Search

There has been renewed interest in Citizen Science (CS) in recent years as it offers an intriguing vision of enabling a scientifically literate population engage in scientific investigations and policy formation. Nonetheless, citizen scientists remain an understudied population, possibly due to the voluntary and part-time nature of their endeavours. Here, a dataset of CS practitioners’ experiences collected using an online survey is presented. The survey sample comprises 100 adults (18+) active in diverse CS projects. The survey contains 47 questions designed for quantitative analysis. Questions cluster around several broad themes - participant demographics, project profiles, experience in citizen science, data collection practices, management, dissemination, knowledge of open research principles, and training received. The dataset offers the potential for further empirical research or as a baseline for subsequent surveys, and will interest anybody planning a CS initiative. The questionnaire constitutes a ready-to-deploy instrument for additional country, region, or initiative-level surveys.

Abstract Citizen science has been studied intensively in recent years. Nonetheless, the voice of citizen scientists is often lost despite their altruistic and indispensable role. To remedy this deficiency, a survey on the overall experiences of citizen scientists was undertaken. Dimensions investigated include activities, open science concepts, and data practices. However, the study prioritizes knowledge and practices of data and data management. When a broad understanding of data is lacking, the ability to make informed decisions about consent and data sharing, for example, is compromised. Furthermore, the potential and impact of individual endeavors and collaborative projects are reduced. Findings indicate that understanding of data management principles is limited. Furthermore, an unawareness of common data and open science concepts was observed. It is concluded that appropriate training and a raised awareness of Responsible Research and Innovation concepts would benefit individual citizen scientists, their projects, and society.

Workload orchestration at the edge of the network has become increasingly challenging with the ever-increasing penetration of resource demanding mobile, and heterogeneous devices offering low latency services. Literature has addressed this challenge assuming the availability of multi-access Mobile Edge Computing (MEC) servers and placing the computing tasks related to such services on the MEC servers. However, to develop a more sustainable and energy-efficient computing paradigm, for applications operating in stochastic environments with unpredictable workloads, it is essential to minimize the MEC servers’ usage, and utilize the available resource-constrained edge devices, to keep the resourceful servers idle for handling any unpredictable larger workload. In this paper, we proposed DEWOrch, a deep reinforcement Learning algorithm for efficient workload orchestration. DEWOrch’s aim is to increase the utilization of resource-constrained edge devices and minimize resource waste for more sustainable and energy efficient computing solution. This model is evaluated in an Extreme Edge Computing environment, where no MEC servers is available and only edge devices with constrained capacity are used to perform tasks. The results show that DEWOrch outperforms the state-of-the-art methods by around 50% decrease in resource waste while improved task success rate, and decreased energy consumption per task in most scenarios.

ObjectiveTo investigate the use of a virtual reality learning environment (VRLE) to enhance medical student knowledge of postpartum hemorrhage (PPH) emergency management and insertion of a postpartum balloon.MethodsA randomized control trial involving medical students from University College Dublin, Ireland. Participants were randomly allocated to the intervention group (VRLE tutorial) or control group (PowerPoint tutorial on the same topic). All participants completed pre-learning experience and post-learning experience surveys. Both groups were timed and assessed on postpartum balloon insertion technique on a model pelvis. The primary outcome was assessment of student knowledge. Secondary outcomes included confidence levels, time taken to complete the task, technique assessment, satisfaction with the learning environment, and side effects of VR.ResultsBoth learning experiences significantly (p

Abstract Augmented Reality in education can support students in a wide range of cognitive tasks–fostering understanding, remembering, applying, analysing, evaluating, and creating learning-relevant information more easily. It can help keep up engagement, and it can render learning more fun. Within the framework of a multi-year investigation encompassing primary and secondary schools across Europe, the ARETE project developed several Augmented Reality applications, providing tools for user interaction and data collection in the education sector. The project developed innovative AR learning technology and methodology, validating these in four comprehensive pilot studies, in total involving more than 2,900 students and teachers. Each pilot made use of a different Augmented Reality application covering specific subjects (English literacy skills, Mathematics and Geography, Positive Behaviour, plus, additionally, an Augmented Reality authoring tool applied in a wide range of subjects). In this paper, we introduce the datasets collected during the pilots, describe how the data enabled the validation of the technology, and how the approach chosen could enhance existing augmented reality applications in data exploration and modelling.

