Pedometry methods for assessing free-living youth

The purpose of this review is to integrate and summarize specific measurement topics (instrument and metric choice, validity, reliability, how many and what types of days, reactivity, and data treatment) appropriate to the study of youth physical activity. Research quality pedometers are necessary to aid interpretation of steps per day collected in a range of young populations under a variety of circumstances. Steps per day is the most appropriate metric choice, but steps per minute can be used to interpret time-in-intensity in specifically delimited time periods (e.g., physical education class). Reported intraclass correlations (ICC) have ranged from .65 over 2 days (although higher values also have been reported for 2 days) to .87 over 8 days (although higher values have been reported for fewer days). Reported ICCs are lower on weekend days (.59) versus weekdays (.75) and lower over vacation days (.69) versus school days (.74). There is no objective evidence of reactivity at this time. Data treatment includes (a) identifying and addressing missing values, (b) identifying outliers and reducing data appropriately if necessary, and (c) transforming the data as required in preparation for inferential analysis. As more pedometry studies in young populations are published, these preliminary methodological recommendations should be modified and refined.

Linking the American Time Use Survey (ATUS) and the Compendium of Physical Activities : methods and rationale

Background: The 2003 Bureau of Labor Statistics American Time Use Survey (ATUS) contains 438 distinct primary activity variables that can be analyzed with regard to how time is spent by Americans. The Compendium of Physical Activities is used to code physical activities derived from various surveys, logs, diaries, etc to facilitate comparison of coded intensity levels across studies. ------ ----- Methods: This paper describes the methods, challenges, and rationale for linking Compendium estimates of physical activity intensity (METs, metabolic equivalents) with all activities reported in the 2003 ATUS. ----- ----- Results: The assigned ATUS intensity levels are not intended to compute the energy costs of physical activity in individuals. Instead, they are intended to be used to identify time spent in activities broadly classified by type and intensity. This function will complement public health surveillance systems and aid in policy and health-promotion activities. For example, at least one of the future projects of this process is the descriptive epidemiology of time spent in common physical activity intensity categories. ----- ----- Conclusions: The process of metabolic coding of the ATUS by linking it with the Compendium of Physical Activities can make important contributions to our understanding of Americans’ time spent in health-related physical activity.

Strength improvement methods for back to back Litesteel beams

LiteSteel Beam (LSB) is a new cold-formed steel beam produced by OneSteel Australian Tube Mills (OATM). The new beam is effectively a channel section with two rectangular hollow flanges and a slender web, and is manufactured using patented dual electric resistance welding and automated roll-forming technologies. OATM is promoting the use of LSBs as flexural members in residential construction. When LSBs are used as back to back built-up sections, they are likely to improve their moment capacity. However, the research project conducted on the flexural behaviour of back to back built-up LSBs showed that the detrimental effects of lateral distortional buckling in single LSB members appear to remain with back to back built-up LSB members. The ultimate moment capacity of back to back LSB member is also affected by lateral distortional buckling failure. Therefore an investigation was conducted with an aim to develop suitable strength improvement methods, which are likely to mitigate lateral distortional buckling effects and hence improve the flexural strengths of back to back LSB members. This paper presents the details of this investigation, the results and recommendations for the most suitable and cost-effective method, which significantly improves the moment capacities of back to back LSB members.

Overview of the INEX 2010 XML mining track : clustering and classification of XML documents

The XML Document Mining track was launched for exploring two main ideas: (1) identifying key problems and new challenges of the emerging field of mining semi-structured documents, and (2) studying and assessing the potential of Machine Learning (ML) techniques for dealing with generic ML tasks in the structured domain, i.e., classification and clustering of semi-structured documents. This track has run for six editions during INEX 2005, 2006, 2007, 2008, 2009 and 2010. The first five editions have been summarized in previous editions and we focus here on the 2010 edition. INEX 2010 included two tasks in the XML Mining track: (1) unsupervised clustering task and (2) semi-supervised classification task where documents are organized in a graph. The clustering task requires the participants to group the documents into clusters without any knowledge of category labels using an unsupervised learning algorithm. On the other hand, the classification task requires the participants to label the documents in the dataset into known categories using a supervised learning algorithm and a training set. This report gives the details of clustering and classification tasks.

