Extraction and characterization of hydroxyapatite-based materials from grey triggerfish skin and black scabbardfish bones

The conversion of food industry by-products to compounds with high added value is nowadays a significant topic, for social, environmental, and economic reasons. In this paper, calcium phosphate-based materials were obtained from black scabbardfish (Aphanopus carbo) bones and grey triggerfish (Balistes capriscus) skin, which are two of the most abundant fish by-products of Madeira Island. Different calcination temperatures between 400 and 1000°C were employed. Materials obtained from calcination of bones of black scabbard fish were composed by homogeneous mixtures of hydroxyapatite (Ca₁₀(PO4)₆(OH)₂, HAp) and β-tricalcium phosphate (β-Ca₃(PO₄)₂, β-TCP). Because of the high biocompatibility of HAp and the good resorbability of β-TCP, these natural biphasic materials could be very relevant in the field of biomaterials, as bone grafts. The ratio between HAp and β-TCP in the biphasic compound was dependent on the calcination temperature. Differently, the material obtained from skin of grey triggerfish contained HAp as the main phase, together with small amounts of other mineral phases, such as halite and rhenanite, which are known to enhance osteogenesis when used as bone substitutes. In both cases, the increase of calcination temperature led to an increase in the particles size with a consequent decrease in their specific surface area. These results demonstrate that from the fish by-products of the most consumed fishes in Madeira Island it is possible to obtain bioceramic materials with tunable composition and particle morphology, which could be promising materials for the biomedical field.     

On the Assumed Natural Strain method to alleviate locking in solid-shell NURBS-based finite elements

In isogeometric analysis (IGA), the functions used to describe the CAD geometry (such as NURBS) are also employed, in an isoparametric fashion, for the approximation of the unknown fields, leading to an exact geometry representation. Since the introduction of IGA, it has been shown that the high regularity properties of the employed functions lead in many cases to superior accuracy per degree of freedom with respect to standard FEM. However, as in Lagrangian elements, NURBS-based formulations can be negatively affected by the appearance of non-physical phenomena that “lock” the solution when constrained problems are considered. In order to alleviate such locking behaviors, the Assumed Natural Strain (ANS) method proposed for Lagrangian formulations is extended to NURBS-based elements in the present work, within the context of solid-shell formulations. The performance of the proposed methodology is assessed by means of a set of numerical examples. The results allow to conclude that the employment of the ANS method to quadratic NURBS-based elements successfully alleviates non-physical phenomena such as shear and membrane locking, significantly improving the element performance.     

Generative face alignment through 2.5D active appearance models

This work addresses the matching of a 3D deformable face model to 2D images through a 2.5D Active Appearance Models (AAM). We propose a 2.5D AAM that combines a 3D metric Point Distribution Model (PDM) and a 2D appearance model whose control points are defined by a full perspective projection of the PDM. The advantage is that, assuming a calibrated camera, 3D metric shapes can be retrieved from single view images. Two model fitting algorithms and their computational efficient approximations are proposed: the Simultaneous Forwards Additive (SFA) and the Normalization Forwards Additive (NFA), both based on the Lucas–Kanade framework. The SFA algorithm searches for shape and appearance parameters simultaneously whereas the NFA projects out the appearance from the error image and searches only for the shape parameters. SFA is therefore more accurate. Robust solutions for the SFA and NFA are also proposed in order to take into account the self-occlusion or partial occlusion of the face. Several performance evaluations for the SFA, NFA and theirs efficient approximations were performed. The experiments include evaluating the frequency of converge, the fitting performance in unseen data and the tracking performance in the FGNET Talking Face sequence. All results show that the 2.5D AAM can outperform both the 2D + 3D combined models and the 2D standard methods. The robust extensions to occlusion were tested on a synthetic sequence showing that the model can deal efficiently with large head rotation.     

Source attribution of submicron organic aerosols during wintertime inversions by advanced factor analysis of aerosol mass spectra

Real-time measurements of submicrometer aerosol were performed using an Aerodyne aerosol mass spectrometer (AMS) during three weeks at an urban background site in Zurich (Switzerland) in January 2006. A hybrid receptor model which incorporates a priori known source composition was applied to the AMS highly time-resolved organic aerosol mass spectra. Three sources and components of submicrometer organic aerosols were identified: the major component was oxygenated organic aerosol (OOA), mostly representing secondary organic aerosol and accounting on average for 52-57% of the particulate organic mass. Radiocarbon (14C) measurements of organic carbon (OC) indicated that approximately 31 and approximately 69% of OOA originated from fossil and nonfossil sources, respectively. OOA estimates were strongly correlated with measured particulate ammonium. Particles from wood combustion (35-40%) and 3-13% traffic-related hydrocarbon-like organic aerosol (HOA) accounted for the other half of measured organic matter (OM). Emission ratios of modeled HOA to measured nitrogen oxides (NOx) and OM from wood burning to levoglucosan from filter analyses were found to be consistent with literature values.     

