Coagulation/flocculation/flotation/nanofiltration processes using Moringa Oleifera as coagulant of Eutrophized river

This study investigated the efficiency of Moringa oleifera (MO) seeds as natural coagulant in coagulation/flocculation/dissolved air flotation (C/F/DAF), followed by nanofiltration (NF) for Microcystis protocystis and microcystin-LR removal. The methodology adopted in this work was performed in two steps: 1) coagulation/flocculation/dissolved air flotation (C/F/DAF) process using the MO extracted in saline solution of potassium chloride (KCl-1M) and sodium chloride (NaCl-1M) in optimum dosage 50 mg·L-1; 2) nanofiltration process using NF90 and NF270 membrane provided Dow Chemical Company®. A working pressure of 8 bar was applied. In all samples were analyzed color, turbidity, pH, cyanobacterial cells count and microcystin concentration. The use of MO seeds as natural coagulant, obtained satisfactory results in the M. protocystis, color and turbidity removal. NF was able to completely remove cyanobacterial cells and microcystins (100 %) from M. protocystis (always under the quantification limit). Therefore, C/F/DAF+NF sequence is a safe barrier against M. protocystis and microcystins in drinking water.

Fucoidan-based particles for diabetes mellitus treatment

Marine organisms are rich in a variety of materials with potential use in Tissue Engineering and Regenerative Medicine. One important example is fucoidan, a sulfated polysaccharide extracted from the cell wall of brown seaweeds.  Fucoidan is composed by L-fucose, sulfate groups and glucuronic acid. It has important bioactive properties such as anti-oxidative, anticoagulant, anticancer and reducing the blood glucose (1). In this work, the biomedical potential of fucoidan-based materials as drug delivery system was assessed by processing modified fucoidan (MFu) into particles by photocrosslinking using superamphiphobic surfaces and visible light. Fucoidan was modified by methacrylation reaction using different concentrations of methacrylate anhydride, namely 8% v/v (MFu1) and 12% v/v (MFu2). Further, MFu particles with and without insulin (5% w/v) were produced by pipetting a solution of 5% MFu with triethanolamine and eosin-y onto a superamphiphobic surface and then photocrosslinking using visible light (2). The developed particles were characterized to assess their chemistry, morphology, swelling behavior, drug release, insulin content and encapsulation efficiency. Moreover, the viability assays of fibroblast L929 cells in contact with MFu particles showed good adhesion and proliferation up to 14 days. Furthermore, the therapeutic potential of these particles using human beta cells is currently under investigation. Results obtained so far suggest that modified fucoidan particles could be a good candidate for diabetes mellitus therapeutic approaches.  

The influence of fibre orientation on the post-cracking tensile behaviour of steel fibre reinforced self-compacting concrete

Adding fibres to concrete provides several advantages, especially in terms of controlling the crack opening width and propagation after the cracking onset. However, distribution and orientation of the fibres toward the active crack plane are significantly important in order to maximize its benefits. Therefore, in this study, the effect of the fibre distribution and orientation on the post-cracking tensile behaviour of the steel fibre reinforced self-compacting concrete (SFRSCC) specimens is investigated. For this purpose, several cores were extracted from distinct locations of a panel and were subjected to indirect (splitting) and direct tensile tests. The local stress-crack opening relationship (σ-w) was obtained by modelling the splitting tensile test under the finite element framework and by performing an Inverse Analysis (IA) procedure. Afterwards the σ-w law obtained from IA is then compared with the one ascertained directly from the uniaxial tensile tests. Finally, the fibre distribution/orientation parameters were determined adopting an image analysis technique.

Using data mining for prediction of hospital length of stay: an application of the CRISP-DM Methodology

Hospitals are nowadays collecting vast amounts of data related with patient records. All this data hold valuable knowledge that can be used to improve hospital decision making. Data mining techniques aim precisely at the extraction of useful knowledge from raw data. This work describes an implementation of a medical data mining project approach based on the CRISP-DM methodology. Recent real-world data, from 2000 to 2013, were collected from a Portuguese hospital and related with inpatient hospitalization. The goal was to predict generic hospital Length Of Stay based on indicators that are commonly available at the hospitalization process (e.g., gender, age, episode type, medical specialty). At the data preparation stage, the data were cleaned and variables were selected and transformed, leading to 14 inputs. Next, at the modeling stage, a regression approach was adopted, where six learning methods were compared: Average Prediction, Multiple Regression, Decision Tree, Artificial Neural Network ensemble, Support Vector Machine and Random Forest. The best learning model was obtained by the Random Forest method, which presents a high quality coefficient of determination value (0.81). This model was then opened by using a sensitivity analysis procedure that revealed three influential input attributes: the hospital episode type, the physical service where the patient is hospitalized and the associated medical specialty. Such extracted knowledge confirmed that the obtained predictive model is credible and with potential value for supporting decisions of hospital managers.

Time-dependent flexural behaviour of cracked steel fibre reinforced self-compacting concrete panels

In the present work are described and discussed the results of an extensive experimental program that aims to study the long-term behaviour of cracked steel fibre reinforced self-compacting concrete, SFRSCC, applied in laminar structures. In a first stage, the influence of the initial crack opening level (wcr = 0.3 and 0.5 mm), applied stress level, fibre orientation/dispersion and distance from the casting point, on the flexural creep behaviour of SFRSCC was investigated. Moreover, in order to evaluate the effects of the creep phenomenon on the residual flexural strength, a series of monotonic tests were also executed. It was found that wcr = 0.5 mm series showed a higher creep coefficient comparing to the series with a lower initial crack opening. Furthermore, the creep performance of the SFRSCC was influenced by the orientation of the extracted prismatic specimens regarding the direction of the concrete flow within the cast panel.

Vision-based hand Wheel-chair control

Several studies have shown that people with disabilities benefit substantially from access to a means of independent mobility and assistive technology. Researchers are using technology originally developed for mobile robots to create easier to use wheelchairs. With this kind of technology people with disabilities can gain a degree of independence in performing daily life activities. In this work a computer vision system is presented, able to drive a wheelchair with a minimum number of finger commands. The user hand is detected and segmented with the use of a kinect camera, and fingertips are extracted from depth information, and used as wheelchair commands.

Using customer lifetime value to improve the prediction of bank deposit subscription in telemarketing campaigns

Customer lifetime value (LTV) enables using client characteristics, such as recency, frequency and monetary (RFM) value, to describe the value of a client through time in terms of profitability. We present the concept of LTV applied to telemarketing for improving the return-on-investment, using a recent (from 2008 to 2013) and real case study of bank campaigns to sell long- term deposits. The goal was to benefit from past contacts history to extract additional knowledge. A total of twelve LTV input variables were tested, un- der a forward selection method and using a realistic rolling windows scheme, highlighting the validity of five new LTV features. The results achieved by our LTV data-driven approach using neural networks allowed an improvement up to 4 pp in the Lift cumulative curve for targeting the deposit subscribers when compared with a baseline model (with no history data). Explanatory knowledge was also extracted from the proposed model, revealing two highly relevant LTV features, the last result of the previous campaign to sell the same product and the frequency of past client successes. The obtained results are particularly valuable for contact center companies, which can improve pre- dictive performance without even having to ask for more information to the companies they serve.

Hand gesture recognition for human computer interaction : a comparative study of different image features

Hand gesture recognition for human computer interaction, being a natural way of human computer interaction, is an area of active research in computer vision and machine learning. This is an area with many different possible applications, giving users a simpler and more natural way to communicate with robots/systems interfaces, without the need for extra devices. So, the primary goal of gesture recognition research is to create systems, which can identify specific human gestures and use them to convey information or for device control. For that, vision-based hand gesture interfaces require fast and extremely robust hand detection, and gesture recognition in real time. In this study we try to identify hand features that, isolated, respond better in various situations in human-computer interaction. The extracted features are used to train a set of classifiers with the help of RapidMiner in order to find the best learner. A dataset with our own gesture vocabulary consisted of 10 gestures, recorded from 20 users was created for later processing. Experimental results show that the radial signature and the centroid distance are the features that when used separately obtain better results, with an accuracy of 91% and 90,1% respectively obtained with a Neural Network classifier. These to methods have also the advantage of being simple in terms of computational complexity, which make them good candidates for real-time hand gesture recognition.

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

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

An integrated system for human action recognition from video using hidden Markov model

In this paper, we present an integrated system for real-time automatic detection of human actions from video. The proposed approach uses the boundary of humans as the main feature for recognizing actions. Background subtraction is performed using Gaussian mixture model. Then, features are extracted from silhouettes and Vector Quantization is used to map features into symbols (bag of words approach). Finally, actions are detected using the Hidden Markov Model. The proposed system was validated using a newly collected real- world dataset. The obtained results show that the system is capable of achieving robust human detection, in both indoor and outdoor environments. Moreover, promising classification results were achieved when detecting two basic human actions: walking and sitting.

On the impact of overlapping access points in detecting node encounters

Data traces, consisting of logs about the use of mobile and wireless networks, have been used to study the statistics of encounters between mobile nodes, in an attempt to predict the performance of opportunistic networks. Understanding the role and potential of mobile devices as relaying nodes in message dissemination and delivery depends on the knowledge about patterns and number of encounters among nodes. Data traces about the use of WiFi networks are widely available and can be used to extract large datasets of encounters between nodes. However, these logs only capture indirect encounters between nodes, and the resulting encounters datasets might not realistically represent the spatial and temporal behaviour of nodes. This paper addresses the impact of overlapping between the coverage areas of different Access Points of WiFi networks in extracting encounters datasets from the usage logs. Simulation and real-world experimental results show that indirect encounter traces extracted directly from these logs strongly underestimate the opportunities for direct node-to- node message exchange in opportunistic networks.

A spectrum-driven damage Identification technique : application and validation through the numerical simulation of the Z24 Bridge

The present paper focuses on a damage identification method based on the use of the second order spectral properties of the nodal response processes. The explicit dependence on the frequency content of the outputs power spectral densities makes them suitable for damage detection and localization. The well-known case study of the Z24 Bridge in Switzerland is chosen to apply and further investigate this technique with the aim of validating its reliability. Numerical simulations of the dynamic response of the structure subjected to different types of excitation are carried out to assess the variability of the spectrum-driven method with respect to both type and position of the excitation sources. The simulated data obtained from random vibrations, impulse, ramp and shaking forces, allowed to build the power spectrum matrix from which the main eigenparameters of reference and damage scenarios are extracted. Afterwards, complex eigenvectors and real eigenvalues are properly weighed and combined and a damage index based on the difference between spectral modes is computed to pinpoint the damage. Finally, a group of vibration-based damage identification methods are selected from the literature to compare the results obtained and to evaluate the performance of the spectral index.

Measurement of Spin Correlation in Top-Antitop Quark Events and Search for Top Squark Pair Production in pp Collisions at s√=8 TeV Using the ATLAS Detector

A measurement of spin correlation in tt¯ production is presented using data collected with the ATLAS detector at the Large Hadron Collider in proton-proton collisions at a center-of-mass energy of 8 TeV, corresponding to an integrated luminosity of 20.3 fb−1. The correlation between the top and antitop quark spins is extracted from dilepton tt¯ events by using the difference in azimuthal angle between the two charged leptons in the laboratory frame. In the helicity basis the measured degree of correlation corresponds to Ahelicity=0.38±0.04, in agreement with the Standard Model prediction. A search is performed for pair production of top squarks with masses close to the top quark mass decaying to predominantly right-handed top quarks and a light neutralino, the lightest supersymmetric particle. Top squarks with masses between the top quark mass and 191 GeV are excluded at the 95% confidence level.

Phase morphology and crystallinity of poly(vinylidene fluoride)/poly(ethylene oxide) piezoelectric blend membranes

Polymer blends based on poly(vinylidene fluoride), PVDF and poly(ethylene oxide), PEO, with varying compositions have been prepared by solvent casting, the polymer blend films being obtained from solutions in dimethyl formamide at 70ºC. Under these conditions PVDF crystallizes from solution while PEO remains in the molten state. Then, PEO crystallizes from the melt confined by PVDF crystalls during cooling to room temperature. PVDF crystallized from DMF solutions adopt predominantly the electroactive β-phase (85%). Nevertheless when PEO is introduced in the polymer blend the β-phase content decreases slightly to 70%. The piezoelectric coefficient (d33) in pristine PVDF is -5 pC/N and decreases with increasing PEO content in the PVDF/PEO blends. Blend morphology, observed by electron and atomic force microscopy, shows the confinement of PEO between the already formed PVDF crystals. On the other hand the sample contraction when PEO is extracted from the blend with water (which is not a solvent for PVDF) allows proving the co-continuity of both phases in the blend. PEO crystallization kinetics have been characterized by DSC both in isothermal and cooling scans experiments showing important differences in crystalline fraction and crystallization rate with sample composition.

Produção de bioplásticos a partir de agro-resíduos

Dissertação de mestrado integrado em Engenharia de Materiais