The possible selves method in evaluating youth development interventions: The use of Integrated Qualitative /Quantitative Data Analytic Strategies (IQ -DAS)

This study investigated the feasibility of using qualitative methods to provide empirical documentation of the long-term qualitative change in the life course trajectories of “at risk” youth in a school based positive youth development program (the Changing Lives Program—CLP). This work draws from life course theory for a developmental framework and from recent advances in the use of qualitative methods in general and a grounded theory approach in particular. Grounded theory provided a methodological framework for conceptualizing the use of qualitative methods for assessing qualitative life change. The study investigated the feasibility of using the Possible Selves Questionnaire-Qualitative Extension (PSQ-QE) for evaluating the impact of the program on qualitative change in participants' life trajectory relative to a non-intervention control group. Integrated Qualitative/Quantitative Data Analytic Strategies (IQ-DAS) that we have been developing a part of our program of research provided the data analytic framework for the study. ^ Change was evaluated in 85 at risk high school students in CLP high school counseling groups over three assessment periods (pre, post, and follow-up), and a non-intervention control group of 23 students over two assessment periods (pre and post). Intervention gains and maintenance and the extent to which these patterns of change were moderated by gender and ethnicity were evaluated using a mixed design Repeated Measures Multivariate Analysis of Variance (RMANOVA) in which Time (pre, post) was the within (repeated) factor and Condition, Gender, and Ethnicity the between group factors. The trends for the direction of qualitative change were positive from pre to post and maintained at the year-end follow-up. More important, the 3-way interaction for Time x Gender x Ethnicity was significant, Roy's Θ =. 205, F(2, 37) = 3.80, p <.032, indicating that the overall pattern of positive change was significantly moderated by gender and ethnicity. Thus, the findings also provided preliminary evidence for a positive impact of the youth development program on long-term change in life course trajectory, and were suggestive with respect to the issue of amenability to treatment, i.e., the identification of subgroups of individuals in a target population who are likely to be the most amenable or responsive to a treatment. ^

XML storage and retrieval using the semantic binary object -oriented database system (SEM-ODB)

Over the past five years, XML has been embraced by both the research and industrial community due to its promising prospects as a new data representation and exchange format on the Internet. The widespread popularity of XML creates an increasing need to store XML data in persistent storage systems and to enable sophisticated XML queries over the data. The currently available approaches to addressing the XML storage and retrieval issue have the limitations of either being not mature enough (e.g. native approaches) or causing inflexibility, a lot of fragmentation and excessive join operations (e.g. non-native approaches such as the relational database approach). ^ In this dissertation, I studied the issue of storing and retrieving XML data using the Semantic Binary Object-Oriented Database System (Sem-ODB) to leverage the advanced Sem-ODB technology with the emerging XML data model. First, a meta-schema based approach was implemented to address the data model mismatch issue that is inherent in the non-native approaches. The meta-schema based approach captures the meta-data of both Document Type Definitions (DTDs) and Sem-ODB Semantic Schemas, thus enables a dynamic and flexible mapping scheme. Second, a formal framework was presented to ensure precise and concise mappings. In this framework, both schemas and the conversions between them are formally defined and described. Third, after major features of an XML query language, XQuery, were analyzed, a high-level XQuery to Semantic SQL (Sem-SQL) query translation scheme was described. This translation scheme takes advantage of the navigation-oriented query paradigm of the Sem-SQL, thus avoids the excessive join problem of relational approaches. Finally, the modeling capability of the Semantic Binary Object-Oriented Data Model (Sem-ODM) was explored from the perspective of conceptually modeling an XML Schema using a Semantic Schema. ^ It was revealed that the advanced features of the Sem-ODB, such as multi-valued attributes, surrogates, the navigation-oriented query paradigm, among others, are indeed beneficial in coping with the XML storage and retrieval issue using a non-XML approach. Furthermore, extensions to the Sem-ODB to make it work more effectively with XML data were also proposed. ^

An experimental study of the effects of double-diffusive convention processes during the solification of binary alloys

Experiments were conducted to show the effects of thermal and geometric boundary conditions on the liquid pool of a binary alloy system which is undergoing phase change, solidification. Transparent analogue solutions were selected for study and experimental apparatus were designed and built. Thermal distribution and concentration data were collected and analysed for the melt pool of various selected geometries and boundary conditions of the systems under study. The data indicate-that characteristic flows develop for both Hypereutectic and Hypoeutectic concentration levels and that the development of macrosegregation and microsegregation defects in continuous casting materials can be minimised by the adjustment of the process variables.

