A proactive intelligent decision support system for predicting the popularity of online news

"Lecture notes in computer science series, ISSN 0302-9743, vol. 9273"

Intelligent decision support to predict patient barotrauma risk in intensive care units

The occurrence of Barotrauma is identified as a major concern for health professionals, since it can be fatal for patients. In order to support the decision process and to predict the risk of occurring barotrauma Data Mining models were induced. Based on this principle, the present study addresses the Data Mining process aiming to provide hourly probability of a patient has Barotrauma. The process of discovering implicit knowledge in data collected from Intensive Care Units patientswas achieved through the standard process Cross Industry Standard Process for Data Mining. With the goal of making predictions according to the classification approach they several DM techniques were selected: Decision Trees, Naive Bayes and Support Vector Machine. The study was focused on identifying the validity and viability to predict a composite variable. To predict the Barotrauma two classes were created: “risk” and “no risk”. Such target come from combining two variables: Plateau Pressure and PCO2. The best models presented a sensitivity between 96.19% and 100%. In terms of accuracy the values varied between 87.5% and 100%. This study and the achieved results demonstrated the feasibility of predicting the risk of a patient having Barotrauma by presenting the probability associated.

Development of an intelligent bio-based packaging system

[Extrat] Currently there is a growing interest in the development of eco-efficient bio-based packaging, being active, smart and intelligent packaging the most highlighted among various innovations. Intelligent packaging has the ability to detect and mark, in real time, changes that might occur within the package/in the food product. Their main purpose is to help the consumer decide whether to buy a certain food product, ensuring that when it is bought it has not suffered significant changes influencing its quality and safety. (...)

Degradation of all-inkjet-printed organic thin-film transistors with TIPS-pentacene under processes applied in textile manufacturing

Printed electronics represent an alternative solution for the manufacturing of low-temperature and large area flexible electronics. The use of inkjet printing is showing major advantages when compared to other established printing technologies such as, gravure, screen or offset printing, allowing the reduction of manufacturing costs due to its efficient material usage and the direct-writing approach without requirement of any masks. However, several technological restrictions for printed electronics can hinder its application potential, e.g. the device stability under atmospheric or even more stringent conditions. Here, we study the influence of specific mechanical, chemical, and temperature treatments usually appearing in manufacturing processes for textiles on the electrical performance of all-inkjet-printed organic thin-film transistors (OTFTs). Therefore, OTFTs where manufactured with silver electrodes, a UV curable dielectric, and 6,13-bis(triisopropylsilylethynyl) pentance (TIPS-pentacene) as the active semiconductor layer. All the layers were deposited using inkjet printing. After electrical characterization of the printed OTFTs, a simple encapsulation method was applied followed by the degradation study allowing a comparison of the electrical performance of treated and not treated OTFTs. Industrial calendering, dyeing, washing and stentering were selected as typical textile processes and treatment methods for the printed OTFTs. It is shown that the all-inkjet-printed OTFTs fabricated in this work are functional after their submission to the textiles processes but with degradation in the electrical performance, exhibiting higher degradation in the OTFTs with shorter channel lengths (L=10 μm).

Inkjet printing technology for flexible pressure sensors

Tese de Doutoramento Ciência e Engenharia de Polímeros e Compósitos.

The role of automatic shape and position recognitionin streamlining manufacturing

The main features of most components consist of simple basic functional geometries: planes, cylinders, spheres and cones. Shape and position recognition of these geometries is essential for dimensional characterization of components, and represent an important contribution in the life cycle of the product, concerning in particular the manufacturing and inspection processes of the final product. This work aims to establish an algorithm to automatically recognize such geometries, without operator intervention. Using differential geometry large volumes of data can be treated and the basic functional geometries to be dealt recognized. The original data can be obtained by rapid acquisition methods, such as 3D survey or photography, and then converted into Cartesian coordinates. The satisfaction of intrinsic decision conditions allows different geometries to be fast identified, without operator intervention. Since inspection is generally a time consuming task, this method reduces operator intervention in the process. The algorithm was first tested using geometric data generated in MATLAB and then through a set of data points acquired by measuring with a coordinate measuring machine and a 3D scan on real physical surfaces. Comparison time spent in measuring is presented to show the advantage of the method. The results validated the suitability and potential of the algorithm hereby proposed

Aplicação de princípios e técnicas de Lean Manufacturing para melhoria do desempenho de linhas de montagem

Dissertação de mestrado integrado em Engenharia e Gestão Industrial

Development of procedures for the design, optimization and manufacturing of customized orthopaedic and trauma implants: Geometrical/anatomical modelling from 3D medical imaging

Tese de Doutoramento (Programa Doutoral em Engenharia Biomédica)

Development of an intelligent earthwork optimization system

Tese de Doutoramento em Engenharia Civil.

A Real-Time intelligent system for tracking patient condition

Hospitals have multiple data sources, such as embedded systems, monitors and sensors. The number of data available is increasing and the information are used not only to care the patient but also to assist the decision processes. The introduction of intelligent environments in health care institutions has been adopted due their ability to provide useful information for health professionals, either in helping to identify prognosis or also to understand patient condition. Behind of this concept arises this Intelligent System to track patient condition (e.g. critic events) in health care. This system has the great advantage of being adaptable to the environment and user needs. The system is focused in identifying critic events from data streaming (e.g. vital signs and ventilation) which is particularly valuable for understanding the patient’s condition. This work aims to demonstrate the process of creating an intelligent system capable of operating in a real environment using streaming data provided by ventilators and vital signs monitors. Its development is important to the physician because becomes possible crossing multiple variables in real-time by analyzing if a value is critic or not and if their variation has or not clinical importance.