Quais as principais características organizacionais das empresas dos diferentes segmentos da construção civil?

Resumo Neste artigo os autores se propõem a identificar os fatores que definem as principais características organizacionais de empresas do subsetor da construção civil, localizadas em Curitiba e região metropolitana, assim como verificar as diferenças existentes entre os cinco segmentos de atividade mais relevantes desse mercado: residencial, industrial e comercial, infraestrutura, serviços especializados e construção industrializada. Para tanto, foi desenvolvida uma pesquisa exploratória e de natureza quantitativa, em nível de mestrado. O método survey foi escolhido como procedimento principal para este estudo, permitindo a obtenção de dados primários por meio da aplicação de um questionário em um grupo de 118 empresas. Esse questionário procurou identificar o perfil da organização e as características organizacionais das empresas avaliadas. Os dados foram analisados com a aplicação de técnicas multivariadas de análise fatorial e análise discriminante. A análise fatorial evidenciou as estratégias de melhoria, o comportamento do indivíduo no trabalho, a estrutura de funcionamento, a dinâmica de crescimento, o estilo de gestão, as relações interpessoais e o posicionamento da empresa perante o mercado como fatores determinantes. A análise discriminante apontou significativa homogeneidade no desenvolvimento organizacional das empresas que representam os diferentes segmentos de atividade

Air pollution and respiratory health of children: the PEACE panel study in Umea., Sweden.

The Pollution Effects on Asthmatic Children in Europe (PEACE) study examined the acute effects of short-term changes in air pollution on symptomatic children. We were one of 14 research centres in Europe that used a common study protocol. Seventy five children in an urban panel and 72 children in a control panel, selected with a screening questionnaire, were characterized with lung function tests and skin-prick tests and followed in a 12 week diary study. Identical air quality monitoring stations were set up in our two study areas. The levels and ranges in concentrations for all air pollution indices were small. A negative correlation was seen between time spent outdoors and pollution levels, which may have weakened the correlation between personal exposure and outdoor concentrations and obscured any effect of air pollution. No consistent pattern of relations between air pollution and adverse effects were found, but particulates and nitrogen dioxide tended to be associated with a nonsignificant increase in symptoms and medication use on the same day. No clear effect of air pollution on respiratory health could be demonstrated

Exploring the time dependence of serum Clara cell protein as a biomarker of pulmonary injury in humans

We have previously demonstrated Clara cell protein (CC16) [secretoglobin 1A1] in serum to be a highly sensitive biomarker of altered lung epithelial permeability after ozone challenge. As a previous experimental study has indicated a diurnal variation in serum CC 16 in humans, the aims of the present investigation were to confirm this observation and to attempt to model the diurnal variation in CC16 concentrations. In IS healthy nonsmoking subjects, peripheral blood samples were drawn at six sampling points over a 15-h period and repeated twice within 3 to 4 weeks. A clear within-day variation was revealed in serum CC16 concentrations, falling significantly from baseline levels between the 11:30 Am and 10:00 Pm time points (p = 0.000). Furthermore, it was shown that this within-day variation was reproducible regardless of subject or day, enabling the diurnal variation in serum CC16 to be modeled and fitted a second-degree polynomial for the observed time span. In conclusion, the present data demonstrate a pronounced time-dependent diurnal variation in serum levels of CC16, which can be mathematically compensated for, when addressing the issue of an air pollution-induced effect on CC16 in field studies

How to Determine the Order-Up-To Level when Demand is Gamma Distributed with Unknown Parameters

Inventory models need information about the demand distribution. In practice, this information is not known with certainty and has to be estimated with often relatively few historical demand observations. Using these estimates leads to underperformance. This paper focuses on gamma distributed demand and a periodic review, order-up-to inventory control policy, where the order-up-to level satisfies a service equation. Under this policy the underperformance is quantified analytically under strong assumptions and with help of simulation if these assumptions are relaxed. The analytical results can be used to improve the attained service level, such that it approaches the desired service level more closely, even if the assumptions are not met. With help of simulation we show that in some cases this improvement results in reaching the desired service level. For the remaining cases, i.e., the cases in which the desired service level is not reached, the underperformance decreases; improvements range from almost 17% up to over 90%. Moreover, with help of simulation and linear regression further improvements can be obtained. The desired service level is reached in more cases and the underperformance in the other cases is decreased even more compared to using only the first improvement. These improvements range from 57% up to 99% compared to the base case (i.e., do not use analytical results) and from 35% up to over 90% compared to using the analytical results, except for a few cases in which the service hardly improved, but in those cases the attained service level was already very close to the desired one. Finally, the method developed in this paper is applied to real demand data using simulation. The total improvements in this case study range from 53% up to 96%.