As the most important indicator of the coastal ecosystem, various studies on phytoplankton have frequently been proposed in coastal waters. In this study, the statistical analysis of phytoplankton biomass (in terms of...
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As the most important indicator of the coastal ecosystem, various studies on phytoplankton have frequently been proposed in coastal waters. In this study, the statistical analysis of phytoplankton biomass (in terms of chlorophyll a) is performed in the Wadden Sea near Lauwersoog (53.4167 degrees N, 6.2000 degrees E), The Netherlands, determined by the 10-year's historical dataset from 2000 through 2009. boxplot analysis is introduced to give insight in the seasonal variations of phytoplankton biomass and physical-chemical conditions. Annually, a big difference is found in chlorophyll a, showing that the maximum values occur in the months of April and July, and the minimum values occur in winter (December, January, and February). The phytoplankton biomass is positively correlated with the physical conditions (salinity, light intensity, and water temperature), and is negatively correlated with the nutrients (nitrate, ammonium, and silicate). Factor analysis distinguishes the driving variables that represent most of the total variance using three extraction methods (principal component analysis, unweighted least squares, and maximum likelihood). Nitrate dominates the main activity in the first rotated component/factor, and ammonium contributes the most in the second rotated component/factor. The findings of this study are significant for understanding the roles of the environmental conditions upon the phytoplankton variability, and for reducing the overlapping information to characterize the driving variables. (C) 2015 Elsevier B.V. All rights reserved.
Citronella oil is derived from the Cymbopogon species which are ***, *** and ***. The aim of this paper is to analyse the citronella oils species using boxplot analysis. The work consists of citronella oils data extra...
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ISBN:
(数字)9781728161334
ISBN:
(纸本)9781728161334
Citronella oil is derived from the Cymbopogon species which are ***, *** and ***. The aim of this paper is to analyse the citronella oils species using boxplot analysis. The work consists of citronella oils data extraction using GC / GC-MS machine for compound identification. After that, a quick observation on the major compound in the samples as significant compound identification was done. Then, it continues with data distribution which is boxplot. In this paper, the statistical works were executed via Matlab software. There are two parameters that required in this study which are the abundance of significant compound (%) and the species of citronella oil either ***, *** or ***. The result showed that, there is clearly can be seen the parameters through the boxplot analysis for data distribution. The techniques also confirmed their potential in statistical analysis for citronella oil significant compounds and proved that the data is applicable for further work especially for classification.
There is no standard definition of outliers, but most authors agree that outliers are points far from other data points. Several outlier detection techniques have been developed mainly for two different purposes. On o...
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There is no standard definition of outliers, but most authors agree that outliers are points far from other data points. Several outlier detection techniques have been developed mainly for two different purposes. On one hand, outliers are considered error measurement observations that should be removed from the analysis, e.g. robust statistics. On the other hand, outliers are the interesting observations, like in fraud detection, and should be modelled by some learning method. In this work, we start from the observation that outliers are affected by the so-called simpson paradox: a trend that appears in different groups of data but disappears or reverses when these groups are combined. Given a data set, we learn a regression tree. The tree grows by partitioning the data into groups more and more homogeneous of the target variable. At each partition defined by the tree, we apply a box plot on the target variable to detect outliers. We would expect that the deeper nodes of the tree would contain less and less outliers. We observe that some points previously signalled as outliers are no more signalled as such, but new outliers appear.
Estimating water quality has been one of the significant challenges faced by the world in recent decades. This paper presents a water quality prediction model utilizing the principal component regression tech-nique. F...
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Estimating water quality has been one of the significant challenges faced by the world in recent decades. This paper presents a water quality prediction model utilizing the principal component regression tech-nique. Firstly, the water quality index (WQI) is calculated using the weighted arithmetic index method. Secondly, the principal component analysis (PCA) is applied to the dataset, and the most dominant WQI parameters have been extracted. Thirdly, to predict the WQI, different regression algorithms are used to the PCA output. Finally, the Gradient Boosting Classifier is utilized to classify the water quality status. The proposed system is experimentally evaluated on a Gulshan Lake-related dataset. The results demonstrate 95% prediction accuracy for the principal component regression method and 100% classification accuracy for the Gradient Boosting Classifier method, which show credible performance compared with the state -of-art models. (c) 2021 The Authors. Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).
This paper characterizes three different groups of stroke level and controlled group consists of healthy subject based on power spectrum density (PSD). In this study, the EEG data sets have been collected from hundred...
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ISBN:
(纸本)9781479930913
This paper characterizes three different groups of stroke level and controlled group consists of healthy subject based on power spectrum density (PSD). In this study, the EEG data sets have been collected from hundred and thirty two subjects during closed eye (CE) session with a time frame approximately 8 minutes for each subject. They are divided into 4 groups which are Early Group (EG), Intermediate Group (IG), Advance Group (AG) and Controlled Group (CG). The results show that PSD technique can be used to distinguish between the stroke subject and healthy subject.
Based on the monitoring data of 78 monitoring stations from 2003 to 2012, five key water quality indexes (biochemical oxygen demand: BOD5, permanganate index: CODMn, dissolved oxygen: DO, ammonium nitrogen: NH3-N, and...
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Based on the monitoring data of 78 monitoring stations from 2003 to 2012, five key water quality indexes (biochemical oxygen demand: BOD5, permanganate index: CODMn, dissolved oxygen: DO, ammonium nitrogen: NH3-N, and total phosphorus: TP) were selected to analyze their temporal and spatial characteristics in the highly disturbed Huaihe River Basin via Mann-Kendall trend analysis and boxplot analysis. The temporal and spatial variations of water pollutant concentrations in the Huaihe River Basin were investigated and analyzed to provide a scientific basis for water pollution control, water environment protection, and ecological restoration. The results indicated that the Yinghe River, Quanhe River, Honghe River, Guohe River, and Baohe River were the most seriously polluted rivers, followed by Hongze Lake, Luoma Lake, Yishuhe River, and Nansi Lake. BOD5, CODMn, and NH3-N were the major pollution indexes, for which the monitoring stations reported that more than 40 % of the water quality concentrations exceeded the class IV level. There were 21, 50, 36, and 21 monitoring stations that recorded significantly decreasing trends for BOD5, CODMn, NH3-N, and TP, respectively, and 39 monitoring stations showed a significantly increasing trend for DO. Moreover, the water quality concentrations had a certain concentricity and volatility according to boxplot analysis for the 20 monitoring stations. The majority of monitoring stations recorded a large fluctuation for the monitoring indexes in 2003 and 2004, which indicated that the water quality concentrations were unstable. According to the seasonal variations of the water quality concentrations in the mainstream of Huaihe River, the monthly variation trends of the BOD5, CODMn, DO, NH3-N, and TP concentrations were basically consistent among the seven monitoring stations. The BOD5, CODMn, NH3 -N, and TP concentrations were affected by the change of the stream discharge;changes in DO and NH3-N concentrations were influenced by t
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