Search Results (12)

Social networks are gaining widespread popularity, with Instagram currently being the most intensively used network. On these platforms, users are continuously exposed to self-relevant information that fosters social comparisons. A distinction is made between ability-based and opinion-based comparison dimensions. To experimentally investigate the influence of these comparison dimensions on users’ subjective well-being, an online exposure experiment (N = 409) was conducted. In a preliminary study (N = 107), valid exposure stimulus material was selected in advance. The results of the main study indicated that the exposure to ability-related social comparisons in the context of social media elicited lower well-being than exposure to opinion-related social comparisons. The theoretical and practical implications of this study consist of including the findings in clinical settings, e.g., affective disorder therapy, and the identification and reduction of ability-related content on social networking sites (SNSs). Future work should include assimilation and contrast effects which might interact with social comparison orientation and well-being. Full article

Nowadays, people are more predisposed to being self-taught due to the availability of online information. With digitalization, information appears not only in its conventional state, as blogs, articles, newspapers, or e-books, but also in more interactive and enticing ways. Video games have become a transmission vehicle for information and knowledge, but they require specific treatment in respect of their presentation and the way in which users interact with them. This treatment includes usability guidelines and heuristics that provide video game properties that are favorable to a better user experience, conducive to captivating the user and to assimilating the content. In this research, usability guidelines and heuristics, complemented with recommendations from educational video game studies, were gathered and analyzed for application to a video game for English language learning called EnLang4All, which was also developed in the scope of this project and evaluated in terms of its reception by users. Full article

This work addresses the problem of vertical wind profile online estimation at a given location. Specifically, the north and east components of the wind are continuously estimated as functions of time and altitude at two waypoints used for landing on the Adolfo Suarez Madrid-Barajas airport. A continuous nowcast of the wind profile is performed in which wind observations are derived from the aircraft states and assimilated into the model. It is well known that wind is one of the utmost contributors to uncertainties in the current and future paradigm of Air Traffic Management. Accurate wind information is key in continuous climb and descent operations, spacing, four dimensional trajectory-based operations, and aircraft performance studies, among others. In this work, wind data are obtained indirectly from the aircraft’s states broadcast by the Mode S and ADS-B aircraft surveillance systems. The Gaussian process regression is adapted to this framework and used to solve the problem. The presented method allows to construct a complete vector wind profile at any specific position that is continuous in time and altitude; namely, there is no need for grid points and time discretisation. The Gaussian process regression is a very flexible estimator which is statistically consistent under general conditions, meaning that it converges to the underground truth when more and more data are dispensed. In addition, the Gaussian process regression approach provides the whole probability distribution of any particular estimation, allowing confidence intervals to be computed naturally. In the case study presented in this paper, in which the wind is constantly estimated, the Gaussian process regression model is iteratively updated every 15 min to capture possible changes in the wind behaviour and give an estimation of the wind profile every half a minute. The method has been validated using a test dataset, achieving a reduction of 50% of the prediction uncertainty in comparison to a baseline model. Moreover, two popular wind profile estimators based on the Kalman filter are also implemented for the sake of comparison. The Kalman filter outperforms the baseline model, but it does not outperform the Gaussian process regression with errors higher by around 35%, in comparison. The obtained results show that the Gaussian process regression of aircraft-derived data reliably nowcast the wind state, which is key in Air Traffic Management. Full article

Irrigation is the primary consumer of freshwater by humans and accounts for over 70% of all annual water use. However, due to the shortage of open critical information in agriculture such as soil, precipitation, and crop status, farmers heavily rely on empirical knowledge to schedule irrigation and tend to excessive irrigation to ensure crop yields. This paper presents WaterSmart-GIS, a web-based geographic information system (GIS), to collect and disseminate near-real-time information critical for irrigation scheduling, such as soil moisture, evapotranspiration, precipitation, and humidity, to stakeholders. The disseminated datasets include both numerical model results of reanalysis and forecasting from HRLDAS (High-Resolution Land Data Assimilation System), and the remote sensing datasets from NASA SMAP (Soil Moisture Active Passive) and MODIS (Moderate-Resolution Imaging Spectroradiometer). The system aims to quickly and easily create a smart, customized irrigation scheduler for individual fields to relieve the burden on farmers and to significantly reduce wasted water, energy, and equipment due to excessive irrigation. The system is prototyped here with an application in Nebraska, demonstrating its ability to collect and deliver information to end-users via the web application, which provides online analytic functionality such as point-based query, spatial statistics, and timeseries query. Systems such as this will play a critical role in the next few decades to sustain agriculture, which faces great challenges from climate change and increased natural disasters. Full article