Elasto-plastic buckling of integrally stiffened panels (ISP): An optimization approach for the design of cross-section profiles

In this work, three optimization algorithms—Levenberg–Marquardt, simulated annealing and a newly developed and proposed hybrid differential evolution particle swarm optimization—are considered in the shape optimizations of cross-sections of integrally stiffened panels (ISP) for aeronautical applications, when subjected to buckling deformation modes within the elasto-plastic range. The proposed algorithm is shown to be robust and more effective, in the tested examples, than conventional optimization algorithms, leading to optimum designs of ISP cross-sections for a pre-defined buckling load-carrying capacity, combined with elasto-plastic nonlinear behaviors.     

Towards defining the whole salivary peptidome

We report the identification of 2294 peptides/proteins in whole saliva in the mass range between 700 and 16 000 Da by LC‐MS and MS/MS using a MALDI‐TOF/TOF mass spectrometer. Most of the identified peptides/proteins are originated from cellular debris or plasma components while only 26% (n = 607) correspond to salivary peptides/proteins species. In spite of the presence of the major salivary peptides in all samples from the six subjects analyzed, each individual presents a different pattern of fragments, many deriving from the same protein sequence. All our data, in particular the large number of fragments found, suggest high proteolytic activity insight the oral cavity. The analysis of samples by gelatin zymography showed that all saliva donors displayed multiple proteolytic bands, two identified as cathepsin D and G by MS. Analysis of the cleavage site distribution on the main peptide sequences based on contingency tables shows that the predominant cleavages occur between Gln‐Gly or Tyr‐Gly. These cleavages are largely associated with proline‐rich proteins peptides and with histatin 1 and P‐B peptide, respectively. However, depending on the peptide class, different cleavage hits were observed suggesting the presence of a set of proteases acting in different ways according to different peptide sequences. Comparing the number of cleavages involving all residues, it is possible to observe that 44% (±10%) of the observed cleavages in histatin, statherin and P‐B peptide in all individuals may be explained by cathepsin D, suggesting a major role for this enzyme in oral cavity proteolysis.     

On an Innovative Optimization Approach for the Design of Cross-section Profiles of Integrally Stiffened Panels Subjected to Elasto-plastic Buckling Deformation Modes

Aircraft fuselage panels are conventionally differential reinforced structures, with aluminum skin and stringers being conventionally joined by riveting operations. As a consequence, these structures can present a number of distinct components, making assembly operations time consuming and expensive. Recently, developments in aluminum extrusion have opened up possibilities for new design procedures for aircraft applications. Particularly, complex-section panels known as Integrally Stiffened Panels (ISP), which are reinforced structures integrally obtained from extrusion, are a good example. In structural terms, ISP may be considered as thin-walled structures in which the stability for compressive loads is strongly dependent on the buckling strength of the cross section itself. Besides the difficulties in the project for buckling deformation modes within the elasto-plastic range, in aeronautical design it is mandatory to find the best compromise between weight and structural performance.In this sense, the present work presents an optimization algorithm based on a Hybrid Differential Evolution and Particle Swarm Optimization (HDEPSO) formulations, applied in finding the optimal cross section shape and dimensions for general ISP structures, under compressive buckling loads and accounting for elasto-plastic behaviors. The results and design guidelines are obtained by a set of parameterized nonlinear finite element simulations in ABAQUS, accounting for buckling carrying load levels as the principal design criteria.     

A specialized on-the-fly algorithm for lexicon and language model composition

This paper presents an algorithm for the composition of weighted finite-state transducers which is specially tailored to speech recognition applications: it composes the lexicon with the language model while simultaneously optimizing the resulting transducer. Furthermore, it performs these computations "on-the-fly" to allow easier management of the tradeoff between offline and online computation and memory. The algorithm is exact for local knowledge integration and optimization operations such as composition and determinization. Minimization and pushing operations are approximated. Our results have confirmed the efficiency of these approximations.     

A nonparametric Riemannian framework on tensor field with application to foreground segmentation

Background modeling on tensor field has recently been proposed for foreground detection tasks. Taking into account the Riemannian structure of the tensor manifold, recent research has focused on developing parametric methods on the tensor domain, e.g. mixture of Gaussians (GMM). However, in some scenarios, simple parametric models do not accurately explain the physical processes. Kernel density estimators (KDEs) have been successful to model, on Euclidean sample spaces, the nonparametric nature of complex, time varying, and non-static backgrounds. Founded on a mathematically rigorous KDE paradigm on general Riemannian manifolds recently proposed in the literature, we define a KDE specifically to operate on the tensor manifold in order to nonparametrically reformulate the existing tensor-based algorithms. We present a mathematically sound framework for nonparametric modeling on tensor field to foreground detection. We endow the tensor manifold with two well-founded Riemannian metrics, i.e. Affine-Invariant and Log-Euclidean. Theoretical aspects are presented and the metrics are compared experimentally. By inducing a space with a null curvature, the Log-Euclidean metric considerably simplifies the scheme, from a practical point of view, while maintaining the mathematical soundness and the excellent segmentation performance. Theoretic analysis and experimental results demonstrate the promise and effectiveness of this framework.