Higher education in industrial engineering in Peru: towards a new model based on skills

This article analyzes the progress of Industrial Engineering in Peru, the relationship to major trends in Europe and North America, and the projected outlook for the future. It is determined that the need for this engineering specialty includes a significant degree of resource management, and the formation of engineers through education requires not only the acquisition and strengthening of technical knowledge, but also the development of the competences that are required by both employers and the recipients of the benefits of engineering: society. Conclusions have been drawn based on state-of-the-art analyses from Europe and North America, and definitions of trends for engineering.

A regularised continuum damage model based on the mesoscopic scale for soft tissue

Material properties of soft fibrous tissues are highly conditioned by the hierarchical structure of this kind of composites. Collagen based tissues present, at decreasing length scales, a complex framework of fibres, fibrils, tropocollagen molecules and amino-acids. Understanding the mechanical behaviour at nano-scale level is critical to accurately incorporate this structural information in phenomenological damage models. In this work we derive a relationship between the mechanical and geometrical properties of the fibril constituents and the soft tissue material parameters at macroscopic scale. A Hodge–Petruska two-dimensional model has been used to describe the fibrils as staggered arrays of tropocollagen molecules. After a mechanical characterisation of each of the fibril components, two fibril failures modes have been defined related with two planes of weakness. A phenomenological continuous damage model with regularised softening was presented along with meso-structurally based definitions for its material parameters. Finally, numerical analysis at fibril, fibre and tissue levels are presented to show the capabilities of the model

Case-based reasoning to support decision making for managing drinking water quality events in distribution systems

Peer reviewed

A model-based approach for assessing in vivo combination therapy interactions

We present an approach for evaluating the efficacy of combination antitumor agent schedules that accounts for order and timing of drug administration. Our model-based approach compares in vivo tumor volume data over a time course and offers a quantitative definition for additivity of drug effects, relative to which synergism and antagonism are interpreted. We begin by fitting data from individual mice receiving at most one drug to a differential equation tumor growth/drug effect model and combine individual parameter estimates to obtain population statistics. Using two null hypotheses: (i) combination therapy is consistent with additivity or (ii) combination therapy is equivalent to treating with the more effective single agent alone, we compute predicted tumor growth trajectories and their distribution for combination treated animals. We illustrate this approach by comparing entire observed and expected tumor volume trajectories for a data set in which HER-2/neu-overexpressing MCF-7 human breast cancer xenografts are treated with a humanized, anti-HER-2 monoclonal antibody (rhuMAb HER-2), doxorubicin, or one of five proposed combination therapy schedules.

The binding conformation of Taxol in β-tubulin: A model based on electron crystallographic density

The chemotherapeutic drug Taxol is known to interact within a specific site on β-tubulin. Although the general location of the site has been defined by photoaffinity labeling and electron crystallography, the original data were insufficient to make an absolute determination of the bound conformation. We have now correlated the crystallographic density with analysis of Taxol conformations and have found the unique solution to be a T-shaped Taxol structure. This T-shaped or butterfly structure is optimized within the β-tubulin site and exhibits functional similarity to a portion of the B9-B10 loop in the α-tubulin subunit. The model provides structural rationalization for a sizeable body of Taxol structure–activity relationship data, including binding affinity, photoaffinity labeling, and acquired mutation in human cancer cells.

3D model based map building

Comunicación presentada en el IX Workshop de Agentes Físicos (WAF'2008), Vigo, 11-12 septiembre 2008.

Chloride Penetration Prediction in Concrete through an Empirical Model Based on Constant Flux Diffusion

An empirical model based on constant flux is presented for chloride transport through concrete in atmospherical exposure conditions. A continuous supply of chlorides is assumed as a constant mass flux at the exposed concrete surface. The model is applied to experimental chloride profiles obtained from a real marine structure, and results are compared with the classical error-function model. The proposed model shows some advantages. It yields a better predictive capacity than the classical error-function model. The previously observed chloride surface concentration increases are compatible with the proposed model. Nevertheless, the predictive capacity of the model can fail if the concrete microstructure changes with time. The model seems to be appropriate for well-maturated concretes exposed to a marine environment in atmospherical conditions.

Assessment of rural nonpoint source pollution : a model based on the Universal soil loss equation /

Mode of access: Internet.

A framework and tool support for the systematic testing of model-based specifications

Formal specifications can precisely and unambiguously define the required behavior of a software system or component. However, formal specifications are complex artifacts that need to be verified to ensure that they are consistent, complete, and validated against the requirements. Specification testing or animation tools exist to assist with this by allowing the specifier to interpret or execute the specification. However, currently little is known about how to do this effectively. This article presents a framework and tool support for the systematic testing of formal, model-based specifications. Several important generic properties that should be satisfied by model-based specifications are first identified. Following the idea of mutation analysis, we then use variants or mutants of the specification to check that these properties are satisfied. The framework also allows the specifier to test application-specific properties. All properties are tested for a range of states that are defined by the tester in the form of a testgraph, which is a directed graph that partially models the states and transitions of the specification being tested. Tool support is provided for the generation of the mutants, for automatically traversing the testgraph and executing the test cases, and for reporting any errors. The framework is demonstrated on a small specification and its application to three larger specifications is discussed. Experience indicates that the framework can be used effectively to test small to medium-sized specifications and that it can reveal a significant number of problems in these specifications.

On a resampling approach for tests on the number of clusters with mixture model-based clustering of tissue samples

We consider the problem of assessing the number of clusters in a limited number of tissue samples containing gene expressions for possibly several thousands of genes. It is proposed to use a normal mixture model-based approach to the clustering of the tissue samples. One advantage of this approach is that the question on the number of clusters in the data can be formulated in terms of a test on the smallest number of components in the mixture model compatible with the data. This test can be carried out on the basis of the likelihood ratio test statistic, using resampling to assess its null distribution. The effectiveness of this approach is demonstrated on simulated data and on some microarray datasets, as considered previously in the bioinformatics literature. (C) 2004 Elsevier Inc. All rights reserved.

Speeding up the EM algorithm for mixture model-based segmentation of magnetic resonance images

Mixture models implemented via the expectation-maximization (EM) algorithm are being increasingly used in a wide range of problems in pattern recognition such as image segmentation. However, the EM algorithm requires considerable computational time in its application to huge data sets such as a three-dimensional magnetic resonance (MR) image of over 10 million voxels. Recently, it was shown that a sparse, incremental version of the EM algorithm could improve its rate of convergence. In this paper, we show how this modified EM algorithm can be speeded up further by adopting a multiresolution kd-tree structure in performing the E-step. The proposed algorithm outperforms some other variants of the EM algorithm for segmenting MR images of the human brain. (C) 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.