Improving quality of medical service with mobile health software

An increasing number of m-Health applications are being developed benefiting health service delivery. In this paper, a new methodology based on the principle of calm computing applied to diagnostic and therapeutic procedure reporting is proposed. A mobile application was designed for the physicians of one of the Portuguese major hospitals, which takes advantage of a multi-agent interoperability platform, the Agency for the Integration, Diffusion and Archive (AIDA). This application allows the visualization of inpatients and outpatients medical reports in a quicker and safer manner, in addition to offer a remote access to information. This project shows the advantages in the use of mobile software in a medical environment but the first step is always to build or use an interoperability platform, flexible, adaptable and pervasive. The platform offers a comprehensive set of services that restricts the development of mobile software almost exclusively to the mobile user interface design. The technology was tested and assessed in a real context by intensivists.

Integrated management systems: A model for maturity assessment

Maturity models are adopted to minimise our complexity perception over a truly complex phenomenon. In this sense, maturity models are tools that enable the assessment of the most relevant variables that impact on the outputs of a specific system. Ideally a maturity model should provide information concerning the qualitative and quantitative relationships between variables and how they affect the latent variable, that is, the maturity level. Management systems (MSs) are implemented worldwide and by an increasing number of companies. Integrated management systems (IMSs) consider the implementation of one or several MSs usually coexisting with the quality management subsystem (QMS). It is intended in this chapter to report a model based on two components that enables the assessment of the IMS maturity, considering the key process agents (KPAs) identified through a systematic literature review and the results collected from two surveys.

DOTA-based Ga(III) and Gd(III) chelates for medical imaging (PET, SPECT and MRI)

PhD Thesis in Sciences Specialization in Chemistry

Tailored magnetic and magnetoelectric responses of polymer-based composites

The manipulation of electric ordering with applied magnetic fields has been realized on magnetoelectric (ME) materials, however, their ME switching is often accompanied by significant hysteresis and coercivity that represents, for some applications, a severe weakness. To overcome this obstacle, this work focus on the development of a new type of ME polymer nanocomposites that exhibits tailored ME response at room temperature. The multiferroic nanocomposites are based on three different ferrite nanoparticles, Zn0.2Mn0.8Fe2O4 (ZMFO), CoFe2O4 (CFO) and Fe3O4 (FO), dispersed in a piezoelectric co-polymer poly(vinylindene fluoride-trifluoroethylene), P(VDF-TrFE), matrix. No substantial differences were detected on the time-stable piezoelectric response of the composites (≈ -28 pC.N−1) with distinct ferrite fillers and for the same ferrite content of 10wt.%. Magnetic hysteresis loops from pure ferrite nanopowders showed different magnetic responses. ME results of the nanocomposite films with 10wt.% ferrite content revealed that the ME induced voltage increases with increasing DC magnetic field until a maximum of 6.5 mV∙cm−1∙Oe−1, at an optimum magnetic field of 0.26 T, and 0.8 mV∙cm−1∙Oe−1, at an optimum magnetic field of 0.15T, for the CFO/P(VDF-TrFE) and FO/P(VDF-TrFE) composites, respectively. On the contrary, the ME response of the ZMFO/P(VDF-TrFE) exposed no hysteresis and high dependence on the ZMFO filler content. Possible innovative applications such as memories and information storage, signal processing, ME sensors and oscillators have been addressed for such ferrite/PVDF nanocomposites.

Discrimination of Brazilian propolis according to the seasoning using chemometrics and machine learning based on UV-Vis scanning data

