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This article addresses some aspects of the potential of Extended Reality (XR) technologies in the context of the accelerated ongoing digital transformation, with public awareness and wider acceptance being prompted by current pandemic, due to the widespread adoption of teleworking, distance learning, and virtual conferences. The state-of-the-art of XR technologies and immersive environments is briefly addressed from the perspective of their sustained adoption in multiple scenarios, including education and training, well-being and active aging, and business.

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Irregularly spaced time series are commonly encountered in the analysis of time series. A particular case is that in which the collection procedure over time depends also on the observed values. In such situations, there is stochastic dependence between the process being modeled and the times at which the observations are made. Ignoring this dependence can lead to biased estimates and misleading inferences. In this paper, we introduce the concept of preferential sampling in the temporal dimension and we propose a model to make inference and prediction. The methodology is illustrated using artificial data as well a real data set.

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AbstractBackgroundHigh-quality data is crucial for guiding decision making and practicing evidence-based healthcare, especially if previous knowledge is lacking. Nevertheless, data quality frailties have been exposed worldwide during the current COVID-19 pandemic. Focusing on a major Portuguese surveillance dataset, our study aims to assess data quality issues and suggest possible solutions.MethodsOn April 27th 2020, the Portuguese Directorate-General of Health (DGS) made available a dataset (DGSApril) for researchers, upon request. On August 4th, an updated dataset (DGSAugust) was also obtained. The quality of data was assessed through analysis of data completeness and consistency between both datasets.ResultsDGSAugust has not followed the data format and variables as DGSApril and a significant number of missing data and inconsistencies were found (e.g. 4,075 cases from the DGSApril were apparently not included in DGSAugust). Several variables also showed a low degree of completeness and/or changed their values from one dataset to another (e.g. the variable ‘underlying conditions’ had more than half of cases showing different information between datasets). There were also significant inconsistencies between the number of cases and deaths due to COVID-19 shown in DGSAugust and by the DGS reports publicly provided daily.ConclusionsThe low quality of COVID-19 surveillance datasets limits its usability to inform good decisions and perform useful research. Major improvements in surveillance datasets are therefore urgently needed - e.g. simplification of data entry processes, constant monitoring of data, and increased training and awareness of health care providers - as low data quality may lead to a deficient pandemic control.

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At a time when communication, new media, and digitalization are transversal to the whole of society, private health care organizations have the possibility of making their business processes evolve. The objective is thus to seize the benefits associated to the active use of patients' electronic health records (EHRs) as the basis for personalized health care. In order to initially validate the health care sector acceptance of a 360 degrees health care information system (HIS), focused on collecting patients' data to create the necessary knowledge for delivering personalized health care procedures and initiatives, a focus group involving a set of health-related professionals was performed. Despite recognizing the immense possibilities associated to EHR and its direct incorporation on a 360 degrees HIS, the referred professionals still highlighted their concerns relative to the maintenance of adequate security and privacy levels. With this in mind, a proposal for a 360 degrees HIS model is presented, and its main functional blocks are described with a focus on triggering patient/customer loyalty.

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Featured Application The robotic bin picking solution presented in this work serves as a stepping stone towards the development of cost-effective, scalable systems for handling entangled objects. This study and its experiments focused on tube-shaped objects, which have a widespread presence in the industry. Abstract Manufacturing and production industries are increasingly turning to robots to carry out repetitive picking operations in an efficient manner. This paper focuses on tackling the novel challenge of automating the bin picking process for entangled objects, for which there is very little research. The chosen case study are sets of freely curved tubes, which are prone to occlusions and entanglement. The proposed algorithm builds a representation of the tubes as an ordered list of cylinders and joints using a point cloud acquired by a 3D scanner. This representation enables the detection of occlusions in the tubes. The solution also performs grasp planning and motion planning, by evaluating post-grasp trajectories via simulation using Gazebo and the ODE physics engine. A force/torque sensor is used to determine how many items were picked by a robot gripper and in which direction it should rotate to solve cases of entanglement. Real-life experiments with sets of PVC tubes and rubber radiator hoses showed that the robot was able to pick a single tube on the first try with success rates of 99% and 93%, respectively. This study indicates that using simulation for motion planning is a promising solution to deal with entangled objects.