Model Assessment Research Articles

A Systematic Review of Simulation in Burn Care: Education, Assessment, and Management.

Assessment and management of burns require nuanced, timely interventions in high-stake settings, creating challenges for trainees. Simulation-based education has become increasingly popular in surgical and nonsurgical subspecialties to supplement training without compromising patient safety. This study aimed to systematically review the literature on existing burn management-related simulations. A systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Articles describing burn-specific surgical and nonsurgical simulation models were included. The model type, study description, simulated skills, assessment methods, fidelity, cost, and outcomes were collected. Of 3,472 articles, 31 met inclusion criteria. The majority of simulations were high-fidelity (n=17, 54.8%). Most were immersive (n=17, 54.8%) and used synthetic benchtop models (n=13, 41.9%), whereas none were augmented/virtual reality. Simulations of acute and early surgical intervention techniques (n=16, 51.6%) and burn wound assessments (n=15, 48.4%) were the most common, whereas burn reconstruction was the least common (n=3, 9.7%). Technical skills were taught more often (n=29, 93.5%) than non-technical skills (n=15, 48.4%). Subjective assessments (n=18, 58.1%) were used more often than objective assessments (n=23, 74.2%). Of the studies that reported costs, 91.7% (n=11) reported low costs. This review identified the need to expand burn simulator options, especially for burn reconstruction, and highlighted the paucity of animal, cadavers, and augmented/virtual reality models. Developing validated, accessible burn simulations to supplement training may improve education, patient safety, and outcomes.

Associations of vegetable and potato intakes with markers of type 2 diabetes risk in the AusDiab cohort.

The associations of vegetable and potato intakes with type 2 diabetes (T2D) appear to be nuanced, depending on vegetable types and preparation method, respectively. We investigated the associations of total vegetable, vegetable subgroup, and potato intakes with 1) markers of T2D at baseline and 2) incident T2D cumulative over a 12-year follow-up period in Australian adults. Using data from the Australian Diabetes, Obesity and Lifestyle Study, intakes of vegetables and potatoes were assessed via a food frequency questionnaire at baseline. Associations between vegetable intake and 1) fasting plasma glucose (FPG), 2-hour post load plasma glucose (PLG), updated homeostasis model assessment of β-cell function (HOMA2-%β), HOMA2 of insulin sensitivity (HOMA2-%S), and fasting insulin levels at baseline and 2) cumulative incident T2D at the end of 12-year follow-up were examined using generalized linear and Cox proportional hazards models, respectively. In total, 8,009 participants were included having median age of 52 years, and vegetable intake of 132 g/day. Higher intake of total vegetable, green leafy, yellow/orange/red, and moderate intakes of cruciferous vegetables was associated with lower PLG. Additionally, higher green leafy vegetable intake was associated with lower HOMA2-%β and serum insulin. Conversely, higher potato fries/chips intakes were associated with higher FPG, HOMA2-%β, serum insulin, and lower HOMA2-%S. Participants with moderate cruciferous vegetables intake had a 25% lower risk of T2D at the end of 12 years follow-up. A higher intake of vegetables, particularly green leafy vegetables, may improve while consuming potato fries/chips, but not potatoes prepared in a healthy way, may worsen glucose tolerance and insulin sensitivity. Our findings suggest a nuanced relationship between vegetable subgroups and their impact on glucose tolerance.

Associations of serum D-dimer and glycosylated hemoglobin levels with third-trimester fetal growth restriction in gestational diabetes mellitus

