Search Results (13)

Background: Physical activity (PA) is associated with lower mortality and cancer recurrence risks. Although evidence shows health benefits for cancer patients before, during, and immediately after treatment, PA recommendations are not regularly included in the standard care. Objective: The study aimed to identify perceived knowledge, barriers, and facilitators of oncology providers’ PA promotion for cancer patients using the 5A (Assess, Advise, Agree, Assist, and Arrange) framework. Methods: A qualitative research design with a phenomenological approach was adopted. A purposive sample of 16 oncology care providers in Nebraska participated in semi-structured interviews via Zoom/phone. Interviews were audio-recorded, transcribed, and imported into MAXQDA 2024 for thematic analysis. Results: Analysis of the qualitative data identified five themes: (i) Broad and inclusive conceptualizations of PA among oncology care providers suggested that they were able to define PA; (ii) Current Practices in PA Counseling included advising on PA and assessment; (iii) Barriers to PA counseling included lack of guideline awareness, insufficient training, low prioritization, uncertainty about responsibility, time constraints, limited resources, lack of referral systems, patient health conditions, and environmental factors; (iv) Facilitators were identified as acknowledged health benefits for cancer survivors, awareness of PA recommendations, access to community resources, and support from interdisciplinary teams; and (v) Expressed desire among oncology care providers for training on incorporating PA into oncology care. Conclusions: Oncology providers recognized PA’s health benefits for cancer survivors but did not promote it due to inadequate knowledge of guidelines and lack of resources. These barriers require improved PA counselling education to help providers incorporate PA into clinical care. Full article

Drawn from a wider study, the research reported here utilised a phenomenology of practice to investigate Australian early childhood teachers’ perceptions of constructed spaces that enhance the Christian education of children. The study was conceptually framed by the notions of constructed space, affordance theory and a theology of the built environment. Four participants were interviewed using Zoom, and their transcripts analysed using a phenomenological framework. The analysis indicated that the constructed space yielded five types of affordances—emotional space, embodied space, physical space, relational space and theological space. The findings suggest that it is the constructed environment of the early childhood centre itself that affords particular types of spaces, such as emotional space, embodies space and so on. The findings also indicate that it is the educator who makes the difference in seeing the possibility for and creating such spaces, and their intentionality in acting to create such spaces. While there are limitations to this study, including the small sample size, the findings nonetheless indicate the importance of the constructed space in enhancing the Christian education of young children in early childhood educational contexts. Full article

Wildlife monitoring can be time-consuming and expensive, but the fast-developing technologies of uncrewed aerial vehicles, sensors, and machine learning pave the way for automated monitoring. In this study, we trained YOLOv5 neural networks to detect points of interest, hare (Lepus europaeus), and roe deer (Capreolus capreolus) in thermal aerial footage and proposed a method to manually assess the parameter mean average precision (mAP) compared to the number of actual false positive and false negative detections in a subsample. This showed that a mAP close to 1 for a trained model does not necessarily mean perfect detection and provided a method to gain insights into the parameters affecting the trained models’ precision. Furthermore, we provided a basic, conceptual algorithm for implementing real-time object detection in uncrewed aircraft systems equipped with thermal sensors, high zoom capabilities, and a laser rangefinder. Real-time object detection is becoming an invaluable complementary tool for the monitoring of cryptic and nocturnal animals with the use of thermal sensors. Full article

In the nine months leading up to COVID-19, our biomedical engineering research group was in the very early stages of development and in-home testing of HUGS, the Hand Use and Grasp Sensor (HUGS) system. HUGS was conceived as a tool to allay parents’ anxiety by empowering them to monitor their infants’ neuromotor development at home. System focus was on the evolving patterns of hand grasp and general upper extremity movement, over time, in the naturalistic environment of the home, through analysis of data captured from force-sensor-embedded toys and 3D video as the baby played. By the end of March, 2020, as the COVID-19 pandemic accelerated and global lockdown ensued, home visits were no longer possible and HUGS system testing ground to an abrupt halt. In the spring of 2021, still under lockdown, we were able to resume recruitment and in-home testing with HUGS-2, a system whose key requirement was that it be contactless. Participating families managed the set up and use of HUGS-2, supported by a detailed library of video materials and virtual interaction with the HUGS team for training and troubleshooting over Zoom. Like the positive/negative poles of experience reported by new parents under the isolation mandated to combat the pandemic, HUGS research was both impeded and accelerated by having to rely solely on distance interactions to support parents, troubleshoot equipment, and securely transmit data. The objective of this current report is to chronicle the evolution of HUGS. We describe a system whose design and development straddle the pre- and post-pandemic worlds of family-centered health technology design. We identify and classify the clinical approaches to infant screening that predominated in the pre-COVID-19 milieu and describe how these procedural frameworks relate to the family-centered conceptualization of HUGS. We describe how working exclusively through the proxy of parents revealed the family’s priorities and goals for child interaction and surfaced HUGS design shortcomings that were not evident in researcher-managed, in-home testing prior to the pandemic. Full article