Integrating imaging sensors and artificial intelligence (AI) have contributed to detecting plant stress symptoms, yet data analysis remains a key challenge. Data challenges include standardized data collection, analysis protocols, selection of imaging sensors and AI algorithms, and finally, data sharing. Here, we present a systematic literature review (SLR) scrutinizing plant imaging and AI for identifying stress responses. We performed a scoping review using specific keywords, namely abiotic and biotic stress, machine learning, plant imaging and deep learning. Next, we used programmable bots to retrieve relevant papers published since 2006. In total, 2,704 papers from 4 databases (Springer, ScienceDirect, PubMed, and Web of Science) were found, accomplished by using a second layer of keywords (e.g., hyperspectral imaging and supervised learning). To bypass the limitations of search engines, we selected OneSearch to unify keywords. We carefully reviewed 262 studies, summarizing key trends in AI algorithms and imaging sensors. We demonstrated that the increased availability of open-source imaging repositories such as PlantVillage or Kaggle has strongly contributed to a widespread shift to deep learning, requiring large datasets to train in stress symptom interpretation. Our review presents current trends in AI-applied algorithms to develop effective methods for plant stress detection using image-based phenotyping. For example, regression algorithms have seen substantial use since 2021. Ultimately, we offer an overview of the course ahead for AI and imaging technologies to predict stress responses. Altogether, this SLR highlights the potential of AI imaging in both biotic and abiotic stress detection to overcome challenges in plant data analysis.

Imagery from Unmanned Aerial Vehicles can be used to generate three-dimensional (3D) point cloud models. However, final data quality is impacted by the flight altitude, camera angle, overlap rate, and data processing strategies. Typically, both overview images and redundant close-range images are collected, which significantly increases the data collection and processing time. To investigate the relationship between input resources and output quality, a suite of seven metrics is proposed including total points, average point density, uniformity, yield rate, coverage, geometry accuracy, and time efficiency. When applied in the field to a full-scale structure, the UAV altitude and camera angle most strongly affected data density and uniformity. A 66% overlapping was needed for successful 3D reconstruction. Conducting multiple flight paths improved local geometric accuracy better than increasing the overlapping rate. The highest coverage was achieved at 77% due to the formation of semi-irregular gridded gaps between point groups as an artefact of the Structure from Motion process. No single set of flight parameters was optimal for every data collection goal. Hence, understanding flight path parameter impacts is crucial to optimal UAV data collection.

Farmers and breeders aim to improve crop responses to abiotic stresses and secure yield under adverse environmental conditions. To achieve this goal and select the most resilient genotypes, plant breeders and researchers rely on phenotyping to quantify crop responses to abiotic stress. Recent advances in imaging technologies allow researchers to collect physiological data non-destructively and throughout time, making it possible to dissect complex plant responses into quantifiable traits. The use of image-based technologies enables the quantification of crop responses to stress in both controlled environmental conditions and field trials. This paper summarizes phenotyping imaging technologies (RGB, multispectral and hyperspectral sensors, among others) that have been used to assess different abiotic stresses including salinity, drought and nitrogen deficiency, while discussing their advantages and drawbacks. We present a detailed review of traits involved in abiotic tolerance, which have been quantified by a range of imaging sensors under high-throughput phenotyping facilities or using unmanned aerial vehicles in the field. We also provide an up-to-date compilation of spectral tolerance indices and discuss the progress and challenges in machine learning, including supervised and unsupervised models as well as deep learning.

