Two-component model for the interstellar scattering in direction to the Vela pulsar

This paper discusses a rigorous treatment of the refractive scintillation of pulsar PSR B0833-45 caused by a two-component interstellar scattering medium. It is assumed that the interstellar scattering medium is composed of a thin screen ISM and an extended interstellar medium. We consider that the scattering of the thin screen concentrates in a thin layer presented by a delta function distribution and that the scattering density of the extended irregular medium satisfies the Gaussian distribution. We investigate and develop equations for the flux density structure function corresponding to this two-component ISM geometry in the scattering density distribution and compare our result with that of the Vela pulsar observations. We conclude that the refractive scintillation caused by this two-component ISM scattering gives a more satisfactory explanation for the observed flux density variation of the Vela pulsar than does the single extended medium model. The level of refractive scintillation is strongly sensitive to the distribution of scattering material along the line of sight. The logarithmic slope of the structure function is sensitive to thin screen location and is relatively insensitive to the scattering strength of the thin screen medium. Therefore, the proposed model can be applied to interpret the structure function of flux density observed in pulsar PSR B0833-45. The result suggests that the medium consists of a discontinuous distribution of plasma turbulence embedded in the Vela supernova remnant. Thus our work provides some insight into the distribution of the scattering along the line of sight to the Vela pulsar.

A reconfigurable component model using reflection

Providing a method of transparent communication and interoperation between distributed software is a requirement for many organisations and several standard and non-standard infrastructures exist for this purpose. Component models do more than just provide a plumbing mechanism for distributed applications, they provide a more controlled interoperation between components. There are very few component models however that have support for advanced dynamic reconfigurability. This paper describes a component model which provides controlled and constrained transparent communication and inter-operation between components in the form of a hierarchical component model. At the same time, the model contains support for advanced run-time reconfigurability of components. The process and benefits of designing a system using the presented model are discussed. A way in which reflective techniques and component frameworks can work together to produce dynamic adaptable systems is explained.

Influence of light in the mixed-layer on the parameters of a three-component model of phytoplankton size class

Phytoplankton size structure is an important indicator of the state of the pelagic ecosystem. Stimulated by the paucity of in situ observations on size structure, and by the sampling advantages of autonomous remote platforms, new efforts are being made to infer the size-structure of the phytoplankton from oceanographic variables that may be measured at high temporal and spatial resolution, such as total chlorophyll concentration. Large-scale analysis of in situ data has revealed coherent relationships between size-fractionated chlorophyll and total chlorophyll that can be quantified using the three-component model of Brewin et al. (2010). However, there are variations surrounding these general relationships. In this paper, we first revise the three-component model using a global dataset of surface phytoplankton pigment measurements. Then, using estimates of the average irradiance in the mixed-layer, we investigate the influence of ambient light on the parameters of the three-component model. We observe significant relationships between model parameters and the average irradiance in the mixed-layer, consistent with ecological knowledge. These relationships are incorporated explicitly into the three-component model to illustrate variations in the relationship between size-structure and total chlorophyll, ensuing from variations in light availability. The new model may be used as a tool to investigate modifications in size-structure in the context of a changing climate.

A shared care model pilot for palliative home care in a rural area:Impact on symptoms, distress, and place of death

Context: Shared care models integrating family physician services with interdisciplinary palliative care specialist teams are critical to improve access to quality palliative home care and address multiple domains of end-of-life issues and needs. Objectives: To examine the impact of a shared care pilot program on the primary outcomes of symptom severity and emotional distress (patient and family separately) over time and, secondarily, the concordance between patient preferences and place of death. Methods: An inception cohort of patients (n = 95) with advanced, progressive disease, expected to die within six months, were recruited from three rural family physician group practices (21 physicians) and followed prospectively until death or pilot end. Serial measurement of symptoms, emotional distress (patient and family), and preferences for place of death was performed, with analysis of changes in distress outcomes assessed using t-tests and general linear models. Results: Symptoms trended toward improvement, with a significant reduction in anxiety from baseline to 14 days noted. Symptom and emotional distress were maintained below high severity (7-10), and a high rate of home death compared with population norms was observed. Conclusion: Future controlled studies are needed to examine outcomes for shared care models with comparison groups. Shared care models build on family physician capacity and as such are promising in the development of palliative home care programs to improve access to quality palliative home care and foster health system integration. © 2011 U.S. Cancer Pain Relief Committee. Published by Elsevier Inc. All rights reserved.

Enhancing family physician capacity to deliver quality palliative home care:An end-of-life, shared-care model

PROBLEM BEING ADDRESSED: Family physicians face innumerable challenges to delivering quality palliative home care to meet the complex needs of end-of-life patients and their families. OBJECTIVE OF PROGRAM: To implement a model of shared care to enhance family physicians' ability to deliver quality palliative home care, particularly in a community-based setting. PROGRAM DESCRIPTION: Family physicians in 3 group practices (N = 21) in Ontario's Niagara West region collaborated with an interprofessional palliative care team (including a palliative care advanced practice nurse, a palliative medicine physician, a bereavement counselor, a psychosocial-spiritual advisor, and a case manager) in a shared-care partnership to provide comprehensive palliative home care. Key features of the program included systematic and timely identification of end-of-life patients, needs assessments, symptom and psychosocial support interventions, regular communication between team members, and coordinated care guided by outcome-based assessment in the home. In addition, educational initiatives were provided to enhance family physicians' knowledge and skills. CONCLUSION: Because of the program, participants reported improved communication, effective interprofessional collaboration, and the capacity to deliver palliative home care, 24 hours a day, 7 days a week, to end-of-life patients in the community.

