Modelling the Mechanical Behavior of Polymer-Based Nanocomposites

Molecular dynamics simulations were employed to analyze the mechanical properties of polymer-based nanocomposites with varying nanofiber network parameters. The study was focused on nanofiber aspect ratio, concentration and initial orientation. The reinforcing phase affects the behavior of the polymeric nanocomposite. Simulations have shown that the fiber concentration has a significant effect on the properties, with higher loadings resulting in higher stress levels and higher stiffness, matching the general behavior from experimental knowledge in this field. The results also indicate that, within the studied range, the observed effect of the aspect ratio and initial orientation is smaller than that of the concentration, and that these two parameters are interrelated.

Coupling Mechanical and Electrical Behavior in the Multi-scale Simulation of Polymer-based CNT Nanocomposites

A numeric model has been proposed to investigate the mechanical and electrical properties of a polymeric/carbon nanotube (CNT) composite material subjected to a deformation force. The reinforcing phase affects the behavior of the polymeric matrix and depends on the nanofiber aspect ratio and preferential orientation. The simulations show that the mechanical behavior of a computer generated material (CGM) depends on fiber length and initial orientation in the polymeric matrix. It is also shown how the conductivity of the polymer/CNT composite can be calculated for each time step of applied stress, effectively providing the ability to simulate and predict strain-dependent electrical behavior of CNT nanocomposites.

Automatic modeling of an orthotic bracing for nonoperative correction of PectusCarinatum

Pectus Carinatum is a deformity of the chest wall, characterized by an anterior protrusion of the sternum, often corrected surgically due to cosmetic motivation. This work presents an alternative approach to the current open surgery option, proposing a novel technique based on a personalized orthosis. Two different processes for the orthosis’ personalization are presented. One based on a 3D laser scan of the patient chest, followed by the reconstruction of the thoracic wall mesh using a radial basis function, and a second one, based on a computer tomography scan followed by a neighbouring cells algorithm. The axial position where the orthosis is to be located is automatically calculated using a Ray-Triangle intersection method, whose outcome is input to a pseudo Kochenek interpolating spline method to define the orthosis curvature. Results show that no significant differences exist between the patient chest physiognomy and the curvature angle and size of the orthosis, allowing a better cosmetic outcome and less initial discomfort

Agronomic performance of 27 cocoa progenies and plant selection based on productivity, self-compatibility and disease resistance

The objective of the present work was to evaluate 27 progenies of cocoa crosses considering the agronomic traits and select F1 plants within superior crosses. The experiment was installed in March 2005, in the Experimental Station Joaquim Bahiana (ESJOB), in Itajuipe, Bahia. The area of the experiment is of approximately 3 ha, with a total of 3240 plants. Thirteen evaluations of vegetative brooms, five of cushion brooms and 15 of number of pods per plant were accomplished. Thirty pollinations were made for each selected plant to test for self-compatibility. The production, based on the number of pods per plant, and resistance to witches´ broom indicated CEPEC 94 x CCN 10, RB 39 x CCN 51 and CCN 10 x VB 1151 as superior progenies. All selections tested were self-compatible. The analyses of progenies and individual tree data, associated to visual field observations, allowed the selection of 17 plants which were included in a network of regional tests to determine the phenotypic stability.

Automatic modeling of pectus excavatum corrective prosthesis using artificial neural networks

Pectus excavatum is the most common deformity of the thorax. Pre-operative diagnosis usually includes Computed Tomography (CT) to successfully employ a thoracic prosthesis for anterior chest wall remodeling. Aiming at the elimination of radiation exposure, this paper presents a novel methodology for the replacement of CT by a 3D laser scanner (radiation-free) for prosthesis modeling. The complete elimination of CT is based on an accurate determination of ribs position and prosthesis placement region through skin surface points. The developed solution resorts to a normalized and combined outcome of an artificial neural network (ANN) set. Each ANN model was trained with data vectors from 165 male patients and using soft tissue thicknesses (STT) comprising information from the skin and rib cage (automatically determined by image processing algorithms). Tests revealed that ribs position for prosthesis placement and modeling can be estimated with an average error of 5.0 ± 3.6 mm. One also showed that the ANN performance can be improved by introducing a manually determined initial STT value in the ANN normalization procedure (average error of 2.82 ± 0.76 mm). Such error range is well below current prosthesis manual modeling (approximately 11 mm), which can provide a valuable and radiation-free procedure for prosthesis personalization.