BACKGROUND Gestational diabetes mellitus (GDM) is a special type of diabetes that commonly occurs in women during pregnancy and involves impaired glucose tolerance and abnormal glucose metabolism; GDM is diagnosed for the first time during pregnancy and can affect fetal growth and development. AIM To investigate the associations of serum D-dimer (D-D) and glycosylated hemoglobin (HbA1c) levels with third-trimester fetal growth restriction (FGR) in GDM patients. METHODS The clinical data of 164 pregnant women who were diagnosed with GDM and delivered at the Obstetrics and Gynecology Hospital of Fudan University from January 2021 to January 2023 were analyzed retrospectively. Among these women, 63 whose fetuses had FGR were included in the FGR group, and 101 women whose fetuses had normal body weights were included in the normal body weight group (normal group). Fasting venous blood samples were collected from the elbow at 28-30 wk gestation and 1-3 d before delivery to measure serum D-D and HbA1c levels for comparative analysis. The diagnostic value of serum D-D and HbA1c levels for FGR was evaluated by receiver operating characteristic analysis, and the influencing factors of third-trimester FGR in GDM patients were analyzed by logistic regression. RESULTS Serum fasting blood glucose, fasting insulin, D-D and HbA1c levels were significantly greater in the FGR group than in the normal group, while the homeostasis model assessment of insulin resistance values were lower (P < 0.05). Regarding the diagnosis of FGR based on serum D-D and HbA1c levels, the areas under the curves (AUCs) were 0.826 and 0.848, the cutoff values were 3.04 mg/L and 5.80%, the sensitivities were 81.0% and 79.4%, and the specificities were 88.1% and 87.1%, respectively. The AUC of serum D-D plus HbA1c levels for diagnosing FGR was 0.928, and the sensitivity and specificity were 84.1% and 91.1%, respectively. High D-D and HbA1c levels were risk factors for third-trimester FGR in GDM patients (P < 0.05). CONCLUSION D-D and HbA1c levels can indicate the occurrence of FGR in GDM patients in the third trimester of pregnancy to some extent, and their combination can be used as an important index for the early prediction of FGR.

Uncertain pedestrian load modeling for structural vibration assessment in footbridge design

The request of material savings in bridge constructions leads to the development towards slender and lightweight structures, which are more sensitive to human-induced vibrations caused by walking pedestrians. The resulting accelerations do not endanger the structural safety, but can be perceived as unpleasant for the user and must therefore be restricted within the limit state of serviceability. For this purpose, the guidelines define acceleration intervals, called as “comfort levels” (CL), which evaluate the comfort of the pedestrians, given the maximum acceleration of the bridge. The model for human-induced loads in the design guideline is based on conservative and deterministic simplifications. In particular, the uncertainties in the human gait parameters are neglected. The main objective of this paper is to develop an uncertain load model for walking pedestrians based on Fourier series. The aleatory and epistemic uncertainties in the human gait parameters are quantified with appropriate uncertainty models relying on available data. In a dynamical finite element analysis, the model is used to simulate groups of pedestrians randomly walking over a bridge to calculate the resulting acceleration amplitudes. The approach is applied to a single span beam and to a real world footbridge using a 3D finite element model. The results are then evaluated within the CLs, enabling new assessment methods.

A novel colorectal cancer screening framework with feature interpretability to identify high-risk populations for colonoscopy.

Risk assessment is of paramount importance for the detection and treatment of colorectal cancer. We developed and validated a feature interpretability screening framework to identify high-risk populations and recommend colonoscopy for them. We utilized a training cohort consisting of 1252605 participants who underwent colonoscopies in Shanghai from 2013 to 2015 to develop the screening framework. We incorporated Shapley additive explanation values into feature selection to provide interpretability for the framework. Two sampling methods were separately employed to mitigate potential model bias caused by class imbalance. Furthermore, we employed various machine learning algorithms to construct risk assessment models and compared their performance. We tested the screening models on an external validation cohort of 359462 samples and conducted comprehensive evaluation and statistical analysis of the validation results. The external validation results demonstrated that the models in the proposed framework achieved sensitivity over 0.734, specificity over 0.790, and area under the receiver operating characteristic curve ranging from 0.808 to 0.859. In the predictions of the best-performing model, the prevalence rates of colorectal cancer were 0.059% and 1.056% in the low- and high-risk groups, respectively. If colonoscopies were performed only on the high-risk group predicted by the model, only 14.36% of total colonoscopies would be needed to detect 74.86% of colorectal cancer cases. We developed and validated a novel framework to identify populations at high risk for colorectal cancer. Those classified as high risk should undergo colonoscopy for further diagnosis.