Digital signal processing implementation for near real-time wavelet transformation system for binary images

Communication has become an essential function in our civilization. With the increasing demand for communication channels, it is now necessary to find ways to optimize the use of their bandwidth. One way to achieve this is by transforming the information before it is transmitted. This transformation can be performed by several techniques. One of the newest of these techniques is the use of wavelets. Wavelet transformation refers to the act of breaking down a signal into components called details and trends by using small waveforms that have a zero average in the time domain. After this transformation the data can be compressed by discarding the details, transmitting the trends. In the receiving end, the trends are used to reconstruct the image. In this work, the wavelet used for the transformation of an image will be selected from a library of available bases. The accuracy of the reconstruction, after the details are discarded, is dependent on the wavelets chosen from the wavelet basis library. The system developed in this thesis takes a 2-D image and decomposes it using a wavelet bank. A digital signal processor is used to achieve near real-time performance in this transformation task. A contribution of this thesis project is the development of DSP-based test bed for the future development of new real-time wavelet transformation algorithms.

As relações da tríplice hélice nas fundações de apoio brasileiras: uma avaliação da dinâmica de captação de recursos para projetos de inovação

Foundations support constitute one of the types of legal entities of private law forged with the purpose of supporting research projects, education and extension and institutional, scientific and technological development of Brazil. Observed as links of the relationship between company, university, and government, foundations supporting emerge in the Brazilian scene from the principle to establish an economic platform of development based on three pillars: science, technology and innovation – ST&I. In applied terms, these ones operate as tools of debureaucratisation making the management between public entities more agile, especially in the academic management in accordance with the approach of Triple Helix. From the exposed, the present study has as purpose understanding how the relation of Triple Helix intervenes in the fund-raising process of Brazilian foundations support. To understand the relations submitted, it was used the interaction models University-Company-Government recommended by Sábato and Botana (1968), the approach of the Triple Helix proposed by Etzkowitz and Leydesdorff (2000), as well as the perspective of the national innovation systems discussed by Freeman (1987, 1995), Nelson (1990, 1993) and Lundvall (1992). The research object of this study consists of 26 state foundations that support research associated with the National Council of the State Foundations of Supporting Research - CONFAP, as well as the 102 foundations in support of IES associated with the National Council of Foundations of Support for Institutions of Higher Education and Scientific and Technological Research – CONFIES, totaling 128 entities. As a research strategy, this study is considered as an applied research with a quantitative approach. Primary research data were collected using the e-mail Survey procedure. Seventy-five observations were collected, which corresponds to 58.59% of the research universe. It is considering the use of the bootstrap method in order to validate the use of the sample in the analysis of results. For data analysis, it was used descriptive statistics and multivariate data analysis techniques: the cluster analysis; the canonical correlation and the binary logistic regression. From the obtained canonical roots, the results indicated that the dependency relationship between the variables of relations (with the actors of the Triple Helix) and the financial resources invested in innovation projects is low, assuming the null hypothesis of this study, that the relations of the Triple Helix do not have interfered positively or negatively in raising funds for investments in innovation projects. On the other hand, the results obtained with the cluster analysis indicate that entities which have greater quantitative and financial amounts of projects are mostly large foundations (over 100 employees), which support up to five IES, publish management reports and use in their capital structure, greater financing of the public department. Finally, it is pertinent to note that the power of the classification of the logistic model obtained in this study showed high predictive capacity (80.0%) providing to the academic community replication in environments of similar analysis.

