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Data Collection Quantitative Data The data used in this study includes historical fire incident records in Karachi, collected from official reports and local fire departments.
Fire Incident Data Table 1.1 Karachi’s Fire Data Incident ID Location Cause Fire Size (sq. ft.)
Limitations: · Models require refinement and more data.
AI in fire safety systems: Leveraging data for predictive fire hazard analysis.
Journal of Artificial Intelligence and Data Science, 9(3), 155-172

The existence of basic data is a requirement for such an evaluation.
Combination of the assessed data in descriptive KPIs 4.
The use of this data connection is limited to Siemens PLCs.
The data acquisition and processing algorithm collects data from the modules in a predefined frequency (sampling rate), transforms the data into readable information (e.g. converting IEE754) and passes the entire information set via Ethernet to a database.
An integrated data processing algorithm permits to aggregate the consumption data on basis of trigger signals from the machine tool prior to data storage.

Predicting the children's blood lead level in a lead and zinc smelting area based on IEUBK model Fei Chen1,a, Hongguang Cheng1,b, Xiangfen Cui1,c 1School of environment, Beijing Normal University, China a15510492759@163.com,bchg@bnu.edu.cn,ccui1987rainny@163.com Keywords: Lead and zinc smelting area,Lead exposure,Children's blood lead level,IEUBK model Abstract.According to environmental lead exposure data and dietary survey data of lead and zinc smelting area,blood lead levels of children (2~7years old) in study area were predicted with IEUBK model.
A total of 123 people, included 42 cases the measured blood lead data.
Method Dietary exposure, lifestyle and behavior patterns of the 123 children were investigated through the form of questionnaire, the atmosphere, soil, water, crops samples in children living areas were collected and measured their lead concentration,estimated children's dietary lead exposure data, we used these data predicted children's blood lead concentration by IEUBK model, the lack of necessary data were added by the model default parameters.
IEUBKwin32 model was used to predicted children's blood lead levels in the study area,this model involved the various parameters and data source as shown in Table 1: Table 1 IEUBK model involved indicators The index type Parameters The data source The concentration of pollutants in environmental media Outdoor dust lead levels According to the survey data interpolation Indoor dust lead levels According to the survey data interpolation Outdoor air lead levels According to the pollution sources calculated the health protection distance, atmospheric environmental protection distance and took standard limit The concentration of pollutants in exposed medium Lead contents in food According to the survey data from village average Lead contents in drinking water According to the survey data from village average Exposure parameters Dietary structure According to the average of children's dietary questionnaires Behavior patterns According to the average of the children's behavior pattern survey
Effect of smelter emission reductions on children's blood lead levels[J].

Looking for data sources by Delphi method and questionnaire method and using the entropy weight method to Process those data.
If it is the even value, the median will be used for data analysis.
Model application prospects Data sources outlook: ① Using Delphi method for basic data and weights.
Ask alliance related personnel to rate the evaluation index, provide data and put forward improvement Suggestions for the index system.
Model application prospects: Data analysis after data collection, Use the mathematical model provided by this paper to analyze the rationality of every evaluation index and each alliance’s existence stable performance.

Basing on this data, absolute values and temperature dependences of the paramagnetic magneto-optical activity (MOA) of the transitions were determined.
On the basis of MCD (Fig. 1) and absorption spectra we have calculated MOA of the observed f-f transitions (parameter A in Eq. 1), using the room temperature data (see Table 1), and we have found temperature dependences of MOA (parameter a in Eq. 1) of the transitions 6H15/2 → 6F5/2 , 6(F7/2 +H5/2) and 6(F9/2+H7/2) (Figs. 2-5).
Reduction of the experimental MOA as compared with the maximum theoretical ones means that there are several contributions in MOA corresponding to several admixed states.
Jf 9/2 9/2 7/2 7/2 5/2 3/2 λ 4 6 4 6 6 6 t 3; 5 5; 7 3; 5 5; 7 5; 7 5; 7 A +4.9; -3.93 +4.5; -3.86 +5.66; -4.53 +5; -4.28 +5.38; -4.61 +5.66; -4.88 Comparing results presented in Table 2 with the experimental data of Table 1 we can find spherical harmonics active in f-f transition allowance.

Based on the analysis of the specific case in the actual business decision-making, this article lists the average in the prediction, reducing work load and the gross profit maximum problems; through the establishment of random variable model, using the theory of mathematical expectation for data measured to choose the best scheme, method and procedure to provide policy makers who make the best choice the best theoretical basis; aiming to make the operators more scientific and rational when choosing investment and catching investment opportunity, Mathematical expectation plays an important role in economic activities.
For example, when p = 0.1, k = 2, 1-E(X)=0.92-0.5=0.31 If the enterprise has 10000, it reduces by3100 times, that is, a reduction of 31% of the workload.
Known from the calculation of the table data above, the more probable the service/useful life of this kind of appliance with the three years is,the more profits of corresponding market gets.
Visible through the calculation of data above, you can use the profit expectation to analyze this problem.

The limit states of all seismic performance levels and their dominating parameters of masonry-infilled frame structures are put forward, and quantified performance index of this structure are obtained on the basis of statistical test data.
Combining with above dominating standards and on the premise of satisfying certain safety guarantee rate, the storey-drift angle limitation about five performance levels of basically well, slightly damaged, moderately damaged, seriously damaged and collapse prevention are respectively 1/1400, 1/500, 1/150, 1/50 and >1/50 on the basis of collecting experimental data[3-5].
The former reflect nonlinear characteristics of structures using force reduction factor, and the calculation process does not need repeated iteration and the results converge well.
Based on the regression analysis of the test data, the storey-drift angle limitation about five performance levels are respectively 1/1400, 1/500, 1/150, 1/50 and >1/50. 3.

The present work presents a comparison of properties and performance data for samples made with different particle size.
Size(um) Sintering temperature(°C) 1 200(1#) 1250 2 200(1#) 1275 3 200(1#) 1300 4 200(1#) 1325 5 200(1#) 1350 6 300(2#) 1250 7 300(2#) 1275 8 300(2#) 1300 9 300(2#) 1325 10 300(2#) 1350 Results and Discussion Corresponding to its processing parameters, a number of data were collected to characterize the microstructure of the ceramic body .The mean pore size, overall porosity, PWP and permeability can all be shown as a function of the sintering temperature, where the smaller sized samples are compared with the larger ones.
Pure water permeation data for the set versus sintering temperature.
The samples were left in the cell and monitored over time so that the PWP recorded was a stable value reached after some amount of flux reduction, often after several hours.

Drying and heating lumped models were proposed and fitted to the experimental data.
Non-linear regression analyzes were performed to verify the consistency of the models to predict the experimental data.
The fitted models presented good agreement with the experimental data.
Table 3 – Parameters of the Eq. 1 obtained after fitting of the drying model to the moisture content experimental data of the sisal fiber.
Table 4 – Parameters of Eq. 2 obtained after fitting of the heating model to the surface temperature experimental data of the sisal fibers.

Field acquisition of the rolling-cut bilateral shear plates with different thickness corresponding to a cutting edge gap related data such as table 1.
According to the shear data to fitting estimation curve, the linear regression equation using a linear regression model.
Among them, the choice of variables to the regression coefficient estimates and the observed data error square and the principle of minimum.
The image data using a linear regression model for data processing of regression model[4].
Figure 1 Residual distribution map Figure 2 Optimization curve From the residual distribution can be seen, the 19 and 23 data residuals from 0 point far away, remove the two data points, the linear regression equation model optimization ,the optimized curve as shown in Figure 2.