scholarly journals Blue Light Hazard and Risk Group Classification of 8 W LED Tubes, Replacing Fluorescent Tubes, through Optical Radiation Measurements

2015 ◽  
Vol 7 (10) ◽  
pp. 13454-13468 ◽  
Author(s):  
Francesco Leccese ◽  
Viola Vandelanotte ◽  
Giacomo Salvadori ◽  
Michele Rocca
Keyword(s):  
Blue Light ◽  
Optical Radiation ◽  
Risk Group ◽  
Group Classification ◽  
Hazard And Risk ◽  
Radiation Measurements

scholarly journals Clinical-genomic Risk Group Classification of Suspicions Lesions on Prostate mpMRI

2019 ◽  
Vol 105 (1) ◽  
pp. S135-S136
Author(s):  
O. Zavala-Romero ◽  
A. Pollack ◽  
D. Kwon ◽  
A.L. Breto ◽  
E. Davicioni ◽  
...  
Keyword(s):  
Risk Group ◽  
Group Classification ◽  
Genomic Risk ◽  
Clinical Genomic

scholarly journals PROGNOSTIC FACTORS AND A NOVEL RISK GROUP CLASSIFICATION OF PAPILLARY THYROID CARCINOMA

2002 ◽  
Vol 28 (1) ◽  
pp. 143-147 ◽  
Author(s):  
Iwao SUGITANI ◽  
Seiichi YOSHIMOTO ◽  
Hiroki MITANI ◽  
Katsuhumi HOKI ◽  
Tomohiko NIGAURI ◽  
...  
Keyword(s):  
Prognostic Factors ◽  
Papillary Thyroid Carcinoma ◽  
Thyroid Carcinoma ◽  
Risk Group ◽  
Group Classification ◽  
Papillary Thyroid

scholarly journals The Evolution of Risk Classification for Neuroblastoma

Children ◽  
2019 ◽  
Vol 6 (2) ◽  
pp. 27 ◽  
Author(s):  
Elizabeth Sokol ◽  
Ami Desai
Keyword(s):  
Clinical Trials ◽  
Risk Group ◽  
Group Classification ◽  
Risk Classification ◽  
Disease Stage ◽  
Cooperative Groups ◽  
Clinical Heterogeneity ◽  
High Risk Disease ◽  
Neuroblastic Tumors

Neuroblastoma is a tumor with great clinical heterogeneity. Patients in North America are risk-stratified using a number of features including age at diagnosis, disease stage, tumor histology, MYCN status (amplified versus nonamplified), and tumor cell ploidy. In this paper, we review the evidence for utilizing these features in the risk classification of neuroblastic tumors. Additionally, we review the clinical and biologic criteria used by various cooperative groups to define low, intermediate, and high-risk disease populations in clinical trials, highlighting the differences in risk classification internationally. Finally, we discuss the development of the International Neuroblastoma Risk Group classification system, designed to begin worldwide standardization of neuroblastoma pretreatment risk classification and allow comparison of clinical trials conducted through different cooperative groups.

Lie group classification of the N-th-order nonlinear evolution equations

2011 ◽  
Vol 54 (12) ◽  
pp. 2553-2572 ◽  
Author(s):  
ShouFeng Shen ◽  
ChangZheng Qu ◽  
Qing Huang ◽  
YongYang Jin
Keyword(s):  
Lie Group ◽  
Evolution Equations ◽  
Nonlinear Evolution ◽  
Group Classification ◽  
Nonlinear Evolution Equations ◽  
Lie Group Classification

scholarly journals Group Classification of a General Bond-Option Pricing Equation of Mathematical Finance

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Tanki Motsepa ◽  
Chaudry Masood Khalique ◽  
Motlatsi Molati
Keyword(s):  
Option Pricing ◽  
Three Dimensional ◽  
Mathematical Finance ◽  
Group Classification ◽  
Lie Point Symmetries ◽  
Group Invariant ◽  
Black Scholes ◽  
Group Invariant Solutions ◽  
Bond Option

We carry out group classification of a general bond-option pricing equation. We show that the equation admits a three-dimensional equivalence Lie algebra. We also show that some of the values of the constants which result from group classification give us well-known models in mathematics of finance such as Black-Scholes, Vasicek, and Cox-Ingersoll-Ross. For all such values of these arbitrary constants we obtain Lie point symmetries. Symmetry reductions are then obtained and group invariant solutions are constructed for some cases.

Lie group classification of second-order ordinary difference equations

2000 ◽  
Vol 41 (1) ◽  
pp. 480-504 ◽  
Author(s):  
Vladimir Dorodnitsyn ◽  
Roman Kozlov ◽  
Pavel Winternitz
Keyword(s):  
Lie Group ◽  
Difference Equations ◽  
Group Classification ◽  
Second Order ◽  
Lie Group Classification

scholarly journals Abductive Networks For Two-Group Classification: A Comparison With Neural Networks

2011 ◽  
Vol 15 (2) ◽  
pp. 1 ◽  
Author(s):  
Anurag Agarwal
Keyword(s):  
Neural Networks ◽  
Linear Mapping ◽  
Group Classification ◽  
Type Ii ◽  
Artificial Intelligence Technique ◽  
Empirical Tests ◽  
Multivariate Discriminant ◽  
Better Than

<span>In this study, a new Artificial Intelligence technique for non-linear mapping called Abductive Networks is used for two-group classification of firms. The results are compared with Neural Networks, another AI technique, which has been shown to perform better than the traditional statistical techniques such as multivariate discriminant analysis and logit. In empirical tests, Abductive Networks perform as well or better than Neural Networks on various criteria of measurement such as Type 1 / Type II accuracy criteria and Distance Between Centroids.</span>

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