Abstract
Objective To assess patients' preferred roles in healthcare-related decision-making in a representative sample of the Portuguese population. Design Population-based nationwide cross-sectional study. Setting and participants A sample of Portuguese people 20 years or older were interviewed face-to-face using a questionnaire with the Problem-Solving Decision-Making scale. Outcomes The primary outcome was patients' preferred role for each vignette of the problem-solving decision-making scale. Sociodemographic factors associated with the preferred roles were the secondary outcomes. Results 599 participants (20-99 years, 53.8% women) were interviewed. Three vignettes of the Problem-Solving Decision-Making scale were compared: morbidity, mortality and quality of life. Most patients preferred a passive role for both the problem-solving and decision-making components of the scale, particularly for the mortality vignette (66.1% in the analysis of the three vignettes), although comparatively more opted to share decision in the decision-making component. For the quality of life vignette, a higher percentage of patients wanted a shared role (44.3%) than with the other two vignettes. In the problem-solving component, preferences were significantly associated with area of residence (p<0.001) and educational level (p=0.013), while in the decision-making, component preferences were associated with age (p=0.020), educational level (p=0.015) and profession (p<0.001). Conclusions In this representative sample of the Portuguese mainland population, most patients preferred a practitioner-controlling role for both the problem-solving and decision-making components. In a life-threatening situation, patients were more willing to let the doctor decide. In contrast, in a less serious situation, there is a greater willingness to participate in decision-making. We have found that shared decision-making is more acceptable to better-educated patients in the problem-solving component and to people who are younger, higher educated and employed, in the decision-making component.

Abstract
Time, cost, and quality are the three indispensable factors for the realization and success of a project. In this context, we propose a framework composed of a multi-objective approach and multi-criteria decision-making methods (MCDM) to solve time-cost-quality trade-off optimization problems. A multi-objective Simulated Annealing (MOSA) algorithm is used to compute an approximation to the Pareto optimal set. The concept of the exploratory grid is introduced in the MOSA to improve its performance. MCDM are used to assist the decision-making process. The Shannon entropy and AHP methods assign weights to criteria. The first methodology is for the inexperienced decision-makers, and the second concedes a personal and flexible weighting of the criteria weights, based on the project manager's assessment. The TOPSIS and VIKOR methods are considered to rank the solutions. Although they have the same purpose, the rankings achieved are different. A tool is implemented to solve a time-cost-quality trade-off problem on a project activities network. The computational experiments are analyzed and the results with the exploratory grid in Simulated Annealing (SA) are promising. Despite the framework aims to solve multi-objective trade-off optimization problems, supporting the decisions of the project manager, the methodologies used can also be applied in other areas.

Abstract
Accurate photovoltaic (PV) power forecasting is crucial to achieving massive PV integration in several areas, which is needed to successfully reduce or eliminate carbon dioxide from energy sources. This paper deals with short-term multi-step PV power forecasts used in model-based predictive control for home energy management systems. By employing radial basis function (RBFs) artificial neural networks (ANN), designed using a multi-objective genetic algorithm (MOGA) with data selected by an approximate convex-hull algorithm, it is shown that excellent forecasting results can be obtained. Two case studies are used: a special house located in the USA, and the other a typical residential house situated in the south of Portugal. In the latter case, one-step-ahead values for unscaled root mean square error (RMSE), mean relative error (MRE), normalized mean average error (NMAE), mean absolute percentage error (MAPE) and R-2 of 0.16, 1.27%, 1.22%, 8% and 0.94 were obtained, respectively. These results compare very favorably with existing alternatives found in the literature.

Abstract
Schizophrenia is a complex and disabling mental disorder estimated to affect 21 million people worldwide. Electroencephalography (EEG) has proven to be an excellent tool to improve and aid the current diagnosis of mental disorders such as schizophrenia. The illness is comprised of various disabilities associated with sensory processing and perception. In this work, the first 10-200 ms of brain activity after the self-generation via button presses (condition 1) and passive presentation (condition 2) of auditory stimuli was addressed. A time-domain analysis of the event-related potentials (ERPs), specifically the MLAEP, N1, and P2 components, was conducted on 49 schizophrenic patients (SZ) and 32 healthy controls (HC), provided by a public dataset. The amplitudes, latencies, and scalp distribution of the peaks were used to compare groups. Suppression, measured as the difference between both conditions’ neural activity, was also evaluated. With the exception of the N1 peak during condition (1), patients exhibited significantly reduced amplitudes in all waveforms analyzed in both conditions. The SZ group also demonstrated a peak delay in the MLAEP during condition (2) and a modestly earlier P2 peak during condition (1). Furthermore, patients exhibited less and more N1 and P2 suppression, respectively. Finally, the spatial distribution of activity in the scalp during the MLAEP peak in both conditions, N1 peak in condition (1) and N1 suppression differed considerably between groups. These findings and measurements will be used with the finality of developing an intelligent system capable of accurately diagnosing schizophrenia.

Abstract
Conflict-free Replicated Data Types (CRDTs) are abstract data types that support developers when designing and reasoning about distributed systems with eventual consistency guarantees. In their core they solve the problem of how to deal with concurrent operations, in a way that is transparent for developers. However in the real world, distributed systems also suffer from other relevant problems, including security and privacy issues and especially when participants can be untrusted. In this paper we present new privacy-preserving CRDT protocols that can be used to help secure distributed cloud-backed applications, including NoSQL geo-replicated databases. Our proposals are based on standard CRDTs, such as sets and counters, augmented with cryptographic mechanisms that allow their operations to be performed on encrypted data. We accompany our proposals with formal security proofs and implement and integrate them in An-tidoteDB, a geo-replicated NoSQL database that leverages CRDTs for its operations. Experimental evaluations based on the Danish Shared Medication Record dataset (FMK) exhibit the tradeoffs that our different proposals make and show that they are ready to be used in practical applications.

Abstract
Recent developments on ethanol-based DNA (etDNA) metabarcoding have shown that it is possible to extract meaningful information about macroinvertebrate community diversity and composition from the ethanol used to preserve bulk samples. The major advantages of this molecular approach are the reduced processing time and costs, and the possibility to keep specimens intact for other experiments. Yet, organisms with highly sclerotised exoskeleton or that are rare in the sample have been found to release a lower amount of DNA into solution and tend to be consistently missed by etDNA metabarcoding, thereby compromising the viability of the method. Few studies have shown that the first steps of the metabarcoding workflow are crucial for the good performance of etDNA-based assays, such as the decision on storage time before sampling and the ethanol phase to be analysed, the inclusion of pre-treatment strategies (i.e., freezing), and the choice of the DNA extraction protocol. In this study, we aimed to evaluate the combined effect of various technical choices on the performance of etDNA metabarcoding, considering factors such as sample volume, ethanol phase of sorted and unsorted samples, pre-capture treatments (evaporation vs filtration) and bioinformatic pipelines. Through the application of decision-tree models, our preliminary data revealed that the increase of volume (by itself) is enough to improve PCR amplification yields and proportion of families matching the morphological identifications, with great impact on the detection of hard-bodied and cased taxa. Also, no major differences among phases with or without a sorting step nor among bioinformatic pipelines were detected, particularly at higher volumes. Our results suggest that the higher performance (with lower observed variation) in taxonomic detection at higher volumes is likely a consequence of a higher availability of longer fragments of DNA in solution. This study highlights the importance of understanding the impact of technical choices to improve the efficiency of a DNA-based method, and reinstates etDNA metabarcoding as a potential method in the context of biomonitoring.