Mathematical modeling and parameters estimation of a car crash using data-based regressive model approach

Interest in the interaction between energy and health within the built environment has been increasing in recent years, in the context of sustainable development. However, in order to promote health and wellbeing across all ages it is necessary to have a better understanding of the association between health and energy at household level. This study contributes to this debate by addressing the case of Portugal using data from the Household Budget Survey (HBS) microdata database. A two-part model is applied to estimate health expenditures based on energy-related expenditures, as well as socioeconomic variables. Additional statistical methods are used to enhance the perception of relevant predictors for health expenditures. Our findings suggest that given the high significance and coefficient value, energy expenditure is a relevant explanatory variable for health expenditures. This result is further validated by a dominance analysis ranking. Moreover, the results show that health gains and medical cost reductions can be a key factor to consider on the assessment of the economic viability of energy efficiency projects in buildings. This is particularly relevant for the older and low-income segments of the population.

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Applications: Analyzing Data Relating to the Challenger Disaster

Mathematics Teacher ◽

10.5951/mt.87.6.0423 ◽

1994 ◽

Vol 87 (6) ◽

pp. 423-426

Author(s):

Linda Tappin

Keyword(s):

Mathematical Modeling ◽

High School ◽

Decision Making ◽

High School Students ◽

Student Involvement ◽

Vital Role ◽

Exponential Functions ◽

School Students ◽

Grade Levels ◽

Using Data

Memories of the Challenger disaster that occurred on 28 January 1986 are still vivid in the minds of many high school students. Thus, using data relating to this event can promote student involvement. This article introduces students to statistics by illustrating its vital role in decision making. Students at various grade levels with varying backgrounds will find this activity motivating and stimulating. Little or no background is necessary to appreciate this application of statistics involving exponential functions, mathematical modeling, probability, and curve sketching.

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System model for analysing construction labour productivity

Construction Innovation ◽

10.1108/ci-07-2015-0040 ◽

2016 ◽

Vol 16 (2) ◽

pp. 203-228 ◽

Cited By ~ 12

Author(s):

Abraham Assefa Tsehayae ◽

Aminah Robinson Fayek

Keyword(s):

Regression Analysis ◽

Labour Productivity ◽

System Model ◽

Model Parameters ◽

System Modelling ◽

Content Type ◽

Activity Models ◽

Influencing Parameters ◽

Using Data ◽

Model Approach

Purpose Despite long-term, sustained research and industry practice, predicting construction labour productivity (CLP) using existing factor and activity modelling approaches remains a challenge. The purpose of this paper is to first demonstrate the limited usefulness of activity models and then to propose a system model approach that integrates factor and activity models for better prediction of CLP. Design/methodology/approach The system model parameters – comprising factors and practices – and work sampling proportions (WSPs) were identified from literature. Field data were collected from 11 projects over a span of 29 months. Activity models based on the relationship between CLP and WSPs were created, and their validity was tested using regression analysis for eight activities in the concreting, electrical and shutdown categories. The proposed system model was developed for concreting activity using the key influencing parameters in conjunction with WSPs. Findings The results of the regression analysis indicate that WSPs, like direct work, are not significantly correlated to CLP and fail to explain its variance. Evaluation of the system model approach for the concreting activity showed improved CLP prediction as compared to existing approaches. Research limitations/implications The system model was tested for concreting activity using data collected from six projects; however, further investigation into the model’s accuracy and efficacy using data collected from other labour-intensive activities is suggested. Originality/value This research establishes the role of WSPs in CLP modelling, and develops a system modelling approach to assist researchers and practitioners in the analysis of productivity-influencing parameters together with WSPs.

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Algebraic modeling in international and local service logistical systems

PROBLEMS IN PROGRAMMING ◽

10.15407/pp2020.04.088 ◽

2020 ◽

pp. 088-097

Author(s):

O.O. Letychevskyi ◽

◽

S.O. Gorbatuk ◽

V.A. Gorbatuk ◽

◽

...

