Theoretical aspects of total time on test transform of weighted variables and applications

summary:Although the total time on test (\textit{TTT}) transform is not a newly discovered concept, it has many applications in various fields. On the other hand, weighted distributions are extensively developed by the statisticians to tackle the insufficiency of the standard statistical distributions in modeling the arising data from real-world problems in the contexts like medicine, ecology, and reliability engineering. This paper develops the \textit{TTT} transform for the weighted random variables and investigates the behavior of the failure rate function of such variables based on the \textit{TTT} transform. In addition, the conditions for establishing the $TTT$ transform ordering for weight variables and its relationship with some stochastic orders have been investigated, and the conditions for establishing the \textit{TTT} transform order as well as the presentation of the new better than used in total time on test transform (\textit{NBUT}) class of the weighted variables have also been studied. Finally, by analyzing the real data sets, applications of the transform introduced in the fit of a model is presented, and it is shown that weighted models have a significant advantage over the base models

Relationship between certain classes of life distributions and some stochastic orderings

Abstract. We consider distribution functions (dfs) in NBU, NBUE, NBUC and NBUT classes of life distributions and study the stochastic orderings of the associated random variables (rvs), their equilibrium and residual life rvs at fixed and at random times. These results offer more insight into the structure of these classes

Studium struktury submikrokrystalických materiálů pomocí rentgenové difrakce

Structure of submicrocrystalline materials was investigated by X-ray powder diffraction, mainly by modelling of widths and shapes of diffraction profiles. The diffraction method was applied to colloid gold nanoparticles, copper samples deformed by equal channel angular pressing and titanium dioxide nanoparticles prepared by various chemical routes. Dislocations and twin faults were identified in the metallic samples from characteristic broadening of diffraction lines. Densities of lattice defects were estimated from the diffraction data. Possibilities and limits of the diffraction method for characterisation of a crystallite size distribution were tested on the titanium dioxide samples. Crystallites of size in the range 3-25 nm could be well characterised. The problems were encountered only for samples with extremely broad size dispersion. Diffraction methods and a computer program were developed and tested, which can be applied also for the analysis of thin films.Rentgenovou práškovou difrakcí byla studována struktura submikrokrystalických materiálů. Pro analýzu byla použita především metoda využívající modelování šířek a tvarů difrakčních profilů. Zkoumány byly koloidní nanočástice zlata, měděné vzorky deformované protlačováním a nanočástice oxidu titaničitého připravené různými chemickými metodami. Z charakteristické anisotropie rozšíření difrakčních profilů byly ve vzorcích mědi a zlata rozpoznány dislokace a růstové vrstevné chyby. Z difrakčních dat bylo možné určit hustoty defektů. Možnosti a omezení určování rozdělení velikostí částic pomocí difrakčních metod byly testovány na vzorcích oxidu titaničitého. Částice o velikosti 3-25 nm bylo možné charakterizovat velmi dobře, problémy se projevily pouze v případě, když vzorky obsahovaly zároveň částice velmi rozdílných velikostí. Byly vyvinuty a otestovány difrakční metody a vytvořen počítačový program, které lze používat i k analýze tenkých vrstev.Katedra fyziky kondenzovaných látekDepartment of Condensed Matter PhysicsFaculty of Mathematics and PhysicsMatematicko-fyzikální fakult

A New Bivariate Class of Life Distributions

Abstract Some concepts of multivariate aging for exchangeable random variables have been considered i

Word mastery in oral reading look on versus audience situation in grade 3.

Thesis (Ed.M.)--Boston Universit

Synthesis of Cationic TetraNHC Complexes of Cobalt, Chromium, Iron, and Platinum for Oxidative Group Transfer

Aziridines are the nitrogen analogue of epoxides, and like epoxides, this functional group is an intermediate in numerous syntheses. Additionally, they are found in several natural products with anticancer activity. Despite their chemical applications, aziridine synthesis faces two big challenges: functional group tolerance is limited and a large excess of one reagent is often needed. This research is focused on ameliorating these two limitations.Previous research demonstrated that square planar, strong donor metal complexes, such as porphyrin or macrocyclic tetracarbene complexes were capable of catalyzing aziridination. In our efforts to develop an ideal catalyst for aziridination, we chose to screen a series of transition metals with our tetracarbene ligand to determine which would be most effective for aziridination.A cobalt(II) tetracarbene complex was synthesized by transmetallation and tested for C2 + N1 catalytic aziridination. The complex was unable to catalyze aziridination; furthermore, it showed no reactivity towards organic azides. The oxidative chemistry of the cobalt complex was explored, and a series of cobalt(III) tetracarbene complexes were synthesized. These complexes proved ineffective at aziridination and were unsuitable for further oxidation to a cobalt(IV) complex.A chromium(III) tetracarbene dichloride complex was tested as an aziridination catalyst. The complex proved capable of performing catalytic aziridination at low alkene substrate loadings and performed aziridination with alkenes and azides containing protic functional groups, such as alcohols. The axial chloride ligand on the chromium was proposed as the key to successful catalysis. This complex represents the first ever C2 + N1 aziridination catalyst on a group 6 metal.Following our hypothesis of the penta-coordinated catalyst, an iron(II) pentacarbene complex was synthesized and characterized for aziridination catalysis. While the complex proved an effective catalyst for aziridination at low alkene loadings, the synthesis of the iron complex proved irreproducible.A series of azidoalkenes compounds were synthesized to study the organic chemistry of ring closing intramolecular aziridination. The aziridination reactions had high conversion of the starting materials. However, the reactions proved to be non-catalytic as the control and catalytic yields were identical

