scholarly journals Investigation of Polar Mesospheric Summer Echoes Using Linear Discriminant Analysis

2021 ◽  
Vol 13 (3) ◽  
pp. 522
Author(s):  
Dorota Jozwicki ◽  
Puneet Sharma ◽  
Ingrid Mann
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Image Data ◽  
Atmospheric Conditions ◽  
Very High Frequency ◽  
Vhf Radar ◽  
Linear Discriminant ◽  
Radar Echoes ◽  
Summer Echoes ◽  
Percent Accuracy

Polar Mesospheric Summer Echoes (PMSE) are distinct radar echoes from the Earth’s upper atmosphere between 80 to 90 km altitude that form in layers typically extending only a few km in altitude and often with a wavy structure. The structure is linked to the formation process, which at present is not yet fully understood. Image analysis of PMSE data can help carry out systematic studies to characterize PMSE during different ionospheric and atmospheric conditions. In this paper, we analyze PMSE observations recorded using the European Incoherent SCATter (EISCAT) Very High Frequency (VHF) radar. The collected data comprises of 18 observations from different days. In our analysis, the image data is divided into regions of a fixed size and grouped into three categories: PMSE, ionosphere, and noise. We use statistical features from the image regions and employ Linear Discriminant Analysis (LDA) for classification. Our results suggest that PMSE regions can be distinguished from ionosphere and noise with around 98 percent accuracy.

scholarly journals Characteristics of layered occurrence ratio of polar mesosphere summer echoes observed by EISCAT VHF 224 MHz Radar

2019 ◽  
Author(s):  
Shucan Ge ◽  
Hailong Li ◽  
Tong Xu ◽  
Mengyan Zhu ◽  
Maoyan Wang ◽  
...  
Keyword(s):  
Very High Frequency ◽  
Vhf Radar ◽  
Seasonal Behavior ◽  
Incoherent Scatter ◽  
K Index ◽  
Radar Echoes ◽  
Threshold Conditions ◽  
Summer Echoes ◽  
Polar Mesosphere Summer Echoes ◽  
Density Threshold

Abstract. Polar Mesosphere Summer Echoes (PMSE) are strong radar echoes observed in polar mesopause during local summer. Measurements of layered PMSE observed by the European Incoherent Scatter Scientific Association Very high frequency (EISCAT VHF) radar from 2004 to 2015 in the latest solar cycle, can be used to study the variations of PMSE occurrence ratio (OR). The seasonal variation of PMSE mono-, double- and tri-layer occurrence ratio was analyzed, and there is different seasonal behavior. A method was given to calculate the PMSE mono-, double- and tri-layer occurrence ratio under different electron density threshold conditions. In addition, the correlation between PMSE layered occurrence ratios and solar 10.7 cm flux index (F10.7), and the correlation between PMSE layered occurrence ratios and geomagnetic K index were analyzed respectively in this study. It can be obtained that PMSE mono-, double- and tri-layer OR are positively correlated with the K index. The correlation coefficient between PMSE mono- and double-layer OR and F10.7 is weak, and the PMSE tri-layer OR has a negative correlation with F10.7.

scholarly journals Characteristics of the layered polar mesosphere summer echoes occurrence ratio observed by EISCAT VHF 224 MHz radar

2019 ◽  
Vol 37 (3) ◽  
pp. 417-427
Author(s):  
Shucan Ge ◽  
Hailong Li ◽  
Tong Xu ◽  
Mengyan Zhu ◽  
Maoyan Wang ◽  
...  
Keyword(s):  
Very High Frequency ◽  
Vhf Radar ◽  
Seasonal Behavior ◽  
Incoherent Scatter ◽  
K Index ◽  
Radar Echoes ◽  
Summer Echoes ◽  
Polar Mesosphere Summer Echoes ◽  
Geomagnetic Activities ◽  
Density Threshold

Abstract. Polar mesosphere summer echoes (PMSEs) are strong radar echoes observed in the polar mesopause during the local summer. Observations of layered PMSEs carried out by the European Incoherent Scatter Scientific Association very-high-frequency (EISCAT VHF) radar during 2004–2015 in the latest solar cycle are used to study the variations of the PMSE occurrence ratio (OR). Different seasonal behavior of PMSEs is found by analyzing the seasonal variation of PMSE mono-, double-, and tri-layer OR. A method was used to calculate the PMSE mono, double-, and tri-layer OR under a different electron density threshold. In addition, a method to analyze the correlation of the layered PMSE OR with the solar 10.7 cm flux index (F10.7) and geomagnetic K index is proposed. Based on it, the correlation of the layered PMSE OR with solar and geomagnetic activities is not expected to be affected by discontinuous PMSEs. It is found that PMSE mono-, double-, and tri-layer ORs are positively correlated with the K index. The correlation of the PMSE mono- and double-layer OR with F10.7 is weak, whereas the PMSE tri-layer OR shows a negative correlation with F10.7.