Keyword(s):

Mathematical Modeling ◽

Software Systems ◽

Algebraic Models ◽

Modern Life ◽

Local Service ◽

Logistics Systems ◽

External Threats ◽

Control Optimization ◽

Security Properties ◽

Model Approach

International and internal service logistics are advancing at a tremendous pace in our modern life, and future forecasts for this area are optimistic. For this reason, we face a task in the control, optimization and safety and security checking of complex logistical systems with many internal agents working in a changing environment. Our paper aims to show how mathematical modeling, especially behavior algebra, can provide an opportunity for predicting the behavior and stability of logistics environments and checking their safety and security properties. The main security properties of complex logistics systems of different capacities and levels are stability of operation, resistance to external threats and detection and elimination of vulnerabilities. During the development of software systems, it is advisable to use a model approach. It involves the creation of models as tools at every stage of software development for the application of verification, testing and validation methods. Algebraic models of logistics systems can be used to analyze the behavior of all agents involved and to prove their ability to fulfill their goals and the ability of the whole system to constantly exist and remain stable. As examples of practical use the application in practice of algebra of behaviors on an example of the operating closed logistic system of a farm is considered.

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Battery Model Parameters Estimation Using Simulated Annealing

TEMA (São Carlos) ◽

10.5540/tema.2017.018.01.0127 ◽

2017 ◽

Vol 18 (1) ◽

pp. 127 ◽

Cited By ~ 4

Author(s):

Marcia De Fatima Brondani ◽

Airam Teresa Zago Romcy Sausen ◽

Paulo Sérgio Sausen ◽

Manuel Osório Binelo

Keyword(s):

Mathematical Modeling ◽

Experimental Data ◽

Simulated Annealing ◽

Visual Analysis ◽

Lithium Ion ◽

Parameters Estimation ◽

Model Parameters ◽

Algorithm Efficiency ◽

Battery Model ◽

Discharge Curves

In this paper, a Simulated Annealing (SA) algorithm is proposed for the Battery model parametrization, which is used for the mathematical modeling of the Lithium Ion Polymer (LiPo) batteries lifetime. Experimental data obtained by a testbed were used for model parametrization and validation. The proposed SA algorithm is compared to the traditional parametrization methodology that consists in the visual analysis of discharge curves, and from the results obtained, it is possible to see the model efficacy in batteries lifetime prediction, and the proposed SA algorithm efficiency in the parameters estimation.

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Computer simulation in informatics lessons as a factor of the continuity of school education in teaching mathematical modeling

Informatics and Education ◽

10.32517/0234-0453-2021-36-5-52-58 ◽

2021 ◽

pp. 52-58

Author(s):

M. E. Korolev

Keyword(s):

Mathematical Modeling ◽

Engineering Students ◽

Applied Mathematics ◽

Simulation Models ◽

Interactive Learning Environments ◽

School Education ◽

Mathematical Education ◽

Transport Direction ◽

Using Data ◽

Academy Of Sciences

The article actualizes the need to use elements of computer modeling from school to university when teaching mathematics to engineering students, analyzes the role of simulation models of applied mathematics. The experience of organizing classes using data visualization is presented, the use of simulators and games for studying mathematics in the context of mathematical education, in which students interact with interactive learning environments, is discussed, examples of using computer simulations in the classroom are discussed. The study lists and characterizes the types of didactic simulations, examines the process of transition of school education using simulators and games into the didactics of mathematics of technical universities using applied mathematics and mathematical modeling to elements of simulation. A pedagogical experiment of the continuity of school education (section “Information systems and programming” of the Donetsk Republican Small Academy of Sciences for Students) was carried out in teaching applied mathematics, informatics and mathematical modeling (the department “Transport technologies”) students of the engineering and transport direction of Automobile and Road Institute of Donetsk National Technical University.

