Hancornia speciosa latex for biomedical applications: physical and chemical properties,biocompatibility assessment and angiogenic activity

The latex obtained from Hancornia speciosa is used in folk medicine for treatment of several diseases, such as acne, warts, diabetes, gastritis and inflammation. In this work, we describe the biocompatibility assessment and angiogenic properties of H. speciosa latex and its potential application in medicine. The physical–chemical characterization was carried out following different methodologies (CHN elemental analyses; thermogravimetric analyses and Fourier transform infrared spectroscopy). The biocompatibility was evaluated through cytotoxicity and genotoxicity tests in fibroblast mouse cells and the angiogenic properties were evaluated using the chick chorioallantoic membrane (CAM) assay model. The physical–chemical results showed that the structure of Hancornia speciosa latex biomembrane is very similar to that of Hevea brasiliensis (commercially available product). Moreover, the cytotoxicity and genotoxicity assays showed that H. speciosa latex is biocompatible with life systems and can be a good biomaterial for medical applications. The CAM test showed the efficient ability of H. speciosa latex in neovascularization of tissues. The histological analysis was in accordance with the results obtained in the CAM assay. Our data indicate that the latex obtained from H. speciosa and eluted in water showed significant angiogenic activity without any cytotoxic or genotoxic effects on life systems. The same did not occur with H. speciosa latex stabilized with ammonia. Addition of ammonia does not have significant effects on the structure of biomembranes, but showed a smaller cell survival and a significant genotoxicity effect. This study contributes to the understanding of the potentialities of H. speciosa latex as a source of new phytomedicines.     

Diffusion of coloured silica nanoparticles into human hair

A novel method for dyeing human hair fibres using coloured silica nanoparticles of 206 nm has been developed. Diffusion of coloured silica nanoparticles into hair fibres has been investigated; silica nanoparticles do not penetrate at all, or do so only sparingly, into the structure of Caucasian virgin hair. However, coloured silica nanoparticles diffused readily into bleached hair fibres. Scanning electron microscopy showed that coloured silica nanoparticles were present mainly in the outer regions of the hair structure (the inner layers of the cuticle and the outer region of the cortex). It was found that the silica nanoparticles were physically entrapped in the hair fibres and results showed a good shampoo fastness to washing.     

Artificial neural networks for machining processes surface roughness modeling

In recent years, several papers on machining processes have focused on the use of artificial neural networks for modeling surface roughness. Even in such a specific niche of engineering literature, the papers differ considerably in terms of how they define network architectures and validate results, as well as in their training algorithms, error measures, and the like. Furthermore, a perusal of the individual papers leaves a researcher without a clear, sweeping view of what the field’s cutting edge is. Hence, this work reviews a number of these papers, providing a summary and analysis of the findings. Based on recommendations made by scholars of neurocomputing and statistics, the review includes a set of comparison criteria as well as assesses how the research findings were validated. This work also identifies trends in the literature and highlights their main differences. Ultimately, this work points to underexplored issues for future research and shows ways to improve how the results are validated.     

Parameter identification and shape optimization

Simulation of metal forming processes using the Finite Element Method (FEM) is a well established procedure, being nowadays possible to develop alternative approaches, such as inverse methodologies, in solving complex problems. In the present paper, two types of inverse approaches will be discussed, namely the parameter identification and the shape optimization problems. The aim of the former is to evaluate the input parameters for material constitutive models that would lead to the most accurate set of results respecting physical experiments. The second category involves determining the initial geometry of a given specimen leading to a desired final geometry after the forming process. The purpose of the present work is then to formulate these inverse problems as optimization problems, introducing a straightforward methodology of process optimization in engineering applications such as metal forming and structural analysis. To reach this goal, an integrated optimization approach, using a finite element code together with a numerical optimization program, was employed. A gradient-based optimization method, as a combination of the steepest-descent method and the Levenberg-Marquardt techniques, was used. Numerical applications in the parameter optimization category include, namely, the characterization of a non-linear elasto-plastic hardening model and the determination of the parameters for a nonlinear hyperelastic model. It is also discussed the simultaneous identification of both constitutive material model parameters and the friction coefficient parameters. From the point of view of shape optimization problems, the determination of the initial geometry of a specimen in a upsetting billing problem as well as a methodology for defining the most suited blank shape to be formed in a square cup, are discussed. The final results for both categories show that this kind of algorithms have great potential for future developments in more demanding and realistic benchmarks. It is also worth noting that the presented integrated methodology can be easily applied to a first introduction of optimization techniques and numerical simulation to undergraduate courses in engineering.     

Views and Patterns in E-Commerce Application Design

Separation of concerns is a well-established principle in software engineering that supports reuse by hiding complexity through abstraction mechanisms. The Abstract Design Views model was created with reuse in mind and allows the designer to apply separation of concerns in a software system from the design to the implementation. In this model, viewed objects represent the basic concern, i.e., the algorithms that provide the essential functionality relevant to an application domain, and viewer objects represent the special concerns related to other software issues, such as user interface presentation, synchronization, and timing. In this paper we use a reuse taxonomy to analyze and validate this model. Using this analysis and the properties of the relationship between viewer and viewed objects, called views, we also indicate how to map the views-based designs into implementations based on design patterns that satisfy the views properties. Finally, we show how to apply the principles of our approach, using views and the design patterns, to design e-commerce applications.     

Mixture models for quantitative HIV RNA data

Clinical investigators are increasing their use of quantitative determinations of HIV viral load in their study populations. The distributions of these measures may be highly skewed, left-censored, and with an extra spike below the detection limit of the assay. We recommended use of a mixture model in this situation, with two sets of explanatory covariates. We extend this model to incorporate multiple measures across time, and to employ shared parameters as a way of increasing model efficiency and parsimony. Data from a cohort of HIV-infected men are used to illustrate these features, and simulations are performed to assess the utility of shared parameters.     

Lamb Meat Quality Assessment by Support Vector Machines

The correct assessment of meat quality (i.e., to fulfill the consumer’s needs) is crucial element within the meat industry. Although there are several factors that affect the perception of taste, tenderness is considered the most important characteristic. In this paper, a Feature Selection procedure, based on a Sensitivity Analysis, is combined with a Support Vector Machine, in order to predict lamb meat tenderness. This real-world problem is defined in terms of two difficult regression tasks, by modeling objective (e.g. Warner–Bratzler Shear force) and subjective (e.g. human taste panel) measurements. In both cases, the proposed solution is competitive when compared with other neural (e.g. Multilayer Perceptron) and Multiple Regression approaches.     

Low complexity Byzantine-resilient consensus

The application of the tolerance paradigm to security - intrusion tolerance - has been raising a reasonable amount of attention in the dependability and security communities. In this paper we present a novel approach to intrusion tolerance. The idea is to use privileged components - generically designated by wormholes - to support the execution of intrusion-tolerant protocols, often called Byzantine-resilient in the literature.The paper introduces the design of wormhole-aware intrusion-tolerant protocols using a classical distributed systems problem: consensus. The system where the consensus protocol runs is mostly asynchronous and can fail in an arbitrary way, except for the wormhole, which is secure and synchronous. Using the wormhole to execute a few critical steps, the protocol manages to have a low time complexity: in the best case, it runs in two rounds, even if some processes are malicious. The protocol also shows how often theoretical partial synchrony assumptions can be substantiated in practical distributed systems. The paper shows the significance of the TTCB as an engineering paradigm, since the protocol manages to be simple when compared with other protocols in the literature.Published online: 29 October 2004This work was partially supported by the EC, through project IST-1999-11583 (MAFTIA), and by the FCT, through the Large-Scale Informatic Systems Laboratory (LASIGE) and projects POSI/1999/CHS/33996 (DEFEATS) and POSI/CHS/39815/2001 (COPE).     

Collision-free spatial hash functions for structural analysis of billion-vertex chemical bond networks

State-of-the-art molecular dynamics (MD) simulations generate massive datasets involving billion-vertex chemical bond networks, which makes data mining based on graph algorithms such as K-ring analysis a challenge. This paper proposes an algorithm to improve the efficiency of ring analysis of large graphs, exploiting properties of K-rings and spatial correlations of vertices in the graph. The algorithm uses dual-tree expansion (DTE) and spatial hash-function tagging (SHAFT) to optimize computation and memory access. Numerical tests show nearly perfect linear scaling of the algorithm. Also a parallel implementation of the DTE + SHAFT algorithm achieves high scalability. The algorithm has been successfully employed to analyze large MD simulations involving up to 500 million atoms.     

Optoelectronic implementation of a 256-channel sonar adaptive-array processor

We present an optoelectronic implementation of an adaptive-array processor that is capable of performing beam forming and jammer nulling in signals of wide fractional bandwidth that are detected by an array of arbitrary topology. The optical system makes use of a two-dimensional scrolling spatial light modulator to represent an array of input signals in 256 tapped delay lines, two acousto-optic modulators for modulating the feedback error signal, and a photorefractive crystal for representing the adaptive weights as holographic gratings. Gradient-descent learning is used to dynamically adapt the holographic weights to optimally form multiple beams and to null out multiple interference sources, either in the near field or in the far field. Space-integration followed by differential heterodyne detection is used for generating the system's output. The processor is analyzed to show the effects of exponential weight decay on the optimum solution and on the convergence conditions. Several experimental results are presented that validate the system's capacity for broadband beam forming and jammer nulling for linear and circular arrays.