Remote Patient Monitoring Based on ZigBee: Lessons from a Real-World Deployment

AIM: This work presents detailed experimental performance results from tests executed in the hospital environment for Health Monitoring for All (HM4All), a remote vital signs monitoring system based on a ZigBee® (ZigBee Alliance, San Ramon, CA) body sensor network (BSN). MATERIALS AND METHODS: Tests involved the use of six electrocardiogram (ECG) sensors operating in two different modes: the ECG mode involved the transmission of ECG waveform data and heart rate (HR) values to the ZigBee coordinator, whereas the HR mode included only the transmission of HR values. In the absence of hidden nodes, a non-beacon-enabled star network composed of sensing devices working on ECG mode kept the delivery ratio (DR) at 100%. RESULTS: When the network topology was changed to a 2-hop tree, the performance degraded slightly, resulting in an average DR of 98.56%. Although these performance outcomes may seem satisfactory, further investigation demonstrated that individual sensing devices went through transitory periods with low DR. Other tests have shown that ZigBee BSNs are highly susceptible to collisions owing to hidden nodes. Nevertheless, these tests have also shown that these networks can achieve high reliability if the amount of traffic is kept low. Contrary to what is typically shown in scientific articles and in manufacturers' documentation, the test outcomes presented in this article include temporal graphs of the DR achieved by each wireless sensor device. CONCLUSIONS: The test procedure and the approach used to represent its outcomes, which allow the identification of undesirable transitory periods of low reliability due to contention between devices, constitute the main contribution of this work.

Automatic modeling of pectus excavatum corrective prosthesis using artificial neural networks

Pectus excavatum is the most common deformity of the thorax. Pre-operative diagnosis usually includes Computed Tomography (CT) to successfully employ a thoracic prosthesis for anterior chest wall remodeling. Aiming at the elimination of radiation exposure, this paper presents a novel methodology for the replacement of CT by a 3D laser scanner (radiation-free) for prosthesis modeling. The complete elimination of CT is based on an accurate determination of ribs position and prosthesis placement region through skin surface points. The developed solution resorts to a normalized and combined outcome of an artificial neural network (ANN) set. Each ANN model was trained with data vectors from 165 male patients and using soft tissue thicknesses (STT) comprising information from the skin and rib cage (automatically determined by image processing algorithms). Tests revealed that ribs position for prosthesis placement and modeling can be estimated with an average error of 5.0 ± 3.6 mm. One also showed that the ANN performance can be improved by introducing a manually determined initial STT value in the ANN normalization procedure (average error of 2.82 ± 0.76 mm). Such error range is well below current prosthesis manual modeling (approximately 11 mm), which can provide a valuable and radiation-free procedure for prosthesis personalization.

Automatic modeling of an orthotic bracing for nonoperative correction of pectus carinatum

Pectus Carinatum is a deformity of the chest wall, characterized by an anterior protrusion of the sternum, often corrected surgically due to cosmetic motivation. This work presents an alternative approach to the current open surgery option, proposing a novel technique based on a personalized orthosis. Two different processes for the orthosis’ personalization are presented. One based on a 3D laser scan of the patient chest, followed by the reconstruction of the thoracic wall mesh using a radial basis function, and a second one, based on a computer tomography scan followed by a neighbouring cells algorithm. The axial position where the orthosis is to be located is automatically calculated using a Ray-Triangle intersection method, whose outcome is input to a pseudo Kochenek interpolating spline method to define the orthosis curvature. Results show that no significant differences exist between the patient chest physiognomy and the curvature angle and size of the orthosis, allowing a better cosmetic outcome and less initial discomfort.

An approach to absorptive capacity based on the Portuguese textile industry

The main purpose of this research is to identify the hidden knowledge and learning mechanisms in the organization in order to disclosure the tacit knowledge and transform it into explicit knowledge. Most firms usually tend to duplicate their efforts acquiring extra knowledge and new learning skills while forgetting to exploit the existing ones thus wasting one life time resources that could be applied to increase added value within the firm overall competitive advantage. This unique value in the shape of creation, acquisition, transformation and application of learning and knowledge is not disseminated throughout the individual, group and, ultimately, the company itself. This work is based on three variables that explain the behaviour of learning as the process of construction and acquisition of knowledge, namely internal social capital, technology and external social capital, which include the main attributes of learning and knowledge that help us to capture the essence of this symbiosis. Absorptive Capacity provides the right tool to explore this uncertainty within the firm it is possible to achieve the perfect match between learning skills and knowledge needed to support the overall strategy of the firm. This study has taken in to account a sample of the Portuguese textile industry and it is based on a multisectorial analysis that makes it possible a crossfunctional analysis to check on the validity of results in order to better understand and capture the dynamics of organizational behavior.