Propolis is a chemically complex biomass produced by honeybees (Apis mellifera) from plant resins added of salivary enzymes, beeswax, and pollen. The biological activities described for propolis were also identified for donor plants resin, but a big challenge for the standardization of the chemical composition and biological effects of propolis remains on a better understanding of the influence of seasonality on the chemical constituents of that raw material. Since propolis quality depends, among other variables, on the local flora which is strongly influenced by (a)biotic factors over the seasons, to unravel the harvest season effect on the propolis chemical profile is an issue of recognized importance. For that, fast, cheap, and robust analytical techniques seem to be the best choice for large scale quality control processes in the most demanding markets, e.g., human health applications. For that, UV-Visible (UV-Vis) scanning spectrophotometry of hydroalcoholic extracts (HE) of seventy-three propolis samples, collected over the seasons in 2014 (summer, spring, autumn, and winter) and 2015 (summer and autumn) in Southern Brazil was adopted. Further machine learning and chemometrics techniques were applied to the UV-Vis dataset aiming to gain insights as to the seasonality effect on the claimed chemical heterogeneity of propolis samples determined by changes in the flora of the geographic region under study. Descriptive and classification models were built following a chemometric approach, i.e. principal component analysis (PCA) and hierarchical clustering analysis (HCA) supported by scripts written in the R language. The UV-Vis profiles associated with chemometric analysis allowed identifying a typical pattern in propolis samples collected in the summer. Importantly, the discrimination based on PCA could be improved by using the dataset of the fingerprint region of phenolic compounds ( = 280-400m), suggesting that besides the biological activities of those secondary metabolites, they also play a relevant role for the discrimination and classification of that complex matrix through bioinformatics tools. Finally, a series of machine learning approaches, e.g., partial least square-discriminant analysis (PLS-DA), k-Nearest Neighbors (kNN), and Decision Trees showed to be complementary to PCA and HCA, allowing to obtain relevant information as to the sample discrimination.

Abstract computation in schizophrenia detection through artificial neural network based systems

Schizophrenia stands for a long-lasting state of mental uncertainty that may bring to an end the relation among behavior, thought, and emotion; that is, it may lead to unreliable perception, not suitable actions and feelings, and a sense of mental fragmentation. Indeed, its diagnosis is done over a large period of time; continuos signs of the disturbance persist for at least 6 (six) months. Once detected, the psychiatrist diagnosis is made through the clinical interview and a series of psychic tests, addressed mainly to avoid the diagnosis of other mental states or diseases. Undeniably, the main problem with identifying schizophrenia is the difficulty to distinguish its symptoms from those associated to different untidiness or roles. Therefore, this work will focus on the development of a diagnostic support system, in terms of its knowledge representation and reasoning procedures, based on a blended of Logic Programming and Artificial Neural Networks approaches to computing, taking advantage of a novel approach to knowledge representation and reasoning, which aims to solve the problems associated in the handling (i.e., to stand for and reason) of defective information.

Metabolic profiling and classification of propolis samples from Southern Brazil: an NMR-based platform coupled with machine learning

The chemical composition of propolis is affected by environmental factors and harvest season, making it difficult to standardize its extracts for medicinal usage. By detecting a typical chemical profile associated with propolis from a specific production region or season, certain types of propolis may be used to obtain a specific pharmacological activity. In this study, propolis from three agroecological regions (plain, plateau, and highlands) from southern Brazil, collected over the four seasons of 2010, were investigated through a novel NMR-based metabolomics data analysis workflow. Chemometrics and machine learning algorithms (PLS-DA and RF), including methods to estimate variable importance in classification, were used in this study. The machine learning and feature selection methods permitted construction of models for propolis sample classification with high accuracy (>75%, reaching 90% in the best case), better discriminating samples regarding their collection seasons comparatively to the harvest regions. PLS-DA and RF allowed the identification of biomarkers for sample discrimination, expanding the set of discriminating features and adding relevant information for the identification of the class-determining metabolites. The NMR-based metabolomics analytical platform, coupled to bioinformatic tools, allowed characterization and classification of Brazilian propolis samples regarding the metabolite signature of important compounds, i.e., chemical fingerprint, harvest seasons, and production regions.

