{"title":"How to Determine the Stiffness of the Pavement's Upper Layer (Base) Based on the Overall Stiffness and the Stiffness of the Lower Layer (Subgrade)","authors":"C. Servin, V. Kreinovich","doi":"10.12988/JITE.2016.6719","DOIUrl":"https://doi.org/10.12988/JITE.2016.6719","url":null,"abstract":"In road construction, it is important to estimate difficult-measure stiffness of the pavement’s upper layer based the easier-to-measure overall stiffness and the stiffness of the lower layer. In situations when the overall stiffness is not yet sufficient, it is also important to estimate how much more we need to add to the upper layer to reach the desired overall stiffness. In this paper, for the cases when a linear approximation is sufficient, we provide analytical formulas for the desired estimations. 1 Formulation of the Problems Need for multiple-layer pavements. Usually, the soil is not stiff enough to serve as a base for the road. Two ideas are used to reach the desired stiffness: • first, the soil is compacted, to increase it stiffness; • second, on top of the compacted soil – which now serves as a subgrade – a stiffer layer of another material (“base”) is placed (and then compacted too). How to characterize stiffness. Stiffness describes how much the pavement is displaced under the external force: the smaller the resulting displacement, the","PeriodicalId":43632,"journal":{"name":"Journal of Information Technology Education-Innovations in Practice","volume":"104 1","pages":"193-203"},"PeriodicalIF":1.2,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73447203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"One Needs to Be Careful When Dismissing Outliers: A Realistic Example","authors":"Carlos Fajardo, O. Kosheleva, V. Kreinovich","doi":"10.12988/JITE.2016.6720","DOIUrl":"https://doi.org/10.12988/JITE.2016.6720","url":null,"abstract":"Traditional approach to eliminating outliers is that we compute the sample mean μ and the sample standard deviation σ, and then, for an appropriate value k0 = 2, 3, 6, etc., we eliminate all data points outside the interval [μ−k0·σ, μ+k0·σ] as outliers. Then, we repeat this procedure with the remaining data, eliminate new outliers, etc., until on some iteration, no new outliers are eliminated. In many applications, this procedure works well. However, in this paper, we provide a realistic example in which this procedure, instead of eliminating all outliers and leaving adequate data points intact, eliminates all the data points. This example shows that one needs to be careful when applying the standard outlier-eliminating procedure. 1 Formulation of the Problem Need to eliminate outliers. In the traditional approach to data analysis, based on the sample, we estimate the means of the corresponding quantities, we estimate the variances, covariance, and correlations; see, e.g., [3]. This usually works well, but sometimes, we have outliers, i.e., values caused, e.g., by the malfunctioning of the measuring instrument. Outliers ruin the estimations. For example, if we are interested in the average temperature, and in addition to 100 measurement results around 20◦ C, we have a (clearly erroneous) value 1000◦ C, then the sample average x becomes x ≈ 20 + . . .+ 20 (100 times) + 1000 101 ≈ 30.","PeriodicalId":43632,"journal":{"name":"Journal of Information Technology Education-Innovations in Practice","volume":"4 1","pages":"205-214"},"PeriodicalIF":1.2,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77665971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Why the Range of a Robust Statistic under Interval Uncertainty is Often Easier to Compute","authors":"O. Kosheleva, V. Kreinovich","doi":"10.12988/JITE.2016.613","DOIUrl":"https://doi.org/10.12988/JITE.2016.613","url":null,"abstract":"In statistical analysis, we usually use the observed sample values x1, . . . , xn to compute the values of several statistics v(x1;:::;xn) { such as sample mean, sample variance, etc. The usual formulas for these statistics implicitly assume that we know the exact values x1;:::;xn. In practice, the sample values e","PeriodicalId":43632,"journal":{"name":"Journal of Information Technology Education-Innovations in Practice","volume":"17 1","pages":"37-43"},"PeriodicalIF":1.2,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73478504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Determination of breast tumor BIRADS variant using physical parameter on the mammography X-ray film","authors":"A. Gunawan, Wayan Supardi","doi":"10.12988/JITE.2016.6936","DOIUrl":"https://doi.org/10.12988/JITE.2016.6936","url":null,"abstract":"The research of the determination breast tumor BIRADS variant using the method of physical parameters on mammogram images are done. Usually, the radiology doctors to determine of BIRADS variant through out visual reading of mammograms. In this research a mathematical model to determine BIRADS variant by using the physical parameters on the mammogram images are derived. In previous studies for determining the stadium and histopathological types of breast cancer using mathematical models with physical