Development and implementation of an empirical ionosphere variability model

2004 ◽  
Vol 33 (6) ◽  
pp. 887-892 ◽  
Author(s):  
J.I. Minow
Keyword(s):  
Variability Model

scholarly journals Effects of BMI and LDL-cholesterol change pattern on cardiovascular disease in normal adults and diabetics

2020 ◽  
Vol 8 (2) ◽  
pp. e001340
Author(s):  
Tae Mi Youk ◽  
Min Jin Kang ◽  
Sun Ok Song ◽  
Eun-Cheol Park
Keyword(s):  
Cardiovascular Disease ◽  
Ldl Cholesterol ◽  
Density Lipoprotein ◽  
Diabetic Control ◽  
Individual Variability ◽  
Control Group ◽  
Simple Method ◽  
Change Pattern ◽  
Patients With Diabetes ◽  
Variability Model

IntroductionTo examine how the risk of cardiovascular disease changes according to degree of change in body mass index (BMI) and low-density lipoprotein (LDL)-cholesterol in patients with diabetes using the health medical examination cohort database of the National Health Insurance Service in Korea. In comparison, the pattern in a non-diabetic control group was also examined.Research design and methodsThe study samples were 13 800 patients with type 2 diabetes and 185 898 non-diabetic controls, and their baseline characteristics and repeatedly measured BMI and LDL-cholesterol until occurrence of cardiovascular disease were collected in longitudinal data. We used the variability model that is joint of mixed effects and regression model, then estimated parameters about variability by Bayesian methods.ResultsThe risk of cardiovascular disease was increased significantly with high average real variability (ARV) of BMI in the patients with diabetes, but the risk of cardiovascular disease was not increased according to degree of ARV in non-diabetic controls. The Bayesian variability model was used to analyze the effects of BMI and LDL-cholesterol change pattern on development of cardiovascular disease in diabetics, showing that variability did not have a statistically significant effect on cardiovascular disease. This shows the danger of the former simple method when interpreting only the mean of the absolute value of the variation.ConclusionsThe approach of simple SD in previous studies for estimation of individual variability does not consider the order of observation. However, the Bayesian method used in this study allows for flexible modeling by superimposing volatility assessments on multistage models.

Compartmental modeling of the hepatic transport of taurocholate in the rat in vivo

1989 ◽  
Vol 257 (2) ◽  
pp. G210-G220 ◽  
Author(s):  
X. Deroubaix ◽  
T. Coche ◽  
E. Depiereux ◽  
E. Feytmans
Keyword(s):  
Excretion Rate ◽  
Compartmental Analysis ◽  
Hepatic Transport ◽  
Lumped Model ◽  
Perfusion Model ◽  
Direct Measurements ◽  
Distribution In Blood ◽  
Variability Model ◽  
Good Agreement

Compartmental analysis was used to study the hepatobiliary transport of taurocholate (TC) in the rat in vivo. The available data are the following: [14C]TC kinetics in blood and bile, weighting factors associated with these data and computed from a theoretical variability model, and TC excretion rate in bile. The lumped model that best fits the data contains five compartments: three compartments for TC distribution in blood and two compartments for the liver. It includes a compartmental representation of the laminar flow of bile in the collecting catheter. This model overestimates TC concentration in blood. A perfusion model that includes a compartment representing explicitly the sinusoidal TC concentration gradient was developed. TC concentration in blood estimated by this model is in good agreement with direct measurements, showing that the perfused model has a better descriptive capacity than the lumped model. The amounts of TC estimated in the two hepatic compartments are similar to values previously published.

scholarly journals Magnetic susceptibility for characterizing areas with different potentials for sugarcane production

2016 ◽  
Vol 51 (9) ◽  
pp. 1349-1358 ◽  
Author(s):  
Diego Silva Siqueira ◽  
José Marques Júnior ◽  
Daniel De Bortoli Teixeira ◽  
Sammy Sidney Rocha Matias ◽  
Livia Arantes Camargo ◽  
...  
Keyword(s):  
Magnetic Susceptibility ◽  
Spatial Variability ◽  
Exchange Capacity ◽  
Strongly Correlated ◽  
Soil Attributes ◽  
Sugarcane Production ◽  
Sugarcane Yield ◽  
Chemical Attributes ◽  
Physical And Chemical ◽  
Variability Model

