MR.Brick: Designing A Remote Mixed-reality Educational Game System for Promoting Children's Social & Collaborative Skills

Children are one of the groups most influenced by COVID-19-related social distancing, and a lack of contact with peers can limit their opportunities to develop social and collaborative skills. However, remote socialization and collaboration as an alternative approach is still a great challenge for children. This paper presents MR.Brick, a Mixed Reality (MR) educational game system that helps children adapt to remote collaboration. A controlled experimental study involving 24 children aged six to ten was conducted to compare MR.Brick with the traditional video game by measuring their social and collaborative skills and analyzing their multi-modal playing behaviours. The results showed that MR.Brick was more conducive to children's remote collaboration experience than the traditional video game. Given the lack of training systems designed for children to collaborate remotely, this study may inspire interaction design and educational research in related fields. © 2023 ACM.

Kaleidoscope: A Reflective Documentation Tool for a User Interface Design Course

Documentation can support design work and create opportunities for learning and reflection. We explore how a novel documentation tool for a remote interaction design course provides insight into design process and integrates strategies from expert practice to support studio-style collaboration and reflection. Using Research through Design, we develop and deploy Kaleidoscope, an online tool for documenting design process, in an upper-level HCI class during the COVID-19 pandemic, iteratively developing it in response to student feedback and needs. We discuss key themes from the real-world deployment of Kaleidoscope, including: tensions between documentation and creation;effects of centralizing discussion;privacy and visibility in shared spaces;balancing evidence of achievement with feelings of overwhelm;and the effects of initial perceptions and incentives on tool usage. These successes and challenges provide insights to guide future tools for design documentation and HCI education that scaffold learning process as an equal partner to execution. © 2023 Owner/Author.

Developing and evaluating the Digitally Enhanced Liberation from VEntilation (DELVE) system: A study of feasibility and user acceptance

Introduction: In common with many aspects of critical illness recovery, there is no universally accepted formula for "weaning," the term used to describe the process of liberating patients from mechanical ventilation.1-3 Understanding a patient's progress during a prolonged wean can be difficult and requires integration of various datasets. Therefore, it is good practice to ensure that weaning prescriptions are clear, easy to follow and adhered to and that weaning-associated data and meta data are recorded accurately and are easy to interpret. The prototype Digitally Enhanced Liberation from VEntilation (DELVE) system has been designed to be used in combination with the Puritan Bennett(TM) 980 (PB980) ventilator (Covidien, USA). DELVE is an open-loop system which provides an interactive weaning chart, combining the weaning prescription entered by the clinical staff, with actual settings recorded from the ventilator in order to display compliance with the prescription (Figure 1). DELVE also collects measured data from the ventilator which could be used to display respiratory performance, both real-time and historical. Figure 1. DELVE set up with the PB980 ventilator (in the simulation suite). Objective(s): This feasibility study was designed to inform development of the first DELVE prototype and a future clinical trial to determine clinical effectiveness and usefulness. The study objectives were to determine whether DELVE could: 1. Present a digital weaning chart that staff could use effectively and would be superior to the current paper version. 2. Record and display the patients' ventilatory performance, both real time and historical, during liberation from mechanical ventilation. Method(s): This was a mixed-methods, prospective feasibility study of a complex intervention.4 Ventilated patients with a tracheostomy, commencing the weaning process, were recruited from an adult intensive care unit. DELVE was used alongside the current paper-based system for weaning planning and data collection. Patients remained in the study until they no longer required the support of the PB980 ventilator. Result(s): Twenty patients were enrolled for between 25 and 270 hours each. There were no safety incidents or data breaches. DELVE was successfully operated by staff, who were able to connect DELVE to the ventilator, prescribe weaning plans and analyse adherence. The digital weaning chart user interface was intuitive and easy to navigate. It was clearer, more complete and easier to interpret when compared to the paper weaning charts (Figure 2). DELVE reliably collected data every ten seconds and safely stored over six million items of measured data and 25000 events, such as alarm triggers and setting changes, in a form that could allow analysis and pictorial or graphical presentation. Conclusion(s): This study supported the feasibility of this and future versions of DELVE to present both a digital weaning chart and to facilitate visual and numerical data presentation. Future iterations of the system could include a user-friendly dashboard representing patient progress during the weaning process. Assimilation of large volumes of data could be used to enhance understanding and inform decision making around the prolonged wean.