Artificial cognitive control system based on the shared circuits model of sociocognitive capacities. A first approach

sharedcircuitmodels is presented in this work. The sharedcircuitsmodelapproach of sociocognitivecapacities recently proposed by Hurley in The sharedcircuitsmodel (SCM): how control, mirroring, and simulation can enable imitation, deliberation, and mindreading. Behavioral and Brain Sciences 31(1) (2008) 1–22 is enriched and improved in this work. A five-layer computational architecture for designing artificialcognitivecontrolsystems is proposed on the basis of a modified sharedcircuitsmodel for emulating sociocognitive experiences such as imitation, deliberation, and mindreading. In order to show the enormous potential of this approach, a simplified implementation is applied to a case study. An artificialcognitivecontrolsystem is applied for controlling force in a manufacturing process that demonstrates the suitability of the suggested approach

A semantic web environment for component model

Component-based development (CBD) has become an important emerging topic in the software engineering field. It promises long-sought-after benefits such as increased software reuse, reduced development time to market and, hence, reduced software production cost. Despite the huge potential, the lack of reasoning support and development environment of component modeling and verification may hinder its development. Methods and tools that can support component model analysis are highly appreciated by industry. Such a tool support should be fully automated as well as efficient. At the same time, the reasoning tool should scale up well as it may need to handle hundreds or even thousands of components that a modern software system may have. Furthermore, a distributed environment that can effectively manage and compose components is also desirable. In this paper, we present an approach to the modeling and verification of a newly proposed component model using Semantic Web languages and their reasoning tools. We use the Web Ontology Language and the Semantic Web Rule Language to precisely capture the inter-relationships and constraints among the entities in a component model. Semantic Web reasoning tools are deployed to perform automated analysis support of the component models. Moreover, we also proposed a service-oriented architecture (SOA)-based semantic web environment for CBD. The adoption of Semantic Web services and SOA make our component environment more reusable, scalable, dynamic and adaptive.

A component model of methods for evaluation of the quality in education

Report published in the Proceedings of the National Conference on "Education in the Information Society", Plovdiv, May, 2013

Addressing issues in sparseness, ecological bias and formulation of the adjacency matrix in Bayesian spatio-temporal analysis of disease counts

The main objective of this PhD was to further develop Bayesian spatio-temporal models (specifically the Conditional Autoregressive (CAR) class of models), for the analysis of sparse disease outcomes such as birth defects. The motivation for the thesis arose from problems encountered when analyzing a large birth defect registry in New South Wales. The specific components and related research objectives of the thesis were developed from gaps in the literature on current formulations of the CAR model, and health service planning requirements. Data from a large probabilistically-linked database from 1990 to 2004, consisting of fields from two separate registries: the Birth Defect Registry (BDR) and Midwives Data Collection (MDC) were used in the analyses in this thesis. The main objective was split into smaller goals. The first goal was to determine how the specification of the neighbourhood weight matrix will affect the smoothing properties of the CAR model, and this is the focus of chapter 6. Secondly, I hoped to evaluate the usefulness of incorporating a zero-inflated Poisson (ZIP) component as well as a shared-component model in terms of modeling a sparse outcome, and this is carried out in chapter 7. The third goal was to identify optimal sampling and sample size schemes designed to select individual level data for a hybrid ecological spatial model, and this is done in chapter 8. Finally, I wanted to put together the earlier improvements to the CAR model, and along with demographic projections, provide forecasts for birth defects at the SLA level. Chapter 9 describes how this is done. For the first objective, I examined a series of neighbourhood weight matrices, and showed how smoothing the relative risk estimates according to similarity by an important covariate (i.e. maternal age) helped improve the model’s ability to recover the underlying risk, as compared to the traditional adjacency (specifically the Queen) method of applying weights. Next, to address the sparseness and excess zeros commonly encountered in the analysis of rare outcomes such as birth defects, I compared a few models, including an extension of the usual Poisson model to encompass excess zeros in the data. This was achieved via a mixture model, which also encompassed the shared component model to improve on the estimation of sparse counts through borrowing strength across a shared component (e.g. latent risk factor/s) with the referent outcome (caesarean section was used in this example). Using the Deviance Information Criteria (DIC), I showed how the proposed model performed better than the usual models, but only when both outcomes shared a strong spatial correlation. The next objective involved identifying the optimal sampling and sample size strategy for incorporating individual-level data with areal covariates in a hybrid study design. I performed extensive simulation studies, evaluating thirteen different sampling schemes along with variations in sample size. This was done in the context of an ecological regression model that incorporated spatial correlation in the outcomes, as well as accommodating both individual and areal measures of covariates. Using the Average Mean Squared Error (AMSE), I showed how a simple random sample of 20% of the SLAs, followed by selecting all cases in the SLAs chosen, along with an equal number of controls, provided the lowest AMSE. The final objective involved combining the improved spatio-temporal CAR model with population (i.e. women) forecasts, to provide 30-year annual estimates of birth defects at the Statistical Local Area (SLA) level in New South Wales, Australia. The projections were illustrated using sixteen different SLAs, representing the various areal measures of socio-economic status and remoteness. A sensitivity analysis of the assumptions used in the projection was also undertaken. By the end of the thesis, I will show how challenges in the spatial analysis of rare diseases such as birth defects can be addressed, by specifically formulating the neighbourhood weight matrix to smooth according to a key covariate (i.e. maternal age), incorporating a ZIP component to model excess zeros in outcomes and borrowing strength from a referent outcome (i.e. caesarean counts). An efficient strategy to sample individual-level data and sample size considerations for rare disease will also be presented. Finally, projections in birth defect categories at the SLA level will be made.

