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Abstract Background Stroke among younger age groups is increasing globally. While there is a focus on research conducted on people under 65 years who have had a stroke, there is a paucity of data on the incidence and risk factors of stroke among younger people (≤ 30 years). This scoping review examines evidence on incidence and risk factors for perinatal, paediatric and young adult stroke globally. Methods The review was guided by the Joanna Briggs Institute’s scoping review methodology. A systematic search was conducted on 23rd March 2022 across Medline Ovid, Embase, PsycINFO and Cumulative Index to Nursing and Allied Health Literature (CINAHL). The eligibility criteria included all study designs providing information on the incidence and risk factors of stroke among young people (≤ 30 years) in the last ten years. Results A total of 5750 articles were identified. After screening, 471 articles (224 cohort studies (47.6%), 164 case studies/case series (34.8%), 35 reviews (7.4%), 30 case-control (6.4%) and 18 combinations of designs (3.8%) were included. There was data from 50 different countries, 199 studies were from high-income countries, upper and middle income (n = 38), lower middle-income (n = 39), low-income (n = 3) countries, international study (n = 7) and a further 185 articles did not state the country of research. Most of the studies (63%) focused on risk factors while incidence constituted 37%. Incidence data were reported heterogeneously across studies, leading to an inability to synthesise data. The three most frequently reported risk factors for perinatal stroke were infections, cardiac conditions, and intrapartum factors. Vasculopathies, infection and cardiac conditions accounted for most reported risk factors for paediatric stroke, while chronic conditions such as diabetes mellitus, vasculopathies and cardiac conditions accounted for the most reported risk factors among young adults. Conclusion This review has highlighted different stroke risk factors for each age cohort of people under 30 years. The low number of epidemiological studies suggests that further research of this type is needed to fully understand the incidence and risk factors in young stroke. A standardised reporting of age groupings of incidence data is imperative to enable the comparison of data from different geographical locations.

Systematic reviews (SRs) are a rigorous method for synthesizing empirical evidence to answer specific research questions. However, they are labor-intensive because of their collaborative nature, strict protocols, and typically large number of documents. Large language models (LLMs) and their applications such as gpt-4/ChatGPT have the potential to reduce the human workload of the SR process while maintaining accuracy. We propose a new hybrid methodology that combines the strengths of LLMs and humans using the ability of LLMs to summarize large bodies of text autonomously and extract key information. This is then used by a researcher to make inclusion/exclusion decisions quickly. This process replaces the typical manually performed title/abstract screening, full-text screening, and data extraction steps in an SR while keeping a human in the loop for quality control. We developed a semi-automated LLM-assisted (Gemini-Pro) workflow with a novel innovative prompt development strategy. This involves extracting three categories of information including identifier, verifier, and data field (IVD) from the formatted documents. We present a case study where our hybrid approach reduced errors compared with a human-only SR. The hybrid workflow improved the accuracy of the case study by identifying 6/390 (1.53%) articles that were misclassified by the human-only process. It also matched the human-only decisions completely regarding the rest of the 384 articles. Given the rapid advances in LLM technology, these results will undoubtedly improve over time.

The current study explores the use of computer vision and artificial intelligence (AI) methods for analyzing 360-degree spherical video-based virtual reality (SVVR) data. The study aimed to explore the potential of AI, computer vision, and machine learning methods (including entropy analysis, Markov chain analysis, and sequential pattern mining), in extracting salient information from SVVR video data. The research questions focused on differences and distinguishing characteristics of autistic and neurotypical usage characteristics in terms of behavior sequences, object associations, and common patterns, and the extent to which the predictability and variability of findings might distinguish the two participant groups and provide provisional insights into the dynamics of their usage behaviors. Findings from entropy analysis suggest the neurotypical group showed greater homogeneity and predictability, and the autistic group displayed significant heterogeneity and variability in behavior. Results from the Markov Chains analysis revealed distinct engagement patterns, with autistic participants exhibiting a wide range of transition probabilities, suggesting varied SVVR engagement strategies, and with the neurotypical group demonstrating more predictable behaviors. Sequential pattern mining results indicated that the autistic group engaged with a broader spectrum of classes within the SVVR environment, hinting at their attraction to a diverse set of stimuli. This research provides a preliminary foundation for future studies in this area, as well as practical implications for designing effective SVVR learning interventions for autistic individuals.

