Developing of Distributed Virtual Laboratories for Smart Sensor System Design Based on Multi-Dimensional Access Method

In the article it is considered preconditions and main principles of creation of virtual laboratories for computer-aided design, as tools for interdisciplinary researches. Virtual laboratory, what are offered, is worth to be used on the stage of the requirements specification or EFT-stage, because it gives the possibility of fast estimating of the project realization, certain characteristics and, as a result, expected benefit of its applications. Using of these technologies already increase automation level of design stages of new devices for different purposes. Proposed computer technology gives possibility to specialists from such scientific fields, as chemistry, biology, biochemistry, physics etc, to check possibility of device creating on the basis of developed sensors. It lets to reduce terms and costs of designing of computer devices and systems on the early stages of designing, for example on the stage of requirements specification or EFT-stage. An important feature of this project is using the advanced multi-dimensional access method for organizing the information base of the Virtual laboratory.

Likelihood-Based Inference for Partially Observed Multi-Type Markov Branching Processes

Thesis (Ph.D.)--University of Washington, 2016-08

Sampling, WLS, and Mixed Models

Mixed models may be defined with or without reference to sampling, and can be used to predict realized random effects, as when estimating the latent values of study subjects measured with response error. When the model is specified without reference to sampling, a simple mixed model includes two random variables, one stemming from an exchangeable distribution of latent values of study subjects and the other, from the study subjects` response error distributions. Positive probabilities are assigned to both potentially realizable responses and artificial responses that are not potentially realizable, resulting in artificial latent values. In contrast, finite population mixed models represent the two-stage process of sampling subjects and measuring their responses, where positive probabilities are only assigned to potentially realizable responses. A comparison of the estimators over the same potentially realizable responses indicates that the optimal linear mixed model estimator (the usual best linear unbiased predictor, BLUP) is often (but not always) more accurate than the comparable finite population mixed model estimator (the FPMM BLUP). We examine a simple example and provide the basis for a broader discussion of the role of conditioning, sampling, and model assumptions in developing inference.

Predicting random effects with an expanded finite population mixed model

Prediction of random effects is an important problem with expanding applications. In the simplest context, the problem corresponds to prediction of the latent value (the mean) of a realized cluster selected via two-stage sampling. Recently, Stanek and Singer [Predicting random effects from finite population clustered samples with response error. J. Amer. Statist. Assoc. 99, 119-130] developed best linear unbiased predictors (BLUP) under a finite population mixed model that outperform BLUPs from mixed models and superpopulation models. Their setup, however, does not allow for unequally sized clusters. To overcome this drawback, we consider an expanded finite population mixed model based on a larger set of random variables that span a higher dimensional space than those typically applied to such problems. We show that BLUPs for linear combinations of the realized cluster means derived under such a model have considerably smaller mean squared error (MSE) than those obtained from mixed models, superpopulation models, and finite population mixed models. We motivate our general approach by an example developed for two-stage cluster sampling and show that it faithfully captures the stochastic aspects of sampling in the problem. We also consider simulation studies to illustrate the increased accuracy of the BLUP obtained under the expanded finite population mixed model. (C) 2007 Elsevier B.V. All rights reserved.

Test result-based sampling: an efficient design for estimating the accuracy of patient safety indicators.

OBJECTIVE: Accuracy studies of Patient Safety Indicators (PSIs) are critical but limited by the large samples required due to low occurrence of most events. We tested a sampling design based on test results (verification-biased sampling [VBS]) that minimizes the number of subjects to be verified. METHODS: We considered 3 real PSIs, whose rates were calculated using 3 years of discharge data from a university hospital and a hypothetical screen of very rare events. Sample size estimates, based on the expected sensitivity and precision, were compared across 4 study designs: random and VBS, with and without constraints on the size of the population to be screened. RESULTS: Over sensitivities ranging from 0.3 to 0.7 and PSI prevalence levels ranging from 0.02 to 0.2, the optimal VBS strategy makes it possible to reduce sample size by up to 60% in comparison with simple random sampling. For PSI prevalence levels below 1%, the minimal sample size required was still over 5000. CONCLUSIONS: Verification-biased sampling permits substantial savings in the required sample size for PSI validation studies. However, sample sizes still need to be very large for many of the rarer PSIs.

