A Financial Protection Strategy for Families That Have a Child With Down Syndrome

2018 ◽  
Vol 29 (1) ◽  
pp. 91-102
Author(s):  
Gao Niu ◽  
Jeyaraj Vadiveloo ◽  
Cary Lakenbach
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Down Syndrome ◽  
Daily Life ◽  
Longevity Risk ◽  
Financial Protection ◽  
Pricing Model ◽  
Effect Factor ◽  
Protection Strategy ◽  
Monte Carlo Simulation Model

Families that have a child with Down syndrome (DS) are facing financial challenges due to the increased life expectancy and daily life dependencies that he or she experiences. This article uses pediatric findings to supplement child mortality impairment assumptions and proposes a combination annuity pricing model to explore an annuity solution for families that have a child with DS. A Markov chain Monte Carlo simulation model is constructed with features such as a fixed death benefit, return of premium, different premium payment patterns, and the widowhood effect factor. The results indicate that such a product is generally affordable for families that have a child with DS to cover their child’s longevity risk and increased dependency needs.

نماذج المحاكاة- محاكاة مونتي كارلو- كأسلوب كمي من أساليب النمذجة واتخاذ القرارات - مقاربة نظرية وتطبيقية

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
محمد الأمين ◽  
بن حامد عبد الغني ◽  
مراس محمد
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Quantitative Methods ◽  
Practical Importance ◽  
Investment Project ◽  
Simulation Method ◽  
Period Analysis ◽  
Investment Projects ◽  
Monte Carlo Simulation Model

Our research aims to try to present the modeling mechanisms in the field of simulation and quantitative methods. The research is a presentation of the role of quantitative methods in making investment project evaluation decisions, more than that and is the use of the Monte Carlo simulation model in evaluation and multi-period analysis of investment projects under conditions Risk and uncertainty. And highlighting the theoretical, scientific and practical importance of the Monte Carlo simulation method in particular, and the importance of using quantitative methods in helping to make decisions in general

Monte Carlo Simulation Model for Predicting Salmonella Contamination of Chicken Liver as a Function of Serving Size for Use in Quantitative Microbial Risk Assessment

2021 ◽  
Author(s):  
Thomas Oscar
Keyword(s):  
Risk Assessment ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Simulation Model ◽  
Chicken Liver ◽  
Quantitative Microbial Risk Assessment ◽  
Microbial Risk Assessment ◽  
Microbial Risk ◽  
Serving Size ◽  
Monte Carlo Simulation Model

The first step in quantitative microbial risk assessment (QMRA) is to determine distribution of pathogen contamination among servings of the food at some point in the farm-to-table chain. In the present study, distribution of Salmonella contamination among servings of chicken liver for use in QMRA was determined at meal preparation. A combination of five methods: 1) whole sample enrichment; 2) quantitative polymerase chain reaction; 3) cultural isolation; 4) serotyping; and 5) Monte Carlo simulation were used to determine Salmonella prevalence (P), number (N), and serotype for different serving sizes. In addition, epidemiological data were used to convert serotype data to virulence (V) values for use in QMRA. A Monte Carlo simulation model based in Excel and simulated with @Risk predicted Salmonella P, N, serotype, and V as a function of serving size from one (58 g) to eight (464 g) chicken livers. Salmonella P of chicken livers was 72.5% (58/80) per 58 g. Four serotypes were isolated from chicken livers: 1) Infantis (P = 28%, V = 4.5); 2) Enteritidis (P = 15%, V = 5); 3) Typhimirium (P = 15%, V = 4.8); and 4) Kentucky (P = 15%, V = 0.8). Median Salmonella N was 1.76 log per 58 g (range: 0 to 4.67 log/58 g) and was not affected ( P > 0.05) by serotype. The model predicted a non-linear increase ( P ≤ 0.05) of Salmonella P from 72.5% per 58 g to 100% per 464 g, minimum N from 0 log per 58 g to 1.28 log per 464 g, and median N from 1.76 log per 58 g to 3.22 log per 464 g. Regardless of serving size, predicted maximum N was 4.74 log, mean V was 3.9, and total N was 6.65 log per lot (10,000 chicken livers). The data acquired and model developed in this study fill an important data and modeling gap in QMRA for Salmonella and chicken liver.

Electricity supplier selection by a household in the Czech Republic in 2017 and 2018 – Monte Carlo simulation approach

2019 ◽  
Author(s):  
Martina Kuncova
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Czech Republic ◽  
Electricity Market ◽  
Electricity Consumption ◽  
Point Of View ◽  
The Czech Republic ◽  
Extra Effort ◽  
The Individual ◽  
Monte Carlo Simulation Model

The situation on the electricity retail market in the Czech Republic is not clear because of the number of suppliers and its products. Although the information about the prices for the electricity consumption for households is available on the web and each household can change the supplier nearly with no extra effort and cost, households are still often not familiar with the individual price items of the products. In this article the analysis of the Czech electricity market from the distribution rate D25d point of view is made for the years 2017-2018 when the household annual consumption is simulated via Monte Carlo simulation model. The aim of this paper is to select such a supplier and product that minimizes the total costs of the electricity for a household for the selected distribution rate and compare it with the results from the previous years.

Monte Carlo Simulation Model of Micro Light Beam Solidification

2008 ◽  
Vol 28 (12) ◽  
pp. 2388-2393
Author(s):  
王翔 Wang Xiang ◽  
裴香涛 Pei Xiangtao ◽  
邵鹏 Shao Peng ◽  
黄文浩 Huang Wenhao
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Simulation Model ◽  
Light Beam ◽  
Monte Carlo Simulation Model

An investigation of the effect of gold nanoparticles with different concentrations on increasing absorbed dose: an empirical and simulation study

2018 ◽  
Vol 18 (02) ◽  
pp. 191-197
Author(s):  
Masoumeh Hoseinnezhad ◽  
Mohammad Mahdavi ◽  
Seyyed R. M. Mahdavi ◽  
Mobarake Mahdavizade
Keyword(s):  
Monte Carlo Simulation ◽  
Gold Nanoparticles ◽  
Monte Carlo ◽  
Absorbed Dose ◽  
Simulation Method ◽  
Dose Enhancement ◽  
Depth Dose ◽  
Optical Ct ◽  
Ct System ◽  
Monte Carlo Simulation Model

AbstractPurposeThe purpose of this study was to determine the dose enhancement factor (DEF) of gold nanoparticles in a dosimeter gel and construct percentage depth dose curves, using the Optical CT system and the Monte Carlo simulation model, to determine the effect of increasing the dose caused by increasing the concentration of gold nanoparticles at depths in the gel.Materials and methodsThe Magic-f Gel was made based on the relevant protocol in the physics lab. To determine the amount of the increase in the absorbed dose, the gold nanoparticles were added to the gel and irradiated. An increase in the dose after adding nanoparticles to the gel vials was estimated both with the Optical CT system and by the Monte Carlo simulation method.ResultsDose enhancement curves for doses of 2, 4 and 6 Gy were prepared for gel vials without adding nanoparticles, and nanoparticle gels at concentrations 0·17, 3 and 6 mM. Also, the DEF was estimated. For the 0·17 mM molar gel, the DEF for 2, 4 and 6 Gy was 0·7, 0·743 and 0·801, respectively. For the 3 mM gel, it was 1·98, 2·5 and 2·2, and for the 6 mM gel, it was 37·4, 4·24 and 4·71, respectively.ConclusionThe enhancement of the dose after adding gold nanoparticles was confirmed both by experimental data and by simulation data.

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