The Role of Physical Modeling in the Mathematical Modeling of the Sacramento-San Joaquin Delta

1980 ◽  
pp. 285-297 ◽  
Author(s):  
Richard C. Kristof
Keyword(s):  
Mathematical Modeling ◽  
Physical Modeling

Technique of scientific and technical forecasting using neural networks

2018 ◽  
pp. 103-110
Author(s):  
V. V. Nefedev
Keyword(s):  
Mathematical Modeling ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Original Method ◽  
Forecasting Methods ◽  
Expert Assessments ◽  
Artificial Neural

For the definition and implementation of breakthrough technologies the most important is the role of scientific and technical forecasting. Well-known forecasting methods based on extrapolation, expert assessments and mathematical modeling are not universal and have a number of significant disadvantages. The article proposes an original method of scientific and technical forecasting based on the use of the methodology of artificial neural networks. 

scholarly journals Role of activated CaMKII in abnormal calcium homeostasis and INa remodeling after myocardial infarction: Insights from mathematical modeling

2008 ◽  
Vol 45 (3) ◽  
pp. 420-428 ◽  
Author(s):  
Thomas J. Hund ◽  
Keith F. Decker ◽  
Evelyn Kanter ◽  
Peter J. Mohler ◽  
Penelope A. Boyden ◽  
...  
Keyword(s):  
Mathematical Modeling ◽  
Myocardial Infarction ◽  
Calcium Homeostasis

THE ROLE OF OXYGEN IN TISSUE MAINTENANCE: MATHEMATICAL MODELING AND QUALITATIVE ANALYSIS

2008 ◽  
Vol 18 (08) ◽  
pp. 1409-1441 ◽  
Author(s):  
AVNER FRIEDMAN ◽  
BEI HU
Keyword(s):  
Mathematical Modeling ◽  
Free Boundary ◽  
Qualitative Analysis ◽  
Phase I ◽  
Stationary Solution ◽  
Oxygen Concentration ◽  
Existence And Uniqueness ◽  
Symmetric Case ◽  
Special Case

The cells in a tissue occupying a region Ωt are divided according to their cycling phase. The density pi of cells in phase i depends on the spatial variable x, the time t, and the time si since the cells entered in phase i. The pi(x, t, si) and the oxygen concentration w(x, t) satisfy a system of PDEs in Ωt, and the boundary of Ωt is a free boundary. We denote by [Formula: see text] the oxygen concentration on the free boundary and consider the radially symmetric case, so that Ωt = {r < R(t)}. We prove that R(t) is always bounded; furthermore, if [Formula: see text] is small, then R(t) → 0 as t → ∞, and if [Formula: see text] is large, then R(t) ≥ c > 0 for all t. Finally, we prove the existence and uniqueness of a stationary solution in a special case.

scholarly journals THEORETICAL AND METHODOLOGICAL APPROACHES TO EVALUATION OF INVESTMENT ATTRACTIVENESS OF REGIONS OF UKRAINE

2021 ◽  
Author(s):  
Liudmyla Sokolova ◽  
Anita Lytvynova
Keyword(s):  
Mathematical Modeling ◽  
Public Administration ◽  
External Environment ◽  
Comparative Assessment ◽  
Financial Resources ◽  
Its Regions ◽  
Methodological Approaches ◽  
Statistical Indicators ◽  
Integral Indicator

Currently, when the Ukrainian economy is developing in an unstable, uncertain, aggressive, dynamic external environment, there is an urgent need to increase the level of investment attractiveness of each region of the country and, as a consequence, the need to increase attention to scientific and methodological support for assessing the integral indicator of investment attractiveness region. In connection with the decentralization of public administration in Ukraine, the role of local authorities in improving the socio-economic situation of its regions is increasing. In the context of real limited financial resources in the country, the problem of increasing the level of investment attractiveness of each region is becoming urgent as a characteristic that allows active investors to form an idea of the state of potential investment objects, as well as ensuring the reliability of obtaining the expected results. It should be noted that the choice of assessment indicators is subjective and depends on the goal of the investor. Such a format for solving this problem requires an assessment of the investment attractiveness of regions based on economic and mathematical modeling. Although there are many developments in this area, today there is no single effective method for assessing the investment attractiveness of the region. The work reveals the essence of the concept of investment attractiveness of a region, defines the methodological provisions of its assessment, the implementation of which requires the use of certain data from official regional statistics. Based on the basic local statistical indicators of the Kharkov, Kiev and Odessa regions of the country, a comparative assessment of their investment attractiveness was carried out. The methodological approaches and parameters determining the investment attractiveness of the regions of Ukraine have been analyzed. The information on the level of investment attractiveness of Kharkiv, Kiev and Odessa regions in 2019 has been systematised, the integral indicator of its evaluation has been defined. In the future, it is recommended to calculate the integral indicator of investment attractiveness for all regions of the country in dynamics.

Mathematical Modeling of Supply Chain Management Systems

2010 ◽  
pp. 344-357
Author(s):  
N. Anbazhagan
Keyword(s):  
Mathematical Modeling ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Mathematical Models ◽  
Stochastic Models ◽  
Management System ◽  
Raw Materials ◽  
Chain Management ◽  
Supply Chain Management System

Supply Chain Management (SCM) is the practice of coordinating the flow of goods, services, information and finances as they move from raw materials to parts supplier to manufacturer to wholesaler to retailer to consumer. Different supply chains have been designed for a variety of firms and this chapter discusses some issues in this regard. This chapter attempts to find why we require different supply chain for different companies. In this chapter we discuss the role of stochastic models in supply chain management system, and also discuss other mathematical models for SCM.

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