A multi-level ecological model of psychotropic prescribing to adults with intellectual disability

Purpose – Simple linear accounts of prescribing do not adequately address reasons “why” doctors prescribe psychotropic medication to people with intellectual disability (ID). Greater understanding of the complex array of factors that influence decisions to prescribe is needed. Design/methodology/approach – After consideration of a number of conceptual frameworks that have potential to better understand prescribing of psychotropic medication to adults with ID, an ecological model of prescribing was developed. A case study is used to outline how the model can provide greater understanding of prescribing processes. Findings – The model presented aims to consider the complexity and multi-dimensional nature of community-based psychotropic prescribing to adults with ID. The utility of the model is illustrated through a consideration of the case study. Research limitations/implications – The model presented is conceptual and is as yet untested. Practical implications – The model presented aims to capture the complexity and multi-dimensional nature of community-based psychotropic prescribing to adults with ID. The model may provide utility for clinicians and researchers as they seek clarification of prescribing decisions. Originality/value – The paper adds valuable insight into factors influencing psychotropic prescribing to adults with ID. The ecological model of prescribing extends traditional analysis that focuses on patient characteristics and introduces multi-level perspectives that may provide utility for clinicians and researchers.

Nonlinear Dynamics of Rotating Multi-Component Pair Plasmas and e-p-i Plasmas

The propagation of small amplitude stationary profile nonlinear electrostatic excitations in a pair plasma is investigated, mainly drawing inspiration from experiments on fullerene pair-ion plasmas. Two distinct pair ion species are considered of opposite polarity and same mass, in addition to a massive charged background species, which is assumed to be stationary, given the frequency scale of interest. In the pair-ion context, the third species is thought of as a background defect (e.g. charged dust) component. On the other hand, the model also applies formally to electron-positron-ion (e-p-i) plasmas, if one neglects electron-positron annihilation. A two-fluid plasma model is employed, incorporating both Lorentz and Coriolis forces, thus taking into account the interplay between the gyroscopic (Larmor) frequency ?c and the (intrinsic) plasma rotation frequency O0. By employing a multi-dimensional reductive perturbation technique, a Zakharov-Kuznetsov (ZK) type equation is derived for the evolution of the electric potential perturbation. Assuming an arbitrary direction of propagation, with respect to the magnetic field, we derive the exact form of nonlinear solutions, and study their characteristics. A parametric analysis is carried out, as regards the effect of the dusty plasma composition (background number density), species temperature(s) and the relative strength of rotation to Larmor frequencies. It is shown that the Larmor and mechanical rotation affect the pulse dynamics via a parallel-to-transverse mode coupling diffusion term, which in fact diverges at ?c ? ±2O0. Pulses collapse at this limit, as nonlinearity fails to balance dispersion. The analysis is complemented by investigating critical plasma compositions, in fact near-symmetric (T- ˜ T+) “pure” (n- ˜ n+) pair plasmas, i.e. when the concentration of the 3rd background species is negligible, case in which the (quadratic) nonlinearity vanishes, so one needs to resort to higher order nonlinear theory. A modified ZK equation is derived and analyzed. Our results are of relevance in pair-ion (fullerene) experiments and also potentially in astrophysical environments, e.g. in pulsars.

Multi-Modal CTL:Completeness, Complexity, and an Application

We define a multi-modal version of Computation Tree Logic (ctl) by extending the language with path quantifiers E and A where d denotes one of finitely many dimensions, interpreted over Kripke structures with one total relation for each dimension. As expected, the logic is axiomatised by taking a copy of a ctl axiomatisation for each dimension. Completeness is proved by employing the completeness result for ctl to obtain a model along each dimension in turn. We also show that the logic is decidable and that its satisfiability problem is no harder than the corresponding problem for ctl. We then demonstrate how Normative Systems can be conceived as a natural interpretation of such a multi-dimensional ctl logic. © 2009 Springer Science+Business Media B.V.

Lower bounds for stability margin of n-dimensional discrete systems

This paper derives lower bounds for the stability margin of n-dimensional discrete systems in the Roesser’s state space setting. The lower bounds for stability margin are derived based on the MacLaurine series expansion. Numerical examples are given to illustrate the results.