Abstract The objective of this work was to evaluate the use of magnetic susceptibility for characterizing the spatial variability of soil attributes and identifying areas with different potentials for sugarcane (Saccharum spp.) production. Samples were collected at 110 points (1 per 7 ha) in the layers of 0.00-0.20 and 0.20-0.40 m, to determine the magnetic susceptibility and physical and chemical attributes of the soil. Fiber content, sucrose polarization (POL), and sugarcane yield were determined in 33 points. The spatial variability model for magnetic susceptibility was 63 and 22% more accurate in delimiting soil potential for sugarcane production than soil physical and chemical attributes at the 0.0-0.2 and 0.2-0.4-m layers, respectively. The spatial variability map for magnetic susceptibility was strongly correlated with clay (0.83 and 0.89, respectively, for the layers) and sand contents (-0.84 and -0.88); moderately correlated with organic matter (-0.25 and -0.35), sum of bases (-0.46 and 0.37), cation exchange capacity (0.22 and 0.47), pH (-0.52 and 0.13), and POL (0.43 and 0.53); and weakly correlated with sugarcane yield (0.26 and 0.23). Magnetic susceptibility can be used to characterize the spatial variability of soil attributes and to identify areas with different potentials for sugarcane production.

Physically Parameterized Prediction Equations for Significant Duration in Active Crustal Regions

2016 ◽  
Vol 32 (4) ◽  
pp. 2057-2081 ◽  
Author(s):  
Kioumars Afshari ◽  
Jonathan P. Stewart
Keyword(s):  
Standard Deviation ◽  
Ground Motions ◽  
Prediction Equations ◽  
Global Database ◽  
Significant Duration ◽  
Crustal Earthquakes ◽  
Aleatory Variability ◽  
Active Tectonic ◽  
Variability Model ◽  
Source Duration

We develop prediction equations for the median and standard deviation of the significant duration of earthquake ground motions from shallow crustal earthquakes in active tectonic regions. We consider significant duration parameters for 5–75%, 5–95%, and 20–80% of the normalized Arias intensity. The equations were derived from a global database with M 3.0–7.9 events. We find significant noise effects on duration parameters that compel us to exclude some records that had been used previously to develop models for amplitude parameters. Our equations include an M-dependent source duration term that also depends on focal mechanism. At small M, the data suggest approximately M-independent source durations that are close to 1 sec. The increase of source durations with M is slower over the range ∼5 to 7.2–7.4 than for larger magnitudes. We adopt an additive path term with breaks in distance scaling at 10 km and 50 km. We include site terms that increase duration for decreasing V S30 and increasing basin depth. Our aleatory variability model captures decreasing between- and within-event standard deviation terms with increasing M.

scholarly journals Evolution of the Linux Kernel Variability Model

2010 ◽  
pp. 136-150 ◽  
Author(s):  
Rafael Lotufo ◽  
Steven She ◽  
Thorsten Berger ◽  
Krzysztof Czarnecki ◽  
Andrzej Wąsowski
Keyword(s):  
Linux Kernel ◽  
Variability Model

Handling Quantity in Variability Models for System-of-Systems

2021 ◽  
Vol 31 (05) ◽  
pp. 693-724
Author(s):  
Daisuke Shimbara ◽  
Motoshi Saeki ◽  
Shinpei Hayashi ◽  
Øystein Haugen
Keyword(s):  
System Of Systems ◽  
Tree Structure ◽  
Entire System ◽  
Simple Experiment ◽  
Domain Specific ◽  
High Degree ◽  
Variability Model ◽  
Very High ◽  
Different Levels ◽  
Parent Node

Problem: Modern systems contain parts that are themselves systems. Such complex systems thus have sets of subsystems that have their own variability. These subsystems contribute to the functionality of a whole system-of-systems (SoS). Such systems have a very high degree of variability. Therefore, a modeling technique for the variability of an entire SoS is required to express two different levels of variability: variability of the SoS as a whole and variability of subsystems. If these levels are described together, the model becomes hard to understand. When the variability model of the SoS is described separately, each variability model is represented by a tree structure and these models are combined in a further tree structure. For each node in a variability model, a quantity is assigned to express the multiplicity of its instances per one instance of its parent node. Quantities of the whole system may refer to the number of subsystem instances in the system. From the viewpoint of the entire system, constraints and requirements written in natural language are often ambiguous regarding the quantities of subsystems. Such ambiguous constraints and requirements may lead to misunderstandings or conflicts in an SoS configuration. Approach: A separate notion is proposed for variability of an SoS; one model considers the SoS as an undivided entity, while the other considers it as a combination of subsystems. Moreover, a domain-specific notation is proposed to express relationships among the variability properties of systems, to solve the ambiguity of quantities and establish the total validity. This notation adapts an approach, named Pincer Movement, which can then be used to automatically deduce the quantities for the constraints and requirements. Validation: The descriptive capability of the proposed notation was validated with four examples of cloud providers. In addition, the proposed method and description tool were validated through a simple experiment on describing variability models with real practitioners.

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