Advantages and Applications of Transforming Empirical Model Input Space with Dimensional Models

Model-based calibration of steady-state engine operation is commonly performed with highly parameterized empirical models that are accurate but not very robust, particularly when predicting highly nonlinear responses such as diesel smoke emissions. To address this problem, and to boost the accuracy of more robust non-parametric methods to the same level, GT-Power was used to transform the empirical model input space into multiple input spaces that simplified the input-output relationship and improved the accuracy and robustness of smoke predictions made by three commonly used empirical modeling methods: Multivariate Regression, Neural Networks and the k-Nearest Neighbor method. The availability of multiple input spaces allowed the development of two committee techniques: a 'Simple Committee' technique that used averaged predictions from a set of 10 pre-selected input spaces chosen by the training data and the "Minimum Variance Committee" technique where the input spaces for each prediction were chosen on the basis of disagreement between the three modeling methods. This latter technique equalized the performance of the three modeling methods. The successively increasing improvements resulting from the use of a single best transformed input space (Best Combination Technique), Simple Committee Technique and Minimum Variance Committee Technique were verified with hypothesis testing. The transformed input spaces were also shown to improve outlier detection and to improve k-Nearest Neighbor performance when predicting dynamic emissions with steady-state training data. An unexpected finding was that the benefits of input space transformation were unaffected by changes in the hardware or the calibration of the underlying GT-Power model.

Design-based stereology to quantify structural properties of artificial and natural snow using thin sections

The quantification of the structural properties of snow is traditionally based on model-based stereology. Model-based stereology requires assumptions about the shape of the investigated structure. Here, we show how the density, specific surface area, and grain boundary area can be measured using a design-based method, where no assumptions about structural properties are necessary. The stereological results were also compared to X-ray tomography to control the accuracy of the method. The specific surface area calculated with the stereological method was 19.8 ± 12.3% smaller than with X-ray tomography. For the density, the stereological method gave results that were 11.7 ± 12.1% larger than X-ray tomography. The statistical analysis of the estimates confirmed that the stereological method and the sampling used are accurate. This stereological method was successfully tested on artificially produced ice beads but also on several snow types. Combining stereology and polarisation microscopy provides a good estimate of grain boundary areas in ice beads and in natural snow, with some limitatio

A Cox Model for Biostatistics of the Future

Professor Sir David R. Cox (DRC) is widely acknowledged as among the most important scientists of the second half of the twentieth century. He inherited the mantle of statistical science from Pearson and Fisher, advanced their ideas, and translated statistical theory into practice so as to forever change the application of statistics in many fields, but especially biology and medicine. The logistic and proportional hazards models he substantially developed, are arguably among the most influential biostatistical methods in current practice. This paper looks forward over the period from DRC's 80th to 90th birthdays, to speculate about the future of biostatistics, drawing lessons from DRC's contributions along the way. We consider "Cox's model" of biostatistics, an approach to statistical science that: formulates scientific questions or quantities in terms of parameters gamma in probability models f(y; gamma) that represent in a parsimonious fashion, the underlying scientific mechanisms (Cox, 1997); partition the parameters gamma = theta, eta into a subset of interest theta and other "nuisance parameters" eta necessary to complete the probability distribution (Cox and Hinkley, 1974); develops methods of inference about the scientific quantities that depend as little as possible upon the nuisance parameters (Barndorff-Nielsen and Cox, 1989); and thinks critically about the appropriate conditional distribution on which to base infrences. We briefly review exciting biomedical and public health challenges that are capable of driving statistical developments in the next decade. We discuss the statistical models and model-based inferences central to the CM approach, contrasting them with computationally-intensive strategies for prediction and inference advocated by Breiman and others (e.g. Breiman, 2001) and to more traditional design-based methods of inference (Fisher, 1935). We discuss the hierarchical (multi-level) model as an example of the future challanges and opportunities for model-based inference. We then consider the role of conditional inference, a second key element of the CM. Recent examples from genetics are used to illustrate these ideas. Finally, the paper examines causal inference and statistical computing, two other topics we believe will be central to biostatistics research and practice in the coming decade. Throughout the paper, we attempt to indicate how DRC's work and the "Cox Model" have set a standard of excellence to which all can aspire in the future.