A Scoping Review of Empirical Studies in Gather.town

There is an increasing interest for people to meet and interact in virtual online environments, including Gather.Town, especially during the COVID-19 pandemic and in the post-pandemic world. We present a scoping review of 11 empirical studies in Gather.town - a 2D Metaverse using a game-like interface with video-chat in which people can create their own virtual spaces and act as avatars to interact with each other for various purposes. To identify knowledge gaps, we summarized the included articles in terms of their application contexts, research issues, research methods, and key findings. We found that most of them were conducted in educational settings with a focus on students' learning experiences and perceptions. The findings of the reviewed studies generally suggested that the use of Gather.town benefited users' engagement. However, the available evidence was mostly based on short interventions (e.g., one session) and self-reported measures (e.g., surveys and interviews). This review concluded by presenting several research gaps for future research (e.g., studies with a longer duration and using more objective measurements of learning achievement and work results). © 2023 IEEE.

Development of Data Logger for COVID Vaccine Temperature Monitoring System

The Data Logger (DL) is a unique tool created to carry out the typical duty of gathering data in a specific area. This common task can include measuring humidity, temperature, pressure or any other physical quantities. Due to the current pandemic situation, its use in temperature monitoring of Covid vaccine will be crucial. According to World Health Organization (WHO) guidelines, COVID vaccine can be stored and transported at -80 °C, -20°C and +2-8°C and shelf life is reduced as vaccine is transferred from one storage temperature to another. So cost effective, efficient and standalone Data Logger (DL) is the need of the hour. The Data logger is proposed to be developed with the use of ESP8266 Node MCU microcontroller. It takes power from a 5V Battery. DS18B20 sensor will be used for temperature sensing. Here we will use Wi-Fi module of ESP8266 Node MCU to send the temperature data from sensor to the Google Sheet over the internet. This real time data will be stored in the format of time and month/date/year. Data logged in Google Sheet will be displayed to the user with the help of graphical user interface (GUI) which is developed using PYTHON scripting language. GUI will allow user to interact with Data Logger through visual graphs. The Data Logger components are mounted on a double layered PCB. © 2022 IEEE.

CoVEffect: interactive system for mining the effects of SARS-CoV-2 mutations and variants based on deep learning.

BACKGROUND: Literature about SARS-CoV-2 widely discusses the effects of variations that have spread in the past 3 years. Such information is dispersed in the texts of several research articles, hindering the possibility of practically integrating it with related datasets (e.g., millions of SARS-CoV-2 sequences available to the community). We aim to fill this gap, by mining literature abstracts to extract-for each variant/mutation-its related effects (in epidemiological, immunological, clinical, or viral kinetics terms) with labeled higher/lower levels in relation to the nonmutated virus. RESULTS: The proposed framework comprises (i) the provisioning of abstracts from a COVID-19-related big data corpus (CORD-19) and (ii) the identification of mutation/variant effects in abstracts using a GPT2-based prediction model. The above techniques enable the prediction of mutations/variants with their effects and levels in 2 distinct scenarios: (i) the batch annotation of the most relevant CORD-19 abstracts and (ii) the on-demand annotation of any user-selected CORD-19 abstract through the CoVEffect web application (http://gmql.eu/coveffect), which assists expert users with semiautomated data labeling. On the interface, users can inspect the predictions and correct them; user inputs can then extend the training dataset used by the prediction model. Our prototype model was trained through a carefully designed process, using a minimal and highly diversified pool of samples. CONCLUSIONS: The CoVEffect interface serves for the assisted annotation of abstracts, allowing the download of curated datasets for further use in data integration or analysis pipelines. The overall framework can be adapted to resolve similar unstructured-to-structured text translation tasks, which are typical of biomedical domains.

Plasmonic Scattering Imaging of Surface-Bonded Nanoparticles at the Solution-Solid Interface.

Imaging nanoscale objects at interfaces is essential for revealing surface-tuned mechanisms in chemistry, physics, and life science. Plasmonic-based imaging, a label-free and surface-sensitive technique, has been widely used for studying the chemical and biological behavior of nanoscale objects at interfaces. However, direct imaging of surface-bonded nanoscale objects remains challenging due to uneven image backgrounds. Here, we present a new surface-bonded nanoscale object detection microscopy that eliminates strong background interference by reconstructing accurate scattering patterns at different positions. Our method effectively functions at low signal-to-background ratios, allowing for optical scattering detection of surface-bonded polystyrene nanoparticles and severe acute respiratory syndrome coronavirus 2 pseudovirus. It is also compatible with other imaging configurations, such as bright-field imaging. This technique complements existing methods for dynamic scattering imaging and broadens the applications of plasmonic imaging techniques for high-throughput sensing of surface-bonded nanoscale objects, enhancing our understanding of the properties, composition, and morphology of nanoparticles and surfaces at the nanoscale.