A biologically inspired object spectral-texture descriptor and its application to vegetation classification in power-line corridors

The use of appropriate features to represent an output class or object is critical for all classification problems. In this paper, we propose a biologically inspired object descriptor to represent the spectral-texture patterns of image-objects. The proposed feature descriptor is generated from the pulse spectral frequencies (PSF) of a pulse coupled neural network (PCNN), which is invariant to rotation, translation and small scale changes. The proposed method is first evaluated in a rotation and scale invariant texture classification using USC-SIPI texture database. It is further evaluated in an application of vegetation species classification in power line corridor monitoring using airborne multi-spectral aerial imagery. The results from the two experiments demonstrate that the PSF feature is effective to represent spectral-texture patterns of objects and it shows better results than classic color histogram and texture features.

Towards a classification of criterion referenced assessment models in mathematics courses : student and academic perspectives

We present the findings of a study into the implementation of explicitly criterion- referenced assessment in undergraduate courses in mathematics. We discuss students' concepts of criterion referencing and also the various interpretations that this concept has among mathematics educators. Our primary goal was to move towards a classification of criterion referencing models in quantitative courses. A secondary goal was to investigate whether explicitly presenting assessment criteria to students was useful to them and guided them in responding to assessment tasks. The data and feedback from students indicates that while students found the criteria easy to understand and useful in informing them as to how they would be graded, it did not alter the way the actually approached the assessment activity.

Physicality in the Information Age : a normative perspective on the patent eligibility of non-physical methods

There has been much conjecture of late as to whether the patentable subject matter standard contains a physicality requirement. The issue came to a head when the Federal Circuit introduced the machine-or-transformation test in In re Bilski and declared it to be the sole test for determining subject matter eligibility. Many commentators criticized the test, arguing that it is inconsistent with Supreme Court precedent and the need for the patent system to respond appropriately to all new and useful innovation in whatever form it arises. Those criticisms were vindicated when, on appeal, the Supreme Court in Bilski v. Kappos dispensed with any suggestion that the patentable subject matter test involves a physicality requirement. In this article, the issue is addressed from a normative perspective: it asks whether the patentable subject matter test should contain a physicality requirement. The conclusion reached is that it should not, because such a limitation is not an appropriate means of encouraging much of the valuable innovation we are likely to witness during the Information Age. It is contended that it is not only traditionally-recognized mechanical, chemical and industrial manufacturing processes that are patent eligible, but that patent eligibility extends to include non-machine implemented and non-physical methods that do not have any connection with a physical device and do not cause a physical transformation of matter. Concerns raised that there is a trend of overreaching commoditization or propertization, where the boundaries of patent law have been expanded too far, are unfounded since the strictures of novelty, nonobviousness and sufficiency of description will exclude undeserving subject matter from patentability. The argument made is that introducing a physicality requirement will have unintended adverse effects in various fields of technology, particularly those emerging technologies that are likely to have a profound social effect in the future.

Panel studies of new venture creation: a methods-focused review and suggestions for future research

Longitudinal panel studies of large, random samples of business start-ups captured at the pre-operational stage allow researchers to address core issues for entrepreneurship research, namely, the processes of creation of new business ventures as well as their antecedents and outcomes. Here, we perform a methods-orientated review of all 83 journal articles that have used this type of data set, our purpose being to assist users of current data sets as well as designers of new projects in making the best use of this innovative research approach. Our review reveals a number of methods issues that are largely particular to this type of research. We conclude that amidst exemplary contributions, much of the reviewed research has not adequately managed these methods challenges, nor has it made use of the full potential of this new research approach. Specifically, we identify and suggest remedies for context-specific and interrelated methods challenges relating to sample definition, choice of level of analysis, operationalization and conceptualization, use of longitudinal data and dealing with various types of problematic heterogeneity. In addition, we note that future research can make further strides towards full utilization of the advantages of the research approach through better matching (from either direction) between theories and the phenomena captured in the data, and by addressing some under-explored research questions for which the approach may be particularly fruitful.