An Expert-Knowledge-based Graph Convolutional Network for Skeleton-based Physical Rehabilitation Exercises Assessment.

Physical therapists play a crucial role in guiding patients through effective and safe rehabilitation processes according to medical guidelines. However, due to the therapist-patient imbalance, it is neither economical nor feasible for therapists to provide guidance to every patient during recovery sessions. Automated assessment of physical rehabilitation can help with this problem, but accurately quantifying patients' training movements and providing meaningful feedback poses a challenge. In this paper, an Expert-knowledge-based Graph Convolutional approach is proposed to automate the assessment of the quality of physical rehabilitation exercises. This approach utilizes experts' knowledge to improve the spatial feature extraction ability of the Graph Convolutional module and a Gated pooling module for feature aggregation. Additionally, a Transformer module is employed to capture long-range temporal dependencies in the movements. The attention scores and weight matrix obtained through this approach can serve as interpretability tools to help therapists understand the assessment model and assist patients in improving their exercises. The effectiveness of the proposed method is verified on the KIMORE dataset, achieving state-of-the-art performance compared to existing models. Experimental results also illustrate the interpretability of the method in both spatial and temporal dimensions.

Development and Assessment of a Prediction Model for Alzheimer's Disease Diagnosis Based on Thermoregulation-Related Genes.

Alzheimer's disease (AD) is a prevalent neurodegenerative condition among the elderly population and the most common form of dementia, however, we lack potent interventions to arrest its inherent pathogenic vectors. Robust evidence indicates thermoregulatory perturbations during and before the onset of symptoms. Therefore, temperature-regulated biomarkers may offer clues to therapeutic targets during the presymptomatic stage. The purpose of this study is to develop and assess a thermoregulation-related gene prediction model for Alzheimer's Disease diagnosis. This study aims to utilize microarray bioinformatic analysis to identify the potential biomarkers of AD by analyzing four microarray datasets (GSE48350, GSE5281, GSE122063, and GSE181279) of AD patients. Furthermore, thermoregulation-associated hub genes were identified, and the expression patterns in the brain were explored. In addition, we explored the infiltration of immune cells with thermoregulation-related hub genes. Diagnostic marker validation was then performed at the single-cell level. Finally, the prediction of targeted drugs was performed based on the hub genes. Through the analysis of four datasets pertaining to AD, a total of five genes associated with temperature regulation were identified. Notably, CCK, CXCR4, SLC27A4, and SLC17A6 emerged as diagnostic markers indicative of AD-related brain injury. Furthermore, in the examination of peripheral blood samples from AD patients, SLC27A4 and CXCR4 were identified as pivotal diagnostic indicators. Regrettably, animal experimentation was not pursued to validate the data; rather, an assessment of temperature regulation-related genes was conducted. Future investigations will be undertaken to establish the correlation between these genes and AD pathology. Overall, CCK, CXCR4, SLC27A4, and SLC17A6 can be considered pivotal biomarkers for diagnosing the pathogenesis and molecular functions of AD.

Residual emissions and carbon removal towards Japan’s net-zero goal: A multi-model analysis

Abstract We study Japan’s net-zero emissions target by 2050 in a multi-model framework, focusing on residual emissions and carbon dioxide removal (CDR). Four energy-economic and integrated assessment models show similar but stronger strategies for the net-zero target, compared to the previous, low-carbon policy target (80% emissions reduction). Results indicate that around 90% (inter-model median) of the current emissions are reduced through abatement, including improved energy efficiency and cleaner electricity and fuels. Models deploy new options such as CDR based on carbon capture and storage (CCS) (bioenergy with CCS and direct air carbon dioxide capture and storage) and hydrogen to achieve net zero. The scale of CCS-based CDR deployment reaches an inter-model median of 132Mt-CO2/yr. The median hydrogen share of final energy in 2050 increases from 0.79% to 6.9% between the low-carbon and net-zero scenarios. The CDR sensitivity analysis reveals that limiting the use of CDR significantly increases the mitigation costs for net zero. Achieving Japan’s net-zero goal will require exploring methods to reduce residual emissions, including demand-side solutions, and accelerating responsible CDR policies.

