Mechanical Behavior of the Lamellar Structure in Semi-Crystalline Polymers

We have employed molecular dynamics simulations to study the behavior of virtual polymeric materials under an applied uniaxial tensile load. Through computer simulations, one can obtain experimentally inaccessible information about phenomena taking place at the molecular and microscopic levels. Not only can the global material response be monitored and characterized along time, but the response of macromolecular chains can be followed independently if desired. The computer-generated materials were created by emulating the step-wise polymerization, resulting in self-avoiding chains in 3D with controlled degree of orientation along a certain axis. These materials represent a simplified model of the lamellar structure of semi-crystalline polymers,being comprised of an amorphous region surrounded by two crystalline lamellar regions. For the simulations, a series of materials were created, varying i) the lamella thickness, ii) the amorphous region thickness, iii) the preferential chain orientation, and iv) the degree of packing of the amorphous region. Simulation results indicate that the lamella thickness has the strongest influence on the mechanical properties of the lamella-amorphous structure, which is in agreement with experimental data. The other morphological parameters also affect the mechanical response, but to a smaller degree. This research follows previous simulation work on the crack formation and propagation phenomena, deformation mechanisms at the nanoscale, and the influence of the loading conditions on the material response. Computer simulations can improve the fundamental understanding about the phenomena responsible for the behavior of polymeric materials, and will eventually lead to the design of knowledge-based materials with improved properties.

Portugal’s Minho-Lima region as a tourist destination: tourism operators’ attitudes towards its management and promotion

The success of tourism development depends on the capacity of a region’s tourism agents to establish and sustain networks, involving both private-sector companies and the public sector. Creating an attractive destination able to compete with others that are better positioned and consolidated requires cooperative behaviour among the various agents involved. This behaviour will facilitate both external and internal competition, which in turn will assure better product quality, continuous product renewal, a strong offer of unique experiences and the efficient use of endogenous resources. In this paper, the authors discuss the results of a survey of restaurant owners and of interviews conducted with the main institutional agents concerned with tourism promotion and the economic development of the Minho–Lima region. Such an approach, the authors argue, can be valuable in identifying the strengths and weaknesses of the area in question with regard to future tourism development. The authors work from the premise that the commitment of tourism agents constitutes a precondition for the success of the strategy to be defined. This is especially applicable to Minho–Lima, which to date has suffered from an absence of commitment and coordination on the part of those agents.

Eigenvalue computations in the context of data-sparse approximations of integral operators

In this work, we consider the numerical solution of a large eigenvalue problem resulting from a finite rank discretization of an integral operator. We are interested in computing a few eigenpairs, with an iterative method, so a matrix representation that allows for fast matrix-vector products is required. Hierarchical matrices are appropriate for this setting, and also provide cheap LU decompositions required in the spectral transformation technique. We illustrate the use of freely available software tools to address the problem, in particular SLEPc for the eigensolvers and HLib for the construction of H-matrices. The numerical tests are performed using an astrophysics application. Results show the benefits of the data-sparse representation compared to standard storage schemes, in terms of computational cost as well as memory requirements.

Application of Gage R&R methodology for improving the training and evaluation of automatic optical inspection machine operators

The current level of demand by customers in the electronics industry requires the production of parts with an extremely high level of reliability and quality to ensure complete confidence on the end customer. Automatic Optical Inspection (AOI) machines have an important role in the monitoring and detection of errors during the manufacturing process for printed circuit boards. These machines present images of products with probable assembly mistakes to an operator and him decide whether the product has a real defect or if in turn this was an automated false detection. Operator training is an important aspect for obtaining a lower rate of evaluation failure by the operator and consequently a lower rate of actual defects that slip through to the following processes. The Gage R&R methodology for attributes is part of a Six Sigma strategy to examine the repeatability and reproducibility of an evaluation system, thus giving important feedback on the suitability of each operator in classifying defects. This methodology was already applied in several industry sectors and services at different processes, with excellent results in the evaluation of subjective parameters. An application for training operators of AOI machines was developed, in order to be able to check their fitness and improve future evaluation performance. This application will provide a better understanding of the specific training needs for each operator, and also to accompany the evolution of the training program for new components which in turn present additional new difficulties for the operator evaluation. The use of this application will contribute to reduce the number of defects misclassified by the operators that are passed on to the following steps in the productive process. This defect reduction will also contribute to the continuous improvement of the operator evaluation performance, which is seen as a quality management goal.

Semi-automatic 3D Segmentation Of Costal Cartilage In CT Data From Pectus Excavatum Patients

One of the current frontiers in the clinical management of Pectus Excavatum (PE) patients is the prediction of the surgical outcome prior to the intervention. This can be done through computerized simulation of the Nuss procedure, which requires an anatomically correct representation of the costal cartilage. To this end, we take advantage of the costal cartilage tubular structure to detect it through multi-scale vesselness filtering. This information is then used in an interactive 2D initialization procedure which uses anatomical maximum intensity projections of 3D vesselness feature images to efficiently initialize the 3D segmentation process. We identify the cartilage tissue centerlines in these projected 2D images using a livewire approach. We finally refine the 3D cartilage surface through region-based sparse field level-sets. We have tested the proposed algorithm in 6 noncontrast CT datasets from PE patients. A good segmentation performance was found against reference manual contouring, with an average Dice coefficient of 0.75±0.04 and an average mean surface distance of 1.69±0.30mm. The proposed method requires roughly 1 minute for the interactive initialization step, which can positively contribute to an extended use of this tool in clinical practice, since current manual delineation of the costal cartilage can take up to an hour.