Modeling of the condyle elements within a biomechanical knee model

The development of a computational multibody knee model able to capture some of the fundamental properties of the human knee articulation is presented. This desideratum is reached by including the kinetics of the real knee articulation. The research question is whether an accurate modeling of the condyle contact in the knee will lead to reproduction of the complex combination of flexion/extension, abduction/adduction, and tibial rotation observed in the real knee. The model is composed by two anatomic segments, the tibia and the femur, whose characteristics are functions of the geometric and anatomic properties of the real bones. The biomechanical model characterization is developed under the framework of multibody systems methodologies using Cartesian coordinates. The type of approach used in the proposed knee model is the joint surface contact conditions between ellipsoids, representing the two femoral condyles, and points, representing the tibial plateau and the menisci. These elements are closely fitted to the actual knee geometry. This task is undertaken by considering a parameter optimization process to replicate experimental data published in the literature, namely that by Lafortune and his coworkers in 1992. Then kinematic data in the form of flexion/extension patterns are imposed on the model corresponding to the stance phase of the human gait. From the results obtained, by performing several computational simulations, it can be observed that the knee model approximates the average secondary motion patterns observed in the literature. Because the literature reports considerable inter-individual differences in the secondary motion patterns, the knee model presented here is also used to check whether it is possible to reproduce the observed differences with reasonable variations of bone shape parameters. This task is accomplished by a parameter study, in which the main variables that define the geometry of condyles are taken into account. It was observed that the data reveal a difference in secondary kinematics of the knee in flexion versus extension. The likely explanation for this fact is the elastic component of the secondary motions created by the combination of joint forces and soft tissue deformations. The proposed knee model is, therefore, used to investigate whether this observed behavior can be explained by reasonable elastic deformations of the points representing the menisci in the model.     

Evolutionary Generalized Radial Basis Function neural networks for improving prediction accuracy in gene classification using feature selection

Radial Basis Function Neural Networks (RBFNNs) have been successfully employed in several function approximation and pattern recognition problems. The use of different RBFs in RBFNN has been reported in the literature and here the study centres on the use of the Generalized Radial Basis Function Neural Networks (GRBFNNs). An interesting property of the GRBF is that it can continuously and smoothly reproduce different RBFs by changing a real parameter τ. In addition, the mixed use of different RBF shapes in only one RBFNN is allowed. Generalized Radial Basis Function (GRBF) is based on Generalized Gaussian Distribution (GGD), which adds a shape parameter, τ, to standard Gaussian Distribution. Moreover, this paper describes a hybrid approach, Hybrid Algorithm (HA), which combines evolutionary and gradient-based learning methods to estimate the architecture, weights and node topology of GRBFNN classifiers. The feasibility and benefits of the approach are demonstrated by means of six gene microarray classification problems taken from bioinformatic and biomedical domains. Three filters were applied: Fast Correlation-Based Filter (FCBF), Best Incremental Ranked Subset (BIRS), and Best Agglomerative Ranked Subset (BARS); this was done in order to identify salient expression genes from among the thousands of genes in microarray data that can directly contribute to determining the class membership of each pattern. After different gene subsets were obtained, the proposed methodology was performed using the selected gene subsets as new input variables. The results confirm that the GRBFNN classifier leads to a promising improvement in accuracy.     

Silicon Nitride Derived from an Organometallic Polymeric Precursor: Preparation and Characterization

Partially crystalline Si₃N₄, with nanosized crystals and a specific surface area greater than 200 m²/g, is obtained by pyrolysis of a commercially available vinylic polysilane in a stream of anhydrous NH₃ to 1000°C. This polymer does not contain N initially. Crystallization to high-purity α-Si₃N₄ proceeds with additional heating above 1400°C under N₂. The changes in crystallinity, powder morphology, infrared spectra, and elemental compositions, for samples annealed from 1000° to 1600°C under N₂, are consistent with an amorphous-to-crystalline transformation. Although macroscopic consolidation and local densification occur at 1400°C, volatilization and accompanying weight loss limit bulk densification. The effect of temperature on specific surface area is examined and related to the sintering process. These results are applicable to pyrolysis, decomposition, and crystallization studies of ceramics synthesized by polymeric precursor routes.     

Development and implementation of clinical guidelines: An artificial intelligence perspective

Clinical practice guidelines in paper format are still the preferred form of delivery of medical knowledge and recommendations to healthcare professionals. Their current support and development process have well identified limitations to which the healthcare community has been continuously searching solutions. Artificial intelligence may create the conditions and provide the tools to address many, if not all, of these limitations.. This paper presents a comprehensive and up to date review of computer-interpretable guideline approaches, namely Arden Syntax, GLIF, PROforma, Asbru, GLARE and SAGE. It also provides an assessment of how well these approaches respond to the challenges posed by paper-based guidelines and addresses topics of Artificial intelligence that could provide a solution to the shortcomings of clinical guidelines. Among the topics addressed by this paper are expert systems, case-based reasoning, medical ontologies and reasoning under uncertainty, with a special focus on methodologies for assessing quality of information when managing incomplete information. Finally, an analysis is made of the fundamental requirements of a guideline model and the importance that standard terminologies and models for clinical data have in the semantic and syntactic interoperability between a guideline execution engine and the software tools used in clinical settings. It is also proposed a line of research that includes the development of an ontology for clinical practice guidelines and a decision model for a guideline-based expert system that manages non-compliance with clinical guidelines and uncertainty.     

