Nonlinear behavior of gelatin networks reveals a hierarchical structure

We investigate the strain hardening behavior of various gelatin networks-namely physical gelatin gel, chemically cross-linked gelatin gel, and a hybrid gel made of a combination of the former two-under large shear deformations using the pre-stress, strain ramp, and large amplitude oscillations shear protocols. Further, the internal structures of physical gelatin gels and chemically cross-linked gelatin gels were characterized by small angle neutron scattering (SANS) to enable their internal structures to be correlated with their nonlinear rheology. The Kratky plots of SANS data demonstrate the presence of small cross-linked aggregates within the chemically cross-linked network whereas, in the physical gelatin gels, a relatively homogeneous structure is observed. Through model fitting to the scattering data, we were able to obtain structural parameters, such as the correlation length (ξ), the cross-sectional polymer chain radius (Rc) and the fractal dimension (df) of the gel networks. The fractal dimension df obtained from the SANS data of the physical and chemically cross-linked gels is 1.31 and 1.53, respectively. These values are in excellent agreement with the ones obtained from a generalized nonlinear elastic theory that has been used to fit the stress-strain curves. The chemical cross-linking that generates coils and aggregates hinders the free stretching of the triple helix bundles in the physical gels.

Pervasive data mining engine

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

Pervasive business intelligence: a marketing intelligence framework proposal

Doctoral Thesis in Information Systems and Technologies Area of Information Systems and Technology

Evaluation of the performance of steel fibre reinforced self-compacting concrete in elevated slab systems; from the material to the structure

Degree of Doctor of Philosophy of Structural/Civil Engineering

Poly(ester-urethane) scaffolds: effect of structure on properties and osteogenic activity of stem cells

The present study aimed to investigate the effect of structure (design and porosity) on the matrix stiffness and osteogenic activity of stem cells cultured on poly(ester-urethane) (PEU) scaffolds. Different three-dimensional (3D) forms of scaffold were prepared from lysine-based PEU using traditional salt-leaching and advanced bioplotting techniques. The resulting scaffolds were characterized by differential scanning calorimetry (DSC), thermogravimetric analysis (TGA), scanning electron microscopy (SEM), mercury porosimetry and mechanical testing. The scaffolds had various pore sizes with different designs, and all were thermally stable up to 300â °C. In vitrotests, carried out using rat bone marrow stem cells (BMSCs) for bone tissue engineering, demonstrated better viability and higher cell proliferation on bioplotted scaffolds compared to salt-leached ones, most probably due to their larger and interconnected pores and stiffer nature, as shown by higher compressive moduli, which were measured by compression testing. Similarly, SEM, von Kossa staining and EDX analyses indicated higher amounts of calcium deposition on bioplotted scaffolds during cell culture. It was concluded that the design with larger interconnected porosity and stiffness has an effect on the osteogenic activity of the stem cells.

Estimativa dos parâmetros de compactação de solos usando técnicas de data mining

Dissertação de mestrado integrado em Engenharia Civil

Exploring the factor structure of neurocognitive measures in older individuals

Here we focus on factor analysis from a best practices point of view, by investigating the factor structure of neuropsychological tests and using the results obtained to illustrate on choosing a reasonable solution. The sample (n=1051 individuals) was randomly divided into two groups: one for exploratory factor analysis (EFA) and principal component analysis (PCA), to investigate the number of factors underlying the neurocognitive variables; the second to test the "best fit" model via confirmatory factor analysis (CFA). For the exploratory step, three extraction (maximum likelihood, principal axis factoring and principal components) and two rotation (orthogonal and oblique) methods were used. The analysis methodology allowed exploring how different cognitive/psychological tests correlated/discriminated between dimensions, indicating that to capture latent structures in similar sample sizes and measures, with approximately normal data distribution, reflective models with oblimin rotation might prove the most adequate.

