Editor’s Choice Articles

Wearable devices now make it possible to record large quantities of physiological data, which can be used to obtain a clearer view of a person’s health status and behavior. However, to the best of our knowledge, there are no open datasets in the literature that provide psycho-physiological data. The Multilevel Monitoring of Activity and Sleep in Healthy people (MMASH) dataset presented in this paper provides 24 h of continuous psycho-physiological data, that is, inter-beat intervals data, heart rate data, wrist accelerometry data, sleep quality index, physical activity (i.e., number of steps per second), psychological characteristics (e.g., anxiety status, stressful events, and emotion declaration), and sleep hormone levels for 22 participants. The MMASH dataset will enable the investigation of possible relationships between the physical and psychological characteristics of people in daily life. Data were validated through different analyses that showed their compatibility with the literature. Full article

Some of the recent developments in data science for worldwide disease control have involved research of large-scale feasibility and usefulness of digital contact tracing, user location tracking, and proximity detection on users’ mobile devices or wearables. A centralized solution relying on collecting and storing user traces and location information on a central server can provide more accurate and timely actions than a decentralized solution in combating viral outbreaks, such as COVID-19. However, centralized solutions are more prone to privacy breaches and privacy attacks by malevolent third parties than decentralized solutions, storing the information in a distributed manner among wireless networks. Thus, it is of timely relevance to identify and summarize the existing privacy-preserving solutions, focusing on decentralized methods, and analyzing them in the context of mobile device-based localization and tracking, contact tracing, and proximity detection. Wearables and other mobile Internet of Things devices are of particular interest in our study, as not only privacy, but also energy-efficiency, targets are becoming more and more critical to the end-users. This paper provides a comprehensive survey of user location-tracking, proximity-detection, and digital contact-tracing solutions in the literature from the past two decades, analyses their advantages and drawbacks concerning centralized and decentralized solutions, and presents the authors’ thoughts on future research directions in this timely research field. Full article

Hydrodynamic forces are an important input value for the design, navigation and station keeping of underwater Remotely Operated Vehicles (ROVs). The experiment investigated the forces imparted by currents (with representative real world turbulence) and waves on a commercially available ROV, namely the BlueROV2 (Blue Robotics, Torrance, USA). Three different distances of a simplified cylindrical obstacle (shading effects) were investigated in addition to the free stream cases. Eight tethers held the ROV in the middle of the 2 m water depth to minimise the influence of the support structure without completely restricting the degrees of freedom (DoF). Each tether was equipped with a load cell and small motions and rotations were documented with an underwater video motion capture system. The paper describes the experimental set-up, input values (current speed and wave definitions) and initial processing of the data. In addition to the raw data, a processed dataset is provided, which includes forces in all three main coordinate directions for each mounting point synchronised with the 6DoF results and the free surface elevations. The provided dataset can be used as a validation experiment as well as for testing and development of an algorithm for position control of comparable ROVs. Full article

With advances in Building Information Modeling (BIM), Virtual Reality (VR) and Augmented Reality (AR) technologies have many potential applications in the Architecture, Engineering, and Construction (AEC) industry. However, the AEC industry, relative to other industries, has been slow in adopting AR/VR technologies, partly due to lack of feasibility studies examining the actual cost of implementation versus an increase in profit. The main objectives of this paper are to understand the industry trends in adopting AR/VR technologies and identifying gaps within the industry. The identified gaps can lead to opportunities for developing new tools and finding new use cases. To achieve these goals, two rounds of a survey at two different time periods (a year apart) were conducted. Responses from 158 industry experts and researchers were analyzed to assess the current state, growth, and saving opportunities for AR/VR technologies for the AEC industry. The findings demonstrate that older generations are significantly more confident about the future of AR/VR technologies and they see more benefits in AR/VR utilization. Furthermore, the research results indicate that Residential and commercial sectors have adopted these tools the most, compared to other sectors and institutional and transportation sectors had the highest growth from 2017 to 2018. Industry experts anticipated a solid growth in the use of AR/VR technologies in 5 to 10 years, with the highest expectations towards healthcare. Ultimately, the findings show a significant increase in AR/VR utilization in the AEC industry from 2017 to 2018. Full article

Germination data are discrete and binomial. Although analysis of variance (ANOVA) has long been used for the statistical analysis of these data, generalized linear mixed models (GzLMMs) provide a more consistent theoretical framework. GzLMMs are suitable for final germination percentages (FGP) as well as longitudinal studies of germination time-courses. Germination indices (i.e., single-value parameters summarizing the results of a germination assay by combining the level and rapidity of germination) and other data with a Gaussian error distribution can be analyzed too. There are, however, different kinds of GzLMMs: Conditional (i.e., random effects are modeled as deviations from the general intercept with a specific covariance structure), marginal (i.e., random effects are modeled solely as a variance/covariance structure of the error terms), and quasi-marginal (some random effects are modeled as deviations from the intercept and some are modeled as a covariance structure of the error terms) models can be applied to the same data. It is shown that: (a) For germination data, conditional, marginal, and quasi-marginal GzLMMs tend to converge to a similar inference; (b) conditional models are the first choice for FGP; (c) marginal or quasi-marginal models are more suited for longitudinal studies, although conditional models lead to a congruent inference; (d) in general, common random factors are better dealt with as random intercepts, whereas serial correlation is easier to model in terms of the covariance structure of the error terms; (e) germination indices are not binomial and can be easier to analyze with a marginal model; (f) in boundary conditions (when some means approach 0% or 100%), conditional models with an integral approximation of true likelihood are more appropriate; in non-boundary conditions, (g) germination data can be fitted with default pseudo-likelihood estimation techniques, on the basis of the SAS-based code templates provided here; (h) GzLMMs are remarkably good for the analysis of germination data except if some means are 0% or 100%. In this case, alternative statistical approaches may be used, such as survival analysis or linear mixed models (LMMs) with transformed data, unless an ad hoc data adjustment in estimates of limit means is considered, either experimentally or computationally. This review is intended as a basic tutorial for the application of GzLMMs, and is, therefore, of interest primarily to researchers in the agricultural sciences. Full article