Groups Of Sample Data Research Articles

The impact of dynamic traffic conditions on the sustainability of urban fire service

Frequent fires pose a significant threat to the safety and sustainability of society. The urban fire service is critical for protecting against fire hazards. Traffic congestion increases the response time of fire service seriously, and dynamic traffic conditions can affect the sustainability of urban fire service. In this study, to evaluate the impact of dynamic traffic conditions on fire service comprehensively, we proposed a method for estimating the total effective coverage area of fire service (TECA) based on real-time traffic data extracted from online maps. To validate the proposed method, a fire station (FS-HN) in Hunan Province, China was selected as the study case. In total, 6625 POIs around FS-HN were characterized as the fire service demand points, and 336 estimating scenarios throughout a week were set. By analyzing the extracted 2,226,000 groups of sample data, TECA for FS-HN was estimated as 7.63 km2 with the assumption that the average travel time of fire trucks is 80% of that of ordinary vehicles. Visual results show that traffic congestion and the lack of transportation infrastructure (only one bridge over a river near FS-HN) seriously reduce the service coverage of FS-HN. To improve fire service, it is necessary to improve traffic conditions and build more transportation infrastructures.

Research on predictive control of energy saving for central heating based on echo state network

It is very important for energy conservation and environmental protection to realize energy-saving control of central heating system by prediction method. In this paper, the system model of central heating is built according to the general principle of secondary heating network, and an energy-saving control scheme based on the combination of dual networks and the regulation of secondary heating network is developed. In order to achieve better energy-saving control, the echo state network method is used to predict the heating water temperature in the heat exchange station controller. In the configuration process of echo state network, the water supply circuit temperature of the secondary heating network is taken as the output, and the 8 parameters of the primary heating network and the secondary heating network are taken as the input. After the stable echo state network is obtained through iterative training, the prediction is completed by using iterative multi-step method. Through the experimental results of three groups of sample data, it can be seen that the echo state network and prediction method constructed in this paper are superior to other methods, and are suitable for energy-saving prediction of central heating system.

Genetic changes in fetal cerebral cortex after maternal exposure to sevoflurane

Purpose: To investigate the acute changes in transcriptome of radial glial progenitor cells after maternal exposure to sevoflurane.Methods: Two groups of sample data were collected from the data set in the GEO database. Pregnant mice in sevoflurane group were exposed to 2.5 % sevoflurane for 6 h on day 14.5 of the pregnancy, while the mice in control group were exposed to 100 % oxygen for 6 h. At the end of the exposure period, the cerebral cortex of the two groups of fetuses was isolated and analyzed by RNA sequencing. Differentially expressed genes (DEG) were analyzed using limma package of R language. The Gene Ontology (GO) and KEGG pathway enrichment were based on DEG through a cluster profile package of R. Moreover, protein-protein interactions (PPI) network construction and central gene prediction were carried out using a string database and R package.Results: Bioinformatics analysis revealed 289 up-regulated genes and 311 down-regulated DEGs, respectively. Gene Ontology and KEGG enrichment analysis revealed terms related to neural development and transcriptional function. Based on the central genes of the PPI network, it was found that certain genes may play significant roles in the regulation of neural development. These genes are hnRNPM, AURKA, NCBP, SRSF6, ASF1B, HNRNPA2B1, DDX21, H3F3B, KPNA2 and ABCE1 (p < 0.05).Conclusion: The findings of this study suggest that hub genes and a variety of signal pathways may play key roles in the development of radial glial progenitor cells.

Evaluation Method of English Talent Training Quality Based on Deep Learning Model

This paper studies and analyzes three aspects: DL model, English talent training, and quality evaluation analysis, so as to get more rigorous and accurate quality evaluation results, and make relevant plans for future research directions. This paper focuses on model and method analysis to carry out experiments and analysis on three aspects: DL, personnel training, and quality evaluation. The experiment and inquiry of deep learning are divided into correct rate and loss. In 0-70 epoch, the highest training correct rate is 0.975, and with the increase of training times, the training correct rate is also increasing. According to the statistical investigation, 42.91% of English teachers are satisfied with their academic level, 38.48% with their oral English level, 38.89% with their teaching quality, 41.02% with their teaching methods, 40.29% with their teaching spirit, and 39.88% with their knowledge structure. At the same time, according to the statistics of students, graduates and teachers’ problems in English talent training, the largest proportion is the poor level of teaching resources, so we should improve the efficiency from the level of teaching resources. In order to improve the efficiency of quality evaluation method, this paper combines AF algorithm and BQ algorithm under deep learning. The error rate of algebraic algorithm is compared. Through six groups of sample data, it can be seen that the highest error rate of AF algorithm is 5.86% and the lowest is 0.92%, the highest error rate of BQ algorithm is 10.70% and the lowest is 1.10%, and the highest error rate of algebraic algorithm is 10.70% and the lowest error rate is 5%. In contrast, the error rate of AF algorithm is lower and more stable. Next, this paper compares and analyzes the performance of the AF algorithm, BQ algorithm, and algebraic algorithm. According to the experimental results, it can be seen that the AF algorithm is more accurate than the BQ algorithm and algebraic algorithm in accuracy, recall rate, F 1 , accuracy rate, etc. Therefore, it is more intuitive and accurate to evaluate the quality of English talents training through AF algorithm under deep learning.