Serving as a conceptual introduction to the ARTS special issue, the article discusses the importance of archaic imagery and poetics of a major avant-garde actor who often symbolizes the main axis of Slavic radical modernism in its Avant-garde phase. Kazimir Malevich has widely explored religious archaic imagery in his oeuvre, engaging in a dialog with a historical tradition of representation. The article discusses Malevich’s iconic legacy, zooming in on the philosophy of Malevich’s suprematist imagery of peasants, Orthodox icons, and the ways of visualizing of an inner Hesychast prayer. In this context, the paper also analyzes Russian philosophy of language, imiaslavie and Hesychasm as it stemmed out from the creative perception of Byzantine philosophical lore developed by Gregory Palamas and several other thinkers. Full article

This paper aims to measure learners’ preferences for a specific teaching format (online, hybrid, or face-to-face) based on their experience, usage, and interaction with e-learning platforms (Moodle/MS Teams), on their participation in e-learning courses delivered via online streaming platforms (Zoom), on teaching staff skills and teaching–learning abilities, as well as on the advantages and disadvantages of those forms of learning during the COVID-19 pandemic. In implementing the research question, a conceptual model was developed, which was further analyzed by means of structural equations modelling via SmartPLS 3.3.9 (SmartPLS GmbH, Boenningstedt, Germany). The data were collected via quantitative research implemented through an online questionnaire addressed to learners (students) from an emerging market during the COVID-19 pandemic. The research contributes to extending social learning theory and the social cognitive learning theory by pinpointing the learners’ preference for the online educational format and by showing how a blended learning environment in universities can be developed by fructifying the gains in terms of digital skills acquisition during the COVID-19 pandemic. The paper highlights the contribution of the online educational environment in extending the use of interactive digital tools and resources, engaging the learners, and creating the opportunity for them to become accountable for their learning experiences. Full article

This study examines the impact of students’ anxiety, due to online learning, in different learning environments: a synchronous (Zoom) and asynchronous learning environment (YouTube) to compare students’ conceptual understanding of electric circuits. Multiple linear regression and factor analyses were conducted to examine the factor of students’ anxiety and conceptual understanding. A sample of 99 vocational students participated in the study, including YouTube (n = 49) and Zoom (n = 50) groups. The DIRECT was used to diagnose test for conceptual understanding in the electric circuits, and OTAI was used to assess anxiety in online learning test. The OTAI consists of three factors: psychological, physiological, and online. The results showed that students’ anxiety, in some factors, affected their conceptual understanding of the electric circuits in both groups. However, there was a significant increase in conceptual understanding in both treatment groups. Although the students’ conceptual understanding had a slight increase, online learning has to improve to reduce the anxiety of learners. Full article

Videoconferencing (VC) is a type of online meeting that allows two or more participants from different locations to engage in live multi-directional audio-visual communication and collaboration (e.g., via screen sharing). The COVID-19 pandemic has induced a boom in both private and professional videoconferencing in the early 2020s that elicited controversial public and academic debates about its pros and cons. One main concern has been the phenomenon of videoconference fatigue. The aim of this conceptual review article is to contribute to the conceptual clarification of VC fatigue. We use the popular and succinct label “Zoom fatigue” interchangeably with the more generic label “videoconference fatigue” and define it as the experience of fatigue during and/or after a videoconference, regardless of the specific VC system used. We followed a structured eight-phase process of conceptual analysis that led to a conceptual model of VC fatigue with four key causal dimensions: (1) personal factors, (2) organizational factors, (3) technological factors, and (4) environmental factors. We present this 4D model describing the respective dimensions with their sub-dimensions based on theories, available evidence, and media coverage. The 4D-model is meant to help researchers advance empirical research on videoconference fatigue. Full article

Stock movement prediction is important in the financial world because investors want to observe trends in stock prices before making investment decisions. However, given the non-linear non-stationary financial time series characteristics of stock prices, this remains an extremely challenging task. A wavelet is a mathematical function used to divide a given function or continuous-time signal into different scale components. Wavelet analysis has good time-frequency local characteristics and good zooming capability for non-stationary random signals. However, the application of the wavelet theory is generally limited to a small scale. The neural networks method is a powerful tool to deal with large-scale problems. Therefore, the combination of neural networks and wavelet analysis becomes more applicable for stock behavior prediction. To rebuild the signals in multiple scales, and filter the measurement noise, a forecasting model based on a stock price time series was provided, employing multiresolution analysis (MRA). Then, the deep learning in the neural network method was used to train and test the empirical data. To explain the fundamental concepts, a conceptual analysis of similar algorithms was performed. The data set for the experiment was chosen to capture a wide range of stock movements from 1 January 2009 to 31 December 2017. Comparison analyses between the algorithms and industries were conducted to show that the method is stable and reliable. This study focused on medium-term stock predictions to predict future stock behavior over 11 days of horizons. Our test results showed a 75% hit rate, on average, for all industries, in terms of US stocks on FORTUNE Global 500. We confirmed the effectiveness of our model and method based on the findings of the empirical research. This study’s primary contribution is to demonstrate the reconstruction model of the stock time series and to perform recurrent neural networks using the deep learning method. Our findings fill an academic research gap, by demonstrating that deep learning can be used to predict stock movement. Full article

The COVID-19 pandemic disrupted face-to-face interactions in healthcare research, with many studies shifting to video-based data collection for qualitative research. This study describes the interactivity achieved in a videoconferencing focus group of seven primary care providers discussing deprescribing opioids and benzodiazepines. Researchers reviewed video footage of a focus group conducted via Zoom and assessed interactivity using Morgan’s framework for focus group communication processes. Two reviewers categorized the type of exchanges as sharing information, comparing experiences, organizing, and conceptualizing the content, as well as validating each other or galvanizing the discussion with “lightning strike” ideas. The conversation dynamics in this focus group included clear examples of interactivity in each of the categories proposed by Morgan (validating, sharing, comparing, organizing, conceptualizing, and lightning strikes) that were observed by two different reviewers with demonstrated high interrater reliability. Conducting focus groups with a skilled moderator using videoconferencing platforms with primary care providers is a viable option that produces sufficient levels of interaction. Full article

The coronavirus disease 2019 (COVID-19) pandemic poses a challenge to the physical and mental well-being of doctors worldwide. Countries around the world introduced severe social restrictions, and significant changes to health service provision in the first wave of the pandemic to suppress the spread of the virus and prioritize healthcare for those who contracted it. This study interviewed 48 hospital doctors who worked in Ireland during the first wave of the pandemic and investigated their conceptualizations of their own well-being during that time (March–May 2020). Doctors were interviewed via Zoom™ or telephone. Interview transcripts were analyzed using structured thematic analysis. Five composite narratives are presented which have been crafted to illustrate themes and experiences emerging from the data. This study found that despite the risks of contracting COVID-19, many doctors saw some improvements to their physical well-being in the first wave of the pandemic. However, most also experienced a decline in their mental well-being due to anxiety, emotional exhaustion, guilt, isolation and poor support. These findings shed light on doctor well-being during COVID-19, and the ways in which they have been affected by the pandemic, both professionally and personally. The paper concludes by highlighting how doctors’ work life and well-being can be better supported during and after the COVID-19 pandemic. Full article

Drought is one of the most typical and serious natural disasters, which occurs frequently in most of mainland China, and it is crucial to explore the evolution characteristics of drought for developing effective schemes and strategies of drought disaster risk management. Based on the application of Cloud theory in the drought evolution research field, the cloud transformation algorithm, and the conception zooming coupling model was proposed to re-fit the distribution pattern of SPI instead of the Pearson-III distribution. Then the spatio-temporal evolution features of drought were further summarized utilizing the cloud characteristics, average, entropy, and hyper-entropy. Lastly, the application results in Northern Anhui province revealed that the drought condition was the most serious during the period from 1957 to 1970 with the SPI12 index in 49 months being less than −0.5 and 12 months with an extreme drought level. The overall drought intensity varied with the highest certainty level but lowest stability level in winter, but this was opposite in the summer. Moreover, drought hazard would be more significantly intensified along the elevation of latitude in Northern Anhui province. The overall drought hazard in Suzhou and Huaibei were the most serious, which is followed by Bozhou, Bengbu, and Fuyang. Drought intensity in Huainan was the lightest. The exploration results of drought evolution analysis were reasonable and reliable, which would supply an effective decision-making basis for establishing drought risk management strategies. Full article

This article presents a methodology to optimize the integration of local scale drainage measures in catchment modelling. The methodology enables to zoom into the processes (physically, spatially and temporally) where detailed physical based computation is required and to zoom out where lumped conceptualized approaches are applied. It allows the definition of parameters and computation procedures on different spatial and temporal scales. Three methods are developed to integrate features of local scale drainage measures in catchment modelling: (1) different types of local drainage measures are spatially integrated in catchment modelling by a data mapping; (2) interlinked drainage features between data objects are enabled on the meso, local and micro scale; (3) a method for modelling multiple interlinked layers on the micro scale is developed. For the computation of flow routing on the meso scale, the results of the local scale measures are aggregated according to their contributing inlet in the network structure. The implementation of the methods is realized in a semi-distributed rainfall-runoff model. The implemented micro scale approach is validated with a laboratory physical model to confirm the credibility of the model. A study of a river catchment of 88 km² illustrated the applicability of the model on the regional scale. Full article