An interpretable model for landslide susceptibility assessment based on Optuna hyperparameter optimization and Random Forest

This study proposed an interpretable model that combines Random Forest (RF), Optuna hyperparameter optimization, and SHapley Additive exPlanations (SHAP) to achieve optimal landslide susceptibility evaluation and provide explanations in the northwest region of Yunnan Province in China. First, an inventory of 4447 landslides and 23 related factors was considered for the landslide susceptibility assessment. Subsequently, a hyperparameter-optimized RF model was developed using the Optuna framework and the training dataset to generate landslide susceptibility maps. The performance of the models were evaluated using accuracy (ACC), precision (PPV), recall (TPR), F1-score (F1), and the Area Under the Curve (AUC) based on the Receiver Operating Characteristic. Furthermore, the interpretability of the model was enhanced through the implementation of SHAP. The proposed model demonstrated outstanding performance on the test set, achieving an ACC of 0.7792, PPV of 0.7448, TPR of 0.8769, F1 of 0.8055, and an AUC of 0.8387. The interpretability analysis revealed that elevation, population density, distance from roads, and normalized difference vegetation index were the primary factors influencing landslide occurrences in the study area. This study provides a comprehensive framework for evaluating landslide susceptibility in specific regions and offers invaluable insights for the prevention and management of landslide disasters.

An Efficient Water Quality Prediction and Assessment Method Based on the Improved Deep Belief Network—Long Short-Term Memory Model

The accuracy of water quality prediction and assessment has always been the focus of environmental departments. However, due to the high complexity of water systems, existing methods struggle to capture the future internal dynamic changes in water quality based on current data. In view of this, this paper proposes a data-driven approach to combine an improved deep belief network (DBN) and long short-term memory (LSTM) network model for water quality prediction and assessment, avoiding the complexity of constructing a model of the internal mechanism of water quality. Firstly, using Gaussian Restricted Boltzmann Machines (GRBMs) to construct a DBN, the model has a better ability to extract continuous data features compared to classical DBN. Secondly, the extracted time-series data features are input into the LSTM network to improve predicting accuracy. Finally, due to prediction errors, noise that randomly follows the Gaussian distribution is added to the assessment results based on the predicted values, and the probability of being at the current water quality level in the future is calculated through multiple evolutionary computations to complete the water quality assessment. Numerical experiments have shown that our proposed algorithm has a greater accuracy compared to classical algorithms in challenging scenarios.

Life cycle assessment of electric kick scooters: consolidating environmental impact quantification and concluding climate-friendly use options

BackgroundThe environmental impact of electric scooters has been the subject of critical debate in the scientific community for the past 5 years. The data published so far are very inhomogeneous and partly methodologically incomplete. Most of the data available in the literature suffer from an average bias of 34%, because end-of-life (EOL) impacts have not been modelled, reported or specified. In addition, the average lifetime mileage of shared fleets of e-scooters, as they are operated in cities around the world, has recently turned out to be much lower than expected. This casts the scooters in an unfavourable light for the necessary mobility transition. Data on impact categories other than the global warming potential (GWP) are scarce. This paper aims to quantify the strengths and weaknesses of e-scooters in terms of their contribution to sustainable transport by more specifically defining and extending the life cycle assessment (LCA) modelling conditions: the modelling is based on two genuine material inventories obtained by dismantling two different e-scooters, one based on a traditional aluminium frame and another, for the first time, based on plastic material.ResultsThis study provides complete inventory data to facilitate further LCA modelling of electric kick scooters. The plastic scooter had a 26% lower lifetime GWP than the aluminium vehicle. A favourable choice of electric motor promises a further reduction in GWP. In addition to GWP, the scooter's life cycles were assessed across seven other impact categories and showed no critical environmental or health impacts compared to a passenger car. On the other hand, only the resource extraction impact revealed clear advantages for electric scooters compared to passenger cars.ConclusionsUnder certain conditions, scooters can still be an important element of the desired mobility transition. To assure a lifetime long enough is the crucial factor to make the electric scooter a favourable or even competitive vehicle in a future sustainable mobility system. A scooter mileage of more than 5400 km is required to achieve lower CO2eq/pkm emissions compared to passenger cars, which seems unlikely in today's standard use case of shared scooter fleets. In contrast, a widespread use of e-scooters as a commuting tool is modelled to be able to save 4% of greenhouse gas (GHG) emissions across the German mobility sector.