Evaluating biomaterial-and microfluidic-based 3D tumor models

Cancer is a major cause of morbidity and mortality worldwide, with a disease burden estimated to increase in the coming decades. Disease heterogeneity and limited information on cancer biology and disease mechanisms are aspects that 2D cell cultures fail to address. We review the current "state-of-the-art" in 3D Tissue Engineering (TE) models developed for and used in cancer research. Scaffold-based TE models and microfluidics, are assessed for their potential to fill the gap between 2D models and clinical application. Recent advances in combining the principles of 3D TE models and microfluidics are discussed, with a special focus on biomaterials and the most promising chip-based 3D models.

An experimental investigation on nano-TiO2 and fly ash based high performance concrete

High performance concrete (HPC) offers several advantages over normal-strength concrete, namely, high mechanical strength and high durability. Therefore, HPC allows for concrete structures with less steel reinforcement and a longer service life, both of which are crucial issues in the eco-efficiency of construction materials. Nevertheless international publications on the field of concrete containing nanoparticles are scarce when compared to Portland cement concrete (around 1%) of the total international publications. HPC nanoparticle-based publications are even scarcer. This article presents the results of an experimental investigation on the mechanical properties and durability of HPC based on nano-TiO2 and fly ash. The durability performance was assessed by means of water absorption by immersion, water absorption by capillarity, ultrasonic pulse velocity, electric resistivity, chloride diffusion and resistance to sulphuric acid attack. The results show that the concretes containing an increased content of nano-TiO2 show decreased durability performance. The results also show that concrete with 1% nano-TiO2 and 30% fly ash as Portland cement replacement show a high mechanical strength (C55/C67) and a high durability. However, it should be noted that the cost of nano-TiO2 is responsible for a severe increase in the cost of concrete mixtures.

A integração de técnicas BIM (Building Information Modeling) no projeto e na construção de estruturas de madeira

Dissertação de mestrado integrado em Engenharia Civil

Hand gesture recognition system based in computer vision and machine learning: Applications on human-machine interaction

Tese de Doutoramento em Engenharia de Eletrónica e de Computadores

Cognitive functioning and efficacy of cognitive intervention in Multiple Sclerosis

Tese de Doutoramento em Psicologia Clínica / Psicologia

Towards an engine for coordination-based architectural reconfigurations

Software reconfigurability became increasingly relevant to the architectural process due to the crescent dependency of modern societies on reliable and adaptable systems. Such systems are supposed to adapt themselves to surrounding environmental changes with minimal service disruption, if any. This paper introduces an engine that statically applies reconfigurations to (formal) models of software architectures. Reconfigurations are specified using a domain specific language— ReCooPLa—which targets the manipulation of software coordinationstructures,typicallyusedinservice-orientedarchitectures(soa).Theengine is responsible for the compilation of ReCooPLa instances and their application to the relevant coordination structures. The resulting configurations are amenable to formal analysis of qualitative and quantitative (probabilistic) properties.

High performance screen printable lithium-ion battery cathode ink based on C-LiFePO4

Lithium-ion battery cathodes have been fabricated by screen-printing through the development of CLiFePO4 inks. It is shown that shear thinning polymer solutions in N-methyl-2-pyrrolidone (NMP) with Newtonian viscosity above 0.4 Pa s are the best binders for formulating a cathode paste with satisfactory film forming properties. The paste shows an elasticity of the order of 500 Pa and, after shear yielding, shows an apparent viscosity of the order of 3 Pa s for shear rates corresponding to those used during screen-printing. The screen-printed cathode produced with a thickness of 26 mm shows a homogeneous distribution of the active material, conductive additive and polymer binder. The total resistance and diffusion coefficient of the cathode are 450 V and 2.5 10 16cm2 s 1, respectively. The developed cathodes show an initial discharge capacity of 48.2 mAh g 1 at 5C and a discharge value of 39.8 mAh g 1 after 50 cycles. The capacity retention of 83% represents 23% of the theoretical value (charge and/or discharge process in twenty minutes), demonstrating the good performance of the battery. Thus, the developed C-LiFePO4 based inks allow to fabricate screen-printed cathodes suitable for printed lithium-ion batteries

Development of multifunctional coatings deposited on polymers based sensors for biomedical applications

Tese de Doutoramento (Programa Doutoral em Engenharia de Materiais)