Key factors in working capital management in the Brazilian market

Many studies have been conducted in corporate finance regarding long-term investment and financing decisions. However, short-term asset investments play a significant role in the balance sheet of companies. Moreover, financial managers dedicate significant amounts of time and effort to the subject of working capital management, balancing current assets and liabilities. This paper provides insights regarding the key factors of working capital management by exploring the internal variables of a number of companies. This study used data from 2,976 Brazilian public companies from 2001 to 2008, and found that debt level, size and growth rate can affect the working capital management of companies.

Nutritional factors in milk from Brazilian mothers delivering small for gestational age neonates

The composition of breast milk from brazilian mothers delivering low birthweight infants and its adequacy as a source of nutrients for this group has not yet been fully elucidated. A total of 209 milk samples from 66 women were analysed. The mothers were divided into three groups: G1, mothers delivering term babies of low birthweight (TSGA, n=16); G2, mothers delivering preterm babies of appropriate birthweight (PTAGA, n=20); G3, mothers delivering term babies of appropriate birthweight (TAGA, n=30). The following factors were analysed: osmolarity, total proteins and protein fractions, creamatocrit, sodium, potassium, calcium and magnesium. Milk samples were collected 48 h and 7, 15, 30 and 60 days after delivery. The groups did not differ significantly in terms of osmolarity, total proteins and fractions, creamatocrit, calcium, magnesium or potassium throughout the study period. Sodium levels were higher in all samples from mothers of TSGA infants and in samples from mothers of PTAGA infants on the 7th, 15th and 30th days than in milk from the TAGA group. The authors consider the needs of the low birthweight and TAGA infants and that these high sodium levels may be necessary for growth of low birthweight infants.

Radiological imaging in digital systems: the effect of exposure parameters in diagnostic quality and patient dose

Esta tese pretende contribuir para o estudo e análise dos factores relacionados com as técnicas de aquisição de imagens radiológicas digitais, a qualidade diagnóstica e a gestão da dose de radiação em sistema de radiologia digital. A metodologia encontra-se organizada em duas componentes. A componente observacional, baseada num desenho do estudo de natureza retrospectiva e transversal. Os dados recolhidos a partir de sistemas CR e DR permitiram a avaliação dos parâmetros técnicos de exposição utilizados em radiologia digital, a avaliação da dose absorvida e o índice de exposição no detector. No contexto desta classificação metodológica (retrospectiva e transversal), também foi possível desenvolver estudos da qualidade diagnóstica em sistemas digitais: estudos de observadores a partir de imagens arquivadas no sistema PACS. A componente experimental da tese baseou-se na realização de experiências em fantomas para avaliar a relação entre dose e qualidade de imagem. As experiências efectuadas permitiram caracterizar as propriedades físicas dos sistemas de radiologia digital, através da manipulação das variáveis relacionadas com os parâmetros de exposição e a avaliação da influência destas na dose e na qualidade da imagem. Utilizando um fantoma contraste de detalhe, fantomas antropomórficos e um fantoma de osso animal, foi possível objectivar medidas de quantificação da qualidade diagnóstica e medidas de detectabilidade de objectos. Da investigação efectuada, foi possível salientar algumas conclusões. As medidas quantitativas referentes à performance dos detectores são a base do processo de optimização, permitindo a medição e a determinação dos parâmetros físicos dos sistemas de radiologia digital. Os parâmetros de exposição utilizados na prática clínica mostram que a prática não está em conformidade com o referencial Europeu. Verifica-se a necessidade de avaliar, melhorar e implementar um padrão de referência para o processo de optimização, através de novos referenciais de boa prática ajustados aos sistemas digitais. Os parâmetros de exposição influenciam a dose no paciente, mas a percepção da qualidade de imagem digital não parece afectada com a variação da exposição. Os estudos que se realizaram envolvendo tanto imagens de fantomas como imagens de pacientes mostram que a sobreexposição é um risco potencial em radiologia digital. A avaliação da qualidade diagnóstica das imagens mostrou que com a variação da exposição não se observou degradação substancial da qualidade das imagens quando a redução de dose é efectuada. Propõe-se o estudo e a implementação de novos níveis de referência de diagnóstico ajustados aos sistemas de radiologia digital. Como contributo da tese, é proposto um modelo (STDI) para a optimização de sistemas de radiologia digital.