(1) Background: The transition from conventional to remote aircraft control will necessitate the development of novel human machine interfaces. When we consider the pilot interface, icons are traditionally used to associate meanings with functions on the flight deck, allowing the pilot to assimilate information effectively. Using established icon design principles, 18 icons, representing key safety-critical functions related to the operation of an aircraft, were designed for integration into a ground station. Pilots were then asked to evaluate these icons based on established icon characteristics. (2) Method: In an online questionnaire study, 29 pilots rated the icons on the icon characteristics of concreteness, complexity, familiarity, meaningfulness, and semantic distance. Alongside these metrics, concept and name agreement were captured for the icon set. (3) Results: Analysis indicated good icon-function fit overall. The findings show that emphasizing concreteness and familiarity improves icon-function fit, as long as the familiarity is directed at aviation-related artifacts. Further, concept agreement appears to be a better measure of icon-function fit in comparison to name agreement. (4) Conclusion: Most of the designed icons were well suited to represent their intended meaning. However, this study emphasizes the need for dedicated standardized icon characteristic norms for aviation systems. Full article

Environmental education, at least in northwest Spain, is often overlooked in the education system from infant schooling onwards and interventions are needed to raise the profile of this subject. The aim of this study was to examine the impact of a learning program designed for primary school students to broaden their ecological awareness and improve digital literacy using gamification tools. The research was developed using a qualitative approach, with data obtained from 156 subjects, including teachers, students and families. The results show that the children assimilated new habits on the better usage of water and electricity and recycling paper and plastic. Moreover, they acquired more efficient strategies for finding information online, by using apps and developing content with digital tools. Gaming dynamics and resources were the key to students’ learning, with the tablet proving an essential tool for boosting motivation, interaction and problem solving. Full article

Freshwater nitrogen (N) pollution is a significant sustainability concern in agriculture. In the U.S. Midwest, large precipitation events during winter and spring are a major driver of N losses. Uncertainty about the fate of applied N early in the growing season can prompt farmers to make additional N applications, increasing the risk of environmental N losses. New tools are needed to provide real-time estimates of soil inorganic N status for corn (Zea mays L.) production, especially considering projected increases in precipitation and N losses due to climate change. In this study, we describe the initial stages of developing an online tool for tracking soil N, which included, (i) implementing a network of field trials to monitor changes in soil N concentration during the winter and early growing season, (ii) calibrating and validating a process-based model for soil and crop N cycling, and (iii) developing a user-friendly and publicly available online decision support tool that could potentially assist N fertilizer management. The online tool can estimate real-time soil N availability by simulating corn growth, crop N uptake, soil organic matter mineralization, and N losses from assimilated soil data (from USDA gSSURGO soil database), hourly weather data (from National Weather Service Real-Time Mesoscale Analysis), and user-entered crop management information that is readily available for farmers. The assimilated data have a resolution of 2.5 km. Given limitations in prediction accuracy, however, we acknowledge that further work is needed to improve model performance, which is also critical for enabling adoption by potential users, such as agricultural producers, fertilizer industry, and researchers. We discuss the strengths and limitations of attempting to provide rapid and cost-effective estimates of soil N availability to support in-season N management decisions, specifically related to the need for supplemental N application. If barriers to adoption are overcome to facilitate broader use by farmers, such tools could balance the need for ensuring sufficient soil N supply while decreasing the risk of N losses, and helping increase N use efficiency, reduce pollution, and increase profits. Full article

#VanguardSTEM is an online community and platform that centers the experiences of women, girls, and non-binary people of color in science, technology, engineering, and mathematics (STEM) fields. We publish original and curated content, using cultural production, to include a multiplicity of identities as worthy of recognition and thus redefine STEM identity and belonging. #VanguardSTEM is rooted firmly in Queer, Black feminisms which delineate that the experiences and critiques of Black women matter and that these insights can foster a restorative and regenerative construction of the cultures in which we exist. In describing how #VanguardSTEM descended from counterspaces, we draw on speculative fiction to define a #VanguardSTEM hyperspace as a fluid “place-time” that is born digital and enabled by social media, but materializes in the physical world for specific purposes. As Black women in STEM, we consider how our situated knowledges and scientific expertise inform our process. We propose an intersectional scientific methodology to address the influence of embodied observation, embedded context and collective impact on scientific inquiry. Through #VanguardSTEM, we assert, without apology, the right of Black, Indigenous, women of color and non-binary people of color to self-advocate by fully representing ourselves and our STEM identities and interests, without assimilation. Full article