Sea surface and the sea floor regionalization of the Southern Ocean by multivariate cluster analysis, links to ArcGIS project files

This study subdivides the Weddell Sea, Antarctica, into seafloor regions using multivariate statistical methods. These regions are categories used for comparing, contrasting and quantifying biogeochemical processes and biodiversity between ocean regions geographically but also regions under development within the scope of global change. The division obtained is characterized by the dominating components and interpreted in terms of ruling environmental conditions. The analysis uses 28 environmental variables for the sea surface, 25 variables for the seabed and 9 variables for the analysis between surface and bottom variables. The data were taken during the years 1983-2013. Some data were interpolated. The statistical errors of several interpolation methods (e.g. IDW, Indicator, Ordinary and Co-Kriging) with changing settings have been compared for the identification of the most reasonable method. The multivariate mathematical procedures used are regionalized classification via k means cluster analysis, canonical-correlation analysis and multidimensional scaling. Canonical-correlation analysis identifies the influencing factors in the different parts of the cove. Several methods for the identification of the optimum number of clusters have been tested. For the seabed 8 and 12 clusters were identified as reasonable numbers for clustering the Weddell Sea. For the sea surface the numbers 8 and 13 and for the top/bottom analysis 8 and 3 were identified, respectively. Additionally, the results of 20 clusters are presented for the three alternatives offering the first small scale environmental regionalization of the Weddell Sea. Especially the results of 12 clusters identify marine-influenced regions which can be clearly separated from those determined by the geological catchment area and the ones dominated by river discharge.

Multivariate statistical analysis on the kaolinite/chlorite ratios from 20 South Atlantic sediment cores

Multivariate statistical analysis on the kaolinite/chlorite ratios from 20 South Atlantic sediment cores allowed for the extraction of two processes controlling the fluctuations of the kaolinite/chlorite ratio during the last 130,000 yrs, (1) the relative strength of North Atlantic Deep Water (NADW) inflow into the South Atlantic Ocean and (2) the influx of aeolian sediments from the south African continent. The NADW fluctuation can be traced in the entire deep South Atlantic while the dust signal is restricted to the vicinity of South Africa. Our data indicate that NADW formation underwent significant changes in response to glacial/interglacial climate changes with enhanced export to the Southern Hemisphere during interglacials. The most pronounced phases with Enhanced South African Dust Export (ESADE) occurred during cold Marine Isotope Stage (MIS) 5d and across the Late Glacial/Holocene transition from 16 ka to 4 ka (MIS 2 to 1). This particular pattern is attributed to the interaction of Antarctic Sea Ice extent, the position of the westerlies and the South African monsoon system.

Fitting model-based psychometric functions to simultaneity and temporal-order judgment data: MATLAB and R routines

Research on temporal-order perception uses temporal-order judgment (TOJ) tasks or synchrony judgment (SJ) tasks in their binary SJ2 or ternary SJ3 variants. In all cases, two stimuli are presented with some temporal delay, and observers judge the order of presentation. Arbitrary psychometric functions are typically fitted to obtain performance measures such as sensitivity or the point of subjective simultaneity, but the parameters of these functions are uninterpretable. We describe routines in MATLAB and R that fit model-based functions whose parameters are interpretable in terms of the processes underlying temporal-order and simultaneity judgments and responses. These functions arise from an independent-channels model assuming arrival latencies with exponential distributions and a trichotomous decision space. Different routines fit data separately for SJ2, SJ3, and TOJ tasks, jointly for any two tasks, or also jointly for the three tasks (for common cases in which two or even the three tasks were used with the same stimuli and participants). Additional routines provide bootstrap p-values and confidence intervals for estimated parameters. A further routine is included that obtains performance measures from the fitted functions. An R package for Windows and source code of the MATLAB and R routines are available as Supplementary Files.

New Advancements of Scalable Statistical Methods for Learning Latent Structures in Big Data

Constant technology advances have caused data explosion in recent years. Accord- ingly modern statistical and machine learning methods must be adapted to deal with complex and heterogeneous data types. This phenomenon is particularly true for an- alyzing biological data. For example DNA sequence data can be viewed as categorical variables with each nucleotide taking four different categories. The gene expression data, depending on the quantitative technology, could be continuous numbers or counts. With the advancement of high-throughput technology, the abundance of such data becomes unprecedentedly rich. Therefore efficient statistical approaches are crucial in this big data era.

