Semantic content of motorcycle riders’ eye fixations during lane-splitting

A naturalistic field observation study is presented, exploring the experienced riders’ eye fixations on specific traffic objects and their relative s     

Simulation of water and nitrogen dynamics in soils during wastewater applications by using a finite-element model

A mathematical model was developed to simulate water movement, mass transport, and nitrogen transformations in soils during wastewater applications. The model is one-dimensional and based on the Galerkin finite-element method. The submodel of mass transport of nitrogen incorporates the convection-dispersion processes of ammonium and nitrate nitrogen, nitrification, denitrification, ammonium exchange and uptake of ammonium and nitrate ions. The accuracy and validity of the proposed model was examined by comparison with an explicit-implicit finite-difference model results. The model was used for simulation of water and nitrogen dynamics during wastewater application in homogeneous and multi-layered soils under different N concentration, rate, duration and scheduling of application.     

The generalized Kuhn model of linear viscoelasticity

We propose a generalization of the Kuhn model of linear viscoelasticity. This generalization, which has four material parameters, is able to provide a near frequency independent response over a wide range of frequencies. It is useful for highly dissipative materials such as asphalt concrete. It is derived by generalizing Lubliner and Panoskaltsis’s modified Kuhn model, but we also show that it is closely related to fractional derivative models. We show that the model admits a rheological approximation, that is, an approximation by classical springs and dashpots. The model and rheological representation are compared to experimental data.     

The ChaLearn gesture dataset (CGD 2011)

This paper describes the data used in the ChaLearn gesture challenges that took place in 2011/2012, whose results were discussed at the CVPR 2012 and ICPR 2012 conferences. The task can be described as: user-dependent, small vocabulary, fixed camera, one-shot-learning. The data include 54,000 hand and arm gestures recorded with an RGB-D \(\hbox {Kinect}^\mathrm{TM}\) camera. The data are organized into batches of 100 gestures pertaining to a small gesture vocabulary of 8–12 gestures, recorded by the same user. Short continuous sequences of 1–5 randomly selected gestures are recorded. We provide man-made annotations (temporal segmentation into individual gestures, alignment of RGB and depth images, and body part location) and a library of function to preprocess and automatically annotate data. We also provide a subset of batches in which the user’s horizontal position is randomly shifted or scaled. We report on the results of the challenge and distribute sample code to facilitate developing new solutions. The data, datacollection software and the gesture vocabularies are downloadable from http://gesture.chalearn.org. We set up a forum for researchers working on these data http://groups.google.com/group/gesturechallenge.     

Use of rosemary extract in preventing oxidation during deep-fat frying of potato chips

The effect of using a rosemary extract on the stability of oil used for frying potato chips has been evaluated. Sliced potatoes were fried intermittently in soybean oil containing a natural extract from rosemary. The conditions used for frying were 185°C for 90 s. Two separate experiments, with and without replenishment of oil, were carried out. Oil samples were taken each day, not only from the frying pans but also by extraction with hexane from the chips. Changes in the induction period of the oil samples (Rancimat method) were determined. The induction period decreased as frying progressed. The reduction in the induction period was higher in the oil free of rosemary extract, and the chips were much darker in color. The oil containing the extract showed greater antioxidant activity, and reduced darkening and rancidity of the oil. Potato chips fried in the oil with added rosemary extract were more acceptable than chips fried in oil containing no extract until the last frying. The free radical scavenging activity of rosemary extract in comparison to other natural antioxidants was also determined by the reduction of O₂ ^.- in a hypoxanthine/xanthine oxidase system.     

An empirical investigation of Technology Readiness among medical staff based in Greek hospitals

Technology readiness (TR) represents an individual’s mental readiness to accept new technologies. Although the TR scale has been used in many studies, its application in the healthcare context is limited. This paper focuses on identifying the TR profiles of medical staff and to model preference TR variations with respect to computer use, computer knowledge and computer feature demands. The study reports results from a nationwide study conducted in Greece, during a three-year period, which sampled responses from 604 physicians and nurses working in 14 Greek hospitals. Exploratory Structural Equation Modelling analysis is used in order to confirm the structure of the Technology Readiness Index. The results confirm the five groups of the TR taxonomy. Statistical differences were found between classes in information and communication technology (ICT) knowledge, ICT feature demands, hours of use per week as well as ICT use performance, but not in the general use of ICT. The results facilitate comprehension of the factors, which influence the use of ICT by medical staff and, in addition, they convey important policy and managerial implications. In conclusion, medical staff should be treated according to its TR taxonomy classes in order to expedite the acceptance and use of an ICT system.     

Advanced product planning: a comprehensive process for systemic definition of new product requirements

This paper reports results of research into the definition of requirements for new consumer products––specifically, electro-mechanical products. The research dealt with the derivation of design requirements that are demonstrably aligned with stakeholder needs. The paper describes a comprehensive process that can enable product development teams to deal with statements of product requirements, as originally collected through market research activities, in a systematic and traceable manner from the early, fuzzy front end, stages of the design process. The process described has been based on principles of systems engineering. A case study from its application and evaluation drawn from the power sector is described in this paper. The case study demonstrates how the process can significantly improve product quality planning practices through revision of captured product requirements, analysis of stakeholder requirements and derivation of design requirements. The paper discusses benefits and issues from the use of the process by product development teams, and identifies areas for further research. Finally, the conclusions drawn from the reported research are presented.

On the discovery of subsumption relations for the alignment of ontologies

For the effective alignment of ontologies, the subsumption mappings between the elements of the source and target ontologies play a crucial role, as much as equivalence mappings do. This paper presents the “Classification-Based Learning of Subsumption Relations” (CSR) method for the alignment of ontologies. Given a pair of two ontologies, the objective of CSR is to learn patterns of features that provide evidence for the subsumption relation among concepts, and thus, decide whether a pair of concepts from these ontologies is related via a subsumption relation. This is achieved by means of a classification task, using state of the art supervised machine learning methods. The paper describes thoroughly the method, provides experimental results over an extended version of benchmarking series of both artificially created and real world cases, and discusses the potential of the method.     

Dropout prediction in e-learning courses through the combination of machine learning techniques

In this paper, a dropout prediction method for e-learning courses, based on three popular machine learning techniques and detailed student data, is proposed. The machine learning techniques used are feed-forward neural networks, support vector machines and probabilistic ensemble simplified fuzzy ARTMAP. Since a single technique may fail to accurately classify some e-learning students, whereas another may succeed, three decision schemes, which combine in different ways the results of the three machine learning techniques, were also tested. The method was examined in terms of overall accuracy, sensitivity and precision and its results were found to be significantly better than those reported in relevant literature.