Biased selection within the social health insurance market in Colombia

Las regulaciones como primaje comunitario, paquetes estandarizados y afiliación abierta, orientadas a reducir el impacto de las fallas en los mercados de seguros, tienen un efecto limitado puesto que abren espacio a la selección sesgada. A partir de 1993, el sistema de seguridad social en salud en Colombia fue reformado hacia un enfoque de mercado con la expectativa de mejorar el desempeño de los monopolios preexistentes exponiéndolos a la competencia de nuevos entrantes. La hipótesis que se maneja en el trabajo es que las fallas de mercado pueden llevar a selección sesgada favoreciendo a los nuevos entrantes. Se analizaron dos encuestas de hogares utilizando el estado de salud auto reportado y la presencia de enfermedad crónica como indicadores prospectivos del riesgo de los afiliados. Se encuentra que hay selección sesgada, llevando a selección adversa entre los aseguradores preexistentes, y a selección favorable entre los nuevos entrantes. Este patrón se observa en 1997 y se incrementa en el 2003. Aunque las entidades preexistentes son entidades públicas, y su tamaño disminuyó sustancialmente entre estos años, se analizan sus implicaciones fiscales en términos de financiación adicional por parte del gobierno.

Detection of neural correlates of self-paced motor activity using empirical mode decomposition phase locking analysis

Transient episodes of synchronisation of neuronal activity in particular frequency ranges are thought to underlie cognition. Empirical mode decomposition phase locking (EMDPL) analysis is a method for determining the frequency and timing of phase synchrony that is adaptive to intrinsic oscillations within data, alleviating the need for arbitrary bandpass filter cut-off selection. It is extended here to address the choice of reference electrode and removal of spurious synchrony resulting from volume conduction. Spline Laplacian transformation and independent component analysis (ICA) are performed as pre-processing steps, and preservation of phase synchrony between synthetic signals. combined using a simple forward model, is demonstrated. The method is contrasted with use of bandpass filtering following the same preprocessing steps, and filter cut-offs are shown to influence synchrony detection markedly. Furthermore, an approach to the assessment of multiple EEG trials using the method is introduced, and the assessment of statistical significance of phase locking episodes is extended to render it adaptive to local phase synchrony levels. EMDPL is validated in the analysis of real EEG data, during finger tapping. The time course of event-related (de)synchronisation (ERD/ERS) is shown to differ from that of longer range phase locking episodes, implying different roles for these different types of synchronisation. It is suggested that the increase in phase locking which occurs just prior to movement, coinciding with a reduction in power (or ERD) may result from selection of the neural assembly relevant to the particular movement. (C) 2009 Elsevier B.V. All rights reserved.

Auto-weighted minimum-variance self-tuning controller

A self-tuning controller which automatically assigns weightings to control and set-point following is introduced. This discrete-time single-input single-output controller is based on a generalized minimum-variance control strategy. The automatic on-line selection of weightings is very convenient, especially when the system parameters are unknown or slowly varying with respect to time, which is generally considered to be the type of systems for which self-tuning control is useful. This feature also enables the controller to overcome difficulties with non-minimum phase systems.

Construction of supramolecular helices and breaking the helicity by forming supramolecular β-sheet structures using suitable self-assembling pseudopeptide building blocks

Bis-valine derivatives or malonamide (Guha,S.; Drew, M.G.B. Small 2008, 4, 1993-2005) and a bis-valine derivative of 1,1-cyclopropone dicarboxamide were used as building blocks for the construction of supramolecular helical structures. The six-membered intramolecular hydrogen-bonded scaffold is formed, and this acts as a unique supramolecular synthon for the construction of a pseudopeptide-based supramolecular helical structure. However, in absence of this intramolecular hydrogen bond. intermolecular hydrogen bonds are formed among the peptide strands. This leads to a supramolecular beta-sheet structure. Proper selection of the supramolecular synthon (six-membered intramolecular hydrogenbonded scaffold) promotes supramolecular helix formation, and a deviation from this molecular structure dictates the disruption of supramolecular helicity. In this study, six crystal structures have been used to demonstrate that a change in the central angle and/or the central core structure of dicarboxamides can be used to design either a supramolecular helix or a beta-sheet.

The influence of anxiety on the initial selection of emotional faces presented in binocular rivalry

Neurocognitive theories of anxiety predict that threat-related information can be evaluated before attentional selection, and can influence behaviour differentially in high anxious compared to low anxious individuals. We investigate this further by presenting emotional and neutral faces in an adapted binocular rivalry paradigm. We show that the initial selection of emotional faces presented in binocular rivalry is highly influenced by self-reported state and trait anxiety-level. Heightened anxiety was correlated with increased perception of angry and fearful faces, and decreased perception of happy expressions. These results are consistent with recent evidence of involuntary selection of threat in anxiety.