parameters on mammogram images are obtained. The model have been tested on the mammogram 284 new patients at Sanglah Hospital and Denpasar Primamedika Hospital in Denpasar. The mathematical models had been determining the breast tumor BIRADS variant with 71.126% sensitivity are resulted. This article is better than in the previous article that uses fractal method, neural network and pattern, because the article is just able to detect the presence of microcalcification.","PeriodicalId":43632,"journal":{"name":"Journal of Information Technology Education-Innovations in Practice","volume":"120 1","pages":"241-250"},"PeriodicalIF":1.2,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74687973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Optimization of maintenance methods to improve the availability of the national electrical network","authors":"Z. Bouzoubaa, A. Soulhi, J. Alami","doi":"10.12988/JITE.2016.612","DOIUrl":"https://doi.org/10.12988/JITE.2016.612","url":null,"abstract":"Classical maintenance methods, largely known and used in industrial units, are based on, among others, preventive or provisional operations. It follows from this that stops immobilizing units being maintained are inevitable, which results in the following costly drawbacks. Loss of profit due to production stop; Decrease in system performance, particularly its operational availability; Creation of operating constraints due to stopping and restarting of units; Deterioration of the brand image of the body in charge of the system in question and possible customers’ dissatisfaction. Our work is an analysis of the different interactions between the maintainability functions and the availability of the electrical distribution network in Morocco. This analysis seeks to identify the new approaches to be adopted in the policy of maintenance of the strategic facilities in the said network in view of optimizing their availability as well as the global output and productivity of the system. 24 Zakaria Bouzoubaa et al. The purpose is to minimize global costs arising from the decrease in productivity and loss of profit directly related to maintenance-related stops.","PeriodicalId":43632,"journal":{"name":"Journal of Information Technology Education-Innovations in Practice","volume":"47 1","pages":"23-35"},"PeriodicalIF":1.2,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81420476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Giancarlo de França Aguiar, B. C. X. C. Aguiar, Luiz Gustavo Silveira Rossa
{"title":"Speed simulation environment","authors":"Giancarlo de França Aguiar, B. C. X. C. Aguiar, Luiz Gustavo Silveira Rossa","doi":"10.12988/JITE.2016.6833","DOIUrl":"https://doi.org/10.12988/JITE.2016.6833","url":null,"abstract":"The study and treatment of data applied to the teaching-learning process are as effective knowledge to the student tools. This work discusses some reflections on the construction and use of concrete materials in engineering education. To develop this work, It was built a speed simulation environment, consisting of a wooden ramp (where the top, a toy car is positioned), and a force sensor to be positioned at the end of the runway. As you scroll through the track, the cart will reach a speed, which in turn, will be measured with the aid of the sensor. The information collected by the sensor is transmitted to an Arduino board, which processes the data and transmits them to the Ethernet Shield (responsible for creating a web page and attach the sensor data). The force sensor used in the work is called LVDT (Linear Variable Differential Transformer), which aims to provide a measure of strength in physical systems. In this work was studied measuring the maximum speed (reached by the trolley) by treatment of the impact data, acceleration, mass of toy car and known track length during simulations","PeriodicalId":43632,"journal":{"name":"Journal of Information Technology Education-Innovations in Practice","volume":"40 1","pages":"227-240"},"PeriodicalIF":1.2,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86928590","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"The trapezoidal fuzzy number linear programming","authors":"Karyati, D. U. Wutsqa, N. Insani","doi":"10.12988/JITE.2016.6825","DOIUrl":"https://doi.org/10.12988/JITE.2016.6825","url":null,"abstract":"Linear Programming (LP) problem is one of optimization problems. Based on its limited resources and other restrictions, we find the optimal solution for the problem. LP problems have very wide applications in our daily problems. However, in practice, these LPs often fail to represent the real solutions. Such failures can be caused by some tight modeling assumptions. One attempt to address this failure is to replace the classical set into fuzzy sets. In this case, we call it Fuzzy Linear Programming. There are some types of fuzzy LP problems. One type is the right sides of the constraints are fuzzy numbers. The other type is the coefficients of the objective function are the fuzzy numbers. The most complicated type is the right side, the coefficients of the variables and the coefficients of