Parametric Analysis of Epoxy/Crumb Rubber Composite by Using Taguchi—GRA Hybrid Technique

Effect of parameters affecting solid particle erosion of crumb rubber epoxy composite is investigated. Five important process parameters—impact velocity, impingement angle, standoff distance, erodent size, and crumb rubber content—are taken into consideration. Erosion rate and erosion efficiency are included as the chief objectives. The Taguchi coupled gray relational analysis type statistical model is implemented to study interaction, parameters' effect on responses, and optimized parameters. ANOVA and regression model affirmed impingement angle and crumb rubber content play a significant role to minimize the erosion. Validity of the proposed model is justified with the standard probability plot and R2 value. A confirmation experiment conducted with A2B2C3D3E3 condition registers noticeable enhancement in GRG to the tune of 0.0893.

Groups with infinite FC-center have the Schmidt property

We show that every countable group with infinite finite conjugacy (FC)-center has the Schmidt property, that is, admits a free, ergodic, measure-preserving action on a standard probability space such that the full group of the associated orbit equivalence relation contains a non-trivial central sequence. As a consequence, every countable, inner amenable group with property (T) has the Schmidt property.

Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods

The notion of uncertainty is of major importance in machine learning and constitutes a key element of machine learning methodology. In line with the statistical tradition, uncertainty has long been perceived as almost synonymous with standard probability and probabilistic predictions. Yet, due to the steadily increasing relevance of machine learning for practical applications and related issues such as safety requirements, new problems and challenges have recently been identified by machine learning scholars, and these problems may call for new methodological developments. In particular, this includes the importance of distinguishing between (at least) two different types of uncertainty, often referred to as aleatoric and epistemic. In this paper, we provide an introduction to the topic of uncertainty in machine learning as well as an overview of attempts so far at handling uncertainty in general and formalizing this distinction in particular.

Predicting the effect of decompression sickness on survival following submarine tower escape

Purpose The purpose of the present study was the development of a methodology for translating predicted rates of decompression sickness (DCS), following tower escape from a sunken submarine, into predicted probability of survival, a more useful statistic for making operational decisions. Design/methodology/approach Predictions were made, using existing models, for the probabilities of a range of DCS symptoms following submarine tower escape. Subject matter expert estimates of the effect of these symptoms on a submariner’s ability to survive in benign weather conditions on the sea surface until rescued were combined with the likelihoods of the different symptoms occurring using standard probability theory. Plots were generated showing the dependence of predicted probability of survival following escape on the escape depth and the pressure within the stricken submarine. Findings Current advice on whether to attempt tower escape is based on avoiding rates of DCS above approximately 5%–10%. Consideration of predicted survival rates, based on subject matter expert opinion, suggests that the current advice might be considered as conservative in the distressed submarine scenario, as DCS rates of 10% are not anticipated to markedly affect survival rates. Originality/value According to the authors’ knowledge, this study represents the first attempt to quantify the effect of different DCS symptoms on the probability of survival in submarine tower escape.

Implicitly adaptive importance sampling

Adaptive importance sampling is a class of techniques for finding good proposal distributions for importance sampling. Often the proposal distributions are standard probability distributions whose parameters are adapted based on the mismatch between the current proposal and a target distribution. In this work, we present an implicit adaptive importance sampling method that applies to complicated distributions which are not available in closed form. The method iteratively matches the moments of a set of Monte Carlo draws to weighted moments based on importance weights. We apply the method to Bayesian leave-one-out cross-validation and show that it performs better than many existing parametric adaptive importance sampling methods while being computationally inexpensive.

The Network Scale-Up Method

The network scale-up method is one of a series of methods that leverage a respondent’s social network to more effectively capture information about specific groups or about the population as a whole. The network scale-up method works with questions that are known as aggregated relational data (ARD). These questions take the form “How many Xs do you know?” That is, ARD are count data consisting of the number of connections between a respondent and individuals with a specific characteristic. Critically, ARD do not involve observing any links in the network and are collected using standard probability sampling techniques. The main focus of this chapter is estimating the size of a group of individuals using ARD and the network scale-up method. As the name implies, the method uses information for survey respondents’ social networks to “scale up” to an entire population.

Relasi Nilai Mata Kuliah Tasawuf dengan Akhlak Mahasiswa Institut Agama Islam Tribakti (IAIT) Kediri

This paper is to answer the relationship between the value of sufism courses with the morals of students. This research is a quantitative approach with field research conducted at IAIT Kediri. Researchers used data collection methods with documentation and distributed questionnaires with a sample of 69 students from 115 students. Research findings, sufism is one of the subjects at IAIT Kediri. Students have high academic grades and a moderate level of morals. Researchers found the relationship between the value of sufism science subjects and the morals of students obtained r count value of 0.139 with a significance of 0.051 so Ha is accepted if using a standard probability of 10%. This means that there is a positive relationship between the value of sufism subjects and the morals of students. The higher the value of sufism courses, the better the morals. Although the magnitude of the correlation coefficient is very weak.

PENGARUH MODEL PEMBELAJARAN FREE INQUIRY TERHADAP HASIL BELAJAR SISWA MATA PELAJARAN MATEMATIKA KELAS III MI BAHRUL ULUM

Learning models are planning to create learning effectively and efficiently. Research focus, to know: a) How to study free inquiry in Madrasah Ibtidaiyah Bahrul Ulum Jombang class III A? b) How is the outcome of students learning MI Bahrul Ulum Jombang class III A? The study used a simple linear regression analysis test and a classic assumption test. This research is a quantitative study of simple regression types. Its population, a total of 718 students, samples of research, using random sampling techniques using probability theory, the sample amounted to 37 students. The research instrument is a poll using a Likert scale. Instruments to collect data in the form of model free inquiry, and student learning results. Analysis of results Using Test-t (T-Test). The results showed that, there is an independent variable influence on dependent variables, from the results of test-t calculations, with the details: a) Learning free inquiry in Madrasah Ibtidaiyah Bahrul Ulum Jombang class III A very good, with the result of 100% achievement. b) The influence of the model of free inquiry towards the outcome of learning outcomes of class III A MI Bahrul Ulum Jombang obtained the value T count = 6.814 that, T count > T table IE 6.814 > 2.021 significant standard/probability value is 0.000 < 0.05 then Ho rejected and Ha accepted

Probability Functions on Posets

In this paper, we define the notion of a probability function on a poset which is similar to the probability function discussed on d-algebras, and obtain three probability functions on posets. Moreover, we define a probability realizer of a poset, and we provide some examples to describe its role for the standard probability function. We apply the notion of a probability function to the ordered plane and obtain three probability functions on it.