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.     

Contrast media-enhanced magnetic resonance imaging visualizes myocardial changes in the course of viral myocarditis

BACKGROUND: The course of tissue changes in acute myocarditis in humans is not well understood. Diagnostic tools currently available are unsatisfactory. We tested the hypothesis that inflammation is reflected by signal changes in contrast-enhanced magnetic resonance imaging (MRI). METHODS AND RESULTS: We assessed 44 consecutive patients with symptoms of acute myocarditis. Nineteen patients met the inclusion criteria revealing ECG changes, reduced myocardial function, elevated creatine kinase, positive troponin T, serological evidence for acute viral infection, exclusion of coronary heart disease, and positive antimyosin scintigraphy. We studied these patients on days 2, 7, 14, 28, and 84 after the onset of symptoms. We obtained ECG-triggered, T1-weighted images before and after application of 0.1 mmol/kg gadolinium. We measured the global relative signal enhancement of the left ventricular myocardium related to skeletal muscle and compared it with measurements in 18 volunteers. The global relative enhancement was higher in patients on days 2 (4.8+/-0.3 [mean+/-SE] versus 2.5+/-0.2; P<.0001); 7 (4.7+/-0.5, P<.0001); 14 (4.6+/-0.5, P<.0002); and 28 (3.9+/-0.4, P=.009) but not on day 84 (3.1+/-0.3; P=NS). On day 2, the enhancement was focal, whereas at later time points, the enhancement was diffuse. In patients with evidence of ongoing disease, the values remained elevated. CONCLUSIONS: Acute myocarditis evolves from a focal to a disseminated process during the first 2 weeks after onset of symptoms. Contrast media-enhanced MRI visualizes the localization, activity, and extent of inflammation and may serve as a powerful noninvasive diagnostic tool in acute myocarditis.     

Exploring the potential of problem-based learning in nurse education

Despite the increased attention that problem-based learning has received as an appropriate pedagogical technique for educating adults for professional practice, reports that evaluate the process are rare and usually relate to professions other than nursing. A study was undertaken in order to discover the graduates' own perceptions of a problem-based learning programme and its effectiveness in preparing them for the reality of their chosen profession. Twelve practising graduate nurses who had completed the programme were interviewed according to the ethnographic method. Three categories were identified from the data: 'and all of a sudden...', which describes the transition from PBL student to staff nurse; 'not an unthinking assistant', where the characteristics that the PBL graduates believe make them different from traditionally trained nurses are described; and 'the buck stops here', which describes the sense of personal responsibility that the graduates experience in terms of their learning and actions.     

Transduction of normal and malignant oral epithelium by particle bombardment

Considerable basic research, mostly in the past 20 years, has elicited greatly increased knowledge concerning the structure and function of cell adhesion molecules. Scores of individual adhesion molecules have been identified and categorized as to major structural features, ligands recognized, and pattern of expression. Recent attention has been focused on the interaction of cell adhesion molecules with intracellular components, and the role of cell adhesion molecules in mediating cell signal transduction. Ongoing efforts to develop specific pharmacological agonists and antagonists for adhesion molecules holds great promise in clinical medicine. Abciximab (Reopro), a monoclonal antibody inhibitor of the platelet integrin alpha IIb beta 3, is currently approved and available to improve vessel patency in patients undergoing angioplasty. Similar approaches to develop adhesion-based therapies to block angiogenesis, tumor progression, and/or metastasis are under development and hold promise for patients with cancer.