Inferring lung cancer risk factor patterns through joint Bayesian spatio-temporal analysis

Background: Preventing risk factor exposure is vital to reduce the high burden from lung cancer. The leading risk factor for developing lung cancer is tobacco smoking. In Australia, despite apparent success in reducing smoking prevalence, there is limited information on small area patterns and small area temporal trends. We sought to estimate spatio-temporal patterns for lung cancer risk factors using routinely collected population-based cancer data. Methods: The analysis used a Bayesian shared component spatio-temporal model, with male and female lung cancer included separately. The shared component reflected exposure to lung cancer risk factors, and was modelled over 477 statistical local areas (SLAs) and 15 years in Queensland, Australia. Analyses were also run adjusting for area-level socioeconomic disadvantage, Indigenous population composition, or remoteness. Results: Strong spatial patterns were observed in the underlying risk factor exposure for both males (median Relative Risk (RR) across SLAs compared to the Queensland average ranged from 0.48-2.00) and females (median RR range across SLAs 0.53-1.80), with high exposure observed in many remote areas. Strong temporal trends were also observed. Males showed a decrease in the underlying risk across time, while females showed an increase followed by a decrease in the final two years. These patterns were largely consistent across each SLA. The high underlying risk estimates observed among disadvantaged, remote and indigenous areas decreased after adjustment, particularly among females. Conclusion: The modelled underlying exposure appeared to reflect previous smoking prevalence, with a lag period of around 30 years, consistent with the time taken to develop lung cancer. The consistent temporal trends in lung cancer risk factors across small areas support the hypothesis that past interventions have been equally effective across the state. However, this also means that spatial inequalities have remained unaddressed, highlighting the potential for future interventions, particularly among remote areas.

Disappeared Persons and Homicide in El Salvador

During 2012-2013, the homicide rate in El Salvador came down from 69.9 to 42.2 per 100,000 population following a truce between the leaders of the two major gangs, “Mara Salvatrucha” and “Barrio 18”, and government. But despite the apparent successes of the truce, it was speculated that the drop in murders could have been due to the killers simply hid the bodies of their victims. This paper aims at determining whether gangs effectively disappeared their victims to cut down the official counts of murders, or they committed these crimes for other reasons. The results from this study suggest that Salvadoran gangs had been using disappearance as a resource to gain sustained social control among residents of already gang-dominated areas, that together with homicide, disappearance is part of a process of territorial spread and strategic strengthening by which these groups are enhancing their capabilities to interfere in the alliances of Mexican drug trafficking organizations with Central American criminal organizations specializing in the transshipment of drugs and in providing access to local markets to distribute and sell drugs. Our findings show that the risk for disappearance has been large even before the truce was in place and that actually, it continues as such and going through a process of geographic expansion.

A multi-component assessment model for monitoring training distress among athletes

We report the initial validation of a multi-component assessment model for monitoring training distress among athletes. The model combines measures of mood disturbance with measures of perceived stress and symptom intensity. Altogether, 492 athletes completed the 10-item Perceived Stress Scale (PSS-10; Cohen et al., 1983), the 24-item Brunel Mood Scale (BRUMS; Terry et al., 2003), and a checklist of 19 symptoms associated with acute overtraining (Fry et al., 1994). Six training distress factors were identified by an exploratory factor analysis: ‘‘depressed mood’’, ‘‘perceived vigour’’, ‘‘physical symptoms’’, ‘‘sleep disturbance’’, ‘‘perceived stress’’, and ‘‘general fatigue’’. Comparisons of group means of these factors with a previously validated inventory were consistent with theoretical predictions and provided evidence of construct validity. Internal consistency of the subscales was also confirmed, with Cronbach alphas ranging from 0.72 to 0.86. Together, these findings suggest that this multi-component model provides a sound conceptual basis for the assessment of training distress among athletes.