Multi-type maltreatment : relationships between familial characteristics, maltreatment and adjustment of children and adults

This thesis examines the nature, extent and impact of multiple forms of maltreatment (multi-type maltreatment) from within a developmental victimological framework. The interrelationships between sexual abuse, physical abuse, psychological maltreatment, neglect, and witnessing family violence are assessed. The role of family variables in predicting maltreatment and the relative contribution of child maltreatment and family variables to adjustment are evaluated. Risk factors for multi-type maltreatment, and the relationship between multi-type maltreatment and adjustment are explored. The major theories of child development are reviewed. As well as exploring the relevance of developmental theories to understanding the impact of child maltreatment, factors influencing the emergence of child psychopathology are reviewed from a developmental psychopathology perspective. Ecological and developmental perspectives on how child maltreatment translates into the behavioural and emotional adjustment problems of children are integrated in the Child Maltreatment: Risk and Protection Model. After exploring some of the relevant conceptual issues, the literature on the prevalence and impact of each maltreatment type is reviewed, and the literature on multi-type maltreatment critiqued. Methodological and ethical concerns with the conduct of research in the field of child maltreatment using direct assessment of children led to the need for an instrument to assess parent perceptions of each of the types of abuse and neglect, as well as adult retrospective reports. Data are presented from two cross-sectional questionnaire-based studies using the Parent and Adult versions of the Family and Life Experiences Questionnaire which was designed to assess perceptions of children's experiences of sexual abuse, physical abuse, psychological maltreatment, neglect and witnessing family violence. Problems with the isolated focus of research on single forms of child maltreatment are addressed by the inclusion of each of these forms of child maltreatment within a single research design. Respondents for both studies were volunteers recruited from counselling agencies, medical, community health, child care and fitness centres and a first year psychology course. Parents (N=50) described their perceptions of primary school children's family characteristics, experiences of maltreatment and adjustment. Children's behavioural adjustment (internalising and externalising), sexual behaviours, emotions, self-esteem, gender identity, family adaptability and cohesion, parental traditionality, parental sexual punitiveness, interparental relationship satisfaction, and demographic characteristics are assessed in the study of Parents' perceptions. A large degree of overlap between the different types of abuse and neglect was found, with a high proportion of parents describing children's experiences of multiple forms of child maltreatment. Using both maltreatment and family characteristics to predict internalising behaviour problems, neglect and family cohesion were the only unique predictors. Family adaptability and cohesion were the only unique predictors of externalising behaviour problems. Physical and sexual abuse were not predicted from family characteristics; neglect was predicted, but no variables provided unique prediction. Unique predictors of psychological maltreatment were family cohesion, parental sexual punitiveness and divorce. Divorce was the only variable with significant unique prediction of the child witnessing family violence. Family background and family functioning were found to predict some forms of maltreatment, but to also be important factors mediating the adjustment of children, independent of maltreatment. The results are interpreted within an ecological framework, integrating risk factors for maltreatment with experiences of abuse and neglect and subsequent adjustment in childhood. Retrospective reports of adults' (N=175) own childhood family characteristics, experiences of maltreatment, and reports of their current adjustment are also studied. Included with the adult version of the FLEQ were the Trauma Symptom Checklist-40, Rosenberg's Self-esteem Scale, and the Family and Adaptability and Cohesion Evaluation Scale-II. Similar results were found in the in the Adult Study. As hypothesised, adult retrospective reports of the five different types of child maltreatment were found to be highly intercorrelated. Family characteristics predicted maltreatment and adjustment scores and discriminated between single and multi-type maltreatment. Maltreatment scores also predicted adult adjustment. As the number of maltreatment types increased, there was an increase in the number of adjustment problems reported. Alternate hypotheses regarding the possible operation of mediating and moderating processes in the relationships between family characteristics, maltreatment and the adjustment of adults were assessed. Finally, the results of these investigations are discussed and interpreted in the light of extant findings previously reviewed. Data from the two major empirical studies are used to demonstrate the overlap between different child maltreatment categories, and the extent and impact of multi-type maltreatment. The results show that children are vulnerable to more than one type of maltreatment. Individuals who experience a number of different forms of maltreatment had greater adjustment problems than those experiencing only one or two different types of abuse or neglect. Dysfunctional families place children at risk of child maltreatment. Negative family characteristics lead to adjustment problems in children and adults. The type of maltreatment having the most damaging effect on children was neglect, and in the long-term, sexual abuse. A multi-dimensional approach to prevention and intervention needs to be adopted, based on the co-morbidity of maltreatment types, and the likelihood of children experiencing further abuse or neglect of a different type. Dysfunctional family dynamics which place children at risk of multi-type maltreatment, and mediate the effects of maltreatment on adjustment, need to be specifically targeted with support and family intervention strategies. Risk-assessment measures used by Child Protection workers must include adequate knowledge of the inter-relationships between maltreatment types, and the particularly negative impact on adjustment of experiencing many forms of abuse or neglect. Suggestions for future clinical and research work in the area of child maltreatment are developed. The importance of assessing all forms of maltreatment when examining the relationships of maltreatment to adjustment is emphasised. It is recommended that prevention and intervention strategies acknowledge the interrelationships between maltreatment types.