Abstract Background COVID-19 is an important public health concern due to its high morbidity, mortality and socioeconomic impact. Its burden varies by geographic location affecting some communities more than others. Identifying these disparities is important for guiding health planning and service provision. Therefore, this study investigated geographical disparities and temporal changes of the percentage of positive COVID-19 tests and COVID-19 incidence risk in North Dakota. Methods COVID-19 retrospective data on total number of tests and confirmed cases reported in North Dakota from March 2020 to September 2021 were obtained from the North Dakota COVID-19 Dashboard and Department of Health, respectively. Monthly incidence risks of the disease were calculated and reported as number of cases per 100,000 persons. To adjust for geographic autocorrelation and the small number problem, Spatial Empirical Bayesian (SEB) smoothing was performed using queen spatial weights. Identification of high-risk geographic clusters of percentages of positive tests and COVID-19 incidence risks were accomplished using Tango’s flexible spatial scan statistic. ArcGIS was used to display and visiualize the geographic distribution of percentages of positive tests, COVID-19 incidence risks, and high-risk clusters. Results County-level percentages of positive tests and SEB incidence risks varied by geographic location ranging from 0.11% to 13.67% and 122 to 16,443 cases per 100,000 persons, respectively. Clusters of high percentages of positive tests were consistently detected in the western part of the state. High incidence risks were identified in the central and south-western parts of the state, where significant high-risk spatial clusters were reported. Additionally, two peaks (August 2020-December 2020 and August 2021-September 2021) and two non-peak periods of COVID-19 incidence risk (March 2020-July 2020 and January 2021-July 2021) were observed. Conclusion Geographic disparities in COVID incidence risks exist in North Dakota with high-risk clusters being identified in the rural central and southwest parts of the state. These findings are useful for guiding intervention strategies by identifying high risk communities so that resources for disease control can be better allocated to communities in need based on empirical evidence. Future studies will investigate predictors of the identified disparities so as to guide planning, disease control and health policy.

Introduction: Our institution was the first in the world to clinically implement MR-guided adaptive radiotherapy (MRgART) in 2014. In 2021, we installed a CT-guided adaptive radiotherapy (CTgART) unit, becoming one of the first clinics in the world to build a dual-modality ART clinic. Herein we review factors that lead to the development of a high-volume dual-modality ART program and treatment census over an initial, one-year period. Materials and Methods: The clinical adaptive service at our institution is enabled with both MRgART (MRIdian, ViewRay, Inc, Mountain View, CA) and CTgART (ETHOS, Varian Medical Systems, Palo Alto, CA) platforms. We analyzed patient and treatment information including disease sites treated, radiation dose and fractionation, and treatment times for patients on these two platforms. Additionally, we reviewed our institutional workflow for creating, verifying, and implementing a new adaptive workflow on either platform. Results: From October 2021 to September 2022, 256 patients were treated with adaptive intent at our institution, 186 with MRgART and 70 with CTgART. The majority (106/186) of patients treated with MRgART had pancreatic cancer, and the most common sites treated with CTgART were pelvis (23/70) and abdomen (20/70). 93.0% of treatments on the MRgART platform were stereotactic body radiotherapy (SBRT), whereas only 72.9% of treatments on the CTgART platform were SBRT. Abdominal gated cases were allotted a longer time on the CTgART platform compared to the MRgART platform, whereas pelvic cases were allotted a shorter time on the CTgART platform when compared to the MRgART platform. Our adaptive implementation technique has led to six open clinical trials using MRgART and seven using CTgART. Conclusions: We demonstrate the successful development of a dual platform ART program in our clinic. Ongoing efforts are needed to continue the development and integration of ART across platforms and disease sites to maximize access and evidence for this technique worldwide.

