Health learning materials support in South Africa compared with other developing countries

An investigation was carried out in to thyroid hormones (TSH, T3, T4) and lipid parameters (total cholesterol, LDL cholesterol, HDL cholesterol, triglyceride) in 136 adolescents (94 femals, average age 13 years). An iodine deficiency (grade II-II) with respect to the daily urine excretion per 1,73 m2 BSA was found in 75%. With few exceptions the serum levels of TSH and T4 were in the normal range. In 36% of the patients we noticed compensatory elevated T3 levels. Correlations between thyroid hormones TSH, T4, renal iodine excretion and the volume of thyroid glands were not detectable, only T3 showed a dignificant positive correlation to the thyroid gland volume. The average values of lipids in patients were found to be higher than in normals. We consider the changed lipids as a sign of a disturbed efficacy of thyroid hormones. The regional insufficient iodine supply causes goiters and to a high degree the observed hyperchole-sterolemia, too. Our results underline the necessity of a common iodine salt prophylaxis as well as the treatment of "harmless" goiters in puberty.     

Iris recognition: Analysis of the error rates regarding the accuracy of the segmentation stage

Iris recognition has been widely used in several scenarios with very satisfactory results. As it is one of the earliest stages, the image segmentation is in the basis of the process and plays a crucial role in the success of the recognition task. In this paper we analyze the relationship between the accuracy of the iris segmentation process and the error rates of three typical iris recognition methods. We selected 5000 images of the UBIRIS, CASIA and ICE databases that the used segmentation algorithm can accurately segment and artificially simulated four types of segmentation inaccuracies. The obtained results allowed us to conclude about a strong relationship between translational segmentation inaccuracies – that lead to errors in phase – and the recognition error rates.     

Outliers detection in environmental monitoring databases

Environmental monitoring is nowadays an important task in many industrial operations. In order to comply with strong environmental laws, they have implemented monitoring systems based on a network of air quality and meteorological stations providing real-time measurements of key variables associated to the distribution of pollutants in surrounding areas. These measurements can be contaminated by outliers, which must be discarded in order to have a consistent set of data. This work presents a nonlinear procedure for outliers detection based on residual analysis of regression with Partial Least Squares and Artificial Neural Networks. In order to minimize the negative effect of outliers in the training dataset a learning algorithm with regularization is proposed. This algorithm is based on a Quasi-Newton optimization method and it was tested on a simulated nonlinear process, on real data from environmental monitoring contaminated with synthetic outliers, and finally applied to a real environmental monitoring data obtained from a monitoring station and having natural outliers. The results are encouraging and further developments are foreseen for including information from neighboring stations and emission source operation.     

Exploring educational immersive videogames: an empirical study with a 3D multimodal interaction prototype

ABSTRACT

Gestural interaction devices emerged and originated various studies on multimodal human–computer interaction to improve user experience (UX). However, there is a knowledge gap regarding the use of these devices to enhance learning. We present an exploratory study which analysed the UX with a multimodal immersive videogame prototype, based on a Portuguese historical/cultural episode. Evaluation tests took place in high school environments and public videogaming events. Two users would be present simultaneously in the same virtual reality (VR) environment: one as the helmsman aboard Vasco da Gama’s fifteenth-century Portuguese ship and the other as the mythical Adamastor stone giant at the Cape of Good Hope. The helmsman player wore a VR headset to explore the environment, whereas the giant player used body motion to control the giant, and observed results on a screen, with no headset. This allowed a preliminary characterisation of UX, identifying challenges and potential use of these devices in multi-user virtual learning contexts. We also discuss the combined use of such devices, towards future development of similar systems, and its implications on learning improvement through multimodal human–computer interaction.     

Principal motion components for one-shot gesture recognition

This paper introduces principal motion components (PMC), a new method for one-shot gesture recognition. In the considered scenario a single training video is available for each gesture to be recognized, which limits the application of traditional techniques (e.g., HMMs). In PMC, a 2D map of motion energy is obtained per each pair of consecutive frames in a video. Motion maps associated to a video are processed to obtain a PCA model, which is used for recognition under a reconstruction-error approach. The main benefits of the proposed approach are its simplicity, easiness of implementation, competitive performance and efficiency. We report experimental results in one-shot gesture recognition using the ChaLearn Gesture Dataset; a benchmark comprising more than 50,000 gestures, recorded as both RGB and depth video with a Kinect?camera. Results obtained with PMC are competitive with alternative methods proposed for the same data set.     

Incorporating technology in service-oriented i* business models: a case study

In recent years, considerable attention has been paid to enterprise information systems. This interest is motivated by the need for achieving better integration of new technologies (hardware and software) with the business processes of an organization. Business processes have become more and more dependent on technologies because technology has a direct impact on business processes, changing the way they are performed and thus also affecting the way analysts design the software system. However, at the present time, there are still some gaps between the definition of business processes and the technologies used in the organization. In practice, organizations have carried out their business processes using different technologies; however, it is sometimes not possible to determine how technologies are useful in achieving current business goals. This is because business models do not explicitly consider the technologies in the organizational requirements. The goal of this paper is to present a systematic process for integrating business processes and technologies at the conceptual level. To validate our approach, we present a case study that describes the processes of the inventory management department of a public research center.     

Robot task plan representation by Petri nets: modelling, identification, analysis and execution

In this paper we introduce a framework to represent robot task plans based on Petri nets. Our approach enables modelling a robot task, analysing its qualitative and quantitative properties and using the Petri net representation for actual plan execution. The overall model is obtained from the composition of simple models, leading to a modular approach. Analysis is applied to a closed loop between the robot controller and the environment Petri net models. We focus here on the quantitative properties, captured by stochastic Petri net models. Furthermore, we introduce a method to identify the environment and action layer parameters of the stochastic Petri net models from real data, improving the significance of the model. The framework building blocks and a single-robot task model are detailed. Results of a case study with simulated soccer robots show the ability of the framework to provide a systematic modelling tool, and of determining, through well-known analysis methods for stochastic Petri nets, relevant properties of the task plan applied to a particular environment.     

Wafer-level fabrication process for fully encapsulated micro-supercapacitors with high specific energy

In this paper a wafer-level process is proposed to fully integrate carbon-based micro-supercapacitor onto silicon substrate. This process relies on the deposition of a paste containing carbon, PVDF and acetone into cavities etched in silicon. After electrolyte deposition in a controlled atmosphere, a wafer-level encapsulation is realized. Cyclic voltammetry performed on non-encapsulated micro-components showed specific energy of 257 mJ cm⁻² for 336 μm deep cavities. The specific encapsulation process developed was tested separately and proved to be efficient in terms of resistance to organic electrolytes and mechanical strength.     

A weight function methodology for the assessment of embedded and surface irregular plane cracks

A low cost numerical tool for the calculation of mode I stress intensity factors K in embedded and surface irregular cracks is presented in this paper. The proposed tool is an extension of the O-integral algorithm due to Oore and Burns for the assessment of embedded plane cracks using the weight function methodology. The performance of the O-integral is assessed first by comparing its K results to exact solutions for embedded elliptical and rectangular cracks. From the analysis of this data it is found that the error in the K results systematically depends on the crack aspect ratio and the local crack front curvature. Based on this evidence a corrective function is derived in order to remediate the limitations of the O-integral. Solutions due to Newman and Raju are used to account for the effects of free surfaces and finite thickness. The accuracy of the proposed procedure is assessed by solving a number of examples and by comparing the obtained results to those available in the literature.