Modeling and evaluation of causal factors in emergency responses to fire accidents involving oil storage system

According to the statistics of 160 typical fire and explosion accidents in oil storage areas at home and abroad nearly 50 years, 122 of them occurred the secondary accidents in the emergency responses. Based on 122 accident cases, 21 causal factors leading to secondary accidents are summarized. In order to quantify the influencing degree of these causal factors on the accident consequences, a multiple linear regression model was established between them. In the modeling process, these factors are decomposed into the criterion layer, variable layer, and bottom layer. The improved analytic hierarchy process (IAHP) was used to establish the relationship between the bottom factors and variable factors, and the regression analysis method was used to establish the relational model between variable layer and criterion layer. For 122 cases of the secondary accidents, this study took the year as a statistical dimension, and obtained 40 groups of sample data. The first 34 groups of sample data were used to build the causal factors model, and the last 6 groups of sample data were tested the generalization ability of the model by using the established regression model combined with grey prediction model. The results show that the prediction ability of the established model was better than that of the grey prediction model alone. Moreover, the relative contribution and change trend of the causal factors were evaluated using the mutation progression method, and corresponding preventive countermeasures were proposed. It was found that human professional skills, knowledge and literacy, environmental issues, and firefighting facilities are the main influencing factors that lead to the secondary accidents. These three kinds of factors show a gradual improvement trend, and the existing prevention measures should be maintained and further improved. The problem of inherent objects or equipment factors has not been effectively improved and has a worsening trend, which is the focus of prevention in the future, and the prevention and control efforts need to be moderately increased. The research results have important guiding significance for understanding the quantitative influences of causal factors on the accident consequences, improving emergency response capabilities, reducing accident losses, and avoiding secondary accidents.

Application of multivariate canonical harmonic trend analysis, singularity analysis with radius-areal metal amount and improved adaptive fuzzy self-organizing mapping to identify geochemical anomaly related to iron polymetallic mineralization in Hunjiang district, Northeastern China

How to more effectively perform anomaly detection of combination information has always been an important issue for the scholars in various fields. In order to identify and extract the geochemical anomaly information related to polymetallic mineralization in the Hunjiang area, this article uses the hybrid method that combines multivariate canonical harmonic trend analysis (MCHTA), singularity analysis with radius-areal metal amount and improved adaptive fuzzy self-organizing map (IAFSOM). First, multiple sets of combination feature information with multi-dimensional variables will be obtained through the MCHTA method, which information is considered as the initial information for the subsequent analysis. Next, the singularity analysis method is used to process the combination concentration value to calculate the singularity indexes. Finally, the singularity indexes are classified by the IAFSOM method, and nine groups of sample data are obtained. The analysis results found that the samples information in fourth group covered most of the low α-values. The main conclusions in this study are as follows: (1) The MCHTA method can effectively detect the combination information related to geochemical anomaly; (2) The application of singularity analysis method with radius-areal metal amount can reveal the significant characteristics of mineralization combination elements; (3) IAFSOM can be used as an effective tool for the classification and identification of geochemical anomaly with combination information; (4) the hybrid method that combines MCHTA method, singularity analysis and IAFSOM model has a good indication significance in the prospecting of geochemical anomalies, and could provide a good method for geochemical prospecting.

Prediction of Characteristic Parameters of the Surge by Landslides

Instability of landslides will lead to high-speed of rock and soil into the water, sparking a huge surge, causing significant harm to residents and infrastructure of coastal areas. With regard to the surge by landslide, the climbing height and the dynamic pressure are dominant characteristic parameters of concern, which influence the scope and extent of damage. The paper selected five important factors which influence characteristic parameters, that are time into water, sliding volume, angle into water, climbing angle, and water depth, based on simulation and MATLAB, established the BP neural network model of the characteristic parameters. Through training the network at various hidden node, the optimal training network has been gained, characteristic parameters at four groups of sample data have been predicted and verified respectively, and the best forecasting network can be selected as the predicting network.

Assessing the quality of training data in the supervised classification of remotely sensed imagery: a correlation analysis

Training data play an important role in the supervised classification process of remotely sensed images. Its quality is an important factor affecting the accuracy of image classification. Therefore, measuring the quality of training data is essential for classification procedures and subsequent operations. This paper discusses a new method for the quality assessment of training data before the classification procedure and investigates the correlation between measures for training data and measures for classified images at category and image level, respectively. Five groups of sample data collected from a Landsat TM image were used in correlation analyses. The results demonstrate that the proposed method is valid for measuring the quality of training data and can, to some extent, reflect the quality of classified images which are obtained through supervised classification with the corresponding training dataset.

Improving Transferability of Safety Performance Functions by Bayesian Model Averaging

A jurisdiction can import a safety performance function from another jurisdiction or another time period through model calibration. For the transfer to be achieved successfully, the calibrated model must sufficiently capture local road and traffic features. As proposed in the AASHTO Highway Safety Manual, the model calibration factor is estimated to be the ratio of the sum of observations in a local sample to the sum of predictions for the sample from the uncalibrated model. Although this approach may be adequate for overall goodness-of-fit measures, achievement of a satisfactory fit over all ranges of the covariates is not guaranteed. This paper seeks to address this limitation by investigating a new methodology for the transfer of models with four groups of sample data from Canada and Italy. First, the calibration factor approach was evaluated by the use of goodness-of-fit tests. Then, local models were developed and evaluated. For these models, a variety of random structures for frequentist and Bayesian approaches was explored with generalized linear regression, nonlinear mixed fitting, or Markov chain Monte Carlo simulation procedures. Finally, a Bayesian model averaging approach that integrated all considered models was investigated as an alternative to traditional model selection. This methodology did improve model transferability over all ranges of covariates, suggesting that Bayesian model averaging can be a sound alternative to conventional model calibration, especially when the flexibility and estimation ease of this technique are considered. Moreover, this approach is conceptually superior to selection of a single best model because it explicitly addresses model uncertainty.