Produto Aquaoli: projeto de design industrial em aquariofilia

Esta dissertação de mestrado, apresenta a elaboração de um projeto de design industrial em aquariofilia - Aquaoli. Pretende-se criar uma nova tipologia de aquários inovadora e distinta, projetando o produto com o intuito deste cumprir com todos os parâmetros exigidos pelo mercado e produção do mesmo. Definiu-se a criança entre os 7 e os 11 anos como público-alvo deste projeto. Assim, pretende-se um resultando promissor para a empresa AQUATLANTIS apostar e lucrar com o seu desenvolvimento. Para que este projeto obtivesse bons resultados, foi realizada uma pesquisa aprofundada, desde a aquariofilia (a sua história, biologia e mercado), do aquariófilo atual (psicologia do consumidor e as suas responsabilidades), até ao estado da arte (da AQUATLANTIS e dos produtos existentes no mercado). Este projeto foi desenvolvido em várias vertentes de design, procurando ser o mais completo possível. Isso foi viável com o desenvolvimento do produto e de todos os seus componentes em três dimensões, o desenvolvimento de maquetes e de protótipos do produto, o desenvolvimento do packaging e campanha promocional, a definição de preço custo e métodos de produção dos objetos, entre outros parâmetros. Obteve-se um projeto de design industrial bastante completo e com potencial para ser implementado no mercado, cativando a atenção do público-alvo e obtendo vendas elevadas. O aquário apresenta-se distinto dos existentes, principalmente no facto de ser modular, muito interativo, pedagógico e incorporar uma estrutura invulgar e particularmente cativante para as crianças. Este produto subdivide-se em dois conjuntos vendidos separadamente (Aquarium Pack e Kit-1 Pack), apresentando assim uma estratégia de marketing que pretende que a criança que o adquira pela primeira vez, venha a desejar adquirir mais produtos desta gama, procurando aumentar a sua estante e/ou o número de aquários e animais que incorpora na mesma.

Comparative analysis between statistical and artificial intelligence models in business failure prediction

A growing number of predicting corporate failure models has emerged since 60s. Economic and social consequences of business failure can be dramatic, thus it is not surprise that the issue has been of growing interest in academic research as well as in business context. The main purpose of this study is to compare the predictive ability of five developed models based on three statistical techniques (Discriminant Analysis, Logit and Probit) and two models based on Artificial Intelligence (Neural Networks and Rough Sets). The five models were employed to a dataset of 420 non-bankrupt firms and 125 bankrupt firms belonging to the textile and clothing industry, over the period 2003–09. Results show that all the models performed well, with an overall correct classification level higher than 90%, and a type II error always less than 2%. The type I error increases as we move away from the year prior to failure. Our models contribute to the discussion of corporate financial distress causes. Moreover it can be used to assist decisions of creditors, investors and auditors. Additionally, this research can be of great contribution to devisers of national economic policies that aim to reduce industrial unemployment.

A Travel Optimization System for Academic Environments

Carpooling initiated in America in the 1970s due to the oil crisis. However, over the past years, carpooling has increased significantly across the world. Some countries have created a High Occupancy Vehicle (HOV) lane to encourage commuters not to travel alone. In additional, carpool websites has been developed to facilitate the connection between the commuters, making it possible to create a compatible match in a faster and efficient manner. This project focuses on carpooling, especially in an academic environment since younger people are more likely to choose carpool. Initially, an intense research was made to examine carpool studies that occurred all over the world, following with a research of higher education institutes that use carpooling as a transportation mode. Most websites created carpools by targeting people from a specific country. These commuters have different origins and destinations making it more complicated to create compatible matches. The objective of this project is to develop a system helping teachers and students from an academic environment to create carpool matches. This objective makes it easier to create carpools because these students and teachers have the same destination. During the research, it was essential to explore, as many as possible, existing carpool websites that are available across the world. After this analysis, several sketches were made to develop the layout and structure of the web application that’s being implemented throughout the project. Once the layout was established, the development of the web application was initiated. This project had its ups and downs but it accomplished all the necessary requirements. This project can be accessed on the link: http://ipcacarpool.somee.com. Once the website was up and running, a web-based survey was developed to study the reasons that motivate people to consider carpooling as an alternative to driving alone. To develop this survey was used a tool called Survey Planet. This survey contained 408 respondents, which 391 are students and 17 are teachers. This study concludes that a majority of the respondents don’t carpool, however they will consider carpooling if there was a dedicated parking space. A majority of the respondents that carpool initiated less than a year ago, indicating that this mean of transportation is recent.

Comparative Analysis between Statistical and Artificial Intelligence Models in Business Failure Prediction

A growing number of predicting corporate failure models has emerged since 60s. Economic and social consequences of business failure can be dramatic, thus it is not surprise that the issue has been of growing interest in academic research as well as in business context. The main purpose of this study is to compare the predictive ability of five developed models based on three statistical techniques (Discriminant Analysis, Logit and Probit) and two models based on Artificial Intelligence (Neural Networks and Rough Sets). The five models were employed to a dataset of 420 non-bankrupt firms and 125 bankrupt firms belonging to the textile and clothing industry, over the period 2003–09. Results show that all the models performed well, with an overall correct classification level higher than 90%, and a type II error always less than 2%. The type I error increases as we move away from the year prior to failure. Our models contribute to the discussion of corporate financial distress causes. Moreover it can be used to assist decisions of creditors, investors and auditors. Additionally, this research can be of great contribution to devisers of national economic policies that aim to reduce industrial unemployment.