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Computed, this represents a reduction of heat loss from the value of 242.6 kW to 92.4 kW, hence a reduction of heat loss by 61.6%.
Fig. 4 Carbon dioxide concentration in ppm in three classrooms during class Evaluation of energy consumption according to energy carriers The documentation of data on energy consumption in this analysis is based on the measured data from operation.
A presentation of the data on energy consumption in a comparison of individual years and the condition of the building in these years mainly concerns whether such a comparison of the condition makes sense, namely, if comparable data is being compared.
However, the window replacement appears in the measured data from operation.
However, we cannot separate the measured data from the operation since we do not have electricity sub meters.

In order to maximize the loss reduction of this process, the laser scribing technics was investigated in details.
In this work, three laser scribing parameters including pulse energy, scanning velocity and scribing spacing, which are highly related to the loss reduction, were selected for the study.
The results indicate that the subsequence of significance of the three factors on core loss reduction is as follows: pulse energy, scribing spacing and scanning velocity.
In this work, we try to get the greatest degree of core loss reduction by directing a laser beam on the surface of the WISCO Company's product 30Q130.
As factors affecting core loss reducing level are sundry and interrelated, an orthogonal method was employed to design the experiment and analyze data.

Introduction RS，a kind of mathematic theory that can analyze data was introduced by Professor Pawlak and some other scientists.
It found relationship between data obtains useful attributes and simplifies information processing by analyzing and reasoning incomplete data.
In order to handle intelligent data, the symbol expression of knowledge must be used.
RS module start fault symptom analog-digital conversion inference/reasoning machine output data transition testing report Network Classification learning knowledge base explanation function new study sample learning sample set input decision table construction continuous attribute discretization RS method calling decision table reduction Fig.2 General structure table of photoelectric equipment fault diagnosis expert system System is mainly composed of knowledge base, inference/reasoning machine, fault symptom parameter, analog-digital conversion, output data transition and RS module. 3.1 Construction of knowledge base Knowledge base is the core of expert system.
Therefore, it must be transferred from physical concept to numerical form. 3.4 Output data transition Output of actual testing result is showed by conception table, however, the figure of output of neurons is between 0 and 1, so it should be transferred to physical concept. 4.

The model was validated experimentally using thermocouples and a data acquisition system, with simulation results closely matching measured data, showing errors below 0.5%.
The test setup comprises the power supply, semiconductor module with a parallel heat sink, thermocouple, multimeter, and data acquisition system.
Temperature measurements are acquired using a multichannel data acquisition system, with ambient conditions maintained at 25 °C.
The red dashed curves represent the numerically predicted junction temperatures, while the solid black curves depict the experimentally measured data.
The data reveals a clear improvement in thermal performance with the addition of fins.

Result and Discussion Beer’s Law equation was applied in this experiment to analyse the absorbance data obtained from UV-VIS spectrophotometer.
(1) Figure 1: Standard calibration curve The standard calibration curve using ammonium stock solution was prepared before undergoing the inhibition and data was recorded at 640 nm.
Meanwhile, urea solution with mixture of urease-guava leaves extract showed reduction in NH3 concentration release.
The reduction happened in small value with different around 0.1 mol/L and the releasing also was almost equal till the end of incubation time.
This small reduction might be because of the concentration of guava leaves extract that been used is slightly low.

Based on the daily precipitation data in Northeast China (NE China) from 1961 to 2010, six extreme precipitation indices (RX1day, Rx5day, R10mm, R20mm, R95T, and R99T) in NE China were calculated, and the temporal and spatial characteristics of extreme precipitation events were analyzed.
Based on the daily observational data of 100 stations in Northeast China (NE China), spatial and temporal variations of extreme precipitation in the last 50 years are analyzed in the present study.
Data and Methods Study Area.
Data.
The data were provided by the National Climatic Center (NCC) of the China Meteorological Administration (CMA).

On the basis of X-ray diffraction data a new phase appears in the initial stage of MnO2 discharge when n*=0.6 [20].
According to other data, the homogeneity range narrows down to n*=0.5 [15].
According to chemical analysis data the reactions (5) and (4) are accompanied by a partial transfer of manganese ions to the contacting solution.
However, the analysis of the entire array of the obtained experimental data leads to the conclusion that the difference in electrochemical behavior between O and MA oxides has a different nature.
Comparing our results with the data presented in the literature one can conclude that mechanoactivated manganese dioxide reveals the electrochemical behavior similar to the behavior of γ-MnO2.

After getting gel-coated iron powder, we can obtain Y2O3-coated iron powder through heating and deacidizing in hydrogen reduction furnace.
Fig. 1 X-ray diffraction pattern of the reduced iron powder (2) Morphology of ODS iron powder before and after reduction Fig.2 shows the SEM images of ODS iron powder before and after reduction.
Fig. 2 SEM images of ODS iron powder before and after reduction: (A), (a): before reduction (B), (b): after reduction.
The cause is the residual carbon in the reduction can form the pearlite in the process of sintering.
Samples Sintering pressure /MPa Sintering temperature /˚C Sintering time /min Relative density /% RH /HRB UTS /MPa elongation /% Pure iron powder 40 950 8 98.6 56 305 30.3 ODS iron powder 40 950 8 98 81 510 5.2 Table.1 shows the performance datas of iron powder before and after dispersion strengthening in the same SPS conditions.

It was decided to collect the data regarding the defects generated in the pump manufacturing process and analyze them using various tools being implemented.
To collect the data, analyze, find out the Root cause and the Rolled Throughput Yield (RTY).
Table 3 and 4 show the data of production collected in the enterprise based on which Fig. 1 Run Chart; Fig. 2 P-Chart; Fig. 3 Pareto Chart were plotted.
AND PLOTTING PARETO CHART Fig. 3 Pareto Chart Fig. 4 Cause and Effect Diagram TABLE 5: COLLECTION OF DATA AFTER IMPLEMENTATION MOTOR ASSEMBLY PROBLEM – Data After Implementation Type of Problem Date 10 11 12 14 15 17 18 19 20 21 22 24 25 26 27 28 SUM AVG % OF TOT PROD % OF TOT DEF.
The data of defects were collected after implementing the measures (Table. 5) and the percentage of rejection has come down from 18.5 to 3.079.

By quantifying the loss of mechanical performance, this study aims to provide essential data for optimizing the durability and reliability of critical infrastructure.
This strategy allows for (i) capturing real synergies between radiation, humidity, thermal cycles, and service loads; (ii) validating accelerated aging protocols using a field benchmark; and (iii) generating direct data for lifetime prediction models in optical and energy infrastructures.
This allows for a direct correlation between material degradation and actual operational performance in critical infrastructures, providing data of greater practical relevance for engineers and designers than mere laboratory simulation.
The observed ranges within the data (e.g., for UTS) also underline the variability inherent in field-aged samples, which can experience non-uniform exposure conditions.
Table 1 ([Insert Table 1 here, summarizing key TGA data like Tonset​, Tmax​, % residue for new and used samples]) summarizes the key thermal degradation parameters.