Loculus : an ontology-based information management framework for the motion picture industry

"How do you film a punch?" This question can be posed by actors, make-up artists, directors and cameramen. Though they can all ask the same question, they are not all seeking the same answer. Within a given domain, based on the roles they play, agents of the domain have different perspectives and they want the answers to their question from their perspective. In this example, an actor wants to know how to act when filming a scene involving a punch. A make-up artist is interested in how to do the make-up of the actor to show bruises that may result from the punch. Likewise, a director wants to know how to direct such a scene and a cameraman is seeking guidance on how best to film such a scene. This role-based difference in perspective is the underpinning of the Loculus framework for information management for the Motion Picture Industry. The Loculus framework exploits the perspective of agent for information extraction and classification within a given domain. The framework uses the positioning of the agent’s role within the domain ontology and its relatedness to other concepts in the ontology to determine the perspective of the agent. Domain ontology had to be developed for the motion picture industry as the domain lacked one. A rule-based relatedness score was developed to calculate the relative relatedness of concepts with the ontology, which were then used in the Loculus system for information exploitation and classification. The evaluation undertaken to date have yielded promising results and have indicated that exploiting perspective can lead to novel methods of information extraction and classifications.

Mobility, accessibility and activity participation : a comparative assessment of methods to identify rural transport disadvantage

Traditionally, transport disadvantage has been identified using accessibility analysis although the effectiveness of the accessibility planning approach to improving access to goods and services is not known. This paper undertakes a comparative assessment of measures of mobility, accessibility, and participation used to identify transport disadvantage using the concept of activity spaces. A 7 day activity-travel diary data for 89 individuals was collected from two case study areas located in rural Northern Ireland. A spatial analysis was conducted to select the case study areas using criteria derived from the literature. The criteria are related to the levels of area accessibility and area mobility which are known to influence the nature of transport disadvantage. Using the activity-travel diary data individuals weekly as well as day to day variations in activity-travel patterns were visualised. A model was developed using the ArcGIS ModelBuilder tool and was run to derive scores related to individual levels of mobility, accessibility, and participation in activities from the geovisualisation. Using these scores a multiple regression analysis was conducted to identify patterns of transport disadvantage. This study found a positive association between mobility and accessibility, between mobility and participation, and between accessibility and participation in activities. However, area accessibility and area mobility were found to have little impact on individual mobility, accessibility, and participation in activities. Income vis-àvis ´ car-ownership was found to have a significant impact on individual levels of mobility, and accessibility; whereas participation in activities were found to be a function of individual levels of income and their occupational status.

Multiscale analysis of protein functions and stochastic modelling of gene transcriptional regulatory networks