Queueing theory

Treballs Finals de Grau de Matemàtiques, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2018, Director: Carles Rovira Escofet[en] This project is aimed to study queueing theory and it is divided in three parts. In the first part we are going to take a theoretical approach to the stochastic processes, specially to the birth and death processes which are indispensable to understand queueing theory. In the second part, we are going to use that knowledge to analyse different types of queues analytically to find some of its characteristic values. Finally in the last part, we are going to do some simulations with the queues we have worked in the previous part to verify if the values we guessed previously were right

Advantages of “Function Domain Sets” Confidence Intervals Over Hypotheses Comparison Tests of One Mean Residual Life (MRL) Function Dominating an Improved Baseline and of Two MRL Functions Comparisons with Applications in Modern Engineered Composite Wood Products: One Sample and Two Sample Cases. Also Exploring General Theory, Insights, and Applications of MRL Functions

In this thesis, we analyze mean residual life (MRL) functions and unique “function domain sets” confidence intervals to identify important opportunities for improving quality of medium density fiberboard (MDF). We stress these tools have tremendous potential for many other forest products (e.g., various composites, natural woods), not just MDF. These “function domain sets” confidence intervals can assess variation in quality where one MRL function dominates an industrial baseline. Assessments of the internal bond of MDF illuminate opportunities for helpful improvements, plus perform valid statistical comparisons of different types of MDF. For example, these MRL methods detect a new, higher-valued MDF product that represents an opportunity for an MDF producer to increase revenues or reduce costs due to excess MRL for a subgroup. These MRL methods can be used as diagnostics of a MDF manufacture process needing adjustments, etc. We provide MAPLE 10 code to implement these MRL procedures. Typical traditional confidence intervals for a MRL function are centered about the function. “Function domain sets” intervals, however, produce novel statements like: “we are 95% confident that the MRL function, e(t), is greater than another function for all t in the domain set [0, θˆ ).” We study “function domain sets” intervals on internal bonds (tensile strengths) for various MDF products. The values of MRL analyses have been demonstrated in a variety of applications beyond MDF production. The usefulness of the MRL function in other areas suggests that it has considerable potential value for the forest products industry. Recent, MRL applications vary from modern accelerated stress testing using proportional MRL modeling, to fuzzy set engineering modeling, to maintenance and replacement of bridges in Europe, to better decision making on materials in nuclear power plants, to general applications in evaluating “degrading” systems. We anticipate that varied analyses of MRL functions and “function domain sets” confidence intervals will furnish practitioners useful tools in many fields. Applications to different areas are highlighted to demonstrate the increasing usefulness and potential of MRL methods in many industries, government agencies, and future academic research

Weakly supervised sentiment analysis and opinion extraction

In recent years, online reviews have become the foremost medium for users to express their satisfaction, or lack thereof, about products and services. The proliferation of user-generated reviews, combined with the rapid growth of e-commerce, results in vast amounts of opinionated text becoming available to consumers, manufacturers, and researchers alike. This has fuelled an increased focus on automated methods that attempt to discover, analyze, and distill opinions found in text. This thesis tackles the tasks of fine-grained sentiment analysis and aspect extraction, and presents a unified framework for the summarization of opinions from multiple user reviews. Two core concepts form the basis of our methodology. Firstly, the use of neural networks, whose ability to learn continuous feature representations from data, without recourse to preprocessing tools or linguistic annotations, has advanced the state-of-the-art of numerous Natural Language Processing tasks. Secondly, our belief that opinion mining systems applied to real-life applications cannot rely on expensive human annotations and should mostly take advantage of freely available review data. Specifically, the main contributions of this thesis are: (i) The creation of OPOSUM, a new Opinion Summarization corpus which contains over one million reviews from multiple domains. To test our methods, we annotated a subset of the data with fine-grained sentiment and aspect labels, as well as extractive gold-standard opinion summaries. (ii) The development of two weakly-supervised hierarchical neural models for the detection and extraction of sentiment-heavy expressions in reviews. Our first model composes segment representations hierarchically and uses an attention mechanism to differentiate between opinions and neutral statements. Our second model is based on Multiple Instance Learning (MIL), and can detect user opinions of potentially opposing polarity. Experiments demonstrate significant benefits from our MIL-based architecture. (iii) The introduction of a neural model for aspect extraction, which requires minimal human involvement. Our proposed formulation uses aspect keywords to help the model target specific aspects, and a multi-tasking objective to further improve its accuracy. (iv) A unified summarization framework which combines our sentiment and aspect detection methods, while taking redundancy into account to produce useful opinion summaries from multiple reviews. Automatic evaluation, on our opinion summarization dataset, shows significant improvements over other summarization systems in terms of extraction accuracy and similarity to reference summaries. A large-scale judgement elicitation study indicates that our summaries are also preferred by human judges