An haptic-based immersive environment for shape analysis and modelling

Currently, the design of aesthetic products is a process that requires a set of activities where digital models and physical mockups play a key role. Typically, these are modified (and built) several times before reaching the desired design, increasing the development time and, consequently, the final product cost. In this paper, we present an innovative design environment for computer-aided design (CAD) surface analysis. Our system relies on a direct visuo-haptic display system, which enables users to visualize models using a stereoscopic view, and allows the evaluation of sectional curves using touch. Profile curves are rendered using an haptic device that deforms a plastic strip, thanks to a set of actuators, to reproduce the curvature of the shape co-located with the virtual model. By touching the strip, users are able to evaluate shape characteristics, such as curvature or discontinuities (rendered using sound), and to assess the surface quality. We believe that future computer-aided systems (CAS)/CAD systems based on our approach will contribute in improving the design process at industrial level. Moreover, these will allow companies to reduce the product development time by reducing the number of physical mockups necessary for the product design evaluation and by increasing the quality of the final product, allowing a wider exploration and comparative evaluation of alternatives in the given time.     

Interactive smart battery storage for a PV and wind hybrid energy management control based on conservative power theory

This paper presents interactive smart battery-based storage (BBS) for wind generator (WG) and photovoltaic (PV) systems. The BBS is composed of an asymmetric cascaded H-bridge multilevel inverter (ACMI) with staircase modulation. The structure is parallel to the WG and PV systems, allowing the ACMI to have a reduction in power losses compared to the usual solution for storage connected at the DC-link of the converter for WG or PV systems. Moreover, the BBS is embedded with a decision algorithm running real-time energy costs, plus a battery state-of-charge manager and power quality capabilities, making the described system in this paper very interactive, smart and multifunctional. The paper describes how BBS interacts with the WG and PV and how its performance is improved. Experimental results are presented showing the efficacy of this BBS for renewable energy applications.     

A dynamic default revision mechanism for speculative computation

In this work a default revision mechanism is introduced into speculative computation to manage incomplete information. The default revision is supported by a method for the generation of default constraints based on Bayesian networks. The method enables the generation of an initial set of defaults which is used to produce the most likely scenarios during the computation, represented by active processes. As facts arrive, the Bayesian network is used to derive new defaults. The objective with such a new dynamic mechanism is to keep the active processes coherent with arrived facts. This is achieved by changing the initial set of default constraints during the reasoning process in speculative computation. A practical example in clinical decision support is described.     

Experimental automobile-pedestrian injuries

Fifteen fresh cadaver impacts were conducted in simulation of pedestrian-automobile accidents. The test sled configuration simulated an automobile bumper and hood in "standard" and "nosedive" situations as well as "hard" and "soft" impact surfaces. Instrumentation and film demonstrated large axial compression forces in the struck leg and considerable angular velocity of the torso in all modes. Dissection revealed primary injury at the impact site at low velocities with added remote injury at high velocities. Fracture of the lateral tibial plateau was most common. Lowering the bumper height offered the greatest protection against injury at moderate impact velocities.     

Diagnostic Tools for Wireless Sensor Networks: A Comparative Survey

The availability of tools to diagnose Wireless Sensor Network (WSN) failures is a key success factor for this type of networks as already demonstrated by several long-running deployments. By nature, WSNs are resource-constrained, fragile, complex to analyse, and failure-prone. Naturally, with the growing number of installations, it is becoming fundamental to efficiently diagnose failures as soon as possible, in order to deal with the underlying causes. In accordance with this, from 2005 onwards, the offer of diagnostic tools has been increasing, as the other base technologies (e.g. networking, operating system, localisation, synchronisation) become reasonably stable. The purpose of this survey is to provide an overview of existing post-deployment WSN diagnostic tools, by briefly presenting their functionality, architecture and constraints, in order to enable a basic understating of each tool. The survey also includes a multi-dimensional comparative analysis of the various tools, based on a proposed classification scheme and evaluation criteria, as well as an identification of the main open research issues. Although the number of diagnostic tools is high and considerable work has been done in this area, we conclude that there are still several challenges concerning post-deployment WSN diagnostic tools, regarding scope, flexibility, generality, mobility and security. Moreover, there is a need for mature, native diagnostic-oriented functionality in WSN platforms and operating systems.     

Using Twitter® as source of information for dietary market research: a study on veganism and plant-based diets

Social media has experienced rapid growth in recent years and has been subjected of discussions on a wide range of topics including new lifestyles and eating habits, providing a great opportunity to obtain spontaneous consumer information. In this sense, the present work aimed to understand the perception of Twitter^® users about themes of veganism and plant-based diets. The social networking data mining methodology was applied to measure the relationship between both terms. The significant differences found were analysed using the global chi-square (χ²) test, and their sources of variation were investigated by the chi-square per cell. The results indicate that the vegan group's posts are more related to the categories of recipes, trends, criticism and negative comments about veganism, being more often citing sources when compared to the other group. The results of the plant-based diet group are more significantly related to the impacts of nutrition, physical activity and consumer health. In conclusion, Twitter^® has proved to be an interesting tool for obtaining data on (re) produced food publications on social media and their results can guide the market and the academic environment in creating new products, services and marketing strategies to answer the needs of specific consumers.     

Higher-Order Adaptive Finite Difference Methods for Fully Nonlinear Elliptic Equations

We introduce generalised finite difference methods for solving fully nonlinear elliptic partial differential equations. Methods are based on piecewise Cartesian meshes augmented by additional points along the boundary. This allows for adaptive meshes and complicated geometries, while still ensuring consistency, monotonicity, and convergence. We describe an algorithm for efficiently computing the non-traditional finite difference stencils. We also present a strategy for computing formally higher-order convergent methods. Computational examples demonstrate the efficiency, accuracy, and flexibility of the methods.