Less-is-more effects in knowledge-based heuristic inference

Inference on the basis of recognition alone is assumed to occur prior to accessing further information (Pachur & Hertwig, 2006). A counterintuitive result of this is the “less-is-more” effect: a drop in the accuracy with which choices are made as to which of two or more items scores highest on a given criterion as more items are learned (Frosch, Beaman & McCloy, 2007; Goldstein & Gigerenzer, 2002). In this paper, we show that less-is-more effects are not unique to recognition-based inference but can also be observed with a knowledge-based strategy provided two assumptions, limited information and differential access, are met. The LINDA model which embodies these assumptions is presented. Analysis of the less-is-more effects predicted by LINDA and by recognition-driven inference shows that these occur for similar reasons and casts doubt upon the “special” nature of recognition-based inference. Suggestions are made for empirical tests to compare knowledge-based and recognition-based less-is-more effects

Design of alternative energy storage systems for hybrid vehicles based on statistical processing of driving cycles information

Hybrid vehicles represent the future for automakers, since they allow to improve the fuel economy and to reduce the pollutant emissions. A key component of the hybrid powertrain is the Energy Storage System, that determines the ability of the vehicle to store and reuse energy. Though electrified Energy Storage Systems (ESS), based on batteries and ultracapacitors, are a proven technology, Alternative Energy Storage Systems (AESS), based on mechanical, hydraulic and pneumatic devices, are gaining interest because they give the possibility of realizing low-cost mild-hybrid vehicles. Currently, most literature of design methodologies focuses on electric ESS, which are not suitable for AESS design. In this contest, The Ohio State University has developed an Alternative Energy Storage System design methodology. This work focuses on the development of driving cycle analysis methodology that is a key component of Alternative Energy Storage System design procedure. The proposed methodology is based on a statistical approach to analyzing driving schedules that represent the vehicle typical use. Driving data are broken up into power events sequence, namely traction and braking events, and for each of them, energy-related and dynamic metrics are calculated. By means of a clustering process and statistical synthesis methods, statistically-relevant metrics are determined. These metrics define cycle representative braking events. By using these events as inputs for the Alternative Energy Storage System design methodology, different system designs are obtained. Each of them is characterized by attributes, namely system volume and weight. In the last part the work, the designs are evaluated in simulation by introducing and calculating a metric related to the energy conversion efficiency. Finally, the designs are compared accounting for attributes and efficiency values. In order to automate the driving data extraction and synthesis process, a specific script Matlab based has been developed. Results show that the driving cycle analysis methodology, based on the statistical approach, allows to extract and synthesize cycle representative data. The designs based on cycle statistically-relevant metrics are properly sized and have satisfying efficiency values with respect to the expectations. An exception is the design based on the cycle worst-case scenario, corresponding to same approach adopted by the conventional electric ESS design methodologies. In this case, a heavy system with poor efficiency is produced. The proposed new methodology seems to be a valid and consistent support for Alternative Energy Storage System design.

Optimum Design of Electromagnets for Magnetic Levitation of Transport Systems based on the Inverse Problem Solutions

Article is devoted to design of optimum electromagnets for magnetic levitation of transport systems. The method of electromagnets design based on the inverse problem solution of electrical equipment is offered. The method differs from known by introducing a stage of minimization the target functions providing the stated levitation force and magnetic induction in a gap, and also the mass of an electromagnet. Initial values of parameters are received, using approximate formulas of the theory of electric devices and electrical equipment. The example of realization of a method is given. The received results show its high efficiency at design. It is practical to use the offered method and the computer program realizing it as a part of system of the automated design of electric equipment for transport with a magnetic levitation.

Reliability-based calibration for a mechanics-based fatigue cracking design procedure

Most pavement design procedures incorporate reliability to account for design inputs-associated uncertainty and variability effect on predicted performance. The load and resistance factor design (LRFD) procedure, which delivers economical section while considering design inputs variability separately, has been recognised as an effective tool to incorporate reliability into design procedures. This paper presents a new reliability-based calibration in LRFD format for a mechanics-based fatigue cracking analysis framework. This paper employs a two-component reliability analysis methodology that utilises a central composite design-based response surface approach and a first-order reliability method. The reliability calibration was achieved based on a number of field pavement sections that have well-documented performance history and high-quality field and laboratory data. The effectiveness of the developed LRFD procedure was evaluated by performing pavement designs of various target reliabilities and design conditions. The result shows an excellent agreement between the target and actual reliabilities. Furthermore, it is clear from the results that more design features need to be included in the reliability calibration to minimise the deviation of the actual reliability from the target reliability.

Classical design thinking to organization design in the tourism sector

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Management from the NOVA – School of Business and Economics

The mixed impact of design thinking on business collaborations

The present work project investigates the implications of design thinking on an interbusiness collaborative project, unveiling both the benefits and fragilities that derive from the use of such methodology within the mentioned context. The study is organized in two parts. The first one starts with a literature review on the two relevant frames of reference – design thinking and business collaboration. It proceeds to the creation of a conceptual framework to assess the potential value of the design-based approach. The second part focuses on the practical application of to the newly developed instrument to the inTRAIN project – R&D for railway interiors.

