Combining dimensional analysis with model based systems engineering

The model based systems engineering (MBSE) approach describes a system using consistent views to provide a holistic model as complete as possible. MBSE methodologies end with the physical architecture of the system, but a physical model is clearly incomplete without the study of its associated physical laws and phenomena related to the whole system or its parts. However, the computational demands could be excessive even for modest projects. Dimensional analysis (DA) is common in fluid dynamics and chemical engineering, but its application to systems engineering is still limited. We describe an engineering methodological process, which incorporates DA as a powerful tool to understand the physical constraints of the system without the burden of complex analytical or numerical calculations. A detailed example describing a microantenna is presented showing the benefits of this approach. The selected example describes a problem rarely covered in modern expositions of DA in order to show the wide benefit of these techniques. The information provided by this analysis is very useful to select the best physically realizable architectures, testing design, and conduct trade-off studies. The complexity of modern systems and systems of systems demands new testing procedures in order to comply with increasingly demanding requirements and regulations. This can be accomplished through research in new DA methods. Finally, this article serves as a fairly comprehensive guide to the use of DA in the context of MBSE, detailing its strengths, limitations, and controversial issues

Necrotrophic fungi associated with epidermal microcracking caused by chilling injury in pickling cucumber fruit

O objetivo deste trabalho foi visualizar a associação entre microfissuras e outros sintomas na epiderme, induzidos pelo frio, e identificar as podridões de pepino (Cucumis sativus L.) por microscopia eletrônica de varredura. O início do desenvolvimento da lesão em pepino é caracterizado por depressões epidérmicas e pelo fendilhamento superficial, provocado pelo colapso das células subepidérmicas. A germinação dos conídios de Alternaria alternata, localizados nas fendas de pepino cultivar Trópico, ocorreu após o início do desenvolvimento dos sintomas dos danos, causados pelo frio, na epiderme do fruto. A germinação dos conídios de Stemphylium herbarum e a penetração do micélio na hipoderme pelas microfissuras ocorreram antes de os sintomas dos danos causados pelo frio se tornarem visíveis. Na cultivar Perichán 121 observou-se o fungo Botrytis cinerea.The objective of this work was to visualize the association between microcracking and other epidermal chilling injury symptoms, and to identify rots in cucumber fruit (Cucumis sativus L.) by scanning electron microscopy (SEM). Depressed epidermal areas and surface cracking due to damages of subepidermal cells characterized the onset of pitting in cucumber fruit. The germination of conidia of Alternaria alternata, with some of them evident on the fractures in the cultivar Trópico, occurred after damaging on the epidermis. Before, the chilling injury symptoms became visible, Stemphylium herbarum conidia germinated, and mycelium penetrated through the hypodermis using the microcracks as pathway. In the cultivar Perichán 121 the fungus was identified as Botrytis cinerea

Necrotrophic fungi associated with epidermal microcracking caused by chilling injury in pickling cucumber fruit

O objetivo deste trabalho foi visualizar a associação entre microfissuras e outros sintomas na epiderme, induzidos pelo frio, e identificar as podridões de pepino (Cucumis sativus L.) por microscopia eletrônica de varredura. O início do desenvolvimento da lesão em pepino é caracterizado por depressões epidérmicas e pelo fendilhamento superficial, provocado pelo colapso das células subepidérmicas. A germinação dos conídios de Alternaria alternata, localizados nas fendas de pepino cultivar Trópico, ocorreu após o início do desenvolvimento dos sintomas dos danos, causados pelo frio, na epiderme do fruto. A germinação dos conídios de Stemphylium herbarum e a penetração do micélio na hipoderme pelas microfissuras ocorreram antes de os sintomas dos danos causados pelo frio se tornarem visíveis. Na cultivar Perichán 121 observou-se o fungo Botrytis cinerea.The objective of this work was to visualize the association between microcracking and other epidermal chilling injury symptoms, and to identify rots in cucumber fruit (Cucumis sativus L.) by scanning electron microscopy (SEM). Depressed epidermal areas and surface cracking due to damages of subepidermal cells characterized the onset of pitting in cucumber fruit. The germination of conidia of Alternaria alternata, with some of them evident on the fractures in the cultivar Trópico, occurred after damaging on the epidermis. Before, the chilling injury symptoms became visible, Stemphylium herbarum conidia germinated, and mycelium penetrated through the hypodermis using the microcracks as pathway. In the cultivar Perichán 121 the fungus was identified as Botrytis cinerea

Hybridization of neural network models for the prediction of Extreme Significant Wave Height segments

This work proposes a hybrid methodology for the detection and prediction of Extreme Significant Wave Height (ESWH) periods in oceans. In a first step, wave height time series is approximated by a labeled sequence of segments, which is obtained using a genetic algorithm in combination with a likelihood-based segmentation (GA+LS). Then, an artificial neural network classifier with hybrid basis functions is trained with a multiobjetive evolutionary algorithm (MOEA) in order to predict the occurrence of future ESWH segments based on past values. The methodology is applied to a buoy in the Gulf of Alaska and another one in Puerto Rico. The results show that the GA+LS is able to segment and group the ESWH values, and the neural network models, obtained by the MOEA, make good predictions maintaining a balance between global accuracy and minimum sensitivity for the detection of ESWH events. Moreover, hybrid neural networks are shown to lead to better results than pure models

Using machine learning methods to determine a typology of patients with HIV-HCV infection to be treated with antivirals

