Discretization Approach and Nonparametric Modeling for Long-Term HIV Dynamic Model

2005 ◽  
pp. 519-527 ◽  
Author(s):  
Jianwei Chen ◽  
Jin-Ting Zhang ◽  
Hulin Wu
Keyword(s):  
Dynamic Model ◽  
Nonparametric Modeling ◽  
Hiv Dynamic Model

STABILITY OF CLEARANCE RATE PARAMETER AND ITS SENSITIVITY BEHAVIOUR FROM THE SIMPLEST HIV DYNAMIC MODEL

2020 ◽  
Vol 9 (10) ◽  
pp. 8789-8798
Author(s):  
S. A. Neomi ◽  
M. Sumathi
Keyword(s):  
Dynamic Model ◽  
Clearance Rate ◽  
Rate Parameter ◽  
Hiv Dynamic Model

scholarly journals Short-Run Needs and Long-Term Goals: A Dynamic Model of Thirst Management

2015 ◽  
Vol 34 (5) ◽  
pp. 702-721 ◽  
Author(s):  
Guofang Huang ◽  
Ahmed Khwaja ◽  
K. Sudhir
Keyword(s):  
Dynamic Model ◽  
Short Run

ANALISIS PENGARUH NILAI SAHAM YANG BEREDAR, STRUKTUR MODAL, RISIKO PASAR, DAN SUKU BUNGA TERHAD AP RETITRN SAHAM DI BEJ DENGAN PENDEKATAN MODEL DINAMIS (Studi Kasus Pada Sektor Aneka lndustri Tahun 1996-2002)

2005 ◽  
Vol 1 (1) ◽  
pp. 20
Author(s):  
Ari Christianti ◽  
Murti Lestari
Keyword(s):  
Capital Structure ◽  
Dynamic Model ◽  
Stock Return ◽  
Negative Impact ◽  
Short Term ◽  
Correction Model ◽  
Industry Sector ◽  
The Interest Rate ◽  
Model Approach

The study aims at empirically proving and analyzing the balance model of Capital Asset Pricing Model (CAPM with the multifactor of risks, consisting of: outstanding stocks value, capital structure represented by Debt EquiQ Ratio (DER), market risk as represented by stock market beta, and the interest rate on company return on stock.This research uses a dynamic model approach considering the existence of the weaknessesin a classic linear model. Since the investment is related to investors behavior that need a lag to market change, the use of the dynamic model approach will be better. It is because the dynamic model uses autoregressive approach containing the lag. The dynamic model used here is Partial Adjustment Model (PAM) and Error Correction Model (ECM).  Based on the estimation of the PAM model it is proven that the model is inefficient in finding the evidence confirming the hypothesis. Subsequently,based on the result of the examination of the ECM model it isconcluded that outstanding stocks value has a positive and signiJicant impact in short term and a negative impact in long term. It means that in the short term outstanding stocks value serves as the consideration for investors in making an investment. However in the long term they are likely to believe that the use of smaller internal capital proportion will be more beneficial for them. The capital structure has only a longierm impact on the return on stock. It means that the impact of DER on stock return on miscellaneous industry sector needs the quite long lag to influence the investors in determining stocks return. It indicates that in the long term they believ:e that the use of increasing number of loan will causes the decrease in company liquidity. Consequently, the opportunity for the company to go bankrupt is bigger Beta stock in the study has a negative impact in the long term. Theoretically, it is not consistent with the parameter direction and indicated that beta stock does notserve as an app;r,pviate prory in measuring the rislcs on. miscellaneous industry sector The interest rate has in the long term a negative impact on stocks return and needs the long lag to influence the investors in determining the return on stocks.Keywords: Stock return, outstanding stock value, DER (Debt Equity Ratio), beta, interest rote, ECM (Eruor Correction Model)

Propagative broad learning for nonparametric modeling of ambient effects on structural health indicators

2020 ◽  
pp. 147592172091692 ◽  
Author(s):  
Sin-Chi Kuok ◽  
Ka-Veng Yuen ◽  
Stephen Roberts ◽  
Mark A Girolami
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Health Indicators ◽  
Operating Conditions ◽  
Learning Approach ◽  
Nonparametric Modeling ◽  
Structural Health ◽  
Model Free ◽  
Free Data

In this article, a novel propagative broad learning approach is proposed for nonparametric modeling of the ambient effects on structural health indicators. Structural health indicators interpret the structural health condition of the underlying dynamical system. Long-term structural health monitoring on in-service civil engineering infrastructures has demonstrated that commonly used structural health indicators, such as modal frequencies, depend on the ambient conditions. Therefore, it is crucial to detrend the ambient effects on the structural health indicators for reliable judgment on the variation of structural integrity. However, two major challenging problems are encountered. First, it is not trivial to formulate an appropriate parametric expression for the complicated relationship between the operating conditions and the structural health indicators. Second, since continuous data stream is generated during long-term structural health monitoring, it is required to handle the growing data efficiently. The proposed propagative broad learning provides an effective tool to address these problems. In particular, it is a model-free data-driven machine learning approach for nonparametric modeling of the ambient-influenced structural health indicators. Moreover, the learning network can be updated and reconfigured incrementally to adapt newly available data as well as network architecture modifications. The proposed approach is applied to develop the ambient-influenced structural health indicator model based on the measurements of 3-year full-scale continuous monitoring on a reinforced concrete building.

scholarly journals Dynamic modelling of cold-hardiness in tea buds by imitating past temperature memory

2020 ◽  
Author(s):  
Kensuke Kimura ◽  
Daisuke Yasutake ◽  
Takahiro Oki ◽  
Koichiro Yoshida ◽  
Masaharu Kitano
Keyword(s):  
Cold Stress ◽  
Cold Acclimation ◽  
Dynamic Model ◽  
Cold Hardiness ◽  
Global Climate ◽  
Effective Means ◽  
Dynamic Modelling ◽  
Short Term ◽  
Mean Square Errors

Abstract Background and Aims Most perennial plants memorize cold stress for a certain period and retrieve the memories for cold acclimation and deacclimation, which leads to seasonal changes in cold-hardiness. Therefore, a model for evaluating cold stress memories is required for predicting cold-hardiness and for future frost risk assessments under warming climates. In this study we develop a new dynamic model of cold-hardiness by introducing a function imitating past temperature memory in the processes of cold acclimation and deacclimation. Methods We formulated the past temperature memory for plants using thermal time weighted by a forgetting function, and thereby proposed a dynamic model of cold-hardiness. We used the buds of tea plants (Camellia sinensis) from two cultivars, ‘Yabukita’ and ‘Yutakamidori’, to calibrate and validate this model based on 10 years of observed cold-hardiness data. Key Results The model captured more than 90 % of the observed variation in cold-hardiness and predicted accurate values for both cultivars, with root mean square errors of ~1.0 °C. The optimized forgetting function indicated that the tea buds memorized both short-term (recent days) and long-term (previous months) temperatures. The memories can drive short-term processes such as increasing/decreasing the content of carbohydrates, proteins and antioxidants in the buds, as well as long-term processes such as determining the bud phenological stage, both of which vary with cold-hardiness. Conclusions The use of a forgetting function is an effective means of understanding temperature memories in plants and will aid in developing reliable predictions of cold-hardiness for various plant species under global climate warming.

scholarly journals Long-term hoarding in the Paridae: a dynamic model

1997 ◽  
Vol 8 (2) ◽  
pp. 178-185 ◽  
Author(s):  
Anders Brodin ◽  
Colin W. Clark
Keyword(s):  
Dynamic Model
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