Effect of Topography on Maize Grains Elemental Profile: A Chemometric Approach

2020 ◽  
Vol 16 (8) ◽  
pp. 1079-1087
Author(s):  
Jorgelina Z. Heredia ◽  
Carlos A. Moldes ◽  
Raúl A. Gil ◽  
José M. Camiña
Keyword(s):  
Cluster Analysis ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Microwave Plasma ◽  
Emission Spectrometry ◽  
Linear Discriminant ◽  
Maize Seeds ◽  
Elemental Profile ◽  
Mineral Profile ◽  
Topographic Characteristics

Background: The elemental composition of maize grains depends on the soil, land and environment characteristics where the crop grows. These effects are important to evaluate the availability of nutrients with complex dynamics, such as the concentration of macro and micronutrients in soils, which can vary according to different topographies. There is available scarce information about the influence of topographic characteristics (upland and lowland) where culture is developed with the mineral composition of crop products, in the present case, maize seeds. On the other hand, the study of the topographic effect on crops using multivariate analysis tools has not been reported. Objective: This paper assesses the effect of topographic conditions on plants, analyzing the mineral profiles in maize seeds obtained in two land conditions: uplands and lowlands. Materials and Methods: The mineral profile was studied by microwave plasma atomic emission spectrometry. Samples were collected from lowlands and uplands of cultivable lands of the north-east of La Pampa province, Argentina. Results: Differentiation of maize seeds collected from both topographical areas was achieved by principal components analysis (PCA), cluster analysis (CA) and linear discriminant analysis (LDA). PCA model based on mineral profile allowed to differentiate seeds from upland and lowlands by the influence of Cr and Mg variables. A significant accumulation of Cr and Mg in seeds from lowlands was observed. Cluster analysis confirmed such grouping but also, linear discriminant analysis achieved a correct classification of both the crops, showing the effect of topography on elemental profile. Conclusions: Multi-elemental analysis combined with chemometric tools proved useful to assess the effect of topographic characteristics on crops.

Colorectal Cancer Classification and Survival Analysis Based on an Integrated RNA and DNA molecular signature

2020 ◽  
Vol 15 ◽  
Author(s):  
Mohanad Mohammed ◽  
Henry Mwambi ◽  
Bernard Omolo
Keyword(s):  
Colorectal Cancer ◽  
Survival Analysis ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Negative Binomial ◽  
Sub Saharan Africa ◽  
Support Vector ◽  
Mutation Status ◽  
Linear Discriminant ◽  
Rnaseq Data

Background: Colorectal cancer (CRC) is the third most common cancer among women and men in the USA, and recent studies have shown an increasing incidence in less developed regions, including Sub-Saharan Africa (SSA). We developed a hybrid (DNA mutation and RNA expression) signature and assessed its predictive properties for the mutation status and survival of CRC patients. Methods: Publicly-available microarray and RNASeq data from 54 matched formalin-fixed paraffin-embedded (FFPE) samples from the Affymetrix GeneChip and RNASeq platforms, were used to obtain differentially expressed genes between mutant and wild-type samples. We applied the support-vector machines, artificial neural networks, random forests, k-nearest neighbor, naïve Bayes, negative binomial linear discriminant analysis, and the Poisson linear discriminant analysis algorithms for classification. Cox proportional hazards model was used for survival analysis. Results: Compared to the genelist from each of the individual platforms, the hybrid genelist had the highest accuracy, sensitivity, specificity, and AUC for mutation status, across all the classifiers and is prognostic for survival in patients with CRC. NBLDA method was the best performer on the RNASeq data while the SVM method was the most suitable classifier for CRC across the two data types. Nine genes were found to be predictive of survival. Conclusion: This signature could be useful in clinical practice, especially for colorectal cancer diagnosis and therapy. Future studies should determine the effectiveness of integration in cancer survival analysis and the application on unbalanced data, where the classes are of different sizes, as well as on data with multiple classes.

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