Acute Respiratory Infection Hubs: A Service Model with Potential to Optimise Infection Management.

Patients with acute respiratory infections (ARI)-including those with upper and lower respiratory infections from both bacterial and viral pathogens-are one of the most common reasons for acute deterioration, with large numbers of potentially avoidable hospital admissions. The acute respiratory infection hubs model was developed to improve healthcare access and quality of care for these patients. This article outlines the implementation of this model and its potential impacts in a number of areas. Firstly, by improving healthcare access for patients with respiratory infections by increasing the capacity for assessment in community and non-emergency department settings and also by providing flexible response to surges in demand and reducing primary and secondary care demand. Secondly, by optimising infection management (including the use of point-of-care diagnostics and standardised best practise guidance to improve appropriate antimicrobial usage) and reducing nosocomial transmission by cohorting those with suspected ARI away from those with non-infective presentations. Thirdly, by addressing healthcare inequalities; in areas of greatest deprivation, acute respiratory infection is strongly linked with increased emergency department attendance. Fourthly, by reducing the National Health Service's (NHS) carbon footprint. Finally, by providing a wonderful opportunity to gather community infection management data to enable large-scale evaluation and research.

Crafting tasteful experiences: Designing artificial intelligence and voice user interfaces for home delivery contexts

This research article crafted, evaluated, and revised a theoretically underpinned design concept with the purpose of enhancing customers' dine-in experiences. The design concept was motivated by the considerable interest in artificial intelligence (AI), voice user interfaces (VUI) within Human-Computer Interaction (HCI), and the rapid digitalization of online food ordering as a result of the COVID-19 pandemic. The study applied the concept-driven design research approach because it offered to make theoretical contributions while at the same time being design and concept-oriented. The result of this research is a revised design concept that has the potential to digitalize the dine-in restaurant business further and add to the understanding of human experience while interacting with a voice user interface. Finally, the research article manifests as an example of how interaction designers make theoretical contributions through design and how technologies can be combined in new contexts. © 2022 Owner/Author.

Demonstration of Remote Control of Critical Care Ventilators and the Integrated Clinical Environment

Introduction: The COVID-19 pandemic posed numerous challenges to patient care, including extensive PPE use, patient care in isolation rooms, inadequate numbers of intensivists particularly in rural communities, use of unfamiliar ventilators that would be partially remedied by the ability to remotely control lung ventilation. The goals of the project were to study the intended use, risk management, usability, cybersecurity for remote control of ventilators and demonstrate the use of a single interface for several different ventilators. Method(s): Clinical scenarios were developed including remote control of the ventilator from an antechamber of an isolation room, nursing station within the same ICU, and remote control from across the country. A risk analysis and was performed and a risk management plan established using the AAMI Consensus Report--Emergency Use Guidance for Remote Control of Medical Devices. A cybersecurity plan is in progress. Testing was done at the MDPNP laboratory. We worked with Nihon Kohden OrangeMed NKV-550, Santa Ana, CA, and Thornhill Medical MOVES SLC, Toronto, Canada. Both companies modified their devices to allow remote control by and application operating on DocBox's Apiary platform. Apiary is a commercially available ICE solution, DocBox Inc, Waltham, MA. An expert panel was created to provide guidance on the design of a single common, simple to use graphical user interface (GUI) for both ventilators. Manufacturers' ventilation modes were mapped to ISO 19223 vocabulary, data was logged using ISO/IEEE 11073-10101 terminology using AAMI 2700-2-1, Medical Devices and Medical Systems - Essential safety and performance requirements for equipment comprising the patient-centric integrated clinical environment (ICE): Part 2-1: Requirements for forensic data logging. Result(s): We demonstrated that both ventilators can be controlled and monitored using common user interface within an institution and across the country. Pressure and flow waveforms were available for the NKV-550 ventilator, and usual ventilator measurements were displayed in near-real time. The interface allowed changing FiO2, ventilation mode, respiratory rate, tidal volume, inspiratory pressure, and alarm settings. At times, increased network latency negatively affected the transmission of waveforms. Conclusion(s): We were able to demonstrate remote control of 2 ventilators with a common user interface. Further work needs to be done on cybersecurity, effects of network perturbations, safety of ventilator remote control, usability implications of having a common UI for different devices needs to be investigated.