The effect of siesta in parameters of cardiac structure and in interpretation of ambulatory arterial blood pressure monitoring

OBJECTIVE: To evaluate the influence of the siesta in ambulatory blood pressure (BP) monitoring and in cardiac structure parameters. METHODS: 1940 ambulatory arterial blood pressure monitoring tests were analyzed (Spacelabs 90207, 15/15 minutes from 7:00 to 22:00 hours and 20/20 minutes from 22:01 to 6.59hours) and 21% of the records indicated that the person had taken a siesta (263 woman, 52±14 years). The average duration of the siesta was 118±58 minutes. RESULTS: (average ± standard deviation) The average of systolic/diastolic pressures during wakefulness, including the napping period, was less than the average for the period not including the siesta (138±16/85±11 vs 139±16/86±11 mmHg, p<0.05); 2) pressure loads during wakefulness including the siesta, were less than those observed without the siesta); 3) the averages of nocturnal sleep blood pressures were similar to those of the siesta, 4) nocturnal sleep pressure drops were similar to those in the siesta including wakefulness with and without the siesta; 5) the averages of BP in men were higher (p<0.05) during wakefulness with and without the siesta, during the siesta and nocturnal sleep in relation to the average obtained in women; 6) patients with a reduction of 0- 5% during the siesta had thickening of the interventricular septum and a larger posterior wall than those with a reduction during the siesta >5%. CONCLUSION: The siesta influenced the heart structure parameters and from a statistical point of view the average of systolic and diastolic pressures and the respective pressure loads of the wakeful period.

Pervasive decision support to predict football corners and goals by means of data mining

Football is considered nowadays one of the most popular sports. In the betting world, it has acquired an outstanding position, which moves millions of euros during the period of a single football match. The lack of profitability of football betting users has been stressed as a problem. This lack gave origin to this research proposal, which it is going to analyse the possibility of existing a way to support the users to increase their profits on their bets. Data mining models were induced with the purpose of supporting the gamblers to increase their profits in the medium/long term. Being conscience that the models can fail, the results achieved by four of the seven targets in the models are encouraging and suggest that the system can help to increase the profits. All defined targets have two possible classes to predict, for example, if there are more or less than 7.5 corners in a single game. The data mining models of the targets, more or less than 7.5 corners, 8.5 corners, 1.5 goals and 3.5 goals achieved the pre-defined thresholds. The models were implemented in a prototype, which it is a pervasive decision support system. This system was developed with the purpose to be an interface for any user, both for an expert user as to a user who has no knowledge in football games.

Towards a pervasive data mining engine: architecture overview

Current data mining engines are difficult to use, requiring optimizations by data mining experts in order to provide optimal results. To solve this problem a new concept was devised, by maintaining the functionality of current data mining tools and adding pervasive characteristics such as invisibility and ubiquity which focus on their users, providing better ease of use and usefulness, by providing autonomous and intelligent data mining processes. This article introduces an architecture to implement a data mining engine, composed by four major components: database; Middleware (control); Middleware (processing); and interface. These components are interlinked but provide independent scaling, allowing for a system that adapts to the user’s needs. A prototype has been developed in order to test the architecture. The results are very promising and showed their functionality and the need for further improvements.

Predicting triage waiting time in maternity emergency care by means of data mining

Healthcare organizations often benefit from information technologies as well as embedded decision support systems, which improve the quality of services and help preventing complications and adverse events. In Centro Materno Infantil do Norte (CMIN), the maternal and perinatal care unit of Centro Hospitalar of Oporto (CHP), an intelligent pre-triage system is implemented, aiming to prioritize patients in need of gynaecology and obstetrics care in two classes: urgent and consultation. The system is designed to evade emergency problems such as incorrect triage outcomes and extensive triage waiting times. The current study intends to improve the triage system, and therefore, optimize the patient workflow through the emergency room, by predicting the triage waiting time comprised between the patient triage and their medical admission. For this purpose, data mining (DM) techniques are induced in selected information provided by the information technologies implemented in CMIN. The DM models achieved accuracy values of approximately 94% with a five range target distribution, which not only allow obtaining confident prediction models, but also identify the variables that stand as direct inducers to the triage waiting times.

