Abstract
The objective of this study is to understand to what extent hedonic and utilitarian consumer profiles are affected by situational factors and how in turn they impact shopping centre patronage. A six step multiple regression analysis corresponding to six different shopping centres has been applied to two clusters of consumers. The data are based on consumers' hedonic/utilitarian customer profile. First, results show that in general the impact on shopping centre patronage is largely affected by proximity, convenience and accessibility variables, which are more relevant among the utilitarian profile consumers. On the other hand, in the hedonic profile segment, affect, that is, the experience of feeling or emotion is the relevant variable explaining patronage. Second, the predictive contribution of these variables on patronage varied according to the shopping centres' positioning. With the findings of the present study, retail managers can formulate marketing strategies, which will attract retail consumers towards their shopping centre and also help them to enhance the significant factors that influence retail store consumer's purchase decision. Also, this investigation contributes to the diagnosis of how consistent is the retailers' in their positioning strategy in targeting the market segments. The present research integrates both situational factors and hedonic as well as utilitarian consumer profiles along with the role of situational dynamics to explain shopping centres' patronage.

Abstract
Industry 4.0 is a new industrial paradigm that brings new challenges for workers as they have to actively collaborate with robots in an interconnected environment. The main purpose of this paper is to give a brief overview of how collaborative robots can be used to support human workers in Industry 4.0 manufacturing environments. The use of collaborative robots certainly brings many advantages as these machines enable more efficient product systems by supporting workers with both physical and cognitive tasks, as is the case of exoskeletons. On the other hand, human–robot interaction might also have some risks if human factors considerations are not well thought through throughout the process. Moreover, it becomes clear that the role that humans have been playing so far in a manufacturing environment is rapidly changing. Human workers will have to adapt to these new systems by acquiring and improving a set of skills that have sometime been neglected until nowadays. © Springer Nature Switzerland AG 2019.

Abstract
The machine learning field has grown considerably in the last years. There are, however, some problems still to be solved. The characteristics of the training sets, for instance, are known to affect the classifiers performance. Here, and inspired by medical applications, we are interested in studying datasets that are both ordinal and imbalanced. Ordinal datasets present labels where only the relative ordering between different values is significant. Imbalanced datasets have very different quantity of examples per class. Building upon our previous work, we make three new contributions, (1) extend the number of classifiers, (2) evaluate two techniques to balance intermediate train sets in binary decomposition methods (often used in multi-class contexts and ordinal ones in particular), and (3) propose a new, iterative, classifier-based oversampling algorithm that we name InCuBAtE. Experiments were made on 6 private datasets, concerning the assessment of response to treatment on oncologic diseases, and 15 public datasets widely used in the literature. When compared with our previous work, results have improved (or remained the same) for 4 of the 6 private datasets and for 11 out of the 15 public datasets.

Abstract

Abstract
The use of specialized accelerator circuits is a feasible solution to address performance and energy issues in embedded systems. This paper extends a previous field-programmable gate array-based approach that automatically generates pipelined customized loop accelerators (CLAs) from runtime instruction traces. Despite efficient acceleration, the approach suffered from high area and resource requirements when offloading a large number of kernels from the target application. This paper addresses this by enhancing the CLA with dynamic partial reconfiguration (DPR) support. Each kernel to accelerate is implemented as a variant of a reconfigurable area of the CLA which hosts all functional units and configuration memory. Evaluation of the proposed system is performed on a Virtex-7 device. We show, for a set of 21 kernels, that when comparing two CLAs capable of accelerating the same subset of kernels, the one which benefits from DPR can be up to 4.3x smaller. Resorting to DPR allows for the implementation of CLAs which support numerous kernels without a significant decrease in operating frequency and does not affect the initiation intervals at which kernels are scheduled. Finally, the area required by a CLA instance can be further reduced by increasing the IIs of the scheduled kernels.

Abstract
Protein-protein interaction (PPI) data is essential to elucidate the complex molecular relationships in living systems, and thus understand the biological functions at cellular and systems levels. The complete map of PPIs that can occur in a living organism is called the interactome. For animals, PPI data is stored in multiple databases (e.g., BioGRID, CCSB, DroID, FlyBase, HIPPIE, HitPredict, HomoMINT, INstruct, Interactome3D, mentha, MINT, and PINA2) with different formats. This makes PPI comparisons difficult to perform, especially between species, since orthologous proteins may have different names. Moreover, there is only a partial overlap between databases, even when considering a single species. The EvoPPI (http://evoppi.i3s.up.pt) web application presented in this paper allows comparison of data from the different databases at the species level, or between species using a BLAST approach. We show its usefulness by performing a comparative study of the interactome of the nine polyglutamine (polyQ) disease proteins, namely androgen receptor (AR), atrophin-1 (ATN1), ataxin 1 (ATXN1), ataxin 2 (ATXN2), ataxin 3 (ATXN3), ataxin 7 (ATXN7), calcium voltage-gated channel subunit alpha1 A (CACNA1A), Huntingtin (HTT), and TATA-binding protein (TBP). Here we show that none of the human interactors of these proteins is common to all nine interactomes. Only 15 proteins are common to at least 4 of these polyQ disease proteins, and 40% of these are involved in ubiquitin protein ligase-binding function. The results obtained in this study suggest that polyQ disease proteins are involved in different functional networks. Comparisons with Mus musculus PPIs are also made for AR and TBP, using EvoPPI BLAST search approach (a unique feature of EvoPPI), with the goal of understanding why there is a significant excess of common interactors for these proteins in humans.