Abstract Fully and accurately reconstructing changes in oceanic productivity and carbon export and their controls is critical to determining the efficiency of the biological pump and its role in the global carbon cycle through time, particularly in modern CO2 source regions like the eastern equatorial Pacific (EEP). Here we present new high-resolution records of sedimentary 230Th-normalized opal and nannofossil carbonate fluxes and [231Pa/230Th]xs ratios from site MV1014-02-17JC in the Panama Basin. We find that, across the last deglaciation, phytoplankton community structure is driven by changing patterns of nutrient (nitrate, iron, and silica) availability which, in turn, are caused by variability in the position of the Intertropical Convergence Zone (ITCZ) and associated changes in biogeochemical cycling and circulation in the Southern Ocean. Our multi-proxy work suggests greater scrutiny is required in the interpretation of common geochemical proxies of productivity and carbon export in the EEP.

Purpose: There is a vital need to train radiation therapy professionals in low- and middle-income countries (LMICs) to develop sustainable cancer treatment capacity and infrastructure. LMICs have started to introduce intensity modulated radiation therapy (IMRT), which is the standard of care in high-income countries, because of improved outcomes and reduced toxicities. This work reports the efficacy of a complementary asynchronous plus synchronous virtual-training approach on improving radiation therapy professions’ self-confidence levels and evaluating participants’ attitudes toward asynchronous and synchronous didactic hands-on learning in 3 LMICs. Methods and Materials: Training was provided to 37 participants from Uganda, Guatemala, and Mongolia, which included 4 theoretical lectures, 4 hands-on sessions, and 8 self-guided online videos. The 36-day training focused on IMRT contouring, site-specific target/organ definition, planning/optimization, and quality assurance. Participants completed pre- and postsession confidence surveys on a 0 to 10 scale, which was converted to a 5-point Likert rating scale to evaluate the training outcomes. The pros and cons of the 3 different training formats were compared. Results: The participants included 15 (40.5%) radiation oncologists, 11 (29.7%) medical physicists, 6 (16.2%) radiation therapists, and 5 (13.5%) dosimetrists. Approximately 50% had more than 10 years of radiation therapy experience, 70.8% had no formal IMRT training, and only 25% had IMRT at their institutions. The average experience and confidence levels in using IMRT at baseline were 3.2 and 2.9, which increased to 5.2 and 4.9 (P < .001) after the theoretical training. After the hands-on training, the experience and confidence levels further improved to 5.4 and 5.5 (P < .001). After the self-guided training, the confidence levels increased further to 6.9 (P < .01). Among the 3 different training sessions, hands-on trainings (58.3%) were most helpful for the development of participants’ IMRT skills, followed by theoretical sessions with 25%. Conclusions: After completing the training sessions, Uganda and Mongolia started IMRT treatments. Remote training provides an excellent and feasible e-learning platform to train radiation therapy professionals in LMICs. The training program improved the IMRT confidence levels and treatment delivery. The hands-on trainings were most preferred.

BackgroundAlthough the burden of the coronavirus disease 2019 (COVID-19) has been different across communities in the US, little is known about the disparities in COVID-19 burden in North Dakota (ND) and yet this information is important for guiding planning and provision of health services. Therefore, the objective of this study was to identify geographic disparities of COVID-19 hospitalization risks in ND.MethodsData on COVID-19 hospitalizations from March 2020 to September 2021 were obtained from the ND Department of Health. Monthly hospitalization risks were computed and temporal changes in hospitalization risks were assessed graphically. County-level age-adjusted and spatial empirical Bayes (SEB) smoothed hospitalization risks were computed. Geographic distributions of both unsmoothed and smoothed hospitalization risks were visualized using choropleth maps. Clusters of counties with high hospitalization risks were identified using Kulldorff's circular and Tango's flexible spatial scan statistics and displayed on maps.ResultsThere was a total of 4,938 COVID-19 hospitalizations during the study period. Overall, hospitalization risks were relatively stable from January to July and spiked in the fall. The highest COVID-19 hospitalization risk was observed in November 2020 (153 hospitalizations per 100,000 persons) while the lowest was in March 2020 (4 hospitalizations per 100,000 persons). Counties in the western and central parts of the state tended to have consistently high age-adjusted hospitalization risks, while low age-adjusted hospitalization risks were observed in the east. Significant high hospitalization risk clusters were identified in the north-west and south-central parts of the state.ConclusionsThe findings confirm that geographic disparities in COVID-19 hospitalization risks exist in ND. Specific attention is required to address counties with high hospitalization risks, especially those located in the north-west and south-central parts of ND. Future studies will investigate determinants of the identified disparities in hospitalization risks.