Creating pedagogically sound, interactive Augmented Reality (AR) experiences supporting situated and experiential learning remains a challenge to teachers without programming skills. To integrate AR in the everyday classroom, teachers need to be capable of designing their own immersive experiences for their students, which is why an analysis of existing authoring toolkits is necessary to identify suitable tools for educational application development and future research directions in terms of educational AR. We identified “easy access”, “GUI-based design”, and “interactive contents” as needs of teachers for designing AR content for the classroom. Based on these needs, we conducted a literature review of 835 documents. Of 80 relevant articles, we included 43 peer-reviewed articles from ACM Digital Library, DBLP, IEEExplore, Scopus, Web of Science, Google Scholar, and miscellaneous other sources in our analysis. We identified 69 different AR authoring toolkits and classified these with regard to their accessibility, their degree of required programming knowledge, and their interactivity. The results show a divergent research landscape with a lack of empirical evaluation. Of 26 openly accessible toolkits, we identified five toolkits addressing the defined needs of teachers for designing interactive AR experiences for the classroom without requiring extensive programming knowledge. We conclude that there are only few tools for the straightforward design of educational AR experiences addressing the needs of teachers and suggest using research-informed and evidence-based criteria for developing AR authoring toolkits for education.

BackgroundThere is a lack of evidence in the literature regarding the learning outcomes of immersive technologies as educational tools for teaching university-level health care students. ObjectiveThe aim of this review is to assess the learning outcomes of immersive technologies compared with traditional learning modalities with regard to knowledge and the participants’ learning experience in medical, midwifery, and nursing preclinical university education. MethodsA systematic review was conducted according to the Cochrane Collaboration guidelines. Randomized controlled trials comparing traditional learning methods with virtual, augmented, or mixed reality for the education of medicine, nursing, or midwifery students were evaluated. The identified studies were screened by 2 authors independently. Disagreements were discussed with a third reviewer. The quality of evidence was assessed using the Medical Education Research Study Quality Instrument (MERSQI). The review protocol was registered with PROSPERO (International Prospective Register of Systematic Reviews) in April 2020. ResultsOf 15,627 studies, 29 (0.19%) randomized controlled trials (N=2722 students) were included and evaluated using the MERSQI tool. Knowledge gain was found to be equal when immersive technologies were compared with traditional learning modalities; however, the learning experience increased with immersive technologies. The mean MERSQI score was 12.64 (SD 1.6), the median was 12.50, and the mode was 13.50. Immersive technology was predominantly used to teach clinical skills (15/29, 52%), and virtual reality (22/29, 76%) was the most commonly used form of immersive technology. Knowledge was the primary outcome in 97% (28/29) of studies. Approximately 66% (19/29) of studies used validated instruments and scales to assess secondary learning outcomes, including satisfaction, self-efficacy, engagement, and perceptions of the learning experience. Of the 29 studies, 19 (66%) included medical students (1706/2722, 62.67%), 8 (28%) included nursing students (727/2722, 26.71%), and 2 (7%) included both medical and nursing students (289/2722, 10.62%). There were no studies involving midwifery students. The studies were based on the following disciplines: anatomy, basic clinical skills and history-taking skills, neurology, respiratory medicine, acute medicine, dermatology, communication skills, internal medicine, and emergency medicine. ConclusionsVirtual, augmented, and mixed reality play an important role in the education of preclinical medical and nursing university students. When compared with traditional educational modalities, the learning gain is equal with immersive technologies. Learning outcomes such as student satisfaction, self-efficacy, and engagement all increase with the use of immersive technology, suggesting that it is an optimal tool for education.