Poverty levels and children's health status: study of risk factors in an urban population of low socioeconomic level

To test the hypothesis that the low socioeconomic population living is shanty towns in Porto Alegre presents different levels of poverty which are reflected on its health status, a cross-sectional study was designed involving 477 families living in Vila Grande Cruzeiro, Porto Alegre, Brazil. The poverty level of the families was measured by using an instrument specifically designed for poor urban populations. Children from families living in extreme poverty (poorest quartile) were found to have higher infant mortality rate, lower birth weights, more hospitalizations, and higher malnutrition rates, in addition to belonging to more numerous families. Thus, the shanty town population of Porto Alegre is not homogeneous, and priority should be given to the more vulnerable subgroups.

Surge damping analysis in pipe systems: modelling and experiments

The current study focuses on the analysis of pressure surge damping in single pipeline systems generated by a fast change of flow, conditions. A dimensionless form of pressurised transient flow equations was developed. presenting the main advantage of being independent of the system characteristics. In lack of flow velocity profiles. the unsteady friction in turbulent regimes is analysed based on two new empirical corrective-coefficients associated with local and convective acceleration terms. A new, surge damping approach is also presented taking into account the pressure peak time variation. The observed attenuation effect in the pressure wave for high deformable pipe materials can be described by a combination of the non-elastic behaviour of the pipe-wall with steady and unsteady friction effects. Several simulations and experimental tests have been carried out. in order to analyse the dynamic response of single pipelines with different characteristics, such as pipe materials. diameters. thickness. lengths and transient conditions.

Assessment of patient dose in digital systems

The assessment of patient dose has gained increased attention, still being an issue of concern that arises from the use of digital systems. The development of digital technology offers the possibility for a reduction of radiation dose around 50% without loss in image quality when compared to a conventional screen–film system. Digital systems give an equivalent or superior diagnostic performance and also several other advantages, but the risk of overexposure with no adverse effect on image quality could be present. This chapter refers to the management of patient dose and provides an explanation of dose-related concepts. In this chapter, exposure influence in dose and image representation and the effects of radiation exposure are also discussed.

Self-optimization for dynamic scheduling in manufacturing systems

Scheduling is a critical function that is present throughout many industries and applications. A great need exists for developing scheduling approaches that can be applied to a number of different scheduling problems with significant impact on performance of business organizations. A challenge is emerging in the design of scheduling support systems for manufacturing environments where dynamic adaptation and optimization become increasingly important. In this paper, we describe a Self-Optimizing Mechanism for Scheduling System through Nature Inspired Optimization Techniques (NIT).

The verification of temporal KBS: SPARSE - a case study in power systems

In this paper we present VERITAS, a tool that focus time maintenance, that is one of the most important processes in the engineering of the time during the development of KBS. The verification and validation (V&V) process is part of a wider process denominated knowledge maintenance, in which an enterprise systematically gathers, organizes, shares, and analyzes knowledge to accomplish its goals and mission. The V&V process states if the software requirements specifications have been correctly and completely fulfilled. The methodologies proposed in software engineering have showed to be inadequate for Knowledge Based Systems (KBS) validation and verification, since KBS present some particular characteristics. VERITAS is an automatic tool developed for KBS verification which is able to detect a large number of knowledge anomalies. It addresses many relevant aspects considered in real applications, like the usage of rule triggering selection mechanisms and temporal reasoning.

Adaptive learning in multiagent systems: a forecasting methodology based on error analysis

Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM is a multi-agent electricity market simu-lator to model market players and simulate their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. MASCEM pro-vides several dynamic strategies for agents’ behaviour. This paper presents a method that aims to provide market players strategic bidding capabilities, allowing them to obtain the higher possible gains out of the market. This method uses an auxiliary forecasting tool, e.g. an Artificial Neural Net-work, to predict the electricity market prices, and analyses its forecasting error patterns. Through the recognition of such patterns occurrence, the method predicts the expected error for the next forecast, and uses it to adapt the actual forecast. The goal is to approximate the forecast to the real value, reducing the forecasting error.

Data mining applications in power systems - case-studies and future trends

Presently power system operation produces huge volumes of data that is still treated in a very limited way. Knowledge discovery and machine learning can make use of these data resulting in relevant knowledge with very positive impact. In the context of competitive electricity markets these data is of even higher value making clear the trend to make data mining techniques application in power systems more relevant. This paper presents two cases based on real data, showing the importance of the use of data mining for supporting demand response and for supporting player strategic behavior.