A MULTILEVEL MODEL TO ADDRESS BATCH EFFECTS IN COPY NUMBER ESTIMATION USING SNP ARRAYS

Submicroscopic changes in chromosomal DNA copy number dosage are common and have been implicated in many heritable diseases and cancers. Recent high-throughput technologies have a resolution that permits the detection of segmental changes in DNA copy number that span thousands of basepairs across the genome. Genome-wide association studies (GWAS) may simultaneously screen for copy number-phenotype and SNP-phenotype associations as part of the analytic strategy. However, genome-wide array analyses are particularly susceptible to batch effects as the logistics of preparing DNA and processing thousands of arrays often involves multiple laboratories and technicians, or changes over calendar time to the reagents and laboratory equipment. Failure to adjust for batch effects can lead to incorrect inference and requires inefficient post-hoc quality control procedures that exclude regions that are associated with batch. Our work extends previous model-based approaches for copy number estimation by explicitly modeling batch effects and using shrinkage to improve locus-specific estimates of copy number uncertainty. Key features of this approach include the use of diallelic genotype calls from experimental data to estimate batch- and locus-specific parameters of background and signal without the requirement of training data. We illustrate these ideas using a study of bipolar disease and a study of chromosome 21 trisomy. The former has batch effects that dominate much of the observed variation in quantile-normalized intensities, while the latter illustrates the robustness of our approach to datasets where as many as 25% of the samples have altered copy number. Locus-specific estimates of copy number can be plotted on the copy-number scale to investigate mosaicism and guide the choice of appropriate downstream approaches for smoothing the copy number as a function of physical position. The software is open source and implemented in the R package CRLMM available at Bioconductor (http:www.bioconductor.org).

A Mechanistic Latent Variable Model for Estimating Drug Concentrations in the Male Genital Tract

The purpose of this study is to develop statistical methodology to facilitate indirect estimation of the concentration of antiretroviral drugs and viral loads in the prostate gland and the seminal vesicle. The differences in antiretroviral drug concentrations in these organs may lead to suboptimal concentrations in one gland compared to the other. Suboptimal levels of the antiretroviral drugs will not be able to fully suppress the virus in that gland, lead to a source of sexually transmissible virus and increase the chance of selecting for drug resistant virus. This information may be useful selecting antiretroviral drug regimen that will achieve optimal concentrations in most of male genital tract glands. Using fractionally collected semen ejaculates, Lundquist (1949) measured levels of surrogate markers in each fraction that are uniquely produced by specific male accessory glands. To determine the original glandular concentrations of the surrogate markers, Lundquist solved a simultaneous series of linear equations. This method has several limitations. In particular, it does not yield a unique solution, it does not address measurement error, and it disregards inter-subject variability in the parameters. To cope with these limitations, we developed a mechanistic latent variable model based on the physiology of the male genital tract and surrogate markers. We employ a Bayesian approach and perform a sensitivity analysis with regard to the distributional assumptions on the random effects and priors. The model and Bayesian approach is validated on experimental data where the concentration of a drug should be (biologically) differentially distributed between the two glands. In this example, the Bayesian model-based conclusions are found to be robust to model specification and this hierarchical approach leads to more scientifically valid conclusions than the original methodology. In particular, unlike existing methods, the proposed model based approach was not affected by a common form of outliers.

A coronary heart disease risk model for predicting the effect of potent antiretroviral therapy in HIV-1 infected men

BACKGROUND: Many HIV-infected patients on highly active antiretroviral therapy (HAART) experience metabolic complications including dyslipidaemia and insulin resistance, which may increase their coronary heart disease (CHD) risk. We developed a prognostic model for CHD tailored to the changes in risk factors observed in patients starting HAART. METHODS: Data from five cohort studies (British Regional Heart Study, Caerphilly and Speedwell Studies, Framingham Offspring Study, Whitehall II) on 13,100 men aged 40-70 and 114,443 years of follow up were used. CHD was defined as myocardial infarction or death from CHD. Model fit was assessed using the Akaike Information Criterion; generalizability across cohorts was examined using internal-external cross-validation. RESULTS: A parametric model based on the Gompertz distribution generalized best. Variables included in the model were systolic blood pressure, total cholesterol, high-density lipoprotein cholesterol, triglyceride, glucose, diabetes mellitus, body mass index and smoking status. Compared with patients not on HAART, the estimated CHD hazard ratio (HR) for patients on HAART was 1.46 (95% CI 1.15-1.86) for moderate and 2.48 (95% CI 1.76-3.51) for severe metabolic complications. CONCLUSIONS: The change in the risk of CHD in HIV-infected men starting HAART can be estimated based on typical changes in risk factors, assuming that HRs estimated using data from non-infected men are applicable to HIV-infected men. Based on this model the risk of CHD is likely to increase, but increases may often be modest, and could be offset by lifestyle changes.

Stochastic knock detection model for spark ignited engines

This report presents the development of a Stochastic Knock Detection (SKD) method for combustion knock detection in a spark-ignition engine using a model based design approach. Knock Signal Simulator (KSS) was developed as the plant model for the engine. The KSS as the plant model for the engine generates cycle-to-cycle accelerometer knock intensities following a stochastic approach with intensities that are generated using a Monte Carlo method from a lognormal distribution whose parameters have been predetermined from engine tests and dependent upon spark-timing, engine speed and load. The lognormal distribution has been shown to be a good approximation to the distribution of measured knock intensities over a range of engine conditions and spark-timings for multiple engines in previous studies. The SKD method is implemented in Knock Detection Module (KDM) which processes the knock intensities generated by KSS with a stochastic distribution estimation algorithm and outputs estimates of high and low knock intensity levels which characterize knock and reference level respectively. These estimates are then used to determine a knock factor which provides quantitative measure of knock level and can be used as a feedback signal to control engine knock. The knock factor is analyzed and compared with a traditional knock detection method to detect engine knock under various engine operating conditions. To verify the effectiveness of the SKD method, a knock controller was also developed and tested in a model-in-loop (MIL) system. The objective of the knock controller is to allow the engine to operate as close as possible to its border-line spark-timing without significant engine knock. The controller parameters were tuned to minimize the cycle-to-cycle variation in spark timing and the settling time of the controller in responding to step increase in spark advance resulting in the onset of engine knock. The simulation results showed that the combined system can be used adequately to model engine knock and evaluated knock control strategies for a wide range of engine operating conditions.

Model to describe the mode I fracture of steel fiber reinforced ultra-high performance concrete

Ultra-high performance fiber reinforced concrete (UHPFRC) has arisen from the implementation of a variety of concrete engineering and materials science concepts developed over the last century. This material offers superior strength, serviceability, and durability over its conventional counterparts. One of the most important differences for UHPFRC over other concrete materials is its ability to resist fracture through the use of randomly dispersed discontinuous fibers and improvements to the fiber-matrix bond. Of particular interest is the materials ability to achieve higher loads after first crack, as well as its high fracture toughness. In this research, a study of the fracture behavior of UHPFRC with steel fibers was conducted to look at the effect of several parameters related to the fracture behavior and to develop a fracture model based on a non-linear curve fit of the data. To determine this, a series of three-point bending tests were performed on various single edge notched prisms (SENPs). Compression tests were also performed for quality assurance. Testing was conducted on specimens of different cross-sections, span/depth (S/D) ratios, curing regimes, ages, and fiber contents. By comparing the results from prisms of different sizes this study examines the weakening mechanism due to the size effect. Furthermore, by employing the concept of fracture energy it was possible to obtain a comparison of the fracture toughness and ductility. The model was determined based on a fit to P-w fracture curves, which was cross referenced for comparability to the results. Once obtained the model was then compared to the models proposed by the AFGC in the 2003 and to the ACI 544 model for conventional fiber reinforced concretes.

Adenovirus-mediated transforming growth factor-beta ameliorates ischemic necrosis of epigastric skin flaps in a rat model

BACKGROUND: Gene therapy has been recently introduced as a novel approach to treat ischemic tissues by using the angiogenic potential of certain growth factors. We investigated the effect of adenovirus-mediated gene therapy with transforming growth factor-beta (TGF-beta) delivered into the subdermal space to treat ischemically challenged epigastric skin flaps in a rat model. MATERIAL AND METHODS: A pilot study was conducted in a group of 5 animals pretreated with Ad-GFP and expression of green fluorescent protein in the skin flap sections was demonstrated under fluorescence microscopy at 2, 4, and 7 days after the treatment, indicating a successful transfection of the skin flaps following subdermal gene therapy. Next, 30 male Sprague Dawley rats were divided into 3 groups of 10 rats each. An epigastric skin flap model, based solely on the right inferior epigastric vessels, was used as the model in this study. Rats received subdermal injections of adenovirus encoding TGF-beta (Ad-TGF-beta) or green fluorescent protein (Ad-GFP) as treatment control. The third group (n = 10) received saline and served as a control group. A flap measuring 8 x 8 cm was outlined on the abdominal skin extending from the xiphoid process proximally and the pubic region distally, to the anterior axillary lines bilaterally. Just prior to flap elevation, the injections were given subdermally in the left upper corner of the flap. The flap was then sutured back to its bed. Flap viability was evaluated seven days after the initial operation. Digital images of the epigastric flaps were taken and areas of necrotic zones relative to total flap surface area were measured and expressed as percentages by using a software program. RESULTS: There was a significant increase in mean percent surviving area between the Ad-TGF-beta group and the two other control groups (P < 0.05). (Ad-TGF-beta: 90.3 +/- 4.0% versus Ad-GFP: 82.2 +/- 8.7% and saline group: 82.6 +/- 4.3%.) CONCLUSIONS: In this study, the authors were able to demonstrate that adenovirus-mediated gene therapy using TGF-beta ameliorated ischemic necrosis in an epigastric skin flap model, as confirmed by significant reduction in the necrotic zones of the flap. The results of this study raise the possibility of using adenovirus-mediated TGF-beta gene therapy to promote perfusion in random portion of skin flaps, especially in high-risk patients.

A system for 3-D reconstruction of a patient-specific surface model from calibrated X-ray images

This paper presents a system for 3-D reconstruction of a patient-specific surface model from calibrated X-ray images. Our system requires two X-ray images of a patient with one acquired from the anterior-posterior direction and the other from the axial direction. A custom-designed cage is utilized in our system to calibrate both images. Starting from bone contours that are interactively identified from the X-ray images, our system constructs a patient-specific surface model of the proximal femur based on a statistical model based 2D/3D reconstruction algorithm. In this paper, we present the design and validation of the system with 25 bones. An average reconstruction error of 0.95 mm was observed.

KNOWLEDGE-BASED MANUFACTURING STRATEGY AND METHODS FOR FOUNDRIES

The European foundry business is a traditional less RTD intensive industry which is dominated by SMEs and which forms a significant part of Europe’s manufacturing industry. The efficient design and manufacturing of cast components and corresponding tooling is a crucial success factor for these companies. To achieve this, information and knowledge around the design, planning and manufacturing of cast components needs to be accessible in a fast and structured way.