Electricity load forecasting is an important part of power system dispatching. Accurately forecasting electricity load have great impact on a number of departments in power systems. Compared to electricity load simulation (white-box model), electricity load forecasting (black-box model) does not require expertise in building construction. The development cycle of the electricity load forecasting model is much shorter than the design cycle of the electricity load simulation. Recent developments in machine learning have lead to the creation of models with strong fitting and accuracy to deal with nonlinear characteristics. Based on the real load dataset, this paper evaluates and compares the two mainstream short-term load forecasting techniques. Before the experiment, this paper first enumerates the common methods of short-term load forecasting and explains the principles of Long Short-term Memory Networks (LSTMs) and Support Vector Machines (SVM) used in this paper. Secondly, based on the characteristics of the electricity load dataset, data pre-processing and feature selection takes place. This paper describes the results of a controlled experiment to study the importance of feature selection. The LSTMs model and SVM model are applied to one-hour ahead load forecasting and one-day ahead peak and valley load forecasting. The predictive accuracy of these models are calculated based on the error between the actual and predicted loads, and the runtime of the model is recorded. The results show that the LSTMs model have a higher prediction accuracy when the load data is sufficient. However, the overall performance of the SVM model is better when the load data used to train the model is insufficient and the time cost is prioritized.

This research is concerned with the novel application and investigation of ‘Soft Actor Critic’ based deep reinforcement learning to control the cooling setpoint (and hence cooling loads) of a large commercial building to harness energy flexibility. The research is motivated by the challenge associated with the development and application of conventional model-based control approaches at scale to the wider building stock. Soft Actor Critic is a model-free deep reinforcement learning technique that is able to handle continuous action spaces and which has seen limited application to real-life or high-fidelity simulation implementations in the context of automated and intelligent control of building energy systems. Such control techniques are seen as one possible solution to supporting the operation of a smart, sustainable and future electrical grid. This research tests the suitability of the technique through training and deployment of the agent on an EnergyPlus based environment of the office building. The agent was found to learn an optimal control policy that was able to minimise energy costs by 9.7% compared to the default rule-based control scheme and was able to improve or maintain thermal comfort limits over a test period of one week. The algorithm was shown to be robust to the different hyperparameters and this optimal control policy was learnt through the use of a minimal state space consisting of readily available variables. The robustness of the algorithm was tested through investigation of the speed of learning and ability to deploy to different seasons and climates. It was found that the agent requires minimal training sample points and outperforms the baseline after three months of operation and also without disruption to thermal comfort during this period. The agent is transferable to other climates and seasons although further retraining or hyperparameter tuning is recommended.

The progression and adoption of innovative learning methodologies signify that a respective part of society is open to new technologies and ideas and thus is advancing. The latest innovation in teaching is the use of Augmented Reality (AR). Applications using this technology have been deployed successfully in STEM (Science, Technology, Engineering, and Mathematics) education for delivering the practical and creative parts of teaching. Since AR technology already has a large volume of published studies about education that reports advantages, limitations, effectiveness, and challenges, classifying these projects will allow for a review of the success in the different educational settings and discover current challenges and future research areas. Due to COVID-19, the landscape of technology-enhanced learning has shifted more toward blended learning, personalized learning spaces and user-centered approach with safety measures. The main findings of this paper include a review of the current literature, investigating the challenges, identifying future research areas, and finally, reporting on the development of two case studies that can highlight the first steps needed to address these research areas. The result of this research ultimately details the research gap required to facilitate real-time touchless hand interaction, kinesthetic learning, and machine learning agents with a remote learning pedagogy.