The dance of trust in high risk multi-partner collaborations : an exploration of the antecedents of trust in cross-sector R&D projects

This paper explores the dance of trust in cross-sector R&D collaborations, and does so by drawing on a multi-method study (involving qualitative research, case studies and a survey of project leaders) of the Australian Cooperative Research Centre (CRC) Program. R&D collaborations formed under this Program involve two main types of risk, venture or performance-related and relational, and these are problematic given power, information and risk impact asymmetries among the partners. Within the CRCs these risks are addressed through formalisation, employing the “right people”, and through relationship building. Trust (theorised following Sako as a multi-dimensional construct) is central to these processes. Where trust is formed among CRC participants, and is reinforced over repeated interactions, then relational and performance risks cease to become a major concern for CRC managers. In the CRCs, trust is formed and sustained as a multi-level process. Engagement and relationship commitment is achieved at an organizational level, and partner reputation, credible commitments and the institutional context are important factors here. At the project level, trust, in conjunction with a task focus, leads to a positive collaboration experience and this is associated with positive project outcomes. Within CRC projects, capabilities for communication and cross-sector management are important for the formation and maintenance of trust. The paper concludes by discussing the managerial implications of the study’s findings and by identifying areas for further research and conceptual development.

Evaluation of housing developments for sustainability using a multi-criteria approach

New housing developments in Australia, particularly on Greenfield sites on the edge of existing urban centres, need to be sustainable in environmental and social terms if long term problems are to be avoided. Sustainability is multi-dimensional and existing analyses have been found to be inadequate in assessing housing developments holistically. This paper describes research which has used five criteria (energy use, resource use, neighbourhood character, neighbourhood connectivity and diversity), representing 31 indicators, to assess three housing precincts of a regional city in southern Australia. The method has been found to produce useful assessments of sustainability. The method has the potential to inform future housing developments and to be used to improve existing suburbs.

Um modelo categorizador intervalar n-dimensional com l-camadas baseado em tesselações

O ICTM (Interval Categorizer Tesselation Model), objeto da presente tese, é um modelo geral para análise de espaços de natureza geométrica, baseado em tesselaçoes, que é capaz de produzir uma categorização confiável de conjunto de pontos de um dado espaço, de acordo com múltiplas características dos pontos, cada característica correspondendo a uma camada do modelo. Por exemplo, na análise de terrenos geográficos, uma região geográfica pode ser analisada de acordo com a sua topografia, vegetaçao, demografia, dados econômicos etc, cada uma gerando uma subdivisão diferente da região. O modelo geral baseado em tesselações não está restrito, porém, a análise de espaços bi-dimensionais. O conjunto dos pontos analisados pode pertencer a um espaço multidimensional, determinando a característica multi-dimensional de cada camada. Um procedimento de projeção das categorizações obtidas em cada camada sobre uma camada básica leva a uma categorização confiavel mais significante, que combina em uma só classificação as análises obtidas para cada característica. Isto permite muitas análises interessantes no que tange a dependência mútua das características. A dimensão da tesselação pode ser arbitrária ou escolhida de acordo com algum critério específico estabelecido pela aplicação. Neste caso, a categorização obtida pode ser refinada, ou pela re-definição da dimensão da tesselação ou tomando cada sub-região resultante para ser analisada separadamente A formalização nos registradores pode ser facilmente recuperada apenas pela indexação dos elementos das matrizes, em qualquer momento da execução. A implementação do modelo é naturalmente paralela, uma vez que a análise é feita basicamente por regras locais. Como os dados de entrada numéricos são usualmente suscetíveis a erros, o modelo utiliza a aritmética intervalar para se ter um controle automático de erros. O modelo ICTM também suporta a extração de fatos sobre as regiões de modo qualitativo, por sentenças lógicas, ou quantitativamente, pela análise de probabilidade. Este trabalho recebe apoio nanceiro do CNPq/CTPETRO e FAPERGS.