the objective function are fuzzy numbers. There are some types of fuzzy numbers. Two of them are trapezoidal fuzzy number and triangular fuzzy number. They are simple, easy to be counted and to be implemented. This research used trapezoidal fuzzy numbers. There are some techniques to solve the fuzzy LP problems. In this research, 124 Karyati, Dhoriva Urwatul Wutsqa and Nur Insani we will exploit ranking function introduced by Yager. Ranking function R is a mapping from a family of fuzzy numbers, which is denoted by F(R), into real number . This paper propose a method to solve trapezoidal fuzzy number linear programming problem. To solve this problem, we use trapezoidal fuzzy numbers. By the properties of the operations which are defined on the linear ranking function, we construct an algorithm to solve the trapezoidal fuzzy number linear programming.","PeriodicalId":43632,"journal":{"name":"Journal of Information Technology Education-Innovations in Practice","volume":"91 1","pages":"123-130"},"PeriodicalIF":1.2,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75721506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Maximum Entropy Approach Is Not As Arbitrary As It May Seem at First Glance","authors":"O. Kosheleva, V. Kreinovich","doi":"10.12988/JITE.2016.51017","DOIUrl":"https://doi.org/10.12988/JITE.2016.51017","url":null,"abstract":"When we only have partial information about the probability distribution, i.e., when several different probability distributions are consistent with our knowledge, then it makes sense to select a distribution with the largest entropy. In particular, when we only know that the quantity is located within a certain interval – and we have no information about the probability of different values within this intervals – then it is reasonable to assume that all these values are equally probable, i.e., that we have a uniform distribution on this interval. The problem with this idea is that if we apply it to the same quantity after a non-linear rescaling, we get a different (non-uniform) distribution in the original scale. In other words, it seems that the results of applying the Maximum Entropy approach are rather arbitrary: they depend on what exactly scale we apply them to. In this paper, we show how to overcome this subjectivity: namely, we propose to take into account that, due to measurement inaccuracy, we always have finitely many possible measurement results, and this finiteness makes the results of applying the Maximum Entropy approach uniquely determined. 1 Maximum Entropy Approach and Its Limitations Need to describe probabilities. One of the main objectives of science is to predict future events based on the available information. In many practical situations, it is not possible to uniquely predict the future events: there are many factors which are difficult to take into account. For example, while we can predict tomorrow’s weather reasonably well, these predictions are not exact. In such situations, when we know that for the same future quantity, several different values are possible, it is desirable to describe the frequency of different possible values, i.e., to describe the probability distribution on the set of all","PeriodicalId":43632,"journal":{"name":"Journal of Information Technology Education-Innovations in Practice","volume":"39 1","pages":"1-7"},"PeriodicalIF":1.2,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86551347","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Some remarks on the asymptotic iteration method","authors":"W. Robin","doi":"10.12988/JITE.2016.6613","DOIUrl":"https://doi.org/10.12988/JITE.2016.6613","url":null,"abstract":"The asymptotic iteration method is shown to arise naturally from the continued fraction approach to solving second-order homogeneous linear ordinary differential equations. This emergence of the asymptotic iteration method from the continued fraction approach follows when the continued fraction method is (a) conjoined with the operator factorization method and (b) ‘completed’ by the explicit consideration of the continued fraction convergents. As well as a specific example being considered, a general discussion of the emergent methodology is presented. Mathematics Subject Classification: 30B70, 33C45, 34A05, 34A25, 40A15","PeriodicalId":43632,"journal":{"name":"Journal of Information Technology Education-Innovations in Practice","volume":"112 1","pages":"251-264"},"PeriodicalIF":1.2,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84652416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Exploration of metacognitive ability at elementary school students in learning mathematics (case study in 1th grade students of elementary school)","authors":"Z. Mz, Wahyudin","doi":"10.12988/JITE.2016.6834","DOIUrl":"https://doi.org/10.12988/JITE.2016.6834","url":null,"abstract":"","PeriodicalId":43632,"journal":{"name":"Journal of Information Technology Education-Innovations in Practice","volume":"64 1","pages":"179-184"},"PeriodicalIF":1.2,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87572990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}