Previous statistical methods for big data often aim to find low dimensional struc- tures in the observed data. For example in a factor analysis model a latent Gaussian distributed multivariate vector is assumed. With this assumption a factor model produces a low rank estimation of the covariance of the observed variables. Another example is the latent Dirichlet allocation model for documents. The mixture pro- portions of topics, represented by a Dirichlet distributed variable, is assumed. This dissertation proposes several novel extensions to the previous statistical methods that are developed to address challenges in big data. Those novel methods are applied in multiple real world applications including construction of condition specific gene co-expression networks, estimating shared topics among newsgroups, analysis of pro- moter sequences, analysis of political-economics risk data and estimating population structure from genotype data.

Methods for Imputing Missing Values and Synthesizing Confidential Values for Continuous and Magnitude Data

Abstract

Continuous variable is one of the major data types collected by the survey organizations. It can be incomplete such that the data collectors need to fill in the missingness. Or, it can contain sensitive information which needs protection from re-identification. One of the approaches to protect continuous microdata is to sum them up according to different cells of features. In this thesis, I represents novel methods of multiple imputation (MI) that can be applied to impute missing values and synthesize confidential values for continuous and magnitude data.

The first method is for limiting the disclosure risk of the continuous microdata whose marginal sums are fixed. The motivation for developing such a method comes from the magnitude tables of non-negative integer values in economic surveys. I present approaches based on a mixture of Poisson distributions to describe the multivariate distribution so that the marginals of the synthetic data are guaranteed to sum to the original totals. At the same time, I present methods for assessing disclosure risks in releasing such synthetic magnitude microdata. The illustration on a survey of manufacturing establishments shows that the disclosure risks are low while the information loss is acceptable.

The second method is for releasing synthetic continuous micro data by a nonstandard MI method. Traditionally, MI fits a model on the confidential values and then generates multiple synthetic datasets from this model. Its disclosure risk tends to be high, especially when the original data contain extreme values. I present a nonstandard MI approach conditioned on the protective intervals. Its basic idea is to estimate the model parameters from these intervals rather than the confidential values. The encouraging results of simple simulation studies suggest the potential of this new approach in limiting the posterior disclosure risk.

The third method is for imputing missing values in continuous and categorical variables. It is extended from a hierarchically coupled mixture model with local dependence. However, the new method separates the variables into non-focused (e.g., almost-fully-observed) and focused (e.g., missing-a-lot) ones. The sub-model structure of focused variables is more complex than that of non-focused ones. At the same time, their cluster indicators are linked together by tensor factorization and the focused continuous variables depend locally on non-focused values. The model properties suggest that moving the strongly associated non-focused variables to the side of focused ones can help to improve estimation accuracy, which is examined by several simulation studies. And this method is applied to data from the American Community Survey.

TEST BINARY IMPORT

Der Müller und die fünf Räuber, Überfall²³

Multivariate Techniques in Biomechanical Analysis

This work outlines the theoretical advantages of multivariate methods in biomechanical data, validates the proposed methods and outlines new clinical findings relating to knee osteoarthritis that were made possible by this approach. New techniques were based on existing multivariate approaches, Partial Least Squares (PLS) and Non-negative Matrix Factorization (NMF) and validated using existing data sets. The new techniques developed, PCA-PLS-LDA (Principal Component Analysis – Partial Least Squares – Linear Discriminant Analysis), PCA-PLS-MLR (Principal Component Analysis – Partial Least Squares –Multiple Linear Regression) and Waveform Similarity (based on NMF) were developed to address the challenging characteristics of biomechanical data, variability and correlation. As a result, these new structure-seeking technique revealed new clinical findings. The first new clinical finding relates to the relationship between pain, radiographic severity and mechanics. Simultaneous analysis of pain and radiographic severity outcomes, a first in biomechanics, revealed that the knee adduction moment’s relationship to radiographic features is mediated by pain in subjects with moderate osteoarthritis. The second clinical finding was quantifying the importance of neuromuscular patterns in brace effectiveness for patients with knee osteoarthritis. I found that brace effectiveness was more related to the patient’s unbraced neuromuscular patterns than it was to mechanics, and that these neuromuscular patterns were more complicated than simply increased overall muscle activity, as previously thought.