Predicting pre-triage waiting time in a maternity emergency room through data mining

An unsuitable patient flow as well as prolonged waiting lists in the emergency room of a maternity unit, regarding gynecology and obstetrics care, can affect the mother and child’s health, leading to adverse events and consequences regarding their safety and satisfaction. Predicting the patients’ waiting time in the emergency room is a means to avoid this problem. This study aims to predict the pre-triage waiting time in the emergency care of gynecology and obstetrics of Centro Materno Infantil do Norte (CMIN), the maternal and perinatal care unit of Centro Hospitalar of Oporto, situated in the north of Portugal. Data mining techniques were induced using information collected from the information systems and technologies available in CMIN. The models developed presented good results reaching accuracy and specificity values of approximately 74% and 94%, respectively. Additionally, the number of patients and triage professionals working in the emergency room, as well as some temporal variables were identified as direct enhancers to the pre-triage waiting time. The imp lementation of the attained knowledge in the decision support system and business intelligence platform, deployed in CMIN, leads to the optimization of the patient flow through the emergency room and improving the quality of services.

Real-Time decision support using data mining to predict blood pressure critical events in intensive medicine patients

Patient blood pressure is an important vital signal to the physicians take a decision and to better understand the patient condition. In Intensive Care Units is possible monitoring the blood pressure due the fact of the patient being in continuous monitoring through bedside monitors and the use of sensors. The intensivist only have access to vital signs values when they look to the monitor or consult the values hourly collected. Most important is the sequence of the values collected, i.e., a set of highest or lowest values can signify a critical event and bring future complications to a patient as is Hypotension or Hypertension. This complications can leverage a set of dangerous diseases and side-effects. The main goal of this work is to predict the probability of a patient has a blood pressure critical event in the next hours by combining a set of patient data collected in real-time and using Data Mining classification techniques. As output the models indicate the probability (%) of a patient has a Blood Pressure Critical Event in the next hour. The achieved results showed to be very promising, presenting sensitivity around of 95%.

Right ventricular structure and function as possible determinants of surgical outcome 30 years after repair of tetralogy of Fallot

OBJECTIVE: To identify the variables that may be involved in the persistence of symptoms (functional class II, III, or IV vs. I) in patients being followed up for 30 years after surgical repair of tetralogy of Fallot. METHODS: Fifty-three patients (27 women), who underwent corrective surgery for tetralogy of Fallot between 1960 and 1970, were studied. Their ages ranged from 7 months to 26 years. At the end of follow-up, 13 patients were asymptomatic and the remaining were in functional class II (N=24), III (N=15), and IV (N=1). To differentiate asymptomatic from symptomatic patients, the following variables were analyzed: age at surgery, need for widening the pulmonary ring and trunk, need for a second (2nd OP) or 3rd operation, residual defect of the interventricular septum, residual regurgitation of the pulmonary valve, systolic gradient through the right ventricular outflow tract, right ventricular dilation or hypertrophy (RVH), cardiothoracic index (CTI), right and left ventricular ejection fraction (RVEF/LVEF), and arrhythmias. RESULTS: The univariate analysis showed an association between the presence of symptoms and the 2nd OP (P=0.03), an increase in the CTI (P=0.0001), moderate to severe RVH (P=0.002), and dilation (P=0.0003). In the logistic regression model, the combination of the 2nd OP (P=0.008), the RVH (P=0.002), and the reduction in RVEF (P=0.01) determined the presence of symptoms. CONCLUSION: Despite the surgical treatment, right ventricular remodeling and performance were the major determinants in the late follow-up of tetralogy of Fallot.

Genetic diversity and population structure in Vicia faba L. landraces and wild related species assessed by nuclear SSRs

Research Article