An approach to absorptive capacity based on the Portuguese textile industry(Final version)

The main purpose of this research is to identify the hidden knowledge and learning mechanisms in the organization in order to disclosure the tacit knowledge and transform it into explicit knowledge. Most firms usually tend to duplicate their efforts acquiring extra knowledge and new learning skills while forgetting to exploit the existing ones thus wasting one life time resources that could be applied to increase added value within the firm overall competitive advantage. This unique value in the shape of creation, acquisition, transformation and application of learning and knowledge is not disseminated throughout the individual, group and, ultimately, the company itself. This work is based on three variables that explain the behaviour of learning as the process of construction and acquisition of knowledge, namely internal social capital, technology and external social capital, which include the main attributes of learning and knowledge that help us to capture the essence of this symbiosis. Absorptive Capacity provides the right tool to explore this uncertainty within the firm it is possible to achieve the perfect match between learning skills and knowledge needed to support the overall strategy of the firm. This study has taken in to account a sample of the Portuguese textile industry and it is based on a multisectorial analysis that makes it possible a crossfunctional analysis to check on the validity of results in order to better understand and capture the dynamics of organizational behavior.

Improving a Cluster Based Directional Channel Model in Realistic Macro-Cell Environment

In this paper a realistic directional channel model that is an extension of the COST 273 channel model is presented. The model uses a cluster of scatterers and visibility region generation based strategy with increased realism, due to the introduction of terrain and clutter information. New approaches for path-loss prediction and line of sight modeling are considered, affecting the cluster path gain model implementation. The new model was implemented using terrain, clutter, street and user mobility information for the city of Lisbon, Portugal. Some of the model's outputs are presented, mainly path loss and small/large-scale fading statistics.

Accounting education in an action-based learning environment: lessons learned

In the context of the Bologna Declaration a change is taking place in the teaching/learning paradigm. From teaching-centered education, which emphasizes the acquisition and transmission of knowledge, we now speak of learning-centered education, which is more demanding for students. This paradigm promotes a continuum of lifelong learning, where the individual needs to be able to handle knowledge, to select what is appropriate for a particular context, to learn permanently and to understand how to learn in new and rapidly changing situations. One attempt to face these challenges has been the experience of ISCAP regarding the teaching/learning of accounting in the course Managerial Simulation. This paper describes the process of teaching, learning and assessment in an action-based learning environment. After a brief general framework that focuses on education objectives, we report the strengths and limitations of this teaching/learning tool. We conclude with some lessons from the implementation of the project.

Learning to manage in knowledge based organizations: the role of portfolios

Paper presented at the 8th European Conference on Knowledge Management, Barcelona, 6-7 Sep. 2008 URL: http://www.academic-conferences.org/eckm/eckm2007/eckm07-home.htm

QSAR modeling of antitubercular activity of diverse organic compounds

Tuberculosis (TB) is a worldwide infectious disease that has shown over time extremely high mortality levels. The urgent need to develop new antitubercular drugs is due to the increasing rate of appearance of multi-drug resistant strains to the commonly used drugs, and the longer durations of therapy and recovery, particularly in immuno-compromised patients. The major goal of the present study is the exploration of data from different families of compounds through the use of a variety of machine learning techniques so that robust QSAR-based models can be developed to further guide in the quest for new potent anti-TB compounds. Eight QSAR models were built using various types of descriptors (from ADRIANA.Code and Dragon software) with two publicly available structurally diverse data sets, including recent data deposited in PubChem. QSAR methodologies used Random Forests and Associative Neural Networks. Predictions for the external evaluation sets obtained accuracies in the range of 0.76-0.88 (for active/inactive classifications) and Q(2)=0.66-0.89 for regressions. Models developed in this study can be used to estimate the anti-TB activity of drug candidates at early stages of drug development (C) 2011 Elsevier B.V. All rights reserved.

Increase of the delivered energy probability in DES using a fuzzy probabilistic modeling

The paper proposes a methodology to increase the probability of delivering power to any load point by identifying new investments in distribution energy systems. The proposed methodology is based on statistical failure and repair data of distribution components and it uses a fuzzy-probabilistic modeling for the components outage parameters. The fuzzy membership functions of the outage parameters of each component are based on statistical records. A mixed integer nonlinear programming optimization model is developed in order to identify the adequate investments in distribution energy system components which allow increasing the probability of delivering power to any customer in the distribution system at the minimum possible cost for the system operator. To illustrate the application of the proposed methodology, the paper includes a case study that considers a 180 bus distribution network.