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
AbstractThe research on software startups has increased lately, focusing on describing how these companies’ unique context influences development practices. The next step for research is the creation of specific practices for these companies grounded in scientific results. An obstacle in this path is which dependent variable these novel practices should improve. A natural answer is these companies’ success. This position paper reviews the literature on new ventures and startups’ success to show that telling if a startup is successful or not is a complex issue. As a solution to this problem, this paper proposes a conceptual framework, suggesting that novel practices should improve success determinants or reduce inhibitors rather than focusing on the startups’ success. Three examples illustrate the framework’s use: hypotheses engineering, microservices, and BizDev. The identification of contributors and inhibitors for success of software startups could enrich the framework and indicate possible avenues for the creation of development practices specific tailored for these companies.

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.

Abstract
The process of building and deploying Machine Learning (ML) models includes several phases and the training phase is taken as one of the most time-consuming. ML models with time series datasets can be used to predict users positions, behaviours or mobility patterns, which implies paths crossing by well-defined positions, and thus, in these cases, syntactic similarity can be used to reduce these models training time. This paper uses the case study of a Mobile Network Operator (MNO) where users mobility are predicted through ML and the use of syntactic similarity withWord2Vec (W2V) framework is tested with Recurrent Neural Network (RNN), Gate Recurrent Unit (GRU), Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) models. Experimental results show that by using framework W2V in these architectures, the training time task is reduced in average between 22% to 43%. Also an improvement on the validation accuracy of mobility prediction of about 3 percentage points in average is obtained.