Satellite-based identification of aerosol particle species using a 2D-space aerosol classification model

2019 ◽  
Vol 219 ◽  
pp. 117057 ◽  
Author(s):  
Qianjun Mao ◽  
Chunlin Huang ◽  
Qixiang Chen ◽  
Hengxing Zhang ◽  
Yuan Yuan
Keyword(s):  
Aerosol Particle ◽  
Classification Model ◽  
Particle Species ◽  
Aerosol Classification ◽  
2D Space

Using a new aerosol relative optical thickness concept to identify aerosol particle species

2014 ◽  
Vol 150 ◽  
pp. 1-11 ◽  
Author(s):  
Yuan Yuan ◽  
Yong Shuai ◽  
Xiao-Wei Li ◽  
Bin Liu ◽  
He-Ping Tan
Keyword(s):  
Aerosol Particle ◽  
Optical Thickness ◽  
Particle Species

scholarly journals HETEAC: The Aerosol Classification Model for EarthCARE

2016 ◽  
Vol 119 ◽  
pp. 01004 ◽  
Author(s):  
Ulla Wandinger ◽  
Holger Baars ◽  
Ronny Engelmann ◽  
Anja Hünerbein ◽  
Stefan Horn ◽  
...  
Keyword(s):  
Classification Model ◽  
Aerosol Classification

scholarly journals Satellite-Based Aerosol Classification for Capital Cities in Asia Using a Random Forest Model

2021 ◽  
Vol 13 (13) ◽  
pp. 2464
Author(s):  
Wonei Choi ◽  
Hyeongwoo Kang ◽  
Dongho Shin ◽  
Hanlim Lee
Keyword(s):  
Random Forest ◽  
Classification Model ◽  
Seasonal Effects ◽  
Classification Models ◽  
Capital Cities ◽  
Microphysical Properties ◽  
Aerosol Classification ◽  
Absorbing Aerosols ◽  
Aerosol Types ◽  
Aerosol Type

Aerosol types in Asian capital cities were classified using a random forest (RF) satellite-based aerosol classification model during 2018–2020 in an investigation of the contributions of aerosol types, with or without Aerosol Robotic Network (AERONET) observations. In this study, we used the recently developed RF aerosol classification model to detect and classify aerosols into four types: pure dust, dust-dominated aerosols, strongly absorbing aerosols, and non-absorbing aerosols. Aerosol optical and microphysical properties for each aerosol type detected by the RF model were found to be reasonably consistent with those for typical aerosol types. In Asian capital cities, pollution-sourced aerosols, especially non-absorbing aerosols, were found to predominate, although Asian cities also tend to be seasonally affected by natural dust aerosols, particularly in East Asia (March–May) and South Asia (March–August). No specific seasonal effects on aerosol type were detected in Southeast Asia, where there was a predominance of non-absorbing aerosols. The aerosol types detected by the RF model were compared with those identified by other aerosol classification models. This study indicates that the satellite-based RF model may be used as an alternative in the absence of AERONET sites or observations.

Visualizing 3D Molecular Structures Using an Augmented Reality App

2019 ◽  
Author(s):  
Kristina Eriksen ◽  
Bjarne Nielsen ◽  
Michael Pittelkow
Keyword(s):  
Augmented Reality ◽  
Small Molecules ◽  
Computer Modelling ◽  
Simple Procedure ◽  
3D Structure ◽  
Molecular Structures ◽  
Free Software ◽  
Programming Skills ◽  
2D Space ◽  
Made In

<p>We present a simple procedure to make an augmented reality app to visualize any 3D chemical model. The molecular structure may be based on data from crystallographic data or from computer modelling. This guide is made in such a way, that no programming skills are needed and the procedure uses free software and is a way to visualize 3D structures that are normally difficult to comprehend in the 2D space of paper. The process can be applied to make 3D representation of any 2D object, and we envisage the app to be useful when visualizing simple stereochemical problems, when presenting a complex 3D structure on a poster presentation or even in audio-visual presentations. The method works for all molecules including small molecules, supramolecular structures, MOFs and biomacromolecules.</p>

A glance on dystonias, how to recognize and handle them

2019 ◽  
pp. 22-29
Author(s):  
F. N. Mercan ◽  
E. Bayram ◽  
M. C. Akbostanci
Keyword(s):  
Differential Diagnosis ◽  
Movement Disorder ◽  
Age Of Onset ◽  
Clinical Manifestations ◽  
Body Part ◽  
Classification Model ◽  
Treatment Choices ◽  
Muscle Groups

Dystonia refers to an involuntary, repetitive, sustained, painful and twisting movements of the affected body part. This movement disorder was first described in 1911 by Hermain Oppenheim, and many studies have been conducted to understand the mechanism, the diagnosis and the treatment of dystonia ever since. However, there are still many unexplained aspects of this phenomenon. Dystonia is diagnosed by clinical manifestations, and various classifications are recommended for the diagnosis and the treatment. Anatomic classification, which is based on the muscle groups involved, is the most helpful classification model to plan the course of the treatment. Dystonias can also be classified based on the age of onset and the cause. These dystonic syndromes can be present without an identified etiology or they can be clinical manifestations of a neurodegenerative or neurometabolic disease. In this review we summarized the differential diagnosis, definition, classifications, possible mechanisms and treatment choices of dystonia.

Comparison of SVM, RF and SGD Methods for Determination of Programmer's Performance Classification Model in Social Media Activities

2020 ◽  
Vol 4 (2) ◽  
pp. 329-335
Author(s):  
Rusydi Umar ◽  
Imam Riadi ◽  
Purwono
Keyword(s):  
Social Media ◽  
Gradient Descent ◽  
Classification Model ◽  
Stochastic Gradient Descent ◽  
Support Vector ◽  
Svm Algorithm ◽  
Vector Machines ◽  
Performance Patterns ◽  
A Company

The failure of most startups in Indonesia is caused by team performance that is not solid and competent. Programmers are an integral profession in a startup team. The development of social media can be used as a strategic tool for recruiting the best programmer candidates in a company. This strategic tool is in the form of an automatic classification system of social media posting from prospective programmers. The classification results are expected to be able to predict the performance patterns of each candidate with a predicate of good or bad performance. The classification method with the best accuracy needs to be chosen in order to get an effective strategic tool so that a comparison of several methods is needed. This study compares classification methods including the Support Vector Machines (SVM) algorithm, Random Forest (RF) and Stochastic Gradient Descent (SGD). The classification results show the percentage of accuracy with k = 10 cross validation for the SVM algorithm reaches 81.3%, RF at 74.4%, and SGD at 80.1% so that the SVM method is chosen as a model of programmer performance classification on social media activities.

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