Critical analysis of the allocation policy for liver transplantation in Brazil

Liver transplantation is now the standard treatment for end-stage liver disease. Given the shortage of liver donors and the progressively higher number of patients waiting for transplantation, improvements in patient selection and optimization of timing for transplantation are needed. Several solutions have been suggested, including increasing the donor pool; a fair policy for allocation, not permitting variables such as age, gender, and race, or third-party payer status to play any role; and knowledge of the natural history of each liver disease for which transplantation is offered. To observe ethical rules and distributive justice (guarantee to every citizen the same opportunity to get an organ), the "sickest first" policy must be used. Studies have demonstrated that death has no relationship with waiting time, but rather with the severity of liver disease at the time of inclusion. Thus, waiting time is no longer part of the United Network for Organ Sharing distribution criteria. Waiting time only differentiates between equally severely diseased patients. The authors have analyzed the waiting list mortality and 1-year survival for patients of the State of São Paulo, from July 1997 through January 2001. Only the chronological criterion was used. According to "Secretaria de Estado da Saúde de São Paulo" data, among all waiting list deaths, 82.2% occurred within the first year, and 37.6% within the first 3 months following inclusion. The allocation of livers based on waiting time is neither fair nor ethical, impairs distributive justice and human rights, and does not occur in any other part of the world.

The role of dispersion into assets allocation

Dispersion of returns has gained a lot of attention as a measure to distinguish good and bad investment opportunities time. In the following dissertation, the cross-sectional returns volatility is analyzed over a fifteen year period across the S&P100 Index composition. The main inference drawn from the data sample is that the canonical measure of dispersion is highly macro-risk driven and therefore more biased towards returns volatility rather than its correlation component.

Dry matter allocation in Jessenia bataua (PALMAE)

There are few assessments of lifetime dry matter production for tropical trees. However, several studies, have been carried out for palms. This study measures dry matter production for Jessenia bataua,a useful palm common in many areas of the Amazon Valley. Palms In the Ducke Forest Reserve Of INPA were studied. Approximately 34% of total aboveground dry matter production in this palm was, alllocated to reproductive effort, eg., the production of in florescences and fruits. The meaning of this percentage, to discussed, relative to percentages identified in other Neotropical palms.

Perception of organizational politics and the effect of authenticity and affective commitment on deviance and task performance in contact centers

This study aimed to understand employees’ reactions to organizational politics in Contact Centers. Drawing from a sample of 187 supervisor-employee dyads, we studied the relationship between employees’ perceptions of organizational politics and supervisor-rated task performance and deviance, and mediation effects by authenticity at work and affective commitment. Results indicate that workers tend to react to workplace politics with deviant behavior and worse task performance. We found that the relationship between perceived politics and task performance was mediated by authenticity. The relationship between perceived politics and supervisor-rated deviance was mediated by affective commitment to the organization. Implications for management are discussed.

Ontology-based representation and generation of workflows for micro-task human-machine computation

Doctoral Program in Computer Science

An evolutionary multi-objective optimization system for earthworks

Earthworks involve the levelling or shaping of a target area through the moving or processing of the ground surface. Most construction projects require earthworks, which are heavily dependent on mechanical equipment (e.g., excavators, trucks and compactors). Often, earthworks are the most costly and time-consuming component of infrastructure constructions (e.g., road, railway and airports) and current pressure for higher productivity and safety highlights the need to optimize earthworks, which is a nontrivial task. Most previous attempts at tackling this problem focus on single-objective optimization of partial processes or aspects of earthworks, overlooking the advantages of a multi-objective and global optimization. This work describes a novel optimization system based on an evolutionary multi-objective approach, capable of globally optimizing several objectives simultaneously and dynamically. The proposed system views an earthwork construction as a production line, where the goal is to optimize resources under two crucial criteria (costs and duration) and focus the evolutionary search (non-dominated sorting genetic algorithm-II) on compaction allocation, using linear programming to distribute the remaining equipment (e.g., excavators). Several experiments were held using real-world data from a Portuguese construction site, showing that the proposed system is quite competitive when compared with current manual earthwork equipment allocation.

Business intelligence in banking: A literature analysis from 2002 to 2013 using Text Mining and latent Dirichlet allocation

telligence applications for the banking industry. Searches were performed in relevant journals resulting in 219 articles published between 2002 and 2013. To analyze such a large number of manuscripts, text mining techniques were used in pursuit for relevant terms on both business intelligence and banking domains. Moreover, the latent Dirichlet allocation modeling was used in or- der to group articles in several relevant topics. The analysis was conducted using a dictionary of terms belonging to both banking and business intelli- gence domains. Such procedure allowed for the identification of relationships between terms and topics grouping articles, enabling to emerge hypotheses regarding research directions. To confirm such hypotheses, relevant articles were collected and scrutinized, allowing to validate the text mining proce- dure. The results show that credit in banking is clearly the main application trend, particularly predicting risk and thus supporting credit approval or de- nial. There is also a relevant interest in bankruptcy and fraud prediction. Customer retention seems to be associated, although weakly, with targeting, justifying bank offers to reduce churn. In addition, a large number of ar- ticles focused more on business intelligence techniques and its applications, using the banking industry just for evaluation, thus, not clearly acclaiming for benefits in the banking business. By identifying these current research topics, this study also highlights opportunities for future research.