A Computer Based Information System for County Equipment Cost Records, Interim Report, HR-173, 1975

This report describes the work accomplished to date on research project HR-173, A Computer Based Information System for County Equipment Cost Records, and presents the initial design for this system. The specific topics discussed here are findings from the analysis of information needs, the system specifications developed from these findings, and the proposed system design based upon the system specifications. The initial system design will include tentative input designs for capturing input data, output designs to show the output formats and the items to be output for use in decision making, file design showing the organization of information to be kept on each piece of equipment in the computer data file, and general system design explaining how the entire system will operate. The Steering Committee appointed by Iowa Highway Research Board is asked to study this report, make appropriate suggestions, and give approval to the proposed design subject to any suggestions made. This approval will permit the designer to proceed promptly with the development of the computer program implementation phase of the design.

Pavement Thickness Design for Local Roads in Iowa, TR-586, 2010

Three pavement design software packages were compared with regards to how they were different in determining design input parameters and their influences on the pavement thickness. StreetPave designs the concrete pavement thickness based on the PCA method and the equivalent asphalt pavement thickness. The WinPAS software performs both concrete and asphalt pavements following the AASHTO 1993 design method. The APAI software designs asphalt pavements based on pre-mechanistic/empirical AASHTO methodology. First, the following four critical design input parameters were identified: traffic, subgrade strength, reliability, and design life. The sensitivity analysis of these four design input parameters were performed using three pavement design software packages to identify which input parameters require the most attention during pavement design. Based on the current pavement design procedures and sensitivity analysis results, a prototype pavement design and sensitivity analysis (PD&SA) software package was developed to retrieve the pavement thickness design value for a given condition and allow a user to perform a pavement design sensitivity analysis. The prototype PD&SA software is a computer program that stores pavement design results in database that is designed for the user to input design data from the variety of design programs and query design results for given conditions. The prototype Pavement Design and Sensitivity Analysis (PA&SA) software package was developed to demonstrate the concept of retrieving the pavement design results from the database for a design sensitivity analysis. This final report does not include the prototype software which will be validated and tested during the next phase.

Typing less common ovarian tumors: A training tool based on a pattern-based algorithm applied to a set of 20 virtual slides.

Context: Ovarian tumors (OT) typing is a competency expected from pathologists, with significant clinical implications. OT however come in numerous different types, some rather rare, with the consequence of few opportunities for practice in some departments. Aim: Our aim was to design a tool for pathologists to train in less common OT typing. Method and Results: Representative slides of 20 less common OT were scanned (Nano Zoomer Digital Hamamatsu®) and the diagnostic algorithm proposed by Young and Scully applied to each case (Young RH and Scully RE, Seminars in Diagnostic Pathology 2001, 18: 161-235) to include: recognition of morphological pattern(s); shortlisting of differential diagnosis; proposition of relevant immunohistochemical markers. The next steps of this project will be: evaluation of the tool in several post-graduate training centers in Europe and Québec; improvement of its design based on evaluation results; diffusion to a larger public. Discussion: In clinical medicine, solving many cases is recognized as of utmost importance for a novice to become an expert. This project relies on the virtual slides technology to provide pathologists with a learning tool aimed at increasing their skills in OT typing. After due evaluation, this model might be extended to other uncommon tumors.

The Art & Science of Learning Design

We live in an era defined by a wealth of open and readily available information, and the accelerated evolution of social, mobile and creative technologies. The provision of knowledge, once a primary role of educators, is now devolved to an immense web of free and readily accessible sources. Consequently, educators need to redefine their role not just ¿from sage on the stage to guide on the side¿ but, as more and more voices insist, as ¿designers for learning¿.The call for such a repositioning of educators is heard from leaders in the field of technology-enhanced learning (TEL) and resonates well with the growing culture of design-based research in Education. However, it is still struggling to find a foothold in educational practice. We contend that the root causes of this discrepancy are the lack of articulation of design practices and methods, along with a shortage of tools and representations to support such practices, a lack of a culture of teacher-as-designer among practitioners, and insufficient theoretical development.The Art and Science of Learning Design (ASLD) explores the frameworks, methods, and tools available for teachers, technologists and researchers interested in designing for learning Learning Design theories arising from findings of research are explored, drawing upon research and practitioner experiences. It then surveys current trends in the practices, methods, and methodologies of Learning Design. Highlighting the translation of theory into practice, this book showcases some of the latest tools that support the learning design process itself.