Abstract
Shack-Hartmann wavefront sensing relies on accurate spot centre measurement. Several algorithms were developed with this aim, mostly focused on precision, i.e. minimizing random errors. In the solar and extended scene community, the importance of the accuracy (bias error due to peak-locking, quantization, or sampling) of the centroid determination was identified and solutions proposed. But these solutions only allow partial bias corrections. To date, no systematic study of the bias error was conducted. This article bridges the gap by quantifying the bias error for different correlation peak-finding algorithms and types of sub-aperture images and by proposing a practical solution to minimize its effects. Four classes of sub-aperture images (point source, elongated laser guide star, crowded field, and solar extended scene) together with five types of peak-finding algorithms (1D parabola, the centre of gravity, Gaussian, 2D quadratic polynomial, and pyramid) are considered, in a variety of signal-to-noise conditions. The best performing peak-finding algorithm depends on the sub-aperture image type, but none is satisfactory to both bias and random errors. A practical solution is proposed that relies on the antisymmetric response of the bias to the sub-pixel position of the true centre. The solution decreases the bias by a factor of similar to 7 to values of less than or similar to 0.02 pix. The computational cost is typically twice of current cross-correlation algorithms.

Abstract
Starting in the 50s, healthcare workforce planning became a major concern for researchers and policy makers, since an imbalance of health professionals may create a serious insufficiency in the health system, and eventually lead to avoidable patient deaths. As such, methodologies and techniques have evolved significantly throughout the years, and simulation, in particular system dynamics, has been used broadly. However, tools such as stochastic agent-based simulation offer additional advantages for conducting forecasts, making it straightforward to incorporate microeconomic foundations and behavior rules into the agents. Surprisingly, we found no application of agent-based simulation to healthcare workforce planning above the hospital level. In this paper we develop a stochastic agent-based simulation model to forecast the supply of physicians and apply it to the Portuguese physician workforce. Moreover, we study the effect of variability in key input parameters using Monte Carlo simulation, concluding that small deviations in emigration or dropout rates may originate disparate forecasts. We also present different scenarios reflecting opposing policy directions and quantify their effect using the model. Finally, we perform an analysis of the impact of existing demographic projections on the demand for healthcare services. Results suggest that despite a declining population there may not be enough physicians to deliver all the care an ageing population may require. Such conclusion challenges anecdotal evidence of a surplus of physicians, supported mainly by the observation that Portugal has more physicians than the EU average.

Abstract
In this work a new trading framework for demand response (DR) aggregators is proposed using a non-probabilistic model. In this model, DR is acquired from consumers to sell it to the purchasers by aggregators. Two programs, i.e., time-of-use (TOU) and reward-based DR program, are implemented to obtain DR from consumers. Then, the obtained DR is sold to buyers via two considered agreements, i.e., fixed DR contracts and DR options. The information-gap decision theory is also employed to consider the uncertainties for risk-averse aggregators. Consumer's participation behavior is considered as an uncertain parameter. A robustness function is proposed to examine the immunity of the model against adverse variations of uncertain parameters. The feasibility of the proposed model is studied on the real-world data.

Abstract
Teenagers have been identified as an audience group that is often excluded from museum curatorial strategies. One strategy to counteract this problem is to involve cultural heritage professionals (CHPs) in the design process of museum based digital experiences targeted at teens. In this paper, 12 CHPs from a local natural history museum took part in a co-design activity over 20 hours, aiming to create and deploy digital tours for teenagers aged between 16-19. We present the three prototypes that derived from these design sessions. These were then tested by both 12 CHPs and 12 teenagers separately, and we report on lessons learned from the evaluation of these prototypes by both groups. © Dupré et al. Published by BCS Learning and Development Ltd. Proceedings of British HCI 2018. Belfast, UK

Abstract

Abstract
Decision making is an important role performed by managers. This chapter will analyze the importance of information systems (IS) on the decision-making process at rural organizations in Portugal's Algarve region. Managers' perceptions were analyzed and compared with the decision-making process model proposed in this chapter, which was based on the models of Simon (1977) and Mintzberg, Raisinghani, and Theorêt (1976). This chapter will discuss the capacity of rural tourism organizations to solve problems, as well as review the time needed to solve problems through the use of IS. This chapter will conclude that IS in the organizational decision-making process is positively related to the identification of the decision-making problem and time needed to solve the problems. This investigation will allow other sectors the opportunity to discuss decision process models based on technology, information capability, and organizational competitiveness.