Background: Virtual Reality (VR) and Augmented Reality (AR) technologies provide a novel experiential learning environment that can revolutionize medical education. These technologies have limitless potential as they provide in effect an infinite number of anatomical models to aid in foundational medical education. The 3D teaching models used within these environments are generated from medical data such as magnetic resonance imaging (MRI) or computed tomography (CT), which can be dissected and regenerated without limitations.Methods: A systematic review was carried out for existing articles until February 11, 2020, in EMBASE, PubMed, Scopus, ProQuest, Cochrane Reviews, CNKI, and OneSearch (University College Dublin Library) using the following search terms: (Virtual Reality OR Augmented Reality OR mixed reality) AND [“head-mounted” OR “face-mounted” OR “helmet-mounted” OR “head-worn” OR oculus OR vive OR HTC OR hololens OR “smart glasses” OR headset AND (training OR teaching OR education)] AND (anatomy OR anatomical OR medicine OR medical OR clinic OR clinical OR surgery OR surgeon OR surgical) AND (trial OR experiment OR study OR randomized OR randomised OR controlled OR control) NOT (rehabilitation OR recovery OR treatment) NOT (“systematic review” OR “review of literature” OR “literature review”). PRISMA guidelines were adhered to in reporting the results. All studies that examined people who are or were medical-related (novel or expert users) were included.Result: The electronic searches generated a total of 1,241 studies. After removing duplicates, 848 remained. Of those, 801 studies were excluded because the studies did not meet the criteria after reviewing the abstract. The full text of the remaining 47 studies was reviewed. After applying inclusion criteria and exclusion criteria, a total of 17 studies (1,050 participants) were identified for inclusion in the review.Conclusion: The systematic review provides the current state of the art on head-mounted device applications in medical education. Moreover, the study discusses trends toward the future and directions for further research in head-mounted VR and AR for medical education.

This paper describes an open source smart grid software infrastructure for co-simulation between cloud-based energy management systems and a building energy model. The core component of the infrastructure is an API, which provides a protocol abstraction as a decoupling mechanism between the control algorithms and the building, thereby facilitating the development and testing of intelligent controllers. The open-source infrastructure can be utilised for the development and benchmarking of smart grid demand response algorithms aimed at reducing the energy consumption and the carbon footprint of buildings, while facilitating the integration of renewable energies into the power system. Keywords: Smart grid, Control algorithm, Building simulation software

In recent years, educational researchers and practitioners have become increasingly interested in new technologies for teaching and learning, including augmented reality (AR). The literature has already highlighted the benefit of AR in enhancing learners’ outcomes in natural sciences, with a limited number of studies exploring the support of AR in social sciences. Specifically, there have been a number of systematic and scoping reviews in the AR field, but no peer-reviewed review studies on the contribution of AR within interventions aimed at teaching or training behavioral skills have been published to date. In addition, most AR research focuses on technological or development issues. However, limited studies have explored how technology affects social experiences and, in particular, the impact of using AR on social behavior. To address these research gaps, a scoping review was conducted to identify and analyze studies on the use of AR within interventions to teach behavioral skills. These studies were conducted across several intervention settings. In addition to this research question, the review reports an investigation of the literature regarding the impact of AR technology on social behavior. The state of the art of AR solutions designed for interventions in behavioral teaching and learning is presented, with an emphasis on educational and clinical settings. Moreover, some relevant dimensions of the impact of AR on social behavior are discussed in more detail. Limitations of the reviewed AR solutions and implications for future research and development efforts are finally discussed.

This paper presents the design of an assessment process and its outcomes to investigate the impact of Educational Robotics activities on students' learning. Through data analytics techniques, the authors will explore the activities' output from a pedagogical and quantitative point of view. Sensors are utilized in the context of an Educational Robotics activity to obtain a more effective robot–environment interaction. Pupils work on specific exercises to make their robot smarter and to carry out more complex and inspirational projects: the integration of sensors on a robotic prototype is crucial, and learners have to comprehend how to use them. In the presented study, the potential of Educational Data Mining is used to investigate how a group of primary and secondary school students, using visual programming (Lego Mindstorms EV3 Education software), design programming sequences while they are solving an exercise related to an ultrasonic sensor mounted on their robotic artifact. For this purpose, a tracking system has been designed so that every programming attempt performed by students' teams is registered on a log file and stored in an SD card installed in the Lego Mindstorms EV3 brick. These log files are then analyzed using machine learning techniques (k-means clustering) in order to extract different patterns in the creation of the sequences and extract various problem-solving pathways performed by students. The difference between problem-solving pathways with respect to an indicator of early achievement is studied.