Likelihood estimate of treatment effects under selection bias

We consider methods for estimating causal effects of treatment in the situation where the individuals in the treatment and the control group are self selected, i.e., the selection mechanism is not randomized. In this case, simple comparison of treated and control outcomes will not generally yield valid estimates of casual effects. The propensity score method is frequently used for the evaluation of treatment effect. However, this method is based onsome strong assumptions, which are not directly testable. In this paper, we present an alternative modeling approachto draw causal inference by using share random-effect model and the computational algorithm to draw likelihood based inference with such a model. With small numerical studies and a real data analysis, we show that our approach gives not only more efficient estimates but it is also less sensitive to model misspecifications, which we consider, than the existing methods.

Self-reported conflict management competence among nursing students on the point of graduating and registered nurses with professional experience

Objective: It has been shown that specific competence is necessary for preventing and managing conflicts in healthcare settings. The aim of this descriptive and correlation study was to investigate and compare the self-reported conflict management competence (CMC) of nursing students who were on the point of graduating (NSPGs), and the CMC of registered nurses (RNs) with professional experience. Methods: The data collection, which consisted of soliciting answers to items measuring CMC in the Nurse Professional Competence (NPC) Scale, was performed as a purposive selection of 11 higher education institutions (HEIs) in Sweden. Three CMC items from the NPC Scale were answered by a total of 569 nursing students who were on the point of graduating and 227 RN registered nurses with professional experience. Results: No significant differences between NSPGs and RNs were found, and both groups showed a similar score pattern, with the lowest score for the item: “How do you perceive your ability to develop the group and strengthen competence in conflict management and problem-solving, based on knowledge of group dynamics?”. RNs with long professional experience (>24 months) rated their overall CMC as significantly better than RNs with short (<24 months) professional experience did (p = .05). NSPGs who had experience of international studies during their nursing education reported higher CMC, compared with those who did not have this experience (p = .03). RNs who reported a high degree of utilisation of CMC during the previous month scored higher regarding self-reported overall CMC (p < .0001). Conclusions: Experience of international studies during nursing education, or long professional experience, resulted in higher self-reported CMC. Hence, the CMC items in the NPC Scale can be suitable for identifying self-reported conflict management competence among NSPGs and RNs

An extended negative selection algorithm for anomaly detection

This paper proposes an extended negative selection algorithm for anomaly detection. Unlike previously proposed negative selection algorithms which do not make use of non-self data, the extended negative selection algorithm first acquires prior knowledge about the characteristics of the Problem space from the historial sample data by using machine learning techniques. Such data consists of both self data and non-self data. The acquired prior knowledge is represented in the form of production rules and thus viewed as common schemata which characterise the two subspaces: self-subspace and non-self-subspace, and provide important information to the generation of detection rules. One advantage of our approach is that it does not rely on the structured representation of the data and can be applied to general anomaly detection. To test the effectiveness, we test our approach through experiments with the public data set iris and KDDrsquo99 published data set.

Process improvement and ICTs in Australian SMEs: a selection and implementation framework

This article describes the development, modification and testing of a tool designed to assist small firms in making more appropriate decisions regarding information and communication technology (ICT) selection and implementation. Using a combination of quantitative and qualitative methods, a number of possible tools were initially developed to support firm-based self-diagnostic exercises. Research outcomes from a joint European–Australian research project were regionalised for Australian conditions through collaborative product development with a number of Australian SME manufacturing firms. This article reports on the pilot implementations and the outcomes achieved with these Australian SMEs. These implementations have shown successful outcomes for the trial SME participants and have led to the creation of an online self-assessment tool to allow wider access by interested SMEs

An efficient approach based on trust and reputation for secured selection of grid resources

Security is a principal concern in offering an infrastructure for the formation of general-purpose computational grids. A number of grid implementations have been devised to deal with the security concerns by authenticating the users, hosts and their interactions in an appropriate fashion. Resource management systems that are sophisticated and secured are inevitable for the efficient and beneficial deployment of grid computing services. The chief factors that can be problematic in the secured selection of grid resources are the wide range of selection and the high degree of strangeness. Moreover, the lack of a higher degree of confidence relationship is likely to prevent efficient resource allocation and utilisation. In this paper, we present an efficient approach for the secured selection of grid resources, so as to achieve secure execution of the jobs. This approach utilises trust and reputation for securely selecting the grid resources. To start with, the self-protection capability and reputation weightage of all the entities are computed, and based on those values, the trust factor (TF) of all the entities are determined. The reputation weightage of an entity is the measure of both the user’s feedback and other entities’ feedback. Those entities with higher TF values are selected for the secured execution of jobs. To make the proposed approach more comprehensive, a novel method is employed for evaluating the user’s feedback on the basis of the existing feedbacks available regarding the entities. This approach is proved to be scalable for an increased number of user jobs and grid entities. The experimentation portrays that this approach offers desirable efficiency in the secured selection of grid resources.

