Statistical Optimization of Iron Electrodes for Alkaline Storage Batteries

A pressed-plate Fe electrode for alkalines storage batteries, designed using a statistical method (fractional factorial technique), is described. Parameters such as the configuration of the base grid, electrode compaction temperature and pressure, binder composition, mixing time, etc. have been optimised using this method. The optimised electrodes have a capacity of 300 plus /minus 5 mA h/g of active material (mixture of Fe and magnetite) at 7 h rate to a cut-off voltage of 8.86V vs. Hg/HgO, OH exp 17 ref.

Thermodynamics of cobalt-manganese alloys using fluoride and oxide electrolytes

Abstract is not available.

Domain Adaptation on the Statistical Manifold

In this paper, we tackle the problem of unsupervised domain adaptation for classification. In the unsupervised scenario where no labeled samples from the target domain are provided, a popular approach consists in transforming the data such that the source and target distributions be- come similar. To compare the two distributions, existing approaches make use of the Maximum Mean Discrepancy (MMD). However, this does not exploit the fact that prob- ability distributions lie on a Riemannian manifold. Here, we propose to make better use of the structure of this man- ifold and rely on the distance on the manifold to compare the source and target distributions. In this framework, we introduce a sample selection method and a subspace-based method for unsupervised domain adaptation, and show that both these manifold-based techniques outperform the cor- responding approaches based on the MMD. Furthermore, we show that our subspace-based approach yields state-of- the-art results on a standard object recognition benchmark.

Annual forecasting of the Australian macadamia crop - integrating tree census data with statistical climate-adjustment models

To facilitate marketing and export, the Australian macadamia industry requires accurate crop forecasts. Each year, two levels of crop predictions are produced for this industry. The first is an overall longer-term forecast based on tree census data of growers in the Australian Macadamia Society (AMS). This data set currently accounts for around 70% of total production, and is supplemented by our best estimates of non-AMS orchards. Given these total tree numbers, average yields per tree are needed to complete the long-term forecasts. Yields from regional variety trials were initially used, but were found to be consistently higher than the average yields that growers were obtaining. Hence, a statistical model was developed using growers' historical yields, also taken from the AMS database. This model accounted for the effects of tree age, variety, year, region and tree spacing, and explained 65% of the total variation in the yield per tree data. The second level of crop prediction is an annual climate adjustment of these overall long-term estimates, taking into account the expected effects on production of the previous year's climate. This adjustment is based on relative historical yields, measured as the percentage deviance between expected and actual production. The dominant climatic variables are observed temperature, evaporation, solar radiation and modelled water stress. Initially, a number of alternate statistical models showed good agreement within the historical data, with jack-knife cross-validation R2 values of 96% or better. However, forecasts varied quite widely between these alternate models. Exploratory multivariate analyses and nearest-neighbour methods were used to investigate these differences. For 2001-2003, the overall forecasts were in the right direction (when compared with the long-term expected values), but were over-estimates. In 2004 the forecast was well under the observed production, and in 2005 the revised models produced a forecast within 5.1% of the actual production. Over the first five years of forecasting, the absolute deviance for the climate-adjustment models averaged 10.1%, just outside the targeted objective of 10%.

Generalized pencils of rays in statistical wave optics

The recently introduced generalized pencil of Sudarshan which gives an exact ray picture of wave optics is analysed in some situations of interest to wave optics. A relationship between ray dispersion and statistical inhomogeneity of the field is obtained. A paraxial approximation which preserves the rectilinear propagation character of the generalized pencils is presented. Under this approximation the pencils can be computed directly from the field conditions on a plane, without the necessity to compute the cross-spectral density function in the entire space as an intermediate quantity. The paraxial results are illustrated with examples. The pencils are shown to exhibit an interesting scaling behaviour in the far-zone. This scaling leads to a natural generalization of the Fraunhofer range criterion and of the classical van Cittert-Zernike theorem to planar sources of arbitrary state of coherence. The recently derived results of radiometry with partially coherent sources are shown to be simple consequences of this scaling.

Abstract concepts made real: A pilot study examining pedagogical approaches in thermodynamics tutorials

BACKGROUND OR CONTEXT Thermodynamics is a core concept for mechanical engineers yet notoriously difficult. Evidence suggests students struggle to understand and apply the core fundamental concepts of thermodynamics with analysis indicating a problem with student learning/engagement. A contributing factor is that thermodynamics is a ‘science involving concepts based on experiments’ (Mayhew 1990) with subject matter that cannot be completely defined a priori. To succeed, students must engage in a deep-holistic approach while taking ownership of their learning. The difficulty in achieving this often manifests itself in students ‘not getting’ the principles and declaring thermodynamics ‘hard’. PURPOSE OR GOAL Traditionally, students practice and “learn” the application of thermodynamics in their tutorials, however these do not consider prior conceptions (Holman & Pilling 2004). As ‘hands on’ learning is the desired outcome of tutorials it is pertinent to study methods of improving their efficacy. Within the Australian context, the format of thermodynamics tutorials has remained relatively unchanged over the decades, relying anecdotally on a primarily didactic pedagogical approach. Such approaches are not conducive to deep learning (Ramsden 2003) with students often disengaged from the learning process. Evidence suggests (Haglund & Jeppsson 2012), however, that a deeper level and ownership of learning can be achieved using a more constructivist approach for example through self generated analogies. This pilot study aimed to collect data to support the hypothesis that the ‘difficulty’ of thermodynamics is associated with the pedagogical approach of tutorials rather than actual difficulty in subject content or deficiency in students. APPROACH Successful application of thermodynamic principles requires solid knowledge of the core concepts. Typically, tutorial sessions guide students in this application. However, a lack of deep and comprehensive understanding can lead to student confusion in the applications resulting in the learning of the ‘process’ of application without understanding ‘why’. The aim of this study was to gain empirical data on student learning of both concepts and application, within thermodynamic tutorials. The approach taken for data collection and analysis was: - 1 Four concurrent tutorial streams were timetabled to examine student engagement/learning in traditional ‘didactic’ (3 weeks) and non-traditional (3 weeks). In each week, two of the selected four sessions were traditional and two non-traditional. This provided a control group for each week. - 2 The non-traditional tutorials involved activities designed to promote student-centered deep learning. Specific pedagogies employed were: self-generated analogies, constructivist, peer-to-peer learning, inquiry based learning, ownership of learning and active learning. - 3 After a three-week period, teaching styles of the selected groups was switched, to allow each group to experience both approaches with the same tutor. This also acted to mimimise any influence of tutor personality / style on the data. - 4 At the conclusion of the trial participants completed a ‘5 minute essay’ on how they liked the sessions, a small questionnaire, modelled on the modified (Christo & Hoang, 2013)SPQ designed by Biggs (1987) and a small formative quiz to gauge the level of learning achieved. DISCUSSION Preliminary results indicate that overall students respond positively to in class demonstrations (inquiry based learning), and active learning activities. Within the active learning exercises, the current data suggests students preferred individual rather than group or peer-to-peer activities. Preliminary results from the open-ended questions such as “What did you like most/least about this tutorial” and “do you have other comments on how this tutorial could better facilitate your learning”, however, indicated polarising views on the nontraditional tutorial. Some student’s responded that they really like the format and emphasis on understanding the concepts, while others were very vocal that that ‘hated’ the style and just wanted the solutions to be presented by the tutor. RECOMMENDATIONS/IMPLICATIONS/CONCLUSION Preliminary results indicated a mixed, but overall positive response by students with more collaborative tutorials employing tasks promoting inquiry based, peer-to-peer, active, and ownership of learning activities. Preliminary results from student feedback supports evidence that students learn differently, and running tutorials focusing on only one pedagogical approached (typically didactic) may not be beneficial to all students. Further, preliminary data suggests that the learning / teaching style of both students and tutor are important to promoting deep learning in students. Data collection is still ongoing and scheduled for completion at the end of First Semester (Australian academic calendar). The final paper will examine in more detail the results and analysis of this project.

Statistical analysis of the associations of hereditary factors with the risk of Type 1 diabetes and Diabetic Nephropathy based on the familial data collected through ascertaiment from population-based registers

In genetic epidemiology, population-based disease registries are commonly used to collect genotype or other risk factor information concerning affected subjects and their relatives. This work presents two new approaches for the statistical inference of ascertained data: a conditional and full likelihood approaches for the disease with variable age at onset phenotype using familial data obtained from population-based registry of incident cases. The aim is to obtain statistically reliable estimates of the general population parameters. The statistical analysis of familial data with variable age at onset becomes more complicated when some of the study subjects are non-susceptible, that is to say these subjects never get the disease. A statistical model for a variable age at onset with long-term survivors is proposed for studies of familial aggregation, using latent variable approach, as well as for prospective studies of genetic association studies with candidate genes. In addition, we explore the possibility of a genetic explanation of the observed increase in the incidence of Type 1 diabetes (T1D) in Finland in recent decades and the hypothesis of non-Mendelian transmission of T1D associated genes. Both classical and Bayesian statistical inference were used in the modelling and estimation. Despite the fact that this work contains five studies with different statistical models, they all concern data obtained from nationwide registries of T1D and genetics of T1D. In the analyses of T1D data, non-Mendelian transmission of T1D susceptibility alleles was not observed. In addition, non-Mendelian transmission of T1D susceptibility genes did not make a plausible explanation for the increase in T1D incidence in Finland. Instead, the Human Leucocyte Antigen associations with T1D were confirmed in the population-based analysis, which combines T1D registry information, reference sample of healthy subjects and birth cohort information of the Finnish population. Finally, a substantial familial variation in the susceptibility of T1D nephropathy was observed. The presented studies show the benefits of sophisticated statistical modelling to explore risk factors for complex diseases.

Statistical properties of turbulence: An overview

We present an introductory overview of several challenging problems in the statistical characterization of turbulence. We provide examples from fluid turbulence in three and two dimensions, from the turbulent advection of passive scalars, turbulence in the one-dimensional Burgers equation, and fluid turbulence in the presence of polymer additives.

Charged-soliton excitations in statistical mechanics

A method is developed for demonstrating how solitons with some internal periodic motion may emerge as elementary excitations in the statistical mechanics of field systems. The procedure is demonstrated in the context of complex scalar fields which can, for appropriate choices of the Lagrangian, yield charge-carrying solitons with such internal motion. The derivation uses the techniques of the steepest-descent method for functional integrals. It is shown that, despite the constraint of some fixed total charge, a gaslike excitation of such charged solitons does emerge.

Time evolution of graphene growth on SiC as a function of annealing temperature

We followed by X-ray Photoelectron Spectroscopy (XPS) the time evolution of graphene layers obtained by annealing 3C SiC(111)/Si(111) crystals at different temperatures. The intensity of the carbon signal provides a quantification of the graphene thickness as a function of the annealing time, which follows a power law with exponent 0.5. We show that a kinetic model, based on a bottom-up growth mechanism, provides a full explanation to the evolution of the graphene thickness as a function of time, allowing to calculate the effective activation energy of the process and the energy barriers, in excellent agreement with previous theoretical results. Our study provides a complete and exhaustive picture of Si diffusion into the SiC matrix, establishing the conditions for a perfect control of the graphene growth by Si sublimation.

GENEHUNTER: your 'one-stop shop' for statistical genetic analysis?

The past decade has brought a proliferation of statistical genetic (linkage) analysis techniques, incorporating new methodology and/or improvement of existing methodology in gene mapping, specifically targeted towards the localization of genes underlying complex disorders. Most of these techniques have been implemented in user-friendly programs and made freely available to the genetics community. Although certain packages may be more 'popular' than others, a common question asked by genetic researchers is 'which program is best for me?'. To help researchers answer this question, the following software review aims to summarize the main advantages and disadvantages of the popular GENEHUNTER package.

Statistical significance of sequential firing patterns in multi-neuronal spike trains

Sequential firings with fixed time delays are frequently observed in simultaneous recordings from multiple neurons. Such temporal patterns are potentially indicative of underlying microcircuits and it is important to know when a repeatedly occurring pattern is statistically significant. These sequences are typically identified through correlation counts. In this paper we present a method for assessing the significance of such correlations. We specify the null hypothesis in terms of a bound on the conditional probabilities that characterize the influence of one neuron on another. This method of testing significance is more general than the currently available methods since under our null hypothesis we do not assume that the spiking processes of different neurons are independent. The structure of our null hypothesis also allows us to rank order the detected patterns. We demonstrate our method on simulated spike trains.