Long-term price range forecast applied to risk management using regression models

Long-term contractual decisions are the basis of an efficient risk management. However those types of decisions have to be supported with a robust price forecast methodology. This paper reports a different approach for long-term price forecast which tries to give answers to that need. Making use of regression models, the proposed methodology has as main objective to find the maximum and a minimum Market Clearing Price (MCP) for a specific programming period, and with a desired confidence level α. Due to the problem complexity, the meta-heuristic Particle Swarm Optimization (PSO) was used to find the best regression parameters and the results compared with the obtained by using a Genetic Algorithm (GA). To validate these models, results from realistic data are presented and discussed in detail.

PLSR Models for Mechanical Strengths of Concrete Composite Materials Reinforced with Pultrusion Wastes

In this paper, we present two Partial Least Squares Regression (PLSR) models for compressive and flexural strength responses of a concrete composite material reinforced with pultrusion wastes. The main objective is to characterize this cost-effective waste management solution for glass fiber reinforced polymer (GFRP) pultrusion wastes and end-of-life products that will lead, thereby, to a more sustainable composite materials industry. The experiments took into account formulations with the incorporation of three different weight contents of GFRP waste materials into polyester based mortars, as sand aggregate and filler replacements, two waste particle size grades and the incorporation of silane adhesion promoter into the polyester resin matrix in order to improve binder aggregates interfaces. The regression models were achieved for these data and two latent variables were identified as suitable, with a 95% confidence level. This technological option, for improving the quality of GFRP filled polymer mortars, is viable thus opening a door to selective recycling of GFRP waste and its use in the production of concrete-polymer based products. However, further and complementary studies will be necessary to confirm the technical and economic viability of the process.

Multiple Linear Regression and Artificial Neural Networks to Predict Time and Efficiency of Soil Vapor Extraction

The prediction of the time and the efficiency of the remediation of contaminated soils using soil vapor extraction remain a difficult challenge to the scientific community and consultants. This work reports the development of multiple linear regression and artificial neural network models to predict the remediation time and efficiency of soil vapor extractions performed in soils contaminated separately with benzene, toluene, ethylbenzene, xylene, trichloroethylene, and perchloroethylene. The results demonstrated that the artificial neural network approach presents better performances when compared with multiple linear regression models. The artificial neural network model allowed an accurate prediction of remediation time and efficiency based on only soil and pollutants characteristics, and consequently allowing a simple and quick previous evaluation of the process viability.

Analyzing Multiple Outcomes: Is it Really Worth the use of Multivariate Linear Regression?

In health related research it is common to have multiple outcomes of interest in a single study. These outcomes are often analysed separately, ignoring the correlation between them. One would expect that a multivariate approach would be a more efficient alternative to individual analyses of each outcome. Surprisingly, this is not always the case. In this article we discuss different settings of linear models and compare the multivariate and univariate approaches. We show that for linear regression models, the estimates of the regression parameters associated with covariates that are shared across the outcomes are the same for the multivariate and univariate models while for outcome-specific covariates the multivariate model performs better in terms of efficiency.

Motivations which influence volunteers' satisfaction

In this paper we attempt to find out which motivations influence volunteers satisfaction. So, in a first moment, we categorize satisfaction through a factor analysis and then use linear regression models to find out the relations previously mentioned. Furthermore we analyse the existence of correlations between some demographic attributes and the other variables. In this research the population under study includes volunteers that work in the health area, more precisely volunteers that work in hospital and have direct contact with patients and their families. We obtained a total of 327 questionnaires and after excluding incomplete answers we get 304 questionnaires which embody a response rate of 36%. The outcomes of our analysis reveal that we can categorize satisfaction into intrinsic and extrinsic categories and show that motivations related to belonging and protection and career recognition are the ones influencing extrinsic satisfaction; motivations associated to development and learning and altruism are the ones with higher effect in intrinsic satisfaction. There are some negative correlations between age and extrinsic satisfaction, between years of participation and extrinsic satisfaction, between education and the motivation related to belonging and protection and between age and the motivation related to career recognition. There is only one positive correlation between hours per week and intrinsic satisfaction. The results offer new insights for research about volunteers’ motivations, motive fulfillment and volunteer satisfaction. Furthermore the outcomes advocate that to ensure satisfied volunteers, their motivations must be identified in a timely and appropriate moment, meaning that should happened as early as possible.