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Mathematical Modeling and Parameters Estimation of Car Crash Using Eigensystem Realization Algorithm and Curve-Fitting Approaches

Mathematical Problems in Engineering ◽

10.1155/2013/262196 ◽

2013 ◽

Vol 2013 ◽

pp. 1-13 ◽

Cited By ~ 6

Author(s):

Bernard B. Munyazikwiye ◽

Hamid Reza Karimi ◽

Kjell Gunnar Robbersmyr

Keyword(s):

Curve Fitting ◽

Parameters Estimation ◽

Crash Test ◽

Physical Parameters ◽

Eigensystem Realization Algorithm ◽

The Real ◽

Car Crash ◽

Acceleration Data ◽

Mass Damper ◽

Vehicle Chassis

An eigensystem realization algorithm (ERA) approach for estimating the structural system matrices is proposed in this paper using the measurements of acceleration data available from the real crash test. A mathematical model that represents the real vehicle frontal crash scenario is presented. The model’s structure is a double-spring-mass-damper system, whereby the front mass represents the vehicle-chassis and the rear mass represents the passenger compartment. The physical parameters of the model are estimated using curve-fitting approach, and the estimated state system matrices are estimated by using the ERA approach. The model is validated by comparing the results from the model with those from the real crash test.

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Safety analysis and risk identification for a tubular reactor using the HAZOP methodology

Chemical Papers ◽

10.2478/s11696-006-0082-0 ◽

2006 ◽

Vol 60 (6) ◽

Cited By ~ 8

Author(s):

J. Labovský ◽

L’. Jelemenský ◽

J. Markoš

Keyword(s):

Mathematical Modeling ◽

Steady State ◽

Ethylene Oxide ◽

Tubular Reactor ◽

Computer Code ◽

Risk Identification ◽

Process Unit ◽

Steady State Analysis ◽

Process Simulator ◽

Model Approach

AbstractA model approach to Hazard and Operability (HAZOP) analysis is presented based on the mathematical modeling of a process unit where both the steady-state analysis, including the analysis of the steady states multiplicity and stability, and the dynamic simulation are used. Heterogeneous tubular reactor for the ethylene oxide production from ethylene and oxygen was chosen to identify potential hazards for real system. The computer code DYNHAZ was developed consisting of a process simulator and a generator of the HAZOP algorithm.

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Predicting Win-Loss outcomes in MLB regular season games – A comparative study using data mining methods

International Journal of Computer Science in Sport ◽

10.1515/ijcss-2016-0007 ◽

2016 ◽

Vol 15 (2) ◽

pp. 91-112 ◽

Cited By ~ 11

Author(s):

C. Soto Valero

Keyword(s):

Data Mining ◽

Cross Validation ◽

Data Contamination ◽

Past Data ◽

Mining Methods ◽

Using Data ◽

New Statistics ◽

Fold Cross Validation ◽

Better Than ◽

Model Approach

Abstract Baseball is a statistically filled sport, and predicting the winner of a particular Major League Baseball (MLB) game is an interesting and challenging task. Up to now, there is no definitive formula for determining what factors will conduct a team to victory, but through the analysis of many years of historical records many trends could emerge. Recent studies concentrated on using and generating new statistics called sabermetrics in order to rank teams and players according to their perceived strengths and consequently applying these rankings to forecast specific games. In this paper, we employ sabermetrics statistics with the purpose of assessing the predictive capabilities of four data mining methods (classification and regression based) for predicting outcomes (win or loss) in MLB regular season games. Our model approach uses only past data when making a prediction, corresponding to ten years of publicly available data. We create a dataset with accumulative sabermetrics statistics for each MLB team during this period for which data contamination is not possible. The inherent difficulties of attempting this specific sports prediction are confirmed using two geometry or topology based measures of data complexity. Results reveal that the classification predictive scheme forecasts game outcomes better than regression scheme, and of the four data mining methods used, SVMs produce the best predictive results with a mean of nearly 60% prediction accuracy for each team. The evaluation of our model is performed using stratified 10-fold cross-validation.

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