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People are subjected to a multitude of interruptions. In order to manage these interruptions it is imperative to predict a person's interruptability – his/her current readiness or inclination to be interrupted. This paper aims to introduce the approach of direct interruptability inference from sensor streams (accelerometer and audio data) in a ubiquitous computing setup and to show that it provides highly accurate and robust predictions.

The authors argue that scenarios are central for evaluating the performance of ubiquitous computing devices (and interruptability predicting devices in particular) and prove this on the setup employed, which was based on that of Kern and Schiele.

The paper demonstrates that scenarios provide the foundation for avoiding misleading results, and provide the basis for a stratified scenario‐based learning model, which greatly speeds up the training of such devices.

The direct prediction seems to be competitive or even superior to indirect prediction methods and no drawbacks have been observed yet.

The paper introduces a method for accurately predicting a person's interruptability directly from simple sensors without any intermediate steps/symbols.

This paper aims to study the effect of processing parameters of an electron beam melting (EBM) machine on the surface roughness, critical pitting temperature and density of Ti-6Al-4V parts produced from the EBM machine.

In this study, statistically designed experiments were used to manufacture Ti-6Al-4V samples in the EBM machine under different process parameters of beam current, beam speed and offset focus. Surface roughness was measured for as-built samples using a 3D profilometer. Then, a potentiostatic test was conducted under 2.40 V vs saturated calomel electrode to determine the critical pitting temperature (CPT) in 3.5 per cent mass NaCl solution for the samples of different processing parameters. Next, density was measured for these samples. Finally, model equations were established to relate EBM’s process parameters to measured properties of surface roughness, CPT and density.

Results showed that offset focus had the main influence on surface roughness more than the beam current and the beam speed. Changing processing parameters did not affect corrosion behavior of EBM Ti-6Al-4V as CPT did not vary widely, although a slight effect on CPT values obtained from the beam current and the beam speed. Density was greatly affected by the offset focus more than the other parameters. It can be concluded that uniform and precise measurements of roughness and density are not achievable through this machine; only a range of these properties can be attained.

EBM machine produces 3D parts in a layer-based building process under high temperature and vacuum atmosphere. Due to the manufacturing technique and conditions, the resulting object has irregularities on the exterior surface and voids that are formed within the part, both of which affect samples’ properties like surface roughness, CPT and density. This study established model equations that can relate parts’ properties to processing parameters so that parts of specific properties are obtained to fit the application they are used for. For each property, ANOVA fits vs linear energy were also obtained.