Compositional data analysis of Holocene sediments from the West Bengal Sundarbans, India: Geochemical proxies for grain-size variability in a delta environment

This paper is part of a special issue of Applied Geochemistry focusing on reliable applications of compositional multivariate statistical methods. This study outlines the application of compositional data analysis (CoDa) to calibration of geochemical data and multivariate statistical modelling of geochemistry and grain-size data from a set of Holocene sedimentary cores from the Ganges-Brahmaputra (G-B) delta. Over the last two decades, understanding near-continuous records of sedimentary sequences has required the use of core-scanning X-ray fluorescence (XRF) spectrometry, for both terrestrial and marine sedimentary sequences. Initial XRF data are generally unusable in ‘raw-format’, requiring data processing in order to remove instrument bias, as well as informed sequence interpretation. The applicability of these conventional calibration equations to core-scanning XRF data are further limited by the constraints posed by unknown measurement geometry and specimen homogeneity, as well as matrix effects. Log-ratio based calibration schemes have been developed and applied to clastic sedimentary sequences focusing mainly on energy dispersive-XRF (ED-XRF) core-scanning. This study has applied high resolution core-scanning XRF to Holocene sedimentary sequences from the tidal-dominated Indian Sundarbans, (Ganges-Brahmaputra delta plain). The Log-Ratio Calibration Equation (LRCE) was applied to a sub-set of core-scan and conventional ED-XRF data to quantify elemental composition. This provides a robust calibration scheme using reduced major axis regression of log-ratio transformed geochemical data. Through partial least squares (PLS) modelling of geochemical and grain-size data, it is possible to derive robust proxy information for the Sundarbans depositional environment. The application of these techniques to Holocene sedimentary data offers an improved methodological framework for unravelling Holocene sedimentation patterns.

Bayesian Dependence Tests for Continuous, Binary and Mixed Continuous-Binary Variables

Tests for dependence of continuous, discrete and mixed continuous-discrete variables are ubiquitous in science. The goal of this paper is to derive Bayesian alternatives to frequentist null hypothesis significance tests for dependence. In particular, we will present three Bayesian tests for dependence of binary, continuous and mixed variables. These tests are nonparametric and based on the Dirichlet Process, which allows us to use the same prior model for all of them. Therefore, the tests are “consistent” among each other, in the sense that the probabilities that variables are dependent computed with these tests are commensurable across the different types of variables being tested. By means of simulations with artificial data, we show the effectiveness of the new tests.

The use of binary polymeric networks in stabilising polyethylene oxide solid dispersions

The objective of this study was to determine if a high Tg polymer (Eudragit® S100) could be used to stabilize amorphous domains of polyethylene oxide (PEO) and hence improve the stability of binary polymer systems containing celecoxib (CX). We propose a novel method of stabilizing the amorphous PEO solid dispersion through inclusion of a miscible, high Tg polymer, namely, that can form strong inter-polymer interactions. The effects of inter-polymer interactions and miscibility between PEO and Eudragit S100 are considered. Polymer blends were first manufactured via hot-melt extrusion at different PEO/S100 ratios (70/30, 50/50, and 30/70 wt/wt). Differential scanning calorimetry and dynamic mechanical thermal analysis data suggested a good miscibility between PEO and S100 polymer blends, particularly at the 50/50 ratio. To further evaluate the system, CX/PEO/S100 ternary mixtures were extruded. Immediately after hot-melt extrusion, a single Tg that increased with increasing S100 content (anti-plasticization) was observed in all ternary systems. The absence of powder X-ray diffractometry crystalline Bragg’s peaks also suggested amorphization of CX. Upon storage (40°C/75% relative humidity), the formulation containing PEO/S100 at a ratio of 50:50 was shown to be most stable. Fourier transform infrared studies confirmed the presence of hydrogen bonding between Eudragit S100 and PEO suggesting this was the principle reason for stabilization of the amorphous CX/PEO solid dispersion system.