Physicochemical Methods for Prediction of Functional Information for Proteins

Structural genomics initiatives are determining thousands of new protein structures. Many of these structures are of unknown function, and computational methods for the rapid determination of functional information from protein structure are needed. We present details of how functional information is obtained from the structure using THEMATICS (Theoretical Microscopic Titration Curves). THEMATICS is a computational procedure that gives information about chemical reactivity, based on solution of the Poisson-Boltzmann equations for the electrical potential function. We show how anomalies in predicted titration curves are established. We show further that when residues with anomalous predicted titration curves form a cluster in physical space, these residues tend to be very highly conserved across species and such clusters are reliable predictors of the active site. Results are given for ten enzymes; detailed results are shown for the enzymes triosephosphate isomerase (from chicken), 6-hydroxymethyl-7,8-dihydropterin pyrophosphokinase (from E. coli), and papain (from papaya).     

Detection and evaluation of the fibers' deposition parameters during wet filament winding

Wet filament winding technology has been extensively used for the manufacture of rotationally symmetric parts made of fiber-reinforced plastics (FRP). As the design and modeling of FRP-parts require numerous assumptions, deviations between calculated and achieved mechanical properties are expected. One aspect that contributes to this discrepancy is the assumption of a homogeneous rectangular cross-section of the fiber-band. In this work, the fiber-band geometry in a wet-winding process of carbon fiber cylinders is analyzed. An Infrared-optical system for the detection of the fiber bandwidth and winding-angle is implemented. The influence of the winding speed and the resin temperature is analyzed. An image processing algorithm for the automatic measurement of the fiber's bandwidth and winding-angle is developed. Manual and adaptive gamma corrections are implemented to improve image quality. A parameter study for the suitable selection of image processing parameters is performed.     

A robot learning from demonstration framework to perform force-based manipulation tasks

This paper proposes an end-to-end learning from demonstration framework for teaching force-based manipulation tasks to robots. The strengths of this work are manyfold. First, we deal with the problem of learning through force perceptions exclusively. Second, we propose to exploit haptic feedback both as a means for improving teacher demonstrations and as a human–robot interaction tool, establishing a bidirectional communication channel between the teacher and the robot, in contrast to the works using kinesthetic teaching. Third, we address the well-known what to imitate? problem from a different point of view, based on the mutual information between perceptions and actions. Lastly, the teacher’s demonstrations are encoded using a Hidden Markov Model, and the robot execution phase is developed by implementing a modified version of Gaussian Mixture Regression that uses implicit temporal information from the probabilistic model, needed when tackling tasks with ambiguous perceptions. Experimental results show that the robot is able to learn and reproduce two different manipulation tasks, with a performance comparable to the teacher’s one.     

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.     

Functional magnetic nanoshells integrated nanosensor for trace analysis of environmental uranium contamination

Transuranic radionuclides such as uranium tend to be a pervasive environmental contaminant. It is absorbed through the intestine or a lung, deposited in the tissues, predominantly kidney and bone, and is carcinogenic. A novel nanosensor system has been developed for voltammetric tracing of environmental uranium contamination.The sensor consists of an organophosphorous ligand, (t-butylphenyl)-N,N-di-(isobutyl) carbamoylmethylphosphineoxide (CMPO) functionalized superparamagnetic core-shell magnetic nanoparticles and magnet based electrodes. It exploits the natural affinity of uranium for phosphate molecules to fabricate a highly specific and reproducible sensor. The small dimension along with a dramatically increased contact surface has lead to a faster response and higher sensitivity. The system uses an external magnetic field gradient for preconcentration and removal of the analyte from the surrounding aqueous media. The redox properties of the analyte are exploited for enumeration of variables by electrochemical techniques such as square wave voltammetry. The detection limit of the system is observed to be in parts-per-billion (ppb) of the uranyl concentration.     

Multiple random crack propagation using a boundary element formulation

This paper proposes a boundary element method (BEM) model that is used for the analysis of multiple random crack growth by considering linear elastic fracture mechanics problems and structures subjected to fatigue. The formulation presented in this paper is based on the dual boundary element method, in which singular and hyper-singular integral equations are used. This technique avoids singularities of the resulting algebraic system of equations, despite the fact that the collocation points coincide for the two opposite crack faces. In fracture mechanics analyses, the displacement correlation technique is applied to evaluate stress intensity factors. The maximum circumferential stress theory is used to evaluate the propagation angle and the effective stress intensity factor. The fatigue model uses Paris’ law to predict structural life. Examples of simple and multi-fractured structures loaded until rupture are considered. These analyses demonstrate the robustness of the proposed model. In addition, the results indicate that this formulation is accurate and can model localisation and coalescence phenomena.