Cell encapsulated hydrogel microspheres as "building blocks" for producing 3D constructs

Cell encapsulation within hydrogel microspheres shows great promise in the field of tissue engineering and regenerative medicine (TERM). However, the assembling of microspheres as building blocks to produce complex tissues is a hard task because of their inability to place along length scales in space. We propose a proof-of-concept strategy to produce 3D constructs using cell encapsulated as building blocks by perfusion based LbL technique. This technique exploits the â bindingâ potential of multilayers apart from coating

Foamed Bitumen: an alternative way of producing asphalt mixtures

With the constant need to improve and make the production of asphalt mixtures more sustainable, new production techniques have been developed, the implementation of which implies the correct knowledge of their performance. One of the most promising asphalt production techniques is the use of foamed bitumen. However, it is essential to understand how this binder will behave when subject to the expansion process. The loss of volume of the foamed bitumen could be translated by a decay curve, which allows to determine the ideal temperature and water content added to the bitumen in order to assure adequate conditions to the mix the bitumen with the aggregates. On the present study, a conventional 160/220 pen grade bitumen was tested by using different temperatures and water contents, and it was concluded that the optimum temperature for the production of foamed bitumen (with the studied bitumen) is 150 ºC, which corresponds to a viscosity of 0.1 Pa.s. The water content mostly influence the half-life of the bitumen foam, resulting in quicker volume reductions for higher water contents.

Business intelligence in banking: A literature analysis from 2002 to 2013 using Text Mining and latent Dirichlet allocation

telligence applications for the banking industry. Searches were performed in relevant journals resulting in 219 articles published between 2002 and 2013. To analyze such a large number of manuscripts, text mining techniques were used in pursuit for relevant terms on both business intelligence and banking domains. Moreover, the latent Dirichlet allocation modeling was used in or- der to group articles in several relevant topics. The analysis was conducted using a dictionary of terms belonging to both banking and business intelli- gence domains. Such procedure allowed for the identification of relationships between terms and topics grouping articles, enabling to emerge hypotheses regarding research directions. To confirm such hypotheses, relevant articles were collected and scrutinized, allowing to validate the text mining proce- dure. The results show that credit in banking is clearly the main application trend, particularly predicting risk and thus supporting credit approval or de- nial. There is also a relevant interest in bankruptcy and fraud prediction. Customer retention seems to be associated, although weakly, with targeting, justifying bank offers to reduce churn. In addition, a large number of ar- ticles focused more on business intelligence techniques and its applications, using the banking industry just for evaluation, thus, not clearly acclaiming for benefits in the banking business. By identifying these current research topics, this study also highlights opportunities for future research.

Producing LFT composite parts for large consumption markets from thermoplastic powder-coated towpregs

Thermoplastic matrix composites are receiving increasing interest in last years. This is due to several advantageous properties and speed of processing of these materials as compared to their thermoset counterparts. Among thermoplastic composites, Long Fibre Thermoplastics (LFTs) have seen the fastest growth, mainly due to developments in the automotive sector. LFTs combine the (semi-)structural material properties of long (>1 cm) fibres, with the ease and speed of thermoplastic processing. This paper reports a study of a novel low-cost LFT technology and resulting composites. A patented powder-coating machine able to produce continuously pre-impregnated materials directly from fibre rovings and polymer powders was used to process glass-fibre reinforced polypropylene (GF/PP) towpregs. Such pre-impregnated materials were then chopped and used to make LFTs in a patented low-cost piston-blender developed by the Centre of Lightweight Structures, TUD-TNO, the Netherlands. The work allowed studying the most relevant towpreg production parameters and establishing the processing window needed to obtain a good quality GF/PP powder coated material. Finally, the processing window that allows producing LFTs of good quality in the piston-blender and the mechanical properties of final stamped GF/PP composite parts were also determined.

Reconstructing transcriptional regulatory networks using data integration and text mining

Transcriptional Regulatory Networks (TRNs) are powerful tool for representing several interactions that occur within a cell. Recent studies have provided information to help researchers in the tasks of building and understanding these networks. One of the major sources of information to build TRNs is biomedical literature. However, due to the rapidly increasing number of scientific papers, it is quite difficult to analyse the large amount of papers that have been published about this subject. This fact has heightened the importance of Biomedical Text Mining approaches in this task. Also, owing to the lack of adequate standards, as the number of databases increases, several inconsistencies concerning gene and protein names and identifiers are common. In this work, we developed an integrated approach for the reconstruction of TRNs that retrieve the relevant information from important biological databases and insert it into a unique repository, named KREN. Also, we applied text mining techniques over this integrated repository to build TRNs. However, was necessary to create a dictionary of names and synonyms associated with these entities and also develop an approach that retrieves all the abstracts from the related scientific papers stored on PubMed, in order to create a corpora of data about genes. Furthermore, these tasks were integrated into @Note, a software system that allows to use some methods from the Biomedical Text Mining field, including an algorithms for Named Entity Recognition (NER), extraction of all relevant terms from publication abstracts, extraction relationships between biological entities (genes, proteins and transcription factors). And finally, extended this tool to allow the reconstruction Transcriptional Regulatory Networks through using scientific literature.

“Learning-by-producing” in the fashion value chain

Proceedings da AUTEX 2015, Bucareste, Roménia.

Text mining na análise de sentimentos em contextos de big data

Dissertação de mestrado integrado em Engenharia e Gestão de Sistemas de Informação