Genomic and proteomic analyses have attracted a great deal of interests in biological research in recent years. Many methods have been applied to discover useful information contained in the enormous databases of genomic sequences and amino acid sequences. The results of these investigations inspire further research in biological fields in return. These biological sequences, which may be considered as multiscale sequences, have some specific features which need further efforts to characterise using more refined methods. This project aims to study some of these biological challenges with multiscale analysis methods and stochastic modelling approach. The first part of the thesis aims to cluster some unknown proteins, and classify their families as well as their structural classes. A development in proteomic analysis is concerned with the determination of protein functions. The first step in this development is to classify proteins and predict their families. This motives us to study some unknown proteins from specific families, and to cluster them into families and structural classes. We select a large number of proteins from the same families or superfamilies, and link them to simulate some unknown large proteins from these families. We use multifractal analysis and the wavelet method to capture the characteristics of these linked proteins. The simulation results show that the method is valid for the classification of large proteins. The second part of the thesis aims to explore the relationship of proteins based on a layered comparison with their components. Many methods are based on homology of proteins because the resemblance at the protein sequence level normally indicates the similarity of functions and structures. However, some proteins may have similar functions with low sequential identity. We consider protein sequences at detail level to investigate the problem of comparison of proteins. The comparison is based on the empirical mode decomposition (EMD), and protein sequences are detected with the intrinsic mode functions. A measure of similarity is introduced with a new cross-correlation formula. The similarity results show that the EMD is useful for detection of functional relationships of proteins. The third part of the thesis aims to investigate the transcriptional regulatory network of yeast cell cycle via stochastic differential equations. As the investigation of genome-wide gene expressions has become a focus in genomic analysis, researchers have tried to understand the mechanisms of the yeast genome for many years. How cells control gene expressions still needs further investigation. We use a stochastic differential equation to model the expression profile of a target gene. We modify the model with a Gaussian membership function. For each target gene, a transcriptional rate is obtained, and the estimated transcriptional rate is also calculated with the information from five possible transcriptional regulators. Some regulators of these target genes are verified with the related references. With these results, we construct a transcriptional regulatory network for the genes from the yeast Saccharomyces cerevisiae. The construction of transcriptional regulatory network is useful for detecting more mechanisms of the yeast cell cycle.

Machine learning techniques to improve software quality

A significant proportion of the cost of software development is due to software testing and maintenance. This is in part the result of the inevitable imperfections due to human error, lack of quality during the design and coding of software, and the increasing need to reduce faults to improve customer satisfaction in a competitive marketplace. Given the cost and importance of removing errors improvements in fault detection and removal can be of significant benefit. The earlier in the development process faults can be found, the less it costs to correct them and the less likely other faults are to develop. This research aims to make the testing process more efficient and effective by identifying those software modules most likely to contain faults, allowing testing efforts to be carefully targeted. This is done with the use of machine learning algorithms which use examples of fault prone and not fault prone modules to develop predictive models of quality. In order to learn the numerical mapping between module and classification, a module is represented in terms of software metrics. A difficulty in this sort of problem is sourcing software engineering data of adequate quality. In this work, data is obtained from two sources, the NASA Metrics Data Program, and the open source Eclipse project. Feature selection before learning is applied, and in this area a number of different feature selection methods are applied to find which work best. Two machine learning algorithms are applied to the data - Naive Bayes and the Support Vector Machine - and predictive results are compared to those of previous efforts and found to be superior on selected data sets and comparable on others. In addition, a new classification method is proposed, Rank Sum, in which a ranking abstraction is laid over bin densities for each class, and a classification is determined based on the sum of ranks over features. A novel extension of this method is also described based on an observed polarising of points by class when rank sum is applied to training data to convert it into 2D rank sum space. SVM is applied to this transformed data to produce models the parameters of which can be set according to trade-off curves to obtain a particular performance trade-off.

Probabilistic subgroup identification using Bayesian finite mixture modelling : a case study in Parkinson’s disease phenotype identification

This article explores the use of probabilistic classification, namely finite mixture modelling, for identification of complex disease phenotypes, given cross-sectional data. In particular, if focuses on posterior probabilities of subgroup membership, a standard output of finite mixture modelling, and how the quantification of uncertainty in these probabilities can lead to more detailed analyses. Using a Bayesian approach, we describe two practical uses of this uncertainty: (i) as a means of describing a person’s membership to a single or multiple latent subgroups and (ii) as a means of describing identified subgroups by patient-centred covariates not included in model estimation. These proposed uses are demonstrated on a case study in Parkinson’s disease (PD), where latent subgroups are identified using multiple symptoms from the Unified Parkinson’s Disease Rating Scale (UPDRS).

UAS Classification: Key to effective airworthiness and operational regulations

This presentation discusses some of the general issues relating to the classification of UAS for the purposes of defining and promulgating safety regulations. One possible approach for the definition of a classification scheme for UAS Type Certification Categories reviewed.