PAH air pollution at a Portuguese urban area: carcinogenic risks and sources identification

This study aimed to characterize air pollution and the associated carcinogenic risks of polycyclic aromatic hydrocarbon (PAHs) at an urban site, to identify possible emission sources of PAHs using several statistical methodologies, and to analyze the influence of other air pollutants and meteorological variables on PAH concentrations.The air quality and meteorological data were collected in Oporto, the second largest city of Portugal. Eighteen PAHs (the 16 PAHs considered by United States Environment Protection Agency (USEPA) as priority pollutants, dibenzo[a,l]pyrene, and benzo[j]fluoranthene) were collected daily for 24 h in air (gas phase and in particles) during 40 consecutive days in November and December 2008 by constant low-flow samplers and using polytetrafluoroethylene (PTFE) membrane filters for particulate (PM10 and PM2.5 bound) PAHs and pre-cleaned polyurethane foam plugs for gaseous compounds. The other monitored air pollutants were SO2, PM10, NO2, CO, and O3; the meteorological variables were temperature, relative humidity, wind speed, total precipitation, and solar radiation. Benzo[a]pyrene reached a mean concentration of 2.02 ngm−3, surpassing the EU annual limit value. The target carcinogenic risks were equal than the health-based guideline level set by USEPA (10−6) at the studied site, with the cancer risks of eight PAHs reaching senior levels of 9.98×10−7 in PM10 and 1.06×10−6 in air. The applied statistical methods, correlation matrix, cluster analysis, and principal component analysis, were in agreement in the grouping of the PAHs. The groups were formed according to their chemical structure (number of rings), phase distribution, and emission sources. PAH diagnostic ratios were also calculated to evaluate the main emission sources. Diesel vehicular emissions were the major source of PAHs at the studied site. Besides that source, emissions from residential heating and oil refinery were identified to contribute to PAH levels at the respective area. Additionally, principal component regression indicated that SO2, NO2, PM10, CO, and solar radiation had positive correlation with PAHs concentrations, while O3, temperature, relative humidity, and wind speed were negatively correlated.

As determinantes da qualidade da informação financeira via imparidade de ativos

Dissertação de Mestrado apresentada ao Instituto de Contabilidade e Administração do Porto para a obtenção do grau de Mestre em Contabilidade e Finanças, sob orientação da Professora Doutora Cláudia Maria Ferreira Pereira

Is mental practice an effective adjunct therapeutic strategy for upper limb motor restoration after stroke? A systematic review and meta- analysis

Stroke is one of the most common conditions requiring rehabilitation, and its motor impairments are a major cause of permanent disability. Hemiparesis is observed by 80% of the patients after acute stroke. Neuroimaging studies showed that real and imagined movements have similarities regarding brain activation, supplying evidence that those similarities are based on the same process. Within this context, the combination of mental practice (MP) with physical and occupational therapy appears to be a natural complement based on neurorehabilitation concepts. Our study seeks to investigate if MP for stroke rehabilitation of upper limbs is an effective adjunct therapy. PubMed (Medline), ISI knowledge (Institute for Scientific Information) and SciELO (Scientific Electronic Library) were terminated on 20 February 2015. Data were collected on variables as follows: sample size, type of supervision, configuration of mental practice, setting the physical practice (intensity, number of sets and repetitions, duration of contractions, rest interval between sets, weekly and total duration), measures of sensorimotor deficits used in the main studies and significant results. Random effects models were used that take into account the variance within and between studies. Seven articles were selected. As there was no statistically significant difference between the two groups (MP vs control), showed a - 0.6 (95% CI: -1.27 to 0.04), for upper limb motor restoration after stroke. The present meta-analysis concluded that MP is not effective as adjunct therapeutic strategy for upper limb motor restoration after stroke.

Predictors of adherence to the Mediterranean diet from the first to the second trimester of pregnancy

Background: Although changes in eating patterns may occur during gestation, predictors of these changes have not been explored. This study aimed to identify predictors of adherence to the Mediterranean diet (MD) from the first to second trimester of pregnancy. Methods: A prospective study was conducted with 102 pregnant women aged 18-40, from the city of Porto, Portugal. Socio-demographic and lifestyle characteristics were assessed through a questionnaire. Food consumption was assessed with a three-day food diary completed during the first and second trimesters. Participants were categorized according to their change in adherence to the MD into the negative change group (i.e., women who had low adherence in each trimester or had high adherence in the first trimester and then low adherence in the second) and the positive change group (i.e., women who had high adherence in both trimesters or had low adherence in the first trimester and then high adherence in the second). Conditional stepwise logistic regression models were performed to assess the potential predictors of negative MD change. Results: Among the 102 women, 39.2% had negative change from the first to the second trimester. The logistic model´s results show that being married (OR=0.26, 95%CI: 0.10, 0.76) and having a higher intake of vegetables in the first trimester (OR=0.17, 95%CI: 0.10, 0.43) were associated with lower odds of having a negative change in adherence to the MD from the first to second trimester. Conclusion: Marital status and vegetable consumption seem to be associated with a lower occurrence of negative change in adherence to the MD from early to middle pregnancy.