Nowadays, the aging, deterioration, and failure of civil structures are challenges of paramount importance, increasingly motivating the search of advanced Structural Health Monitoring (SHM) tools. In this work, we propose a SHM strategy for online structural damage detection and localization, combining Deep Learning (DL) and Model-Order Reduction (MOR). The developed data-based procedure is driven by the analysis of vibration and temperature recordings, shaped as multivariate time series and collected on the fly through pervasive sensor networks. Damage detection and localization are treated as a supervised classification task considering a finite number of predefined damage scenarios. During a preliminary offline phase, for each damage scenario, a collection of synthetic structural responses and temperature distributions, is numerically generated through a physics-based model. Several loading and thermal conditions are considered, thanks to a suitable parametrization of the problem, which controls the dependency of the model on operational and environmental conditions. Because of the huge amount of model evaluations, MOR techniques are employed in order to relieve the computational burden that is associated to the dataset construction. Finally, a deep neural network, featuring a stack of convolutional layers, is trained by assimilating both vibrational and thermal data. During the online phase, the trained DL network processes new incoming recordings in order to classify the actual state of the structure, thus providing information regarding the presence and localization of the damage, if any. Numerical performances of the proposed approach are assessed on the monitoring of a two-storey frame under low intensity seismic excitation. Full article

The research focuses on identifying the way in which Romanian universities managed to provide knowledge during the Coronavirus pandemic, when, in a very short time, universities had to adapt the educational process for exclusively online teaching and learning. In this regard, we analyzed students’ perception regarding online learning, their capacity to assimilate information, and the use of E-learning platforms. An online survey based on a semi-structured questionnaire was conducted. Data was collected from 762 students from two of the largest Romanian universities. The results of the research revealed that higher education institutions in Romania were not prepared for exclusively online learning. Thus, the advantages of online learning identified in other studies seem to diminish in value, while disadvantages become more prominent. The hierarchy of problems that arise in online learning changes in the context of the crisis caused by the pandemic. Technical issues are the most important, followed by teachers’ lack of technical skills and their teaching style improperly adapted to the online environment. However, the last place was assigned by students to the lack of interaction with teachers or poor communication with them. Based on these findings, research implications for universities and researchers are discussed. Full article

Satellite remote sensing and model data play an important role in research and applications of tropical meteorology and climatology over vast, data-sparse oceans and remote continents. Since the first weather satellite was launched by NASA in 1960, a large collection of NASA’s Earth science data is freely available to the research and application communities around the world, significantly improving our overall understanding of the Earth system and environment. Established in the mid-1980s, the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), located in Maryland, USA, is a data archive center for multidisciplinary, satellite and model assimilation data products. As one of the 12 NASA data centers in Earth sciences, GES DISC hosts several important NASA satellite missions for tropical meteorology and climatology such as the Tropical Rainfall Measuring Mission (TRMM), the Global Precipitation Measurement (GPM) Mission and the Modern-Era Retrospective analysis for Research and Applications (MERRA). Over the years, GES DISC has developed data services to facilitate data discovery, access, distribution, analysis and visualization, including Giovanni, an online analysis and visualization tool without the need to download data and software. Despite many efforts for improving data access, a significant number of challenges remain, such as finding datasets and services for a specific research topic or project, especially for inexperienced users or users outside the remote sensing community. In this article, we list and describe major NASA satellite remote sensing and model datasets and services for tropical meteorology and climatology along with examples of using the data and services, so this may help users better utilize the information in their research and applications. Full article

Since the submarine has become the major threat to maritime security, there is an urgent need to find a more efficient method of anti-submarine warfare (ASW). The digital twin theory is one of the most outstanding information technologies, and has been quite popular in recent years. The most influential change produced by digital twin is the ability to enable real-time dynamic interactions between the simulation world and the real world. Digital twin can be regarded as a paradigm by means of which selected online measurements are dynamically assimilated into the simulation world, with the running simulation model guiding the real world adaptively in reverse. By combining digital twin theory and random finite sets (RFSs) closely, a new framework of sensor control in ASW is proposed. Two key algorithms are proposed for supporting the digital twin-based framework. First, the RFS-based data-assimilation algorithm is proposed for online assimilating the sequence of real-time measurements with detection uncertainty, data association uncertainty, noise, and clutters. Second, the computation of the reward function by using the results of the proposed data-assimilation algorithm is introduced to find the optimal control action. The results of three groups of experiments successfully verify the feasibility and effectiveness of the proposed approach. Full article