Cyber-ambient intelligent training of operators in power systems control centres

Cyber-Physical Systems and Ambient Intelligence are two of the most important and emerging paradigms of our days. The introduction of renewable sources gave origin to a completely different dimension of the distribution generation problem. On the other hand, Electricity Markets introduced a different dimension in the complexity, the economic dimension. Our goal is to study how to proceed with the Intelligent Training of Operators in Power Systems Control Centres, considering the new reality of Renewable Sources, Distributed Generation, and Electricity Markets, under the emerging paradigms of Cyber-Physical Systems and Ambient Intelligence. We propose Intelligent Tutoring Systems as the approach to deal with the intelligent training of operators in these new circumstances.

Autonomic computing for scheduling in manufacturing systems

We describe a novel approach to scheduling resolution by combining Autonomic Computing (AC), Multi-Agent Systems (MAS) and Nature Inspired Optimization Techniques (NIT). Autonomic Computing has emerged as paradigm aiming at embedding applications with a management structure similar to a central nervous system. A natural Autonomic Computing evolution in relation to Current Computing is to provide systems with Self-Managing ability with a minimum human interference. In this paper we envisage the use of Multi-Agent Systems paradigm for supporting dynamic and distributed scheduling in Manufacturing Systems with Autonomic properties, in order to reduce the complexity of managing systems and human interference. Additionally, we consider the resolution of realistic problems. The scheduling of a Cutting and Treatment Stainless Steel Sheet Line will be evaluated. Results show that proposed approach has advantages when compared with other scheduling systems.

Pediculus capitis infestation according to sex and social factors in Argentina

OBJECTIVE: To determine the intensity of Pediculus capitis infestation (abundance) among Argentinean schoolchildren. Children's sex and social stratum were analyzed as modifiers of the general prevalence and degree of parasitism. METHODS: The study included 1,370 schoolchildren (692 girls, 678 boys) from 26 schools of the province of La Rioja (21 public schools, five private schools). Classic prevalence was obtained as the percentage of children with nits and/or lice. Moreover, five degrees of parasitism were classified: 0) children with no signs of pediculosis; 0+) children with evidence of past infestation; 1) children with a recent infestation and low probability of active parasitism; 2) children with a recent infestation and high probability of active parasitism; 3) children with mobile lice (active pediculosis). RESULTS: The general prevalence was 61.4% (girls: 79%; boys: 44%, p<0.001). Private schools showed lower prevalence than public schools (p=0.02), especially due to the low prevalence in boys. Fifty percent of children were classified in classes 0 and 0+, 22% in class 1; and 28% in grades 2 and 3. The proportion of children in grade 3 was higher in public schools than in private schools. There were significant sexual differences in the intensity of parasitism for grades 2 and 3, where girls' rates exceeded twice those of boys'. CONCLUSIONS: Sex and social stratum are important modifiers of P. capitis general prevalence and degree of infestation. The classification of children by intensity of infestation allowed a more precise delimitation of this condition, which is especially important for disease surveillance and application of control measures.

AI in power systems and energy markets

In a world increasingly conscientious about environmental effects, power and energy systems are undergoing huge transformations. Electric energy produced from power plants is transmitted and distributed to end users through a power grid. The power industry performs the engineering design, installation, operation, and maintenance tasks to provide a high-quality, secure energy supply while accounting for its systems’ abilities to withstand uncertain events, such as weather-related outages. Competitive, deregulated electricity markets and new renewable energy sources, however, have further complicated this already complex infrastructure.Sustainable development has also been a challenge for power systems. Recently, there has been a signifi cant increase in the installation of distributed generations, mainly based on renewable resources such as wind and solar. Integrating these new generation systems leads to more complexity. Indeed, the number of generation sources greatly increases as the grid embraces numerous smaller and distributed resources. In addition, the inherent uncertainties of wind and solar energy lead to technical challenges such as forecasting, scheduling, operation, control, and risk management. In this special issue introductory article, we analyze the key areas in this field that can benefi t most from AI and intelligent systems now and in the future.We also identify new opportunities for cross-fertilization between power systems and energy markets and intelligent systems researchers.

Volunteers and management factors in nonprofit organisations: a brief literature review

The objective of this paper is to review and discuss the literature about the management factors that can influence volunteer work. First we present the different management factors. This discussion is followed by the identification of the key management factors in volunteering: recruitment, training and rewarding. Finally, we present two main gaps in the literature that justify the scope for further research: (i) how management factors (recruitment, training and rewarding) influence volunteers’ satisfaction and retention; and (ii) predominance of the investigations in the North American context, followed by English and Australian context.