Constructing a user experience user experience-based mobile learning environment

Mobile learning, in the past defined as learning with mobile devices, now refers to any type of learning-on-the-go or learning that takes advantage of mobile technologies. This new definition shifted its focus from the mobility of technology to the mobility of the learner (O'Malley and Stanton 2002; Sharples, Arnedillo-Sanchez et al. 2009). Placing emphasis on the mobile learner’s perspective requires studying “how the mobility of learners augmented by personal and public technology can contribute to the process of gaining new knowledge, skills, and experience” (Sharples, Arnedillo-Sanchez et al. 2009). The demands of an increasingly knowledge based society and the advances in mobile phone technology are combining to spur the growth of mobile learning. Around the world, mobile learning is predicted to be the future of online learning, and is slowly entering the mainstream education. However, for mobile learning to attain its full potential, it is essential to develop more advanced technologies that are tailored to the needs of this new learning environment. A research field that allows putting the development of such technologies onto a solid basis is user experience design, which addresses how to improve usability and therefore user acceptance of a system. Although there is no consensus definition of user experience, simply stated it focuses on how a person feels about using a product, system or service. It is generally agreed that user experience adds subjective attributes and social aspects to a space that has previously concerned itself mainly with ease-of-use. In addition, it can include users’ perceptions of usability and system efficiency. Recent advances in mobile and ubiquitous computing technologies further underline the importance of human-computer interaction and user experience (feelings, motivations, and values) with a system. Today, there are plenty of reports on the limitations of mobile technologies for learning (e.g., small screen size, slow connection), but there is a lack of research on user experience with mobile technologies. This dissertation will fill in this gap by a new approach in building a user experience-based mobile learning environment. The optimized user experience we suggest integrates three priorities, namely a) content, by improving the quality of delivered learning materials, b) the teaching and learning process, by enabling live and synchronous learning, and c) the learners themselves, by enabling a timely detection of their emotional state during mobile learning. In detail, the contributions of this thesis are as follows: • A video codec optimized for screencast videos which achieves an unprecedented compression rate while maintaining a very high video quality, and a novel UI layout for video lectures, which together enable truly mobile access to live lectures. • A new approach in HTTP-based multimedia delivery that exploits the characteristics of live lectures in a mobile context and enables a significantly improved user experience for mobile live lectures. • A non-invasive affective learning model based on multi-modal emotion detection with very high recognition rates, which enables real-time emotion detection and subsequent adaption of the learning environment on mobile devices. The technology resulting from the research presented in this thesis is in daily use at the School of Continuing Education of Shanghai Jiaotong University (SOCE), a blended-learning institution with 35.000 students.

Volcanic forcing for climate modeling: a new microphysics-based data set covering years 1600–present

As the understanding and representation of the impacts of volcanic eruptions on climate have improved in the last decades, uncertainties in the stratospheric aerosol forcing from large eruptions are now linked not only to visible optical depth estimates on a global scale but also to details on the size, latitude and altitude distributions of the stratospheric aerosols. Based on our understanding of these uncertainties, we propose a new model-based approach to generating a volcanic forcing for general circulation model (GCM) and chemistry–climate model (CCM) simulations. This new volcanic forcing, covering the 1600–present period, uses an aerosol microphysical model to provide a realistic, physically consistent treatment of the stratospheric sulfate aerosols. Twenty-six eruptions were modeled individually using the latest available ice cores aerosol mass estimates and historical data on the latitude and date of eruptions. The evolution of aerosol spatial and size distribution after the sulfur dioxide discharge are hence characterized for each volcanic eruption. Large variations are seen in hemispheric partitioning and size distributions in relation to location/date of eruptions and injected SO2 masses. Results for recent eruptions show reasonable agreement with observations. By providing these new estimates of spatial distributions of shortwave and long-wave radiative perturbations, this volcanic forcing may help to better constrain the climate model responses to volcanic eruptions in the 1600–present period. The final data set consists of 3-D values (with constant longitude) of spectrally resolved extinction coefficients, single scattering albedos and asymmetry factors calculated for different wavelength bands upon request. Surface area densities for heterogeneous chemistry are also provided.

Synchronous and Time-Lagged Effects between Occupational Self-Efficacy and Objective and Subjective Career Success: Findings from a Four-Wave and 9-Year Longitudinal Study

We integrated research on the dimensionality of career success into social-cognitive career theory and explored the positive feedback loop between occupational self-efficacy and objective and subjective career success over time (self-efficacy → objective success → subjective success → self-efficacy). Furthermore, we theoretically accounted for synchronous and time-lagged effects, as well as indirect reciprocity between the variables. We tested the proposed model by means of longitudinal structural equation modeling in a 9-year four-wave panel design, by applying a model comparison approach and indirect effect analyses (N = 608 professionals). The findings supported the proposed positive feedback loop between occupational self-efficacy and career success. Supporting our time-based reasoning, the findings showed that unfolding effects between occupational self-efficacy and objective career success take more time (i.e., time-lagged or over time) than unfolding effects between objective and subjective career success, as well as between subjective career success and occupational self-efficacy (i.e., synchronous or concurrently). Indirect effects of past on future occupational self-efficacy via objective and subjective career success were significant, providing support for an indirect reciprocity model. Results are discussed with respect to extensions of social-cognitive career theory and occupational self-efficacy development over time.