Abstract
The feasibility of measuring arsenic and selenium contents in a single nail clipping was investigated using a small-focus portable X-ray fluorescence (XRF) instrument with monochromatic excitation beams. Nail clipping phantoms supplemented with arsenic and selenium to produce materials with 0, 5, 10, 15, and 20 mu g/g were used for calibration purposes. In total, 10 different clippings were analyzed at two different measurement positions. Energy spectra were fit with detection peaks for arsenic K-alpha, selenium K-alpha, arsenic K-beta, selenium K-beta, and bromine K-alpha characteristic X-rays. Data analysis was performed under two distinct conditions of fitting constraint. Calibration lines were established from the amplitude of each of the arsenic and selenium peaks as a function of the elemental contents in the clippings. The slopes of the four calibration lines were consistent between the two conditions of analysis. The calculated minimum detection limit (MDL) of the method, when considering the Ka peak only, ranged from 0.210 +/- 0.002 mu g/g selenium under one condition of analysis to 0.777 +/- 0.009 mu g/g selenium under another. Compared with previous portable XRF nail clipping studies, MDLs were substantially improved for both arsenic and selenium. The new measurement technique had the additional benefits of being short in duration (similar to 3 min) and requiring only a single nail clipping. The mass of the individual clipping used did not appear to play a major role in signal strength, but positioning of the clipping is important.

Abstract
Model-based methodologies, supported by automatic generation, have been proposed as a solution to reduce software development costs. In the case of interactive computing systems specific challenges arise. On the one hand, a high level of automation requires the use of detailed models, which is contrary to the iterative development process, based on the progressive refinement of user interface mockups, typical of user centered development processes. On the other hand, layered software architectures imply a distinction between the models used in the business logic and in the user interface, raising consistency problems between the models at each level. This article proposes a tool supported approach to user interface generation directly from the architectural models of the business logic. In many situations, user interfaces provide similar features inside a specific domain. The identification of the application domain is thus a key factor in supporting the automation of the generation process. © 2017 Association for Computing Machinery.

Abstract
In recent years, safer and more reliable biometric methods have been developed. Apart from the need for enhanced security, the media and entertainment sectors have also been applying biometrics in the emerging market of user-adaptable objects/systems to make these systems more user-friendly. However, the complexity of some state-of-the-art biometric systems (e.g., iris recognition) or their high false rejection rate (e.g., fingerprint recognition) is neither compatible with the simple hardware architecture required by reduced-size devices nor the new trend of implementing smart objects within the dynamic market of the Internet of Things (IoT). It was recently shown that an individual can be recognized by extracting features from their electrocardiogram (ECG). However, most current ECG-based biometric algorithms are computationally demanding and/or rely on relatively large (several seconds) ECG samples, which are incompatible with the aforementioned application fields. Here, we present a computationally low-cost method (patent pending), including simple mathematical operations, for identifying a person using only three ECG morphology-based characteristics from a single heartbeat. The algorithm was trained/tested using ECG signals of different duration from the Physionet database on more than 60 different training/test datasets. The proposed method achieved maximal averaged accuracy of 97.450% in distinguishing each subject from a ten-subject set and false acceptance and rejection rates (FAR and FRR) of 5.710 +/- 1.900% and 3.440 +/- 1.980%, respectively, placing Beat-ID in a very competitive position in terms of the FRR/FAR among state-of-the-art methods. Furthermore, the proposed method can identify a person using an average of 1.020 heartbeats. It therefore has FRR/FAR behavior similar to obtaining a fingerprint, yet it is simpler and requires less expensive hardware. This method targets low-computational/energy-cost scenarios, such as tiny wearable devices (e.g., a smart object that automatically adapts its configuration to the user). A hardware proof-of concept implementation is presented as an annex to this paper.

Abstract

Abstract
In interactive systems, the ability to select virtual objects is essential. In immersive virtual environments, object selection is usually done at arm's length in mid-air by directly intersecting the desired object with the user's hand. However, selecting objects outside user's arm-reach still poses significant challenges, which direct approaches fail to address. Techniques proposed to overcome such limitations often follow an arm-extension metaphor or favor selection volumes combined with ray-casting. Nonetheless, while these approaches work for room sized environments, they hardly scale up to larger scenarios with many objects. In this paper, we introduce a new taxonomy to classify existing selection techniques. In its wake, we propose PRECIOUS, a novel mid-air technique for selecting out-of-reach objects, featuring iterative refinement in Virtual Reality, an hitherto untried approach in this context. While comparable techniques have been developed for non-stereo and non-immersive environments, these are not suitable to Immersive Virtual Reality. Our technique is the first to employ an iterative progressive refinement in such settings. It uses cone-casting to select multiple objects and moves the user closer to them in each refinement step, to allow accurate selection of the desired target. A user evaluation showed that PRECIOUS compares favorably against state-of-the-art approaches. Indeed, our results indicate that PRECIOUS is a versatile approach to out-of-reach target acquisition, combining accurate selection with consistent task completion times across different scenarios.

Abstract
We consider a Unit Commitment Problem (UCP) addressing not only the economic objective of minimizing the total production costs-as is done in the standard UCP-but also addressing environmental concerns. Our approach utilizes a multi-objective formulation and includes in the objective function a criterion to minimize the emission of pollutants. Environmental concerns are having a significant impact on the operation of power systems related to the emissions from fossil-fuelled power plants. However, the standard UCP, which minimizes just the total production costs, is inadequate to address environmental concerns. We propose to address the UCP with environmental concerns as a multi-objective problem and use a metaheuristic approach combined with a non-dominated sorting procedure to solve it. The metaheuristic developed is a variant of an evolutionary algorithm, known as Biased Random Key Genetic Algorithm. Computational experiments have been carried out on benchmark problems with up to 100 generation units for a 24 h scheduling horizon. The performance of the method, as well as the quality, diversity and the distribution characteristics of the solutions obtained are analysed. It is shown that the method proposed compares favourably against alternative approaches in most cases analysed.