Self-assembly and morphology in double crystalline diblock copolymer/homopolymer blends with competitive hydrogen bonding

Block copolymers are of particular interest due to their ability to form a rich variety of nanostructures via self-assembly [1]. The self-assembly via competitive hydrogen bonding is a novel concept which is based on the competition between different blocks of the block copolymer to form more than one kind of intermolecular interaction with the complimentary polymer in the system. Recently, Guo and co-workers have proven that careful selection of the polymers specifically the block copolymer, and the experimental conditions can lead to self-assembled structures in blends and complexes exhibiting competitive hydrogen bonding [2-5].

The importance of neighbourhood size in self organising systems

In recent times, the analysis of SOM (self-organising map) performance has concentrated on optimising the gain decay, rather than the size, form and decay of the neighbourhood function. We propose that the size, form and decay of region size plays a much more significant role in the learning, and especially in the development, of topographic feature maps. In this paper, a biologically-derived SOM model is presented. This model is able to select a single winning neuron and to form Gaussian outputs about this winner, without the need for a meta-level decision-making structure to artificially select a winner and fit a Gaussian output to that winner. Using this model, some fundamental characteristics of the relationship between neighbourhood size and SOM output states are demonstrated.

Enzyme-assisted self-assembly under thermodynamic control

The production of functional molecular architectures through self-assembly is commonplace in biology, but despite advances^{1, 2, 3}, it is still a major challenge to achieve similar complexity in the laboratory. Self-assembled structures that are reproducible and virtually defect free are of interest for applications in three-dimensional cell culture^{4, 5}, templating⁶, biosensing⁷ and supramolecular electronics⁸. Here, we report the use of reversible enzyme-catalysed reactions to drive self-assembly. In this approach, the self-assembly of aromatic short peptide derivatives^{9, 10} provides a driving force that enables a protease enzyme to produce building blocks in a reversible and spatially confined manner. We demonstrate that this system combines three features: (i) self-correction—fully reversible self-assembly under thermodynamic control; (ii) component-selection—the ability to amplify the most stable molecular self-assembly structures in dynamic combinatorial libraries^{11, 12, 13}; and (iii) spatiotemporal confinement of nucleation and structure growth. Enzyme-assisted self-assembly therefore provides control in bottom-up fabrication of nanomaterials that could ultimately lead to functional nanostructures with enhanced complexities and fewer defects.

On self-propagating methodological flaws in performance normalization for strength and power sports

Performance in strength and power sports is greatly affected by a variety of anthropometric factors. The goal of performance normalization is to factor out the effects of confounding factors and compute a canonical (normalized) performance measure from the observed absolute performance. Performance normalization is applied in the ranking of elite athletes, as well as in the early stages of youth talent selection. Consequently, it is crucial that the process is principled and fair. The corpus of previous work on this topic, which is significant, is uniform in the methodology adopted. Performance normalization is universally reduced to a regression task: the collected performance data are used to fit a regression function that is then used to scale future performances. The present article demonstrates that this approach is fundamentally flawed. It inherently creates a bias that unfairly penalizes athletes with certain allometric characteristics, and, by virtue of its adoption in the ranking and selection of elite athletes, propagates and strengthens this bias over time. The main flaws are shown to originate in the criteria for selecting the data used for regression, as well as in the manner in which the regression model is applied in normalization. This analysis brings into light the aforesaid methodological flaws and motivates further work on the development of principled methods, the foundations of which are also laid out in this work.

A new energy efficient cluster-head and backup selection scheme in WSN

Despite significant advancements in wireless sensor networks (WSNs), energy conservation remains one of the most important research challenges. Proper organization of nodes (clustering) is one of the major techniques to expand the lifespan of the whole network through aggregating data at the cluster head. The cluster head is the backbone of the entire cluster. That means if a cluster head fails to accomplish its function, the received and collected data by cluster head can be lost. Moreover, the energy consumption following direct communications from sources to base stations will be increased. In this paper, we propose a type-2 fuzzy based self-configurable cluster head selection (SCCH) approach to not only consider the selection criterion of the cluster head but also present the cluster backup approach. Thus, in case of cluster failure, the system still works in an efficient way. The novelty of this protocol is the ability of handling communication uncertainty, which is an inherent operational aspect of sensor networks. The experiment results indicate SCCH performs better than other recently developed methods.