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Perturbation Procedures in the Dynamic Analysis of a Toroidal Shell Segment Pressurized by a Step Load
Anthony Monday Ette,
Joy Ulumma Chukwuchekwa,
Williams Ifeanyichukwu Osuji,
Atulegwu Chukwudi Osuji
Issue:
Volume 5, Issue 1, March 2020
Pages:
1-11
Received:
22 November 2019
Accepted:
13 December 2019
Published:
17 January 2020
Abstract: This paper uses perturbation techniques in asymptotic procedures to determine the normal displacement, the associated Airy stress function and the dynamic buckling load of an imperfect, finite toroidal shell segment pressurized by a step load. The adoption of asymptotic and perturbation procedure is made possible by the presence of small non-dimensional parameter on which asymptotic expansions are made possible. It is assumed here that the imperfection can be regarded as the first term in the Fourier Sine series expansion. The buckling modes are also assumed to be strictly in the shape of the imperfection which is in turn given in the shape of the classical buckling mode. In the final analysis, a simple but implicit formula for determining the dynamic buckling load was obtained. The dynamic buckling load was related to the corresponding static buckling load and that relationship is independent of the imperfection parameter. It is observed, that this procedure, perhaps more than other ones, can be used to analyze relatively more complicated problems particularly where more demands and restrictions are placed on the imperfection parameter. The results are strictly and are valid as far as the imperfection parameter is relatively small compared to unity.
Abstract: This paper uses perturbation techniques in asymptotic procedures to determine the normal displacement, the associated Airy stress function and the dynamic buckling load of an imperfect, finite toroidal shell segment pressurized by a step load. The adoption of asymptotic and perturbation procedure is made possible by the presence of small non-dimens...
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A Comparative Study on Additive and Mixed Models in Descriptive Time Series
Kelechukwu Celestine Nosike Dozie,
Maxwell Azubuike Ijomah
Issue:
Volume 5, Issue 1, March 2020
Pages:
12-17
Received:
7 January 2020
Accepted:
27 January 2020
Published:
11 February 2020
Abstract: Time series analyses are statistical methods used to assess trends in repeated measurements taken at equally spaced time intervals and their relationships with other trends or events, taking account of the temporal structure of such data. An important aspect of descriptive time series analysis is the choice of model for time series decomposition. This paper examined the challenges in choosing between additive and mixed models in time series decomposition. Most of the existing studies have focused on how to choose between additive and multiplicative models with little or no regards on mixed model. The ultimate objective of this study is therefore, to compare the row, column and overall means and variances of the Buys-Ballot table for additive and mixed models. Table 1 shows that the column variances of Buys-Ballot table is constant for additive model but depends on slope and seasonal effects for mixed model. Results show that seasonal variances of the Buys-Ballot table is constant for additive model and a function of slope and seasonal effects for mixed model. Also, when there is no trend (b=0), the estimates of row, column and overall means are the same for the two models while the estimates of seasonal indices are not the same for both additive and mixed models.
Abstract: Time series analyses are statistical methods used to assess trends in repeated measurements taken at equally spaced time intervals and their relationships with other trends or events, taking account of the temporal structure of such data. An important aspect of descriptive time series analysis is the choice of model for time series decomposition. T...
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Modeling of Average Survival Time for a Loss to Be Handled in Insurance Company
James Akuma Bogonko,
George Orwa,
Anthony Wanjoya
Issue:
Volume 5, Issue 1, March 2020
Pages:
18-21
Received:
28 October 2019
Accepted:
21 November 2019
Published:
18 February 2020
Abstract: Most insurance companies find it hard and hectic to pay claims that had not being anticipated. In order for the companies to be able to make enough reserves to cater for the claims, the average survival time for a claim to occur and then settled in an automobile insurance companies need to be determined. Therefore, the project used survival analysis techniques to analyze this problem. The techniques that were employed include both the product limit estimator and the cox proportional hazard model. The variables that were analyzed in this study were primarily; type of vehicle ownership, type of policy issued, nature of the claim, size of the vehicle and place of residence for the respective customers. The objectives of the study was to compare statistically and graphically the Kaplan Meier survival graphs of different covariate groups and the time a certain vehicle takes for a loss to occur mostly after occurrence of the insured risk and also used a cox-regression to test for their significance. The study used on secondary data that was acquired from one of the insurance Company in Kenya. The review was motor vehicle claims data for 2018 where the information was coded and analyzed using descriptive statistics. The study showed that ownership and residence were significant risk factors that contribute to the occurrence of a loss but they are insignificant in claim settlement using Cox regression model and log rank test. The size of the vehicle and the type of policy given out were significant covariates that influence claim settlement time.
Abstract: Most insurance companies find it hard and hectic to pay claims that had not being anticipated. In order for the companies to be able to make enough reserves to cater for the claims, the average survival time for a claim to occur and then settled in an automobile insurance companies need to be determined. Therefore, the project used survival analysi...
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A Satisfaction Degree Evaluation Model of Knowledge Payment Platform for College Students in Guangzhou City
Qiansheng Zhang,
Yaxian Chen,
Guoming Wu,
Huahong Chen,
Xiaoya Sun,
Huijuan Zang
Issue:
Volume 5, Issue 1, March 2020
Pages:
22-28
Received:
25 February 2020
Accepted:
24 March 2020
Published:
14 April 2020
Abstract: With the rapid development of knowledge information and internet technology, a variety of online knowledge paid products and knowledge payment platforms are widely used by college students. The satisfaction evaluation of knowledge payment platform not only helps college students choose a suitable knowledge payment platform, but also helps the knowledge payment platform improve competitiveness and standardization. This paper collects customer satisfaction data by designing questionnaires and conducting customer satisfaction surveys. Using statistical analysis methods such as descriptive statistics, parameter testing and reliability analysis, the customer satisfaction data are analyzed from different dimensions. Based on American customer satisfaction index (ACSI) and the characteristics of knowledge payment, content quality, technical quality, update frequency and advertising marketing quality are extracted as important factors for satisfaction evaluation. Then, a satisfaction evaluation model of knowledge payment platform is constructed to evaluate the satisfaction degree of college students on the knowledge payment platform by employing factor analysis and multivariable regression analysis. The proposed model can predict the development trend of knowledge payment platforms and provide valuable suggestions for the healthy development and the selection of knowledge payment platforms.
Abstract: With the rapid development of knowledge information and internet technology, a variety of online knowledge paid products and knowledge payment platforms are widely used by college students. The satisfaction evaluation of knowledge payment platform not only helps college students choose a suitable knowledge payment platform, but also helps the knowl...
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