Comparing the performance of geostatistical models with additional information from covariates for sewage plume characterization

In this work, kriging with covariates is used to model and map the spatial distribution of salinity measurements gathered by an autonomous underwater vehicle in a sea outfall monitoring campaign aiming to distinguish the effluent plume from the receiving waters and characterize its spatial variability in the vicinity of the discharge. Four different geostatistical linear models for salinity were assumed, where the distance to diffuser, the west-east positioning, and the south-north positioning were used as covariates. Sample variograms were fitted by the Mat`ern models using weighted least squares and maximum likelihood estimation methods as a way to detect eventual discrepancies. Typically, the maximum likelihood method estimated very low ranges which have limited the kriging process. So, at least for these data sets, weighted least squares showed to be the most appropriate estimation method for variogram fitting. The kriged maps show clearly the spatial variation of salinity, and it is possible to identify the effluent plume in the area studied. The results obtained show some guidelines for sewage monitoring if a geostatistical analysis of the data is in mind. It is important to treat properly the existence of anomalous values and to adopt a sampling strategy that includes transects parallel and perpendicular to the effluent dispersion.

Performance of state space and ARIMA models for consumer retail sales forecasting

Forecasting future sales is one of the most important issues that is beyond all strategic and planning decisions in effective operations of retail businesses. For profitable retail businesses, accurate demand forecasting is crucial in organizing and planning production, purchasing, transportation and labor force. Retail sales series belong to a special type of time series that typically contain trend and seasonal patterns, presenting challenges in developing effective forecasting models. This work compares the forecasting performance of state space models and ARIMA models. The forecasting performance is demonstrated through a case study of retail sales of five different categories of women footwear: Boots, Booties, Flats, Sandals and Shoes. On both methodologies the model with the minimum value of Akaike's Information Criteria for the in-sample period was selected from all admissible models for further evaluation in the out-of-sample. Both one-step and multiple-step forecasts were produced. The results show that when an automatic algorithm the overall out-of-sample forecasting performance of state space and ARIMA models evaluated via RMSE, MAE and MAPE is quite similar on both one-step and multi-step forecasts. We also conclude that state space and ARIMA produce coverage probabilities that are close to the nominal rates for both one-step and multi-step forecasts.

O impacto do capital intelectual na performance

Para as organizações sobreviverem num novo ambiente competitivo, tiveram que deixar de procurar obter vantagens competitivas nos seus recursos tangíveis para as procurarem nos ativos intangíveis. Assim, várias investigações comprovam a necessidade de se dar a devida importância ao conceito de Capital Intelectual como fonte de criação de vantagens competitivas para as organizações. Esta importância aplica-se a vários níveis de análise entre os quais o setor público. Esta investigação visa analisar a importância relativa de cada dimensão do capital intelectual de uma Instituição de Ensino, bem como o impacto do mesmo na performance (percecionada pelos seus stakeholders). Nesta dissertação adotamos o método de Estudo de Caso, utilizando uma abordagem mista – quantitativa e qualitativa. Utilizámos variadas técnicas na recolha de dados, tais como: entrevista, registo de áudio e questionários. Relativamente à análise de dados, efetuamos uma análise de conteúdo, análise de documentos e análise estatística. Ao nível de resultados, a análise qualitativa sugere que as três dimensões do Capital Intelectual (Capital Humano, Capital Estrutural e Capital Relacional) devem possuir a mesma importância relativa para avaliar o capital intelectual de uma Instituição de Ensino. Os resultados quantitativos sugerem que o capital intelectual tem impacto na performance, o que está em linha com a literatura sobre o tema. Finalmente, considerámos que esta tese contribui para uma sensibilização relativamente à importância do capital intelectual para melhorar a gestão e performance em contextos educacionais.