Fatigue Prediction on Fibrin Poly-ε-caprolactone Macroporous Scaffolds

Tissue engineering applications rely on scaffolds that during its service life, either for in-vivo or in vitro applications, are under mechanical solicitations. The variation of the mechanical condition of the scaffold is strongly relevant for cell culture and has been scarcely addressed. Fatigue life cycle of poly-ε-caprolactone, PCL, scaffolds with and without fibrin as filler of the pore structure were characterized both dry and immersed in liquid water. It is observed that the there is a strong increase from 100 to 500 in the number of loading cycles before collapse in the samples tested in immersed conditions due to the more uniform stress distributions within the samples, the fibrin loading playing a minor role in the mechanical performance of the scaffolds

Contribution towards a reflection about methodological strategies in design education and the function of drawing in design

This work presents a reflection on Design education and specifically on the role of Drawing in this area. As a subject, Design has expanded its field of action expanding into new areas such as Experience Design or Service Design. It became necessary for the designer to have more than an education based on technological knowledge or know-how. Many authors like Meredith Davis, Don Norman or Jamie Hobson point out the urgency to review the curricula of Design courses because nowadays “… design is more than appearance, design is about interaction, about strategy and about services. Designers change social behavior” (Norman 2011). When shifting from a product-centered design to a person-centered design (in a structure, a service or in a relationship) what should the function of drawing in a design course be? What should its curriculum be? Our work methodology will be to confront today’s perspectives on design theory and practice in an attempt to add to the discussion on the methodological strategies in design teaching in the contemporary context.

Contribution towards a reflection about methodological strategies in Design Education and the function of Drawing in Design

This work presents a reflection on Design education and specifically on the role of Drawing in this area. As a subject, Design has expanded its field of action expanding into new areas such as Experience Design or Service Design. It became necessary for the designer to have more than an education based on technological knowledge or know-how. Many authors like Meredith Davis, Don Norman or Jamie Hobson point out the urgency to review the curricula of Design courses because nowadays “ … design is more than appearance, design is about interaction, about strategy and about services. Designers change social behavior” (Norman, 2011) When shifting from a product-centered design to a person-centered design (in a structure, a service or in a relationship) what should the function of drawing in a design course be? What should its curriculum be? Our work methodology will be to confront today’s perspectives on design theory and practice in an attempt to add to the discussion on the methodological strategies in design teaching in the contemporary context.

Thermal degradation of Pb(Zr0.53Ti0.47)O3/poly(vinylidene fluoride) composites as a function of ceramic grain size and concentration

Poly(vinylidene fluoride)/Pb(Zr0.53Ti0.47)O3,([PVDF]1−x/[PZT]x) composites of volume fractions x and (0–3) type connectivity were prepared in the form of thin films. PZT powders with average grain sizes of 0.2, 0.84, and 2.35 μm in different volume fraction of PZT up to 40 % were mixed with the polymeric matrix. The influence of the inorganic particle size and its content on the thermal degradation properties of the composites was then investigated by means of thermo-gravimetric analysis. It is observed that filler size affects more than filler concentration the degradation temperature and activation energy of the polymer. In the same way and due to their larger specific area, smaller particles leave larger solid residuals after the polymer degradation. The polymer degradation mechanism is not significantly modified by the presence of the inorganic fillers. On the other hand, an inhibition effect occurs due to the presence of the fillers, affecting particularly the activation energy of the process.

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.

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.