CHAOS MEASURE IN AUTONOMOUS LPA MODEL

2020 ◽  
Vol 33 (02) ◽  
Author(s):  
A Ansari ◽  
Keyword(s):  
Lpa Model

Two-parameter bifurcations in LPA model

2017 ◽  
Vol 75 (5) ◽  
pp. 1235-1251 ◽  
Author(s):  
Veronika Hajnová ◽  
Lenka Přibylová
Keyword(s):  
Two Parameter ◽  
Lpa Model

The LPA model

2004 ◽  
pp. 29-55
Author(s):  
J. Cushing
Keyword(s):  
Lpa Model

Bifurcation and mixed tracking of the discrete fractional LPA model

2018 ◽  
Vol 7 (1) ◽  
pp. 308-312
Author(s):  
Ibiyinka Fuwape ◽  
Samuel Ogunjo
Keyword(s):  
Lpa Model

scholarly journals Radiomic Feature Analysis for Distinguishing Solitary Pulmonary Capillary Hemangioma From Lepidic-Predominant Lung Adenocarcinoma in Ground Glass Lung Nodules

2020 ◽  
Author(s):  
Mong-Wei Lin ◽  
Hao-Jen Wang ◽  
Yi-Chang Chen ◽  
Li-Wei Chen ◽  
Min-Shu Hsieh ◽  
...  
Keyword(s):  
Decision Tree ◽  
Capillary Hemangioma ◽  
Classification Model ◽  
Feature Analysis ◽  
Ground Glass ◽  
Support Vector ◽  
Benign Tumors ◽  
Baseline Model ◽  
Pulmonary Capillary ◽  
Lpa Model

Abstract Solitary pulmonary capillary hemangioma (SPCH) is a benign lung tumor that presents as ground-glass nodules (GGN) on computed tomography (CT) images, mimicking lepidic-predominant adenocarcinoma (LPA). This study aimed to establish a discriminant model using a radiomic feature analysis to distinguish SPCH from LPA in lung GGNs. This study included 13 and 49 patients who underwent complete resection for lung SPCH and LPA, respectively. An SPCH/LPA classification model was proposed based on a two-level decision tree and 26 radiomic features extracted from each segmented lesion, including 5 and 21 features from the histogram and co-occurrence matrix, respectively. The two-level decision tree was constructed based on the training data with a support vector machine (SVM) as the classifier in each tree node. For comparison, a baseline model was built with the same 26 features using an SVM as the classifier. Both models were assessed by the leave-one-out cross-validation method. The area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity of the proposed SPCH/LPA model were 0.954, 91.9%, 92.3%, and 91.8%. The proposed SPCH/LPA model significantly outperformed the baseline model (p<0.05). Our results may help surgeons to preoperatively discriminate SPCH from LPA, thus avoiding unnecessary surgery for benign tumors.

Population Dynamics in Diffusive Coupled Insect Population

2018 ◽  
Vol 6 (4) ◽  
pp. 149-159
Author(s):  
Dongwook Kim ◽  
Dong-Hoon Shin
Keyword(s):  
Population Dynamics ◽  
Diffusion Coefficient ◽  
Population Density ◽  
Chaotic Dynamics ◽  
Total Population ◽  
Chaotic Behavior ◽  
Insect Population ◽  
Bifurcation Parameter ◽  
Diffusion Term ◽  
Lpa Model

A variety of ecological models exhibit chaotic dynamics because of nonlinearities in population growth and interactions. Here, we will study the LPA model (beetle Tribolium). The LPA model is known to exhibit chaos. In this project, we investigate two things which are the effect of noise constant and the effect of diffusion combined with the LPA model. The effect of noise is not only to change the dynamics of total population density but also to blur the bifurcation diagram. Numerical simulations of the model have shown that diffusion can drive the total population of insects into complex patterns of variability in time. We will compare these simulations with simulations without diffusion. And we conclude that the diffusion coefficient is a bifurcation parameter and that there exist parameter regions with chaotic behavior and periodic solutions. This study demonstrates how diffusion term can be used to influence the chaotic dynamics of an insect population.

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