Instructional Usability and Learner-User eXperience Assessment in a Virtual Reality Educational Milieu: A Deductive Tech-Instructionality Model from EdTech

Educational Technology (EdTech) lacks a foundational, formal, scientific, epistemic theory. Therefore, it lacks native constructs/variables and an epistemological object of study for scientifically deploying its work. This study determines the existence (ontology) of the theorized constructs Instructional Usability (UsI) and Learner-User eXperience (LUX) and defines their characterization (epistemology). Both constructs were modeled and instrumented. Furthermore, a Tech-Instructionality Model (TIM) was theorized and developed in this paper, both analytically and empirically. The model integrates UsI and LUX as two pairs of constructs linked with two EdTech epistemological objects of study, the instructional interface and the instructional interaction in two assessment modalities, testing mode (user-learner view) and inspection mode (expert/designer view). Two instruments were developed and validated in this study for testing mode, the Instructional Usability Scale (SUsI) and the Learner-User eXperience Questionnaire (QLUX). Both instruments were tested in a non-immersive virtual reality educational milieu during the academic lockdown of the Covid19 pandemic. The results show that both SUsI and QLUX consistently measured UsI and LUX, thus, providing a valid assessment for tech-instructionality and a foundation for constructing a scientific theory of EdTech. © 2023 IEEE.

Integrating System Dynamics with Data Science via Graphical User Interface

This paper builds on the PySD project, which seeks to bring together System Dynamics and Data Science by migrating models into a programming environment in Python. The authors develop an interactive tool, built on top of the PySD module as a step toward accessibility, which helps further the ability of (1) simulation to be intuitive for non-experts and (2) Data Science to facilitate model structure understanding. This tool is meant to serve as a conduit through which Data Science can be leveraged in Systems Dynamics modeling efforts.

Caring the Sharing Apps–Exploration of the Factors Affecting User Satisfaction with Sharing Economy Apps

Sharing economy-based services are more prevalent in contemporary society, especially after the covid-19 pandemic. However, from the user's perspective, it is still unclear what factors define the success of sharing economy-based apps. To address this issue, we conducted a study using an integrated theoretical framework that incorporated cognitive load theory, social network theory, and theory of planned behavior. 448 samples were collected from three tourist destinations in India to test the model. The results showed that mobile app user interface, interaction, and social networking would positively impact user satisfaction with sharing economy-based apps. User satisfaction also leads to recommendation and continuance intention. The study findings have several implications and recommendations for future studies on sharing economy apps. © 2023 Taylor & Francis Group, LLC.

Staff quality of working life and turnover intentions in municipal nursing care and social welfare: a cross-sectional study.

BACKGROUND: Nurses and social workers are two common professions with a university degree working within municipal nursing care and social welfare. Both groups have high turnover intention rates, and there is a need to better understand their quality of working life and turnover intentions in general and more specifically during the Covid-19 pandemic. This study investigated associations between working life, coping strategies and turnover intentions of staff with a university degree working within municipal care and social welfare during the Covid-19 pandemic. METHODS: A cross-sectional design; 207 staff completed questionnaires and data were analyzed using multiple linear regression analyses. RESULTS: Turnover intentions were common. For registered nurses 23% thought of leaving the workplace and 14% the profession 'rather often' and 'very often/always'. The corresponding figures for social workers were 22% (workplace) and 22% (profession). Working life variables explained 34-36% of the variance in turnover intentions. Significant variables in the multiple linear regression models were work-related stress, home-work interface and job-career satisfaction (both for the outcome turnover intentions profession and workplace) and Covid-19 exposure/patients (turnover intentions profession). For the chosen coping strategies, 'exercise', 'recreation and relaxation' and 'improving skills', the results (associations with turnover) were non-significant. However, comparing the groups social workers reported that they used 'recreation and relaxation' more often than were reported by registered nurses. CONCLUSIONS: More work-related stress, worse home-work interface and less job-career satisfaction together with Covid-19 exposure/patients (Covid-19 only for turnover profession) increase turnover intentions. Recommendations are that managers should strive for better home-work interface and job-career satisfaction, monitor and counteract work-related stress to prevent turnover intentions.

The impact of smartphone use on learning effectiveness: A case study of primary school students.

