close to 3.7V and the other near 3.3V appear, resulting from the reduction of Ni ions and Mn, respectively.

As the overall billet geometry remains nearly constant during ECAE, multiple passes through the die are possible without any reduction in cross-sectional area.
The grain boundary character distributions were also plotted from the EBSD data.
Processing through both the routes A and BC led to a reduction of grain size in every pass.
The grain and phase boundaries in two-phase materials do not get distinguished so clearly in EBSD maps, therefore the measured EBSD data was partitioned for both the constituent phases.
The (111) pole figures and the relevant sections of the ODFs obtained from the partitioned EBSD data are displayed in Fig 4 (b) and 5 (b) for α phase.

Vibration data are collected through a data acquisition system, then tested and analyzed through statistical analysis.
For convenience of recording and processing the experimental data, each variable are coded as A, B, C, D and E.
MINITAB software was employed to analyze the experimental data.
Based on 95% confidence interval (α=0.05), the samples data were computed and analyzed.
Since the p-value is greater than commonly chosen α-levels, there is no evidence for a difference in deviation of actual laboratory data versus data calculated from model.

Specifically, the transferred and processed data is used by automated driving systems installed onboard to lead the vehicle through its path, by applying all necessary commands.
As data generated by scheduling and monitoring systems and vehicles increased, managing this data efficiently was crucial for decision-making [11].
Data-driven learning methods can identify multiple patterns and enable quick and efficient decision making to choose from those patterns.
External factors might influence V2X communications, due to distance, weather or other scenarios in which data transfer is not ideal, creating a delay in receiving and processing data.
A less probable delay is still possible – the reaction of the vehicle from when the data is received, to when the required actions are implemented.

The same experimental design was also used to acquire a data set for ANN.
The plot of experimental data versus RSM (A) and ANN (B) model predicted data.
The original data set (17 runs) was divided into three subsets – training (9 data points), validation (4 data points) and testing sets (4 data points).
The data splitting was carried out to estimate ANN performance for prediction of “unobserved” data that were not originally used for training.
Likewise, RMSE is determined as 0.0745 for all data sets.

Recently, with the aim of improving performance and reducing production cost, new technologies, such as: reductions of microalloyed elements, tailored components, and mechanical properties forecast, have been developed in China auto industry.
The logical relationship between three models, related variables, input and transfer of data is shown in Fig.5b.
a relationships between models b data transferred between models Fig.5 The hot forging process program and logical relation between prediction models According to the characteristics of Heating Working, AGS forecast model consists of four parts, dynamic recrystallization (DRX), static recrystallization (SRX), meta-dynamic recrystallization (MDRX) and grain growth (GG).

However, the main drawback of this group of damage detection methods is its limited applicability to operational data.
Its application together with classical modal filter allows for damage detection using operational data with other type of excitation.
However, the main drawback of this group of damage detection methods is its limited applicability to operational data.
In the simulation zero initial conditions were specified, exponential window was applied to the output data.
Comparison of ODS filter and modal filter applied on the same operational data As far as operational data is concerned, method presented in the article seems to be working better, in comparison with modal filter designed mostly for FRF data, as fewer distortions can be spotted in the output, especially around natural frequencies of a system.

The advantages of these WUSN are; easy to deploy, concealment, timeliness of data, coverage density and reliability [6].
The paper further formulates a nonlinear program to determine the optimal data (events) extraction in a grid based WUSNs.
Data Optimization Analysis in Wireless Underground Sensor Network This section describes the optimization method for data collection using relay nodes in WUSN.
Fig. 13: Maximum Energy Bound with Data Extraction Fig. 14: Minimum Data Bound with Energy Consumption Fig. 15: Optimal Data extracted versus Energy consumed 6.
The increase in energy efficiency (Kbits/Joules) is because of more data being collected by the mobile sink and the reduction of energy consumption per node due to reduced communication range between the nodes.

The VO2 (M) petaloid clusters, which were composed of ultrathin flakes with several nanometers in diameter, were prepared by solvothermal reduction of self-synthesized nano-V2O5 and absolute EtOH.
Therefore, VO2 has wide potential applications in different areas, such as temperature sensing devices [3], optical or electrical modulators [4], optical data storage media [5] and intelligent thermochromic windows [6].
The data revealed that the peak at 516.3 eV was attributed to V2p3/2, and the V2p1/2 peak was attributed at 522.6-524.0 eV; and the peak at 530.3 eV was associated to O1s.
The data revealed that the vibrational absorption bands from 400 to 1000 cm-1 disappeared above 341 K and appeared again below 323 K.