An Examination of Mental Stress in College Students: Utilizing Intelligent Perception Data and the Mental Stress Scale

In order to solve the problems of traditional mental stress detection in college students that are time-consuming, random, and subjective, this paper proposes an intelligent perception-driven mental stress assessment method for college students. First, we analyze the factors in SRQ and SCL-90, which can be measured by intelligent sensing methods, including sleep, exercise, social interaction, and environment, and then perform feature extraction. Secondly, we use machine learning methods to build a mental stress assessment model. The Shapley additive explanations (SHAP) model is used to explain the training results. Experimental results show that the model proposed in this article can effectively assess the mental stress state of college students. This means that the collection of intelligent perception data based on the mental stress scale can effectively evaluate the mental stress state of college students and provide a new research idea for further developing a non-intrusive and real-time mental stress assessment for college students.

Fatigue enhancement of welded thin-walled tubular joints made of lean duplex steel

In the present work, the fatigue strength and enhancement techniques of welded thin-walled (wall thickness of t = 2 mm) square hollow section (SHS) joints made of lean duplex stainless steel (grade EN 1.4162) are experimentally and numerically studied. Experimental fatigue tests were carried out on gas metal arc-welded tubular X-joints subjected to fully reversed (applied stress ratio of R = −1) constant amplitude in-plane bending moment. Geometrical improvement techniques were investigated by applying fully penetrating fillet welds and using structural reinforcements. The applications of high-frequency mechanical impact (HFMI) treatment were examined in the context of further enhancing the fatigue strength of the geometrically improved joints. In addition to the experimental work carried out in this study, the fatigue test data of welded thin-walled SHS and rectangular hollow section (RHS) joints were extracted from literature. Structural stress concentration factors were numerically and analytically obtained to validate the fatigue assessment model for welded thin-walled tubular joints using the structural hot-spot stress method. In addition, the effective notch stress (ENS) concept with the reference radius of rref = 0.05 mm was implemented. Compared to the joints in the as-welded state, fatigue strength improvements of 1.53–2.7 were found in the geometrically improved joints. On the other hand, the HFMI treatment did not lead to an additional increase in the fatigue strength capacity of the studied AW joints. The CIDECT guidelines for the structural hot-spot stress method were found conservative for thin-walled joints (t < 3 mm) and could be recommended for fatigue design.

A review of explainable fashion compatibility modeling methods

The paper reviews methods used in the fashion compatibility recommendation domain. We select methods based on reproducibility, explainability, and novelty aspects and then organize them chronologically and thematically. We presented general characteristics of publicly available datasets that are related to the fashion compatibility recommendation task. Finally, we analyzed the representation bias of datasets, fashion-based algorithms’ sustainability, and explainable model assessment. The paper describes practical problem explanations, methodologies, and published datasets that may serve as an inspiration for further research. The proposed structure of the survey organizes knowledge in the fashion recommendation domain and will be beneficial for those who want to learn the topic from scratch, expand their knowledge, or find a new field for research. Furthermore, the information included in this paper could contribute to developing an effective and ethical fashion-based recommendation system.

Flood Disaster Risk Assessment in Wuhan City Based on GIS Analysis and Indicator Ranking Using Random Forