Qualitative risk assessment during polymer mortar test specimens preparation - methods comparison

Polymer binder modification with inorganic nanomaterials (NM) could be a potential and efficient solution to control matrix flammability of polymer concrete (PC) materials without sacrificing other important properties. Occupational exposures can occur all along the life cycle of a NM and “nanoproducts” from research through scale-up, product development, manufacturing, and end of life. The main objective of the present study is to analyse and compare different qualitative risk assessment methods during the production of polymer mortars (PM) with NM. The laboratory scale production process was divided in 3 main phases (pre-production, production and post-production), which allow testing the assessment methods in different situations. The risk assessment involved in the manufacturing process of PM was made by using the qualitative analyses based on: French Agency for Food, Environmental and Occupational Health & Safety method (ANSES); Control Banding Nanotool (CB Nanotool); Ecole Polytechnique Fédérale de Lausanne method (EPFL); Guidance working safely with nanomaterials and nanoproducts (GWSNN); Istituto Superiore per la Prevenzione e la Sicurezza del Lavoro, Italy method (ISPESL); Precautionary Matrix for Synthetic Nanomaterials (PMSN); and Stoffenmanager Nano. It was verified that the different methods applied also produce different final results. In phases 1 and 3 the risk assessment tends to be classified as medium-high risk, while for phase 2 the more common result is medium level. It is necessary to improve the use of qualitative methods by defining narrow criteria for the methods selection for each assessed situation, bearing in mind that the uncertainties are also a relevant factor when dealing with the risk related to nanotechnologies field.

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.

Automated network resilience optimization using computational intelligence methods

This paper presents an automated optimization framework able to provide network administrators with resilient routing configurations for link-state protocols, such as OSPF or IS-IS. In order to deal with the formulated NP-hard optimization problems, the devised framework is underpinned by the use of computational in- telligence optimization engines, such as Multi-objective Evolutionary Algorithms (MOEAs). With the objective of demonstrating the framework capabilities, two il- lustrative Traffic Engineering methods are described, allowing to attain routing con- figurations robust to changes in the traffic demands and maintaining the network stable even in the presence of link failure events. The presented illustrative results clearly corroborate the usefulness of the proposed automated framework along with the devised optimization methods.

Comparison of different numerical methods for the solution of the time-fractional reaction-diffusion equation with variable diffusion coefficient

In this work we perform a comparison of two different numerical schemes for the solution of the time-fractional diffusion equation with variable diffusion coefficient and a nonlinear source term. The two methods are the implicit numerical scheme presented in [M.L. Morgado, M. Rebelo, Numerical approximation of distributed order reaction- diffusion equations, Journal of Computational and Applied Mathematics 275 (2015) 216-227] that is adapted to our type of equation, and a colocation method where Chebyshev polynomials are used to reduce the fractional differential equation to a system of ordinary differential equations

Modeling microbes: New methods for integrated metabolic and regulatory network reconstruction

PhD Thesis in Bioengineering

Improvement of in silico strain engineering methods in Saccharomyces cerevisiae

PhD thesis in Bioengineering

In silico constraint-based strain optimization methods: the quest for optimal cell factories

Shifting from chemical to biotechnological processes is one of the cornerstones of 21st century industry. The production of a great range of chemicals via biotechnological means is a key challenge on the way toward a bio-based economy. However, this shift is occurring at a pace slower than initially expected. The development of efficient cell factories that allow for competitive production yields is of paramount importance for this leap to happen. Constraint-based models of metabolism, together with in silico strain design algorithms, promise to reveal insights into the best genetic design strategies, a step further toward achieving that goal. In this work, a thorough analysis of the main in silico constraint-based strain design strategies and algorithms is presented, their application in real-world case studies is analyzed, and a path for the future is discussed.

PKO: alternative method for isolating mycobacteria from sputum

We elaborated an alternative culture method, which we denominated PKO (initials in tribute of respect to Petroff, Kudoh and Ogawa), for isolating Mycobacterium tuberculosis from sputum for diagnosis of pulmonary tuberculosis (TB), and to compare its performance with the Swab and Petroff methods. For the technique validation, sputum samples from patients suspected of pulmonary TB cases were examined by acid-fast microscopy (direct and concentrated smear), PKO, Swab and Petroff methods. We found that Petroff and PKO methods have parity in the effectiveness of M. tuberculosis isolation. However, by the PKO method, 65% of isolated strains were detected in a period of £15 days, while by the Petroff method the best detection was in an interval of 16-29 days (71%). In positive smear samples, the average time of PKO isolation is only superior to the one related for Bactec 460TB. In conclusion, the exclusion of the neutralization stage of pH in the PKO reduces the manipulation of the samples, diminishes the execution time of the culture according to the Petroff method and facilitates the qualification of professionals involved in the laboratorial diagnosis of Tuberculosis.

Project management under uncertainty: a study on solution methods

Project Management involves onetime endeavors that demand for getting it right the first time. On the other hand, project scheduling, being one of the most modeled project management process stages, still faces a wide gap from theory to practice. Demanding computational models and their consequent call for simplification, divert the implementation of such models in project management tools from the actual day to day project management process. Special focus is being made to the robustness of the generated project schedules facing the omnipresence of uncertainty. An "easy" way out is to add, more or less cleverly calculated, time buffers that always result in project duration increase and correspondingly, in cost. A better approach to deal with uncertainty seems to be to explore slack that might be present in a given project schedule, a fortiori when a non-optimal schedule is used. The combination of such approach to recent advances in modeling resource allocation and scheduling techniques to cope with the increasing flexibility in resources, as can be expressed in "Flexible Resource Constraint Project Scheduling Problem" (FRCPSP) formulations, should be a promising line of research to generate more adequate project management tools. In reality, this approach has been frequently used, by project managers in an ad-hoc way.

On the Hill estimator: a comparison of methods

Extreme value theory (EVT) deals with the occurrence of extreme phenomena. The tail index is a very important parameter appearing in the estimation of the probability of rare events. Under a semiparametric framework, inference requires the choice of a number k of upper order statistics to be considered. This is the crux of the matter and there is no definite formula to do it, since a small k leads to high variance and large values of k tend to increase the bias. Several methodologies have emerged in literature, specially concerning the most popular Hill estimator (Hill, 1975). In this work we compare through simulation well-known procedures presented in Drees and Kaufmann (1998), Matthys and Beirlant (2000), Beirlant et al. (2002) and de Sousa and Michailidis (2004), with a heuristic scheme considered in Frahm et al. (2005) within the estimation of a different tail measure but with a similar context. We will see that the new method may be an interesting alternative.

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

Comparing different methods to measure biofilm thickness: the techniques are: optical coherence tomography, confocal laser scanning microscopy and low load compression test

Dissertação de mestrado integrado em Engenharia Biomédica (área de especialização em Engenharia Clínica)

Enzymatic phosphorylation of hair keratin enhances fast adsorption of cationic moieties

The current study describes the in vitro phosphorylation of a human hair keratin, using protein kinase for the first time. Phosphorylation of keratin was demonstrated by 31P NMR (Nuclear Magnetic Resonance) and Diffuse Reflectance Infrared Fourier Transform (DRIFT) techniques. Phosphorylation induced a 2.5 fold increase of adsorption capacity in the first 10 minutes for cationic moiety like Methylene Blue (MB). Thorough description of MB adsorption process was performed by several isothermal models. Reconstructed fluorescent microscopy images depict distinct amounts of dye bound to the differently treated hair. The results of this work suggest that the enzymatic phosphorylation of keratins might have significant implications in hair shampooing and conditioning, where short application times of cationic components are of prime importance.

Gold nanoparticles functionalised with fast water exchanging Gd3+ chelates: Linker effects on the relaxivity

The relaxivity displayed by Gd3+ chelates immobilized onto gold nanoparticles is the result of complex interplay between nanoparticle size, water exchange rate and chelate structure. In this work we study the effect of the length of -thioalkyl linkers, anchoring fast water exchanging Gd3+ chelates onto gold nanoparticles, on the relaxivity of the immobilized chelates. Gold nanoparticles functionalized with Gd3+ chelates of mercaptoundecanoyl and lipoyl amide conjugates of the DO3A-N-(-amino)propionate chelator were prepared and studied as potential CA for MRI. High relaxivities per chelate, of the order of magnitude 28-38 mM-1s-1 (30 MHz, 25 ºC) were attained thanks to simultaneous optimization of the rotational correlation time and of the water exchange rate. Fast local rotational motions of the immobilized chelates around connecting linkers (internal flexibility) still limit the attainable relaxivity. The degree of internal flexibility of the immobilized chelates seems not to be correlated with the length of the connecting linkers. Biodistribution and MRI studies in mice suggest that the in vivo behavior of the gold nanoparticles is determined mainly by size. Small nanoparticles (HD= 3.9 nm) undergo fast renal clearance and avoidance of the RES organs while larger nanoparticles (HD= 4.8 nm) undergo predominantly hepatobiliary excretion. High relaxivities, allied to chelate and nanoparticle stability and fast renal clearance in vivo suggests that functionalized gold nanoparticles hold great potential for further investigation as MRI Contrast Agents. This study contributes to understand the effect of linker length on the relaxivity of gold nanoparticles functionalized with Gd3+ complexes. It is a relevant contribution towards “design rules” for nanostructures functionalized with Gd3+ chelates as Contrast Agents for MRI and multimodal imaging.

Finite integration methods for isospectral flows

In this paper we consider the approximate computation of isospectral flows based on finite integration methods( FIM) with radial basis functions( RBF) interpolation,a new algorithm is developed. Our method ensures the symmetry of the solutions. Numerical experiments demonstrate that the solutions have higher accuracy by our algorithm than by the second order Runge- Kutta( RK2) method.