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This paper proposes a symptom-based reliability analysis method for deteriorating reinforced concrete (RC) structures on the basis of monitored data.
By far, the most popular use of monitored data appeared to be damage detection.
This paper proposes a symptom-based reliability analysis method for deteriorating reinforced concrete (RC) structures on the basis of monitored data.
On the other hand, the reliability increases when the monitored data is below the prediction.
Influence of symptom reliability by monitoring symptom data.

New stringent emission limits require the drastic reduction (80 to 95%) of harmful emissions of NOx and PM.
For this latter purpose, on-board monitoring data will be combined with open-source data such as AIS and waterway characteristics.
Furthermore, many vessel engines are so old, that no type approval was ever performed and no emission data is available.
Obtaining precise data about the power output of the engine is one of the problems that occur in measurement setups on a vessel.
Conclusions New stringent emission limits require the drastic reduction (80 to 95%) of harmful emissions of NOx and PM.

The area reduction at fracture was higher in 6063 than 7030, although the uniform elongation was larger in 7030. 6063 showed almost no strain rate dependence of the ductility and a limited reduction with increased temperature. 7030 showed markedly increasing area reduction with increasing temperature and decreasing values with increasing strain rate.
(a) (b) Figure 3: Area reduction at fracture as a function of (a) strain rate and (b) temperature.
The ductility as a function of temperature and strain rate was measured in terms of area reduction at fracture 0 0 A AA RA f− = , (2) where A0 is the original cross-sectional area and Af is the area after fracture.
On the other hand, fig. 1 shows a much higher uniform elongation for 7030, contradictory to the RA data.
The area reduction at fracture is higher in 6063 than in 7030, although the uniform elongation is larger in 7030. 6063 shows almost no strain rate dependence of the ductility and a limited reduction with increased temperature. 7030 shows markedly increasing area reduction with increasing temperature and decreasing values with increasing strain rate. 4.

All the following reported data refer to MOSFET with W/L = 1000/50.
This result indicates that moving towards the conduction band the interface trap distribution in samples #2 and #3 is lower than that of sample #1, in agreement with the data previously obtained on MOS capacitors [5].
Therefore, the approaching of the threshold voltage to the ideal values obtained in the implanted MOSFETs can be explained by an interface state reduction due to the N.
The improvement of the mobility is clearly correlated with the N presence at the interface which produces a strong Dit reduction.
The µFE increase up to 290 °C is related to a reduction of the Coulomb scattering term, which is due to acceptor interface states present in the upper part of the gap.

Synergistic effects between Al(MEP) and silica are obtained leading to the increase of LOI and to the reduction of combustion time.
The TGA data demonstrate that silica can enhance the thermal stability of the EP/Al(MEP) and increase the char residue formation at high temperature.
Nevertheless, the reduction in flammability of EP is a major concern.
Figure 1 gives TGA and DTG curves and data of Al(MEP), neat EP and EP/Al(MEP) with and without silica.
Comparison of silica and RH silica as fillers in EP/Al(MEP) composites (EP/Al(MEP)/silica=100/20/10) Filler LOI (%) t1+2 (s) FS (MPa) FM (GPa) NIS (kJ/m2) Ti (°C) Tmax (°C) DTGmax (%/°C) Char at 700°C (%) Silica 39.3 5.2 86.4 2.24 10.3 325 362 0.60 38.7 RH Silica 38.2 6.6 78.9 2.23 10.9 322 363 0.78 36.2 Data in Table2 show that silica filled EP/Al(MEP) exhibits higher LOI value, shorter burning time and higher flexural strength than RH silica filled EP/Al(MEP).

Fig.1 The framework for EEG signals classification 1.2 EEG Data Acquisitions and Preprocessing The experiment data collected from the subjects aged 21~30 are elicited by three different simple schematic faces (happy, neutral, sad) using neuroscan with 66 electrodes.
Fig. 2 illustrates the EEG data acquisition processing using visual stimulus [7].
Fig.2 The EEG data acquisition The purpose of preprocessing the selected EEG signals is getting clean EEG signals without other noises.
Thank EEG data provided by Dr.
[5] Koelstra S, Muhl CandPatras I,in: EEG analysis for implicit tagging of video data, in: Affective Computing and Intelligent Interaction and Workshops, 2009

There are plenty of data need to compute.
The button bar is loading the data button, the icon from left to right is : open the test data, load, delete; open the calculations, load; open the optimal results, load; refresh and so on. 2.5.2 The block of frequency response function calculation.
It can complete respectively the finite element model updating base on the modal data and frequency response data.
Reduction of Stiffness and Mass Matrices [J].A1AA Journal, 1965, 3(2): 380-383
Reduction of Structural Frequency Equations.

This study explains the process computing direct economic losses causing from structural seismic damage and then presents how to obtain data required for the process and how to manage the obtained data.
Then, this paper suggests a procedure to calculate economic loss due to the seismic damage of structures, and applying demand data derived from this methodology.
The value reduction factors are summarized in Table 2.
Table 2 - Reduction factors in example building Seismic Damage Levels Slight Moderate Extensive Complete performance reduction factor Structure - - 0.6 0.8 Non-Structure 0.2 0.4 1.0 1.0 Finally, building loss costs are calculated.
The building loss cost for each return period and damage state is calculated by multiplying the current cost of the building by the value reduction factor.

The analyzed OFE copper has been industrially engineered, being successively hot rolled and cold rolled to ∆=70 % and ∆=90 % reduction.
The software OIMTM analysis from the TSL Company has been used for the qualitative study of the EBSD data.
Intergranular nucleation and growth have been observed in the beginning of the recrystallization process for both reduction levels (70 and 90 %).
a),b) 70 % reduction. c),d) 90 % reduction.
For this reduction level, the second mechanism observed corresponds to the nucleation and growth of strongly recovered cube subgrains.

Introduction SMED refers to the theory and techniques used for the reduction of equipment setup times [1].
Data collection of the setup activities is performed to identify the internal and external elements.
Collect data for categorizing internal and external operations (Step 2) In the Company A, the machine setup process is divided into internal and external setup as refer in Figure 2.
For analyzing the new mold preparation process, PFMEA was used to analyze the causes and effects based on the observation and data collected in step1 and 2.
Furthermore, the reduction cart will free up spaces in the store room which is used to store the carts.