Comparison of single and multi-objective evolutionary algorithms for robust link-state routing

Traffic Engineering (TE) approaches are increasingly impor- tant in network management to allow an optimized configuration and resource allocation. In link-state routing, the task of setting appropriate weights to the links is both an important and a challenging optimization task. A number of different approaches has been put forward towards this aim, including the successful use of Evolutionary Algorithms (EAs). In this context, this work addresses the evaluation of three distinct EAs, a single and two multi-objective EAs, in two tasks related to weight setting optimization towards optimal intra-domain routing, knowing the network topology and aggregated traffic demands and seeking to mini- mize network congestion. In both tasks, the optimization considers sce- narios where there is a dynamic alteration in the state of the system, in the first considering changes in the traffic demand matrices and in the latter considering the possibility of link failures. The methods will, thus, need to simultaneously optimize for both conditions, the normal and the altered one, following a preventive TE approach towards robust configurations. Since this can be formulated as a bi-objective function, the use of multi-objective EAs, such as SPEA2 and NSGA-II, came nat- urally, being those compared to a single-objective EA. The results show a remarkable behavior of NSGA-II in all proposed tasks scaling well for harder instances, and thus presenting itself as the most promising option for TE in these scenarios.

Task clustering on ETL systems – A pattern-oriented approach

Usually, data warehousing populating processes are data-oriented workflows composed by dozens of granular tasks that are responsible for the integration of data coming from different data sources. Specific subset of these tasks can be grouped on a collection together with their relationships in order to form higher- level constructs. Increasing task granularity allows for the generalization of processes, simplifying their views and providing methods to carry out expertise to new applications. Well-proven practices can be used to describe general solutions that use basic skeletons configured and instantiated according to a set of specific integration requirements. Patterns can be applied to ETL processes aiming to simplify not only a possible conceptual representation but also to reduce the gap that often exists between two design perspectives. In this paper, we demonstrate the feasibility and effectiveness of an ETL pattern-based approach using task clustering, analyzing a real world ETL scenario through the definitions of two commonly used clusters of tasks: a data lookup cluster and a data conciliation and integration cluster.

Off-line simulation inspires insight: a neurodynamics approach to efficient robot task learning

There is currently an increasing demand for robots able to acquire the sequential organization of tasks from social learning interactions with ordinary people. Interactive learning-by-demonstration and communication is a promising research topic in current robotics research. However, the efficient acquisition of generalized task representations that allow the robot to adapt to different users and contexts is a major challenge. In this paper, we present a dynamic neural field (DNF) model that is inspired by the hypothesis that the nervous system uses the off-line re-activation of initial memory traces to incrementally incorporate new information into structured knowledge. To achieve this, the model combines fast activation-based learning to robustly represent sequential information from single task demonstrations with slower, weight-based learning during internal simulations to establish longer-term associations between neural populations representing individual subtasks. The efficiency of the learning process is tested in an assembly paradigm in which the humanoid robot ARoS learns to construct a toy vehicle from its parts. User demonstrations with different serial orders together with the correction of initial prediction errors allow the robot to acquire generalized task knowledge about possible serial orders and the longer term dependencies between subgoals in very few social learning interactions. This success is shown in a joint action scenario in which ARoS uses the newly acquired assembly plan to construct the toy together with a human partner.

A new perspective of students allocation satisfaction in engineering courses in Portugal

Publicado em "AIP Conference Proceedings" Vol. 1648

Estudo de fidedignidade do instrumento neuropsicológico Iowa Gambling Task

OBJETIVO: O objetivo do presente estudo foi verificar evidências de fidedignidade do instrumento neuropsicológico Iowa Gambling Task (IGT) a partir do método teste-reteste. MÉTODO: Participaram 50 indivíduos saudáveis, de 19 a 75 anos de idade, com no mínimo cinco anos de educação formal. A aplicação foi realizada de forma individual, em dois encontros, com intervalo de um a seis meses entre teste e reteste. RESULTADOS: Os resultados evidenciaram uma correlação positiva moderada significativa entre teste-reteste no cálculo global. Na análise por segmentos, os blocos 4 e 5 apresentaram uma correlação positiva moderada, mas não foram observadas correlações significativas nos blocos 1, 2 e 3. CONCLUSÃO: Esses dados corroboram estudos atuais que encontraram correlações moderadas entre teste-reteste em medidas de funções executivas e sugerem que o IGT pode ser empregado para avaliar o processo de tomada de decisão de forma confiável ao longo do tempo, desde que sejam considerados estudos de fidedignidade com populações saudáveis mais amplas e com populações clínicas.

Expression und funktionelle Relevanz von Zwei-Poren K+-Kanälen der Task-Familie in thalamischen Schaltneutronen

Thalamus, thalamocortical relay neurons, TASK-channels, Two-Pore-K+-channels, HCN-channels, Halothane, Muscarin, Bupivacaine, Spermine, computer modelling