Abstract
Cognitive health status is a determining factor for quality of life. Since early identification of mild cognitive impairment is critical to better manage further decline, and to guide therapeutic and rehabilitation interventions, there is the need for efficient clinical instruments able to monitor the individuals, particularly older adults, in their residential environments. This paper presents a scoping review to identify of innovative digital solutions to detect cognitive impairments and that might be used as a screening tool for age related cognitive impairment of community-dwelling older adults. Nineteen articles were included in this scoping review. As a main conclusion, most of the included articles report digital solutions to support the application of existing paper-based clinical instruments to assess specific or multiple cognitive functions. However, six studies explore new approaches to assess cognitive functions including virtual reality and serious games.

Abstract
With the significant increase in wind speed usage as a clean source of energy, an accurate wind speed forecasting system is a must for more effective utilization of this energy. Failure to consider the inherent spatio-temporal features of wind speed time series leads to the lack of generalization capacity for current wind speed forecasting approaches. This paper proposes an end-to-end deep neural network framework, i.e., convolutional rough long short-term memory (ConvRLSTM), to extract spatio-temporal wind correlations and mitigate the inherent uncertainties in wind time series by incorporating the Rough set theory into a combination of convolution neural network (CNN) and LSTM units. Our proposed model receives the historical data of wind speed for a 20x20 array of wind turbines in North Carolina, US. Several ConvRLSTM layers extract the most relevant features for the forecasting task, and finally, fully connected layers predict 400 wind speed values using the spatial features obtained by the CNN and temporal features computed by the LSTM. Through analyzing the numerical forecasting results, it can be inferred that the proposed approach outperforms the mainstream and recently published forecasting strategies in terms of the RMSE metric.

Abstract
Although most Natural Language Processing tasks, such as Text Classification and Natural Language Translation, have experienced a major performance improvement due to recent advances in neural network architectures, Temporal Relation Extraction remains an open challenge. This leaves the door open for new research questions. In this paper, we provide a brief summary of the task and some of the recent efforts that have been made to solve it. In addition, some research opportunities yet to be explored are also discussed.

Abstract

Abstract
Current chemometrics and artificial intelligence methods are unable to deal with complex multi-scale interference of blood constituents in visible shortwave near-infrared spectroscopy point-of-care technologies. The major difficulty is to access the rich information in the spectroscopy signal, unscrambling and interpreting spectral interference to provide analytical quality quantifications. We present a new self-learning artificial intelligence method for spectral processing based on the search of covariance modes with direct correspondence to the BeerLambert law. Dog and cat hemograms were analyzed by impedance flow cytometry and standard laboratory methods (erythrocytes counts, hemoglobin, and hematocrit). Spectral records were performed for the same samples. The methodology was benchmarked against state-of-the-art chemometrics: a multivariate linear model of hemoglobin bands, similarity, partial least squares, local partial least squares, and artificial neural networks. The new method outperforms the state-of-the-art, providing analytical quality quantifications according to desired veterinary pathology guidelines (total errors of 1.69% to 7.14%), whereas chemometric methods cannot. The method finds relevant samples and spectral information that hold the quantitative information for a particular interference mode, in contrast to the current methods that do not hold a relationship with the BeerLambert law. It allows the interpretation of interference bands used in quantification, providing the capacity to determine if the composition of an unknown sample is predictable. This research is especially relevant for improving current optical point-of-care technologies that are affected by spectral interference and moving towards micro-sampling and reagent-less technologies in healthcare and veterinary medicine diagnosis.

Abstract
The consumer's profile and behaviour have undergone drastic changes in recent years, as a result of rapid technological developments associated with the proliferation of the Internet, which have boosted the growth of e-commerce. Retail needed to adopt new strategies to respond to new consumer demands leading to the development of omnichannel. The omnichannel strategy is centred on the consumer and aims to offer him/her a unique, consistent, and quality experience, through any contact point and wherever the consumer wants. However, most Portuguese retailers still opt for a multichannel strategy, where the physical shop and the online shop operate independently from each other. Although there are already some successful cases in Portugal, it can be considered that the use of omnichannel is still at an early stage. More publicity is needed so that retailers are aware of the concept and above all recognise the advantages of this new strategy. Before disclosure it is important to understand why companies work with both online and offline channels and do not opt for an omnichannel strategy. This study aims to analyse Portuguese companies that have not yet adopted an omnichannel strategy, to understand the barriers that make them unwilling to adopt this strategy. To this end, a quantitative research was carried out through the application of surveys to companies in different districts of Portugal to understand their position in relation to omnichannel and the reasons for not moving to this structure. The results obtained made it possible to describe the importance of omnichannel as a commercial and distribution strategy and analyse the reasons why its use by companies in Portugal is still extremely low. The lack of knowledge about the structure and management issues emerged as the biggest barriers.