This study explored how to program for generalization using a fully immersive virtual reality (VR) intervention for teaching transportation skills to autistic adults related to using a university shuttle. Using multi-methods, this research sought to identify similarities and differences in behavior between the VR environment and the real-world, participants’ perceptions of telepresence in the VR environment, and how participants characterized similarities and differences between the VR environment and the real-world. Male adult participants (N ​= ​6) completed training and then engaged in two VR-based training sessions of increasing complexity, after which they enacted what was learned in the real-world. Fidelity of implementation was high across VR and real-world sessions and no significant differences were found in behaviors between the VR and real-world sessions, providing evidence for skills generalization from the contrived VR setting to the naturalistic real-world setting. Participants reported high perceptions of telepresence (e.g., being there) and social presence (e.g., being there with others), and qualitative evidence suggests they made connections between the virtual world and real-world. Implications and directions for future research are discussed.

Objective: This study aimed to determine if a novel intervention that combined individualised exercise training with behaviour change counselling based on Health Action Process Approach (HAPA) constructs could elicit long-term increase in physical activity (PA) and reduce comorbidity development among people requiring hip or knee arthroplasty. Method: A pre-registered two arm, parallel group, randomised controlled trial comparing the effect of a 12-week individualised exercise program combined with behavioural counselling delivered by accredited exercise physiologists, versus usual care to Osteoarthritis (OA) patients on public surgery waitlists. Participants were followed up at 6 months after baseline (pre-surgery) and again at 6 months post-surgery. Within and between group differences in post-surgery PA (as measured by ActivPal accelerometer), pain, function, quality of life, HAPA-based behavioural and psychological constructs, and health risk factors were analysed. Results: 63 participants (34 Female; Mean age ​= ​66.4 ​± ​7.2 ​yrs) consented to participate in this study. At 6 months post baseline and 6 months post-surgery there were significant improvements in PA, pain, function, and quality of life, however there were no significant differences in the between group responses. Significant between group changes were observed in several psychological constructs related to volition at 6 months post baseline; however, these had disappeared by 6 months post-surgery. Conclusions: An exercise program and HAPA guided counselling intervention can improve psychological constructs related to exercise behaviour; however, these did not result in significant between group changes in PA at the timepoints measured. Further research with larger sample size is required.Trial Registration: Australian New Zealand Clinical Trials Registry (ACTRN 12617000357358) Date of registration: 08/03/2017.

Egypt’s power system is facing major changes. Rising demand, scarcity of fossil resources and the falling capital costs of solar and wind energy technologies pose the potential to fundamentally remake the national power fleet. Against this backdrop, the paper at hand investigates potential pathways of the domestic power system with a particular focus on regional deployment and associated impacts on the transmission network. To this end, the open-source modelling framework OSeMOSYS was adapted and deployed. The analysis evaluates renewable energy potentials for 320 sites across Egypt in eight different sub-regions. The study concludes that wind and PV installations prove to be cost-competitive and capable of shouldering a large share of projected demand growth, while their regional deployment should take required grid investments into consideration. The results also indicate that a restrained expansion of renewable energy technologies targeted by the government proves to be more costly than a more aggressive deployment pathway.

Smart workplace Internet of Things (IoT) solutions rely on several sensors deployed efficiently in the workplace environment to collect accurate data to meet system goals. A vital issue for these sensor-based IoT solutions is privacy. Ideally, the occupants must be monitored discreetly, and the strategies for maintaining privacy are dependent on the nature of the data required. This paper proposes a new sensor design approach for IoT solutions in the workplace that protects occupants’ privacy. We focus on a novel sensor that autonomously detects and captures human movements in the office to monitor a person’s sedentary behavior. The sensor guides an eHealth solution that uses continuous feedback about desk behaviors to prompt healthy movement breaks for seated workers. The proposed sensor and its privacy-preserving characteristics can enhance the eHealth solution system’s performance. Compared to self-reporting, intrusive, and other data collection techniques, this sensor can collect the information reliably and timely. We also present the data analysis specific to this new sensor that measures two physical distance parameters in real-time and uses their difference to determine human actions. This architecture aims to collect precise data at the sensor design level rather than to protect privacy during the data analysis phase.

This study presents a strengths-based framework for designing virtual reality experiences tailored to the needs and abilities of autistic individuals. Recognizing the potential of virtual reality to provide engaging and immersive learning environments, the framework aligns the strengths and preferences of autistic users with the affordances of virtual reality platforms. Drawing on the existing literature and empirical findings, the framework highlights key areas of alignment, including visual perception, anxiety management, attention to differences, concrete thinking, and response to positive feedback. The framework emphasizes the importance of involving autistic individuals in the co-design and co-creation of virtual reality technologies to ensure a more tailored and preferred user experience. By adopting a strengths-based approach and actively involving autistic individuals, the design and implementation of virtual reality interventions can better address their unique needs and foster positive outcomes. The study concludes by advocating for continued research and collaboration to advance the field of virtual reality technology for autistic individuals and to work toward shared goals with the autistic community.

This paper explores the adoption of an entangled eclecticism perspective in Learning Experience Design (LXD), integrating a sociotechnical-pedagogical systems approach. It emphasizes the significance of considering the sociocultural, technological, and pedagogical dimensions of learning as a cohesive, interconnected ecology to design effective learning experiences. The authors discuss the implications of this perspective on learning outcomes, methodologies, and the selection of relevant theories for design. The aim is to guide the creation of learning experiences that are not only effective, efficient, and appealing but also deeply empathetic, culturally sensitive, and responsive to the evolving dynamics of learning environments. Concluding that LXD is a complementary approach to traditional learning design, the paper underscores its evolution, drawing from multiple traditions to offer a holistic framework for educational design and technology. This approach strives for learning experiences that are not just effective and efficient, but also culturally sensitive and empathetic, adapting to the dynamic nature of learning environments. Broader implications of this approach and directions for future research are discussed.

We report findings from an eDelphi study that aimed to explore 16 expert panelists' perspectives regarding the key attributes of learning experience design (LXD) as it relates to the following: design, disciplines, methods, and theory. Findings suggest consensus was reached regarding LXD's focus on learner-centrism and incorporating human-centered design practices to design learning environments. LXD practitioners adapt methods and theories from fields such as human--computer interaction and user experience. Implications suggest a need to develop specific methods and theories within our own field.

Interest in the use of virtual reality technologies for individuals with autism spectrum disorders has been increasing for over two decades. Recently, research interest has been growing in the area of head mounted display-based virtual reality technologies, thanks to increased availability and affordability. Affordances and theorized benefits of headset-based virtual reality for individuals with autism spectrum disorders are quite promising. However, very little attention has been given in the literature to implementation safety and ethics. This is a particular concern in light of documented adverse effects associated with headset-based virtual reality. To approach this gap, this article details how the authors approached the issue of minimizing adverse effects with related and overlapping methods, but from two separate, independent research sites—one in the United States and one in the United Kingdom. A structured within- and across-case analysis of the two independent studies was conducted to identify central implementation processes and procedures. Analysis resulted in development of a model for minimizing potential adverse effects of headset-based virtual reality for this population. We assert that our model could provide clarity in terms of design and implementation of headset-based virtual reality for individuals with autism spectrum disorders, guide implementations of future researchers and practitioners, and contribute to minimizing and controlling for potential adverse effects.

Dosimetric accuracy of a volumetric modulated arc therapy (VMAT) plan is directly related to the beam model, particularly with multileaf collimator characterization. Inappropriate dosimetric leaf gap (DLG) value can lead to a suboptimal beam model, with significant failure in patient-specific quality assurance (PSQA) of VMAT plans. This study addressed the systematic issue of beam modeling and developed a practical method to determine the optimal DLG value for a beam model. Several complex VMAT plans were selected for the quality assurance analysis using the variable DLG values. The results of three-dimensional (3D) Gamma analysis as a function of the DLG at 3%/3 mm, 2%/2 mm, and 1%/1 mm criteria were fitted by a polynomial curve. The DLG value corresponding to the maximum Gamma passing rate for each polynomial fitting function was derived, and the average was calculated to be the optimal DLG value for each model. The 3D Gamma analysis was repeated with the optimal DLG value to verify the dosimetric accuracy of each VMAT case by PSQA. Gamma passing rates are seen to vary considerably with the DLG values and different analysis criteria (3%/3 mm, 2%/2 mm, and 1%/1 mm) for each case. The optimal DLG derived for each model was 1.16 mm and 1.10 mm, much larger than the measured value (about 0.3 mm). The beam models with the optimal DLG was able to produce an average Gamma passing rate of 97.1% (range, 94.6%– 99.1%) at 3%/3 mm and 93.5% (range, 89.0%– 96.5%) at 2%/2 mm for one beam model, and 97.1% (range, 94.8%– 99.1%) at 3%/3 mm, and 93.3% (range, 88.8%– 96.7%) at 2%/2 mm for another. The overall accuracy of dose calculation for VMAT plans should be optimized with a compromise of varied modulation complexities in a beam model. We have developed a practical method to derive the optimal DLG value for each beam model based on the Gamma passing criterion. This technique should be applicable in general for all beam energies and patient cases.

The purpose of this study was to explore the competencies of those professionals working in the nascent area of Learning Experience Design (LXD). Following systematic procedures and inspiration from our conceptual framework of LXD that emphasizes knowledge, skill, and ability statements (KSA), we collected and coded N = 388 unique LXD job announcements over a four-month period. Using LXD job announcements as the unit of analysis, this research used a concurrent mixed-methods approach first to qualitatively code LXD job announcements on the presence or absence of a range KSA statements and then used Exploratory Factor Analysis (EFA) for dichotomously scored data to discern the core competencies required among LXD professionals. Our processes resulted in a total of 69 knowledge statements, 38 skills statements, and 72 ability statements. Our findings unveil core competencies across the KSA domains such as knowledge of multimedia production technologies and software, emerging technology design and development skills, and the ability to apply human-centered design to create learning products and experiences. We discuss our findings in relation to the more established domain of instructional design (ID) and present potentially unique facets of LXD in this regard. Limitations and delimitations are provided along with closing remarks.

Germany has experienced rapid growth in its renewable electricity generation capacity in the past fifteen years. This development has been accompanied by a drop in wholesale electricity prices and significant net export surpluses. This situation has given rise to discussions in policy circles concerning the drivers behind these dynamics. The paper at hand empirically analyzes historical cross-border commercial flows (CBCF) of electricity from Germany by applying a Ridge Regression model to parse the effects of the growing shares of renewable energy capacity on the commercial electricity exchange between 2012 and 2016. In case of PV, the fraction of PV generation (marginal effect of PV on export) that is exported correlates positively with the level of PV generation. However, the marginal effect of wind on export is negatively correlated with the level of wind generation. A possible explanation for this finding as it relates to wind feed-in could involve the high degree to which the grid is constrained at times of high wind penetration.