Abstract
In this paper, the unrelated parallel machine scheduling problem considering machine-dependent and job sequence-dependent setup times is addressed. This problem involves the scheduling of n jobs on m unrelated machines with setup times in order to minimize the makespan. The Simulated Annealing algorithm is used to solve four sets of small scheduling problems, from the literature, on two unrelated machines: the first one has six jobs, the second has seven jobs and the third and fourth has eight and nine jobs, respectively. The results seem promising when compared with other methods referred in literature. © 2020, Springer Nature Switzerland AG.

Abstract
While deep learning has led to breakthroughs in many areas of computer science, its power has yet to be fully exploited in the area of adaptive optics (AO) and astronomy as a whole. In this paper we describe the first steps taken to apply deep, convolutional neural networks to the problem of wavefront reconstruction and prediction and demonstrate their feasibility of use in simulation. Our preliminary results show we are able to reconstruct wavefronts comparably well to current state of the art methods. We further demonstrate the ability to predict future wavefronts up to five simulation steps with under 1nm RMS wavefront error.

Abstract
The growth of intermittent renewable power generation has been drawing attention to the design of balancing markets. Portugal is an interesting case study because, while wind generation already accounts for a high fraction of demand (23%), there are still no economic incentives for efficient wind forecast (wind balancing costs are passed to end consumers). We analyze the evolution of the balancing market from 2012 to 2016. Using actual costs provided by the Portuguese TSO, we find wind imbalance costs in the range of 2 to 4 EUR/MWh. These results surprisingly suggest that, even with large wind penetration and socialized imbalance costs, wind forecast errors can have a relatively low cost.

Abstract
Differential diagnosis between Idiopathic Parkinson's disease (IPD) and Vascular Parkinsonism (VaP) is a difficult task, especially early in the disease. There is growing evidence to support the use of gait assessment in diagnosis and management of movement disorder diseases. The aim of this study is to evaluate the effectiveness of some machine learning strategies in distinguishing IPD and VaP gait. Wearable sensors positioned on both feet were used to acquire the gait data from 15 IPD, 15 VaP, and 15 healthy subjects. A comparative classification analysis was performed by applying two supervised machine learning algorithms: Multiple Layer Perceptrons (MLPs) and Deep Belief Networks (DBNs). The decisional space was composed of the gait variables, with or without neuropsychological evaluation (Montreal cognitive assessment (MoCA) score), top-ranked in an error incremental analysis. In the classification task of characterizing parkinsonian gait by distinguishing between patients (IPD+VaP) and healthy control, from the all strides classification of the gait performed by the person, high accuracy (93% with or without MoCA) was obtained for both algorithms. In the classification task of the two groups of patients (VaP vs. IPD), DBN classifier achieved higher performance (73% with MoCA). To the best of our knowledge, this is the first study on gait classification that includes a VaP group. DBN classifiers are not frequently applied in literature to similar studies, but the results here obtained demonstrate that the use of DBN classifiers based on gait analysis is promising to be a good support to the neurologist in distinguishing VaP and IPD.

Abstract

Abstract
This paper addresses a phase-related feature that is time-shift invariant, and that expresses the relative phases of all harmonics with respect to that of the fundamental frequency. We identify the feature as Normalized Relative Delay (NRD) and we show that it is particularly useful to describe the holistic phase properties of voiced sounds produced by a human speaker, notably vowel sounds. We illustrate the NRD feature with real data that is obtained from five sustained vowels uttered by 20 female speakers and 17 male speakers. It is shown that not only NRD coefficients carry idiosyncratic information, but also their estimation is quite stable and robust for all harmonics encompassing, for most vowels, at least the first four formant frequencies. The average NRD model that is estimated using data pertaining to all speakers in our database is compared to that of the idealized Liljencrants-Fant (LF) and Rosenberg glottal models. We also present results on the phase effects of linear-phase FIR and IIR vocal tract filter models when a plausible source excitation is used that corresponds to the derivative of the L-F glottal flow model. These results suggest that the shape of NRD feature vectors is mainly determined by the glottal pulse and only marginally affected by either the group delay of the vocal tract filter model, or by the acoustic coupling between glottis and vocal tract structures. Copyright