Commercial buildings incorporate Building Energy Management Systems (BEMS) to monitor indoor environment conditions as well as controlling Heating Ventilation and Air Conditioning (HVAC) systems. Measurements of temperature, humidity and energy consumption are typically stored within BEMS. These measurements include underlying information regarding building thermal response, which is crucial for the calculation of heating and cooling loads. Forecasting of building thermal loads can be achieved using data records from BEMS. Accurate predictions can be produced when introducing these data records to data-mining predictive models. Incomplete datasets are often acquired when extracting data from the BEMS; hence detailed representations of commercial buildings can be implemented using EnergyPlus. For the purposes of the research described in this paper, different types of commercial buildings in various climates are examined to investigate the scalability of the predictive models.

This article presents an overview of educational robotics (ER) in primary and preschool education. As ER seems to be gaining popularity for its effectiveness as a learning tool, more research needs to be done in this area. Recent results from ER pilot projects advocate for the integration of ER in K-12 education curricula. On the other hand, teachers may face various difficulties in carrying out such activities due to lack of experience or knowledge in this field. Previous research has shown that ER is still an open field for exploration. Even though an increasing number of experiences are available for the use of robotic tools in early education, there is not enough empirical evidence on the features they need to present for young learners to perceive them as attractive and easy to use. In addition, the high cost of some tools may prevent educational institutions from using them systematically. To detect possible gaps in the current research, in the context of this work, 21 articles representing ER applications and frameworks were collected and reviewed between 2011 and 2021. The results of this study demonstrate that ER can be a valuable tool for supporting primary and preschool students. However, the review supports that more research is needed on the technical features that a robotic tool must have to be successfully introduced to students of this age. Moreover, future work is needed to develop low-cost ER tools so they can become more accessible to educational institutions.

Breast density estimation with visual evaluation is still challenging due to low contrast and significant fluctuations in the mammograms’ fatty tissue background. The primary key to breast density classification is to detect the dense tissues in the mammographic images correctly. Many methods have been proposed for breast density estimation; nevertheless, most of them are not fully automated. Besides, they have been badly affected by low signal-to-noise ratio and variability of density in appearance and texture. This study intends to develop a fully automated and digitalized breast tissue segmentation and classification using advanced deep learning techniques. The conditional Generative Adversarial Networks (cGAN) network is applied to segment the dense tissues in mammograms. To have a complete system for breast density classification, we propose a Convolutional Neural Network (CNN) to classify mammograms based on the standardization of Breast Imaging-Reporting and Data System (BI-RADS). The classification network is fed by the segmented masks of dense tissues generated by the cGAN network. For screening mammography, 410 images of 115 patients from the INbreast dataset were used. The proposed framework can segment the dense regions with an accuracy, Dice coefficient, Jaccard index of 98%, 88%, and 78%, respectively. Furthermore, we obtained precision, sensitivity, and specificity of 97.85%, 97.85%, and 99.28%, respectively, for breast density classification. This study’s findings are promising and show that the proposed deep learning-based techniques can produce a clinically useful computer-aided tool for breast density analysis by digital mammography.

This paper introduces the application of an agent-based software system for monitoring the process of gellan gum production. Gellan gum (biopolymer) is produced in industrial scale in bioreactors (sealed vessels) where the microbial culture is grown in a liquid fermentation medium under controlled environmental conditions (temperature, pH, aeration and agitation). The multi agent system will view the monitoring problem as the interaction of simple independent software entities, for effective use of the available data. The outcome of this agent – based solution will include the automatic on-line data acquisition and correlation of the most important parameters. Within such a dynamic process, like the gellan gum production, certan parameters (such as biomass, gellan and glucose concentration) change continuously and have to be measured and controlled. Also automatic knowledge derivation from past cases through the multi agent software system can be of future benefit.