This study investigated the effects of smartphone use on the perceived academic performance of elementary school students. Following the derivation of four hypotheses from the literature, descriptive analysis, t testing, one-way analysis of variance (ANOVA), Pearson correlation analysis, and one-way multivariate ANOVA (MANOVA) were performed to characterize the relationship between smartphone behavior and academic performance with regard to learning effectiveness. All coefficients were positive and significant, supporting all four hypotheses. We also used structural equation modeling (SEM) to determine whether smartphone behavior is a mediator of academic performance. The MANOVA results revealed that the students in the high smartphone use group academically outperformed those in the low smartphone use group. The results indicate that smartphone use constitutes a potential inequality in learning opportunities among elementary school students. Finally, in a discussion of whether smartphone behavior is a mediator of academic performance, it is proved that smartphone behavior is the mediating variable impacting academic performance. Fewer smartphone access opportunities may adversely affect learning effectiveness and academic performance. Elementary school teachers must be aware of this issue, especially during the ongoing COVID-19 pandemic. The findings serve as a reference for policymakers and educators on how smartphone use in learning activities affects academic performance.

Knowledge Based Expert System for Diagnosis of COVID-19

The recent pandemic caused by the Coronavirus Disease (COVID-19) first surfaced in Wuhan, China in December 2019. This paper presents an expert system for the diagnosis of COVID-19 based on its symptoms to aid people in taking precautionary measures. When experts are not available, an expert system that can effectively diagnose the disease is crucial. It takes the place of one or more experts in decision-making and problem-solving. An expert system for diagnosis of COVID-19 is a system developed to recognize early COVID-19 symptoms that individuals may experience by allowing users to directly check the disease with results that can serve as a foundation for additional testing. This study's primary goal is to identify useful COVID-19 detection patterns or knowledge by examining the historical data we have obtained from the Kaggle dataset. The patterns are presented as rules, which are given to the expert system after consultation with a domain expert. A total of 1,048,575 pieces of data were used for model training and testing. To detect COVID-19 disease, we employ a PART rule-based algorithm, which performed 92.47% accurately in a 10-fold cross-validation test. We can therefore draw the conclusion that the algorithm produces a promising result and that the expert system aids in the diagnosis of the disease. The system offers a suggestion in line with the identified symptom. [ FROM AUTHOR] Copyright of Journal of Pharmaceutical Negative Results is the property of ResearchTrentz and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Design and Development of Toroidal Core Winding Machine with Digital Interface

The small size and inherent superior electrical characteristics of a toroid has made it the first choice for many Original Equipment Manufacturers (OEMs). However, the lack of knowledge regarding the toroidal coil winding equipment is still hampering the growth of toroid as the first choice for transformers, inductors and other electrical applications. Additionally, due to Covid-19 pandemic and lockdown situation, small scale companies are lacking skilled manpower for the high precision task of toroidal core winding and taping. Although the machine is readily available in the market, the cost is still very high. Toroidal core winding machine is an equipment used for the purpose of winding toroidal cores which is used in various electrical machines such as current transformers, power transformers, isolation transformers, inductors and chokes, auto transformers, etc. This project aims to develop a low-cost toroidal winding machine with a user-friendly digital interface for selection of winding parameters as per the user input. The winding machine developed in this project is efficient and reliable with high-speed performance and negligible error. © 2022 IEEE.

Federated Learning for Healthcare Domain - Pipeline, Applications and Challenges

Federated learning is the process of developing machine learning models over datasets distributed across data centers such as hospitals, clinical research labs, and mobile devices while preventing data leakage. This survey examines previous research and studies on federated learning in the healthcare sector across a range of use cases and applications. Our survey shows what challenges, methods, and applications a practitioner should be aware of in the topic of federated learning. This paper aims to lay out existing research and list the possibilities of federated learning for healthcare industries.© 2022 Copyright held by the owner/author(s).

External Human-Machine Interfaces for Automated Vehicles in Shared Spaces: A Review of the Human-Computer Interaction Literature.

Given the rise of automated vehicles from an engineering and technical perspective, there has been increased research interest concerning the Human and Computer Interactions (HCI) between vulnerable road users (VRUs, such as cyclists and pedestrians) and automated vehicles. As with all HCI challenges, clear communication and a common understanding-in this application of shared road usage-is critical in order to reduce conflicts and crashes between the VRUs and automated vehicles. In an effort to solve this communication challenge, various external human-machine interface (eHMI) solutions have been developed and tested across the world. This paper presents a timely critical review of the literature on the communication between automated vehicles and VRUs in shared spaces. Recent developments will be explored and studies analyzing their effectiveness will be presented, including the innovative use of Virtual Reality (VR) for user assessments. This paper provides insight into several gaps in the eHMI literature and directions for future research, including the need to further research eHMI effects on cyclists, investigate the negative effects of eHMIs, and address the technical challenges of eHMI implementation. Furthermore, it has been underlined that there is a lack of research into the use of eHMIs in shared spaces, where the communication and interaction needs differ from conventional roads.