This study demonstrates that the “cumulative deceleration area” performs poorly and the study data calls for scientific classification of FHR decelerations

2020 ◽  
pp. 1-2 ◽  
Author(s):  
Shashikant L. Sholapurkar
Keyword(s):  
Study Data ◽  
Scientific Classification

MEDIATEXT IS A TOOL THAT DETERMINES THE QUALITY OF INFORMATION

2020 ◽  
Vol 6 (3) ◽  
pp. 158-164
Author(s):  
Navruza Yakhyayeva ◽  
Keyword(s):  
Functional Properties ◽  
Linguistic Features ◽  
Text Linguistics ◽  
Quality Of Information ◽  
The Creation ◽  
Linguistic Units ◽  
Scientific Classification

The quality and content of information in the article media text is based on scientific classification of linguistic features. The study of functional styles of speech, the identification of their linguistic signs, the discovery of the functional properties of linguistic units and their separation on the basis of linguistic facts is one of thetasks that modern linguistics is waiting for a solution. Text Linguistics, which deals with the creation, modeling of its structure and the study of the process of such activity, is of interest to journalists today as a science.

A data mining approach to the diagnosis of failure modes for two serial fastened sandwich composite plates

2016 ◽  
Vol 51 (20) ◽  
pp. 2853-2862 ◽  
Author(s):  
Serkan Ballı
Keyword(s):  
Data Mining ◽  
Random Forest ◽  
Failure Modes ◽  
Composite Plates ◽  
Study Data ◽  
Sandwich Composite ◽  
Support Vector ◽  
Geometrical Parameters ◽  
Mining Methods

The aim of this study is to diagnose and classify the failure modes for two serial fastened sandwich composite plates using data mining techniques. The composite material used in the study was manufactured using glass fiber reinforced layer and aluminum sheets. Obtained results of previous experimental study for sandwich composite plates, which were mechanically fastened with two serial pins or bolts were used for classification of failure modes. Furthermore, experimental data from previous study consists of different geometrical parameters for various applied preload moments as 0 (pinned), 2, 3, 4, and 5 Nm (bolted). In this study, data mining methods were applied by using these geometrical parameters and pinned/bolted joint configurations. Therefore, three geometrical parameters and 100 test data were used for classification by utilizing support vector machine, Naive Bayes, K-Nearest Neighbors, Logistic Regression, and Random Forest methods. According to experiments, Random Forest method achieved better results than others and it was appropriate for diagnosing and classification of the failure modes. Performances of all data mining methods used were discussed in terms of accuracy and error ratios.

scholarly journals A SCIENTIFIC CLASSIFICATION OF VOLATILITY MODELS

2010 ◽  
Vol 24 (1) ◽  
pp. 192-195
Author(s):  
Massimiliano Caporin ◽  
Michael McAleer
Keyword(s):  
Volatility Models ◽  
Scientific Classification

Identification, investigation and classification of surface depressions and chalk dissolution features using integrated LiDAR and geophysical methods

2020 ◽  
Vol 53 (4) ◽  
pp. 620-644 ◽  
Author(s):  
Zoe Elizabeth Jeffery ◽  
Stephen Penn ◽  
David Peter Giles ◽  
Linley Hastewell
Keyword(s):  
Ground Penetrating Radar ◽  
Image Interpretation ◽  
Geophysical Methods ◽  
Integrated Approach ◽  
Study Data ◽  
Aerial Photographs ◽  
Geophysical Techniques ◽  
Karstic Features ◽  
Ground Penetrating

The chalk bedrock of the Hampshire Basin, southern England is an important aquifer and is highly susceptible to dissolution, making the development and presence of karstic features a widespread occurrence. These features are hazardous because they provide possible pathways to the underlying aquifer and therefore present potential site-specific contamination risks. There is also evidence of extensive extraction, through both mining and surface quarrying, of chalk, flint and clay over many centuries. Geophysical techniques consisting of electromagnetic (EM31) and ground-penetrating radar surveys were used to identify and characterize target features identified from desk study data. The ground-penetrating radar and EM31 interpretations allowed the classification of non-anthropogenic target features, such as diffuse buried sinkholes with disturbed and subsiding clay-rich infill and varying symmetrical and asymmetrical morphologies. We describe here the investigations of such features identified at Holme Farm, Stansted House, Hampshire. The combination of EM31 data and ground-penetrating radar profiles facilitated the identification of a palaeovalley, cavities and irregular rockhead. This investigation identified locations of aquifer contamination risk as some sinkholes have been sites for the illegal dumping of waste or the infiltration of fertilizers, leaking sewage pipes or animal waste. This potential source of contamination utilizes the sinkhole as a pathway into the highly transmissive White Chalk Subgroup of Hampshire and has caused contamination of the aquifer. We conclude that our integrated approach of geophysical techniques linked to aerial photographs and LiDAR image interpretation was highly effective in the location and characterization of dissolution structures, infilled former quarries and mining features at this site.

Classification of COPD Patients According to ALAT and GOLD Staging Systems Using PUMA Study Data

2017 ◽  
Vol 53 (3) ◽  
pp. 98-106
Author(s):  
María Montes de Oca ◽  
María Victorina López Varela ◽  
María Eugenia Laucho-Contreras ◽  
Alejandro Casas ◽  
Eduardo Schiavi ◽  
...  
Keyword(s):  
Study Data ◽  
Copd Patients ◽  
Staging Systems

Quality of data on intra-EU trade in goods

2019 ◽  
Vol 64 (6) ◽  
pp. 5-15
Author(s):  
Iwona Markowicz ◽  
Paweł Baran
Keyword(s):  
Poor Quality ◽  
Study Data ◽  
Quality Of Data ◽  
Member States ◽  
Eu Member States ◽  
Country Level ◽  
The Poor ◽  
Made In

Official statistics on trade in goods between EU member states are collect-ed on country-level and then aggregated by Eurostat. Methodology of data collecting differs slightly between member states(e.g. various statistical thresholds and coverage), including differences in exchange rates as well as undeclared or late-declared transac-tions, errors in classification of goods and other mistakes. It often involves incomparability of mirror data (nominally concerning the same transactions recorded in statistics of both dispatcher and receiver countries). A huge part of these differences can be explained with the variable quality of data resources in the Eurostat database. In the study data quality on intra-EU trade in goods for 2017 was compared between Poland and neigh-bouring EU countries, i.e.:Germany, Czech Republic, Slovakia, Lithuania,and other Baltic states–Latvia and Estonia. The additional aim was to indicate the directions hav-ing the greatestinfluence on the observed differences in mirror data. The results of the study indicate that the declarations made in Estonia affect the poor quality of data on trade in goods between the countries mentioned above to the greatest extent.

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