The influence of the supply chain agents on the new product development's performance: an analysis based on the multi-group moderation

Este estudo buscou verificar a influencia dos agentes da cadeia de suprimentos no desempenho do desenvolvimento de novos produtos quando os agentes são analisados em conjunto. A motivação desta pesquisa veio de estudos que alertaram para a consideração da integração da cadeia de suprimentos como um constructo multidimensional, englobando o envolvimento da manufatura, fornecedores e clientes no desenvolvimento de novos produtos; e devido à falta de informação sobre as influencias individuais destes agentes no desenvolvimento de novos produtos. Sob essas considerações, buscou-se construir um modelo analítico baseado na Teoria do Capital Social e Capacidade Absortiva, construir hipóteses a partir da revisão da literatura e conectar constructos como cooperação, envolvimento do fornecedor no desenvolvimento de novos produtos (DNP), envolvimento do cliente no DNP, envolvimento da manufatura no DNP, antecipação de novas tecnologias, melhoria contínua, desempenho operacional do DNP, desempenho de mercado do NPD e desempenho de negócio do DNP. Para testar as hipóteses foram consideradas três variáveis moderadoras, tais como turbulência ambiental (baixa, média e alta), indústria (eletrônicos, maquinários e equipamentos de transporte) e localização (América, Europa e Ásia). Para testar o modelo foram usados dados do projeto High Performance Manufacturing que contém 339 empresas das indústrias de eletrônicos, maquinários e equipamentos de transporte, localizadas em onze países. As hipóteses foram testadas por meio da Análise Fatorial Confirmatória (AFC) incluindo a moderação muti-grupo para as três variáveis moderadoras mencionadas anteriormente. Os principais resultados apontaram que as hipóteses relacionadas com cooperação foram confirmadas em ambientes de média turbulência, enquanto as hipóteses relacionadas ao desempenho no DNP foram confirmadas em ambientes de baixa turbulência ambiental e em países asiáticos. Adicionalmente, sob as mesmas condições, fornecedores, clientes e manufatura influenciam diferentemente no desempenho de novos produtos. Assim, o envolvimento de fornecedores influencia diretamente no desempenho operacional e indiretamente no desempenho de mercado e de negócio em baixos níveis de turbulência ambiental, na indústria de equipamentos de transporte em países da Americanos e Europeus. De igual forma, o envolvimento do cliente influenciou diretamente no desempenho operacional e indiretamente no desempenho de mercado e do negócio em médio nível de turbulência ambiental, na indústria de maquinários e em países Asiáticos. Fornecedores e clientes não influenciam diretamente no desempenho de mercado e do negócio e não influenciam indiretamente no desempenho operacional. O envolvimento da manufatura não influenciou nenhum tipo de desempenho do desenvolvimento de novos produtos em todos os cenários testados.

Multi-objective Estimation of Distribution Algorithm Based on Joint Modeling of Objectives and Variables

This paper proposes a new multi-objective estimation of distribution algorithm (EDA) based on joint modeling of objectives and variables. This EDA uses the multi-dimensional Bayesian network as its probabilistic model. In this way it can capture the dependencies between objectives, variables and objectives, as well as the dependencies learnt between variables in other Bayesian network-based EDAs. This model leads to a problem decomposition that helps the proposed algorithm to find better trade-off solutions to the multi-objective problem. In addition to Pareto set approximation, the algorithm is also able to estimate the structure of the multi-objective problem. To apply the algorithm to many-objective problems, the algorithm includes four different ranking methods proposed in the literature for this purpose. The algorithm is applied to the set of walking fish group (WFG) problems, and its optimization performance is compared with an evolutionary algorithm and another multi-objective EDA. The experimental results show that the proposed algorithm performs significantly better on many of the problems and for different objective space dimensions, and achieves comparable results on some compared with the other algorithms.