Several European countries have established criteria for prioritising initiation of treatment in patients infected with the hepatitis C virus (HCV) by grouping patients according to clinical characteristics. Based on neural network techniques, our objective was to identify those factors for HIV/HCV co-infected patients (to which clinicians have given careful consideration before treatment uptake) that have not being included among the prioritisation criteria. This study was based on the Spanish HERACLES cohort (NCT02511496) (April-September 2015, 2940 patients) and involved application of different neural network models with different basis functions (product-unit, sigmoid unit and radial basis function neural networks) for automatic classification of patients for treatment. An evolutionary algorithm was used to determine the architecture and estimate the coefficients of the model. This machine learning methodology found that radial basis neural networks provided a very simple model in terms of the number of patient characteristics to be considered by the classifier (in this case, six), returning a good overall classification accuracy of 0.767 and a minimum sensitivity (for the classification of the minority class, untreated patients) of 0.550. Finally, the area under the ROC curve was 0.802, which proved to be exceptional. The parsimony of the model makes it especially attractive, using just eight connections. The independent variable "recent PWID" is compulsory due to its importance. The simplicity of the model means that it is possible to analyse the relationship between patient characteristics and the probability of belonging to the treated group

The entrepreneur in the regional innovation system. Acomparative study for high- and low-income regions

This paper investigates the influence of entrepreneurs’characteristics oninnovation in regions with different levels of development. By doing so,this work seeks to contribute to a better understanding of the role ofentrepreneurs in the functioning and performance of regional innovationsystems. The influence of entrepreneurs’personal characteristics andtheir perceptions of the business environment onfirm innovation areinvestigated via a survey of companies carried out in six Spanish regions.The results allow the identifying of significant differences in the maindeterminants of innovation in the high-income regions and low-incomeregions studied. Entrepreneurs’generalized trust stimulates innovationonly in high-income regions, where necessity motivation has also anegative effect on innovation. Growth ambition seems to play a highlypositive role only in the case of low-income regions. Human capital andinfrastructure are perceived by the entrepreneurs as the main bottle-necks for innovation in low-income regions, whereas in the case of high-income regions the legal,fiscal andfinancial systems are considered thekey institutional barriers. These differences in the entrepreneurial factorshould be taken into account in order to design and implement policiesto stimulate and foster innovation in different regional contexts.Ministerio de Economía y Competitividad (MINECO). España ECO2013-42889-PJunta de Andalucía P09-SEJ-485

Multilogistic Regression using Initial and Radial Basis Function covariates

This paper proposes a hybrid multilogistic model, named MultiLogistic Regression using Initial and Radial Basis Function covariates (MLRIRBF). The process for obtaining the coefficients is carried out in several steps. First, an Evolutionary Programming (EP) algorithm is applied, aimed to produce a RBF Neural Network (RBFNN) with a reduced number of RBF transformations and the simplest structure possible. Then, the input space is transformed by adding the nonlinear transformations of the input variables given by the RBFs of the best individual in the last generation. Finally, a maximum likelihood optimization method determines the coefficients associated with a multilogistic regression model built on this transformed input space. In this final step, two different multilogistic regression algorithms are applied, one that considers all initial and RBF covariates (MLRIRBF) and another one that incrementally constructs the model and applies cross-validation, resulting in an automatic covariate selection (MLRIRBF*). The methodology proposed is tested using six benchmark classification problems from well-known machine learning problems. The results are compared with the corresponding multilogistic regression methodologies applied over the initial input space, to the RBFNNs obtained by the EP algorithm (RBFEP) and to other competitive machine learning techniques. The MLRIRBF* models are found to be better than the corresponding multilogistic regression methodologies and the RBFEP method for almost all datasets, and obtain the highest mean accuracy rank when compared to the rest of methods in all datasets

LEGO© Mindstorms NXT and Q-Learning: a teaching approach for robotics in engineering

Robotics has become a common subject in many engineering degrees and postgraduate programs. Although at undergraduate levels the students are introduced to basic theoretical concepts and tools, at postgraduate courses more complex topics have to be covered. One of those advanced subjects is Cognitive Robotics, which covers aspects like automatic symbolic reasoning, decision-making, task planning or machine learning. In particular, Reinforcement Learning (RL) is a machine learning and decision-making methodology that does not require a model of the environment where the robot operates, overcoming this limitation by making observations. In order to get the greatest educational benefit, RL theory should be complemented with some hands-on RL task that uses a real robot, so students get a complete vision of the learning problem, as well as of the issues that arise when dealing with a physical robotic platform. There are several RL techniques that can be studied in such a subject; we have chosen Q-learning, since is a simple, effective and well-known RL algorithm. In this paper we present a minimalist implementation of the Q-learning method for a Lego Mindstorms NXT mobile robot, focused on simplicity and applicability, and flexible enough to be adapted to several tasks. Starting from a simple wandering problem, we first design an off-line model of the learning process in which the Q-learning parameters are studied. After that, we implement the algorithm on the robot, gradually enlarging the number of states-actions of the problem. The final result of this work is a teaching framework for developing practical activities regarding Q-learning in our Robotics subjects, which will improve our teaching.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

An Orientation Service for Dependent People Based on an Open Service Architecture

This article describes a service architecture for ambient assisted liv ing and in particular an orientation navigation service in open places for persons with memory problems such as those patients suffering from Alzheimer’s in its early stages. The service has the following characteristics: one-day system autonomy; self-adjusting interfaces for simple interaction with patients, based on behavioural patterns to predict routes and destinations and to detect lost situations; easy browsing through simple spoken commands and use of photo graphs for reorientation, and independence of GISs (Geographic Information Systems) to reduce costs and increase accessibility. Initial testing results of the destination prediction algorithm are very positive. This system is integrated in a global e-health/e-care home service architecture platform (OSGi) that enables remote management of services and devices and seamless integration with other home service domains.Ministerio de Educación y Ciencia TSI2006-13390-C02-0