Resolution proof transformation for compression and interpolation

Verification methods based on SAT, SMT, and theorem proving often rely on proofs of unsatisfiability as a powerful tool to extract information in order to reduce the overall effort. For example a proof may be traversed to identify a minimal reason that led to unsatisfiability, for computing abstractions, or for deriving Craig interpolants. In this paper we focus on two important aspects that concern efficient handling of proofs of unsatisfiability: compression and manipulation. First of all, since the proof size can be very large in general (exponential in the size of the input problem), it is indeed beneficial to adopt techniques to compress it for further processing. Secondly, proofs can be manipulated as a flexible preprocessing step in preparation for interpolant computation. Both these techniques are implemented in a framework that makes use of local rewriting rules to transform the proofs. We show that a careful use of the rules, combined with existing algorithms, can result in an effective simplification of the original proofs. We have evaluated several heuristics on a wide range of unsatisfiable problems deriving from SAT and SMT test cases.     

Variability for oil and fatty acid composition in castorbean varieties

Thirty-six castorbean varieties were surveyed for oil and fatty acid composition, in order to determine variability of these seed compounds. A large variability of seed oil percentage was observed, ranging from 39.6 to 59.5%. Concerning the fatty acids, little variability was observed for ricinoleic acid, which was the most abundant in the oil, ranging from 83.65 to 90.00%. The other fatty acids appeared in small concentrations and showed a small range: 0.87 to 2.35, 0.68 to 1.84, 2.96 to 5.64, 3.19 to 5.98, and 0.34 to 0.91%, for palmitic, stearic, oleic, linoleic, and linolenic acid, respectively. Non-significant correlations were observed between fatty acids and seed oil percentage. However, significant correlations were observed among fatty acid concentrations: positive and negative ones. These significant correlations could be associated with the biosynthetic pathways of the fatty acids, which are not fully elucidated. They suggest, however, that selection for a particular fatty acid will tend to increase those positively correlated, and decrease those negative ones. Selection and plant breeding techniques could then be applied to modify the oil content of the castorbean seeds, considering the variability observed. For the fatty acid composition, however, the variability was not large enough to make substantial changes in their concentrations by selection procedures. More varieties should be surveyed to find out if such variability is available.     

Model falsification using set‐valued observers for a class of discrete‐time dynamic systems: a coprime factorization approach

This article introduces a new method for model falsification using set‐valued observers, which can be applied to a class of discrete linear time‐invariant dynamic systems with time‐varying model uncertainties. In comparison with previous results, the main advantages of this approach are as follows: The computation of the convex hull of the set‐valued estimates of the state can be avoided under certain circumstances; to guarantee convergence of the set‐valued estimates of the state, the required number of previous steps is at most as large as the number of states of the nominal plant; and it provides a straightforward nonconservative method to falsify uncertain models of dynamic systems, including open‐loop unstable plants. The results obtained are illustrated in simulation, emphasizing the advantages and shortcomings of the suggested method. Copyright © 2013 John Wiley & Sons, Ltd.     

Well-defined polymer precursors synthesized by RAFT polymerization of N,N-dimethylacrylamide/N-acryloxysuccinimide: random and block copolymers

N,N-dimethylacrylamide (DMA) and N-acryloxysuccinimide (NAS) were copolymerized by the reversible addition–fragmentation chain transfer (RAFT) polymerization technique, to obtain random and block copolymer precursors onto which different side-groups may be statistically grafted via the reactive NAS units. These reactive copolymers have interesting applications in various fields such as coatings and paints, water purification and biology. Random poly(DMA-co-NAS) copolymer chains were synthesized with a 75/25 molar ratio, high conversion, an excellent molecular weight (MW) control from 5000 to 130 000 g mol⁻¹, and low polydispersity index (M_w/M_n<1.1). Poly(DMA-b-NAS) block copolymers were synthesized by a two step method, in which a poly(DMA) homopolymer was prepared first and then used as macro-chain transfer agent to polymerize NAS. For example, a poly(DMA-b-NAS) sample was obtained with an average molecular weight of 44 300/7400 g mol⁻¹ corresponding to 447 DMA and 44 NAS units. Such block copolymers had not yet been synthesized by any controlled polymerization technique. They can be used to prepare polymers with exactly the same backbone and an increasing number of different side groups (e.g. hydrophobic, ionic or fluorescent).     

OpenCOPI: middleware integration for Ubiquitous Computing

In this paper, we present OpenCOPI (Open COntext Platform Integration), a Service-Oriented Architecture-based middleware platform that supports the integration of services provided by distinct sources, ranging from services offered by simple systems to more complex services provided by context-provision middleware. OpenCOPI offers selection and composition mechanisms to, respectively, select and compose services provided by different sources, considering applications of both Quality of Service and Quality of Context requirements. It also offers an adaptation mechanism that enables to adapt the application execution due to service failures, service quality fluctuation and user mobility. OpenCOPI allows the definition of applications in a higher abstraction level by the specification of a semantic workflow that contains abstract activities. This paper illustrates the use of OpenCOPI in an application from the Gas & Oil Industry and it also shows the evaluation of the main mechanisms of OpenCOPI: the service selection, composition, adaptation and workflow execution.     

A pseudo-spectral scheme for the approximate solution of a time-fractional diffusion equation

We consider an initial-boundary-value problem for a time-fractional diffusion equation with initial condition u₀(x) and homogeneous Dirichlet boundary conditions in a bounded interval [0, L]. We study a semidiscrete approximation scheme based on the pseudo-spectral method on Chebyshev–Gauss–Lobatto nodes. In order to preserve the high accuracy of the spectral approximation we use an approach based on the evaluation of the Mittag-Leffler function on matrix arguments for the integration along the time variable. Some examples are presented and numerical experiments illustrate the effectiveness of the proposed approach.