Regularized model learning in EDAs for continuous and multi-objective optimization

Probabilistic modeling is the de�ning characteristic of estimation of distribution algorithms (EDAs) which determines their behavior and performance in optimization. Regularization is a well-known statistical technique used for obtaining an improved model by reducing the generalization error of estimation, especially in high-dimensional problems. `1-regularization is a type of this technique with the appealing variable selection property which results in sparse model estimations. In this thesis, we study the use of regularization techniques for model learning in EDAs. Several methods for regularized model estimation in continuous domains based on a Gaussian distribution assumption are presented, and analyzed from di�erent aspects when used for optimization in a high-dimensional setting, where the population size of EDA has a logarithmic scale with respect to the number of variables. The optimization results obtained for a number of continuous problems with an increasing number of variables show that the proposed EDA based on regularized model estimation performs a more robust optimization, and is able to achieve signi�cantly better results for larger dimensions than other Gaussian-based EDAs. We also propose a method for learning a marginally factorized Gaussian Markov random �eld model using regularization techniques and a clustering algorithm. The experimental results show notable optimization performance on continuous additively decomposable problems when using this model estimation method. Our study also covers multi-objective optimization and we propose joint probabilistic modeling of variables and objectives in EDAs based on Bayesian networks, speci�cally models inspired from multi-dimensional Bayesian network classi�ers. It is shown that with this approach to modeling, two new types of relationships are encoded in the estimated models in addition to the variable relationships captured in other EDAs: objectivevariable and objective-objective relationships. An extensive experimental study shows the e�ectiveness of this approach for multi- and many-objective optimization. With the proposed joint variable-objective modeling, in addition to the Pareto set approximation, the algorithm is also able to obtain an estimation of the multi-objective problem structure. Finally, the study of multi-objective optimization based on joint probabilistic modeling is extended to noisy domains, where the noise in objective values is represented by intervals. A new version of the Pareto dominance relation for ordering the solutions in these problems, namely �-degree Pareto dominance, is introduced and its properties are analyzed. We show that the ranking methods based on this dominance relation can result in competitive performance of EDAs with respect to the quality of the approximated Pareto sets. This dominance relation is then used together with a method for joint probabilistic modeling based on `1-regularization for multi-objective feature subset selection in classi�cation, where six di�erent measures of accuracy are considered as objectives with interval values. The individual assessment of the proposed joint probabilistic modeling and solution ranking methods on datasets with small-medium dimensionality, when using two di�erent Bayesian classi�ers, shows that comparable or better Pareto sets of feature subsets are approximated in comparison to standard methods.

Methods of visualization and analysis of cardiac depolarization in the three dimensional space

The master thesis presents methods for intellectual analysis and visualization 3D EKG in order to increase the efficiency of ECG analysis by extracting additional data. Visualization is presented as part of the signal analysis tasks considered imaging techniques and their mathematical description. Have been developed algorithms for calculating and visualizing the signal attributes are described using mathematical methods and tools for mining signal. The model of patterns searching for comparison purposes of accuracy of methods was constructed, problems of a clustering and classification of data are solved, the program of visualization of data is also developed. This approach gives the largest accuracy in a task of the intellectual analysis that is confirmed in this work. Considered visualization and analysis techniques are also applicable to the multi-dimensional signals of a different kind.

An adaptive and dynamic dimensionality reduction method for high-dimensional indexing

The notorious "dimensionality curse" is a well-known phenomenon for any multi-dimensional indexes attempting to scale up to high dimensions. One well-known approach to overcome degradation in performance with respect to increasing dimensions is to reduce the dimensionality of the original dataset before constructing the index. However, identifying the correlation among the dimensions and effectively reducing them are challenging tasks. In this paper, we present an adaptive Multi-level Mahalanobis-based Dimensionality Reduction (MMDR) technique for high-dimensional indexing. Our MMDR technique has four notable features compared to existing methods. First, it discovers elliptical clusters for more effective dimensionality reduction by using only the low-dimensional subspaces. Second, data points in the different axis systems are indexed using a single B+-tree. Third, our technique is highly scalable in terms of data size and dimension. Finally, it is also dynamic and adaptive to insertions. An extensive performance study was conducted using both real and synthetic datasets, and the results show that our technique not only achieves higher precision, but also enables queries to be processed efficiently. Copyright Springer-Verlag 2005

A Limit Theorem for Multi-Type Subcritical Age-Dependent Branching Processes with two Types of Immigration

Марусия Н. Славчова-Божкова - В настоящата работа се обобщава една гранична теорема за докритичен многомерен разклоняващ се процес, зависещ от възрастта на частиците с два типа имиграция. Целта е да се обобщи аналогичен резултат в едномерния случай като се прилагат “coupling” метода, теория на възстановяването и регенериращи процеси.

A Multi-View Learning Algorithm

Report published in the Proceedings of the National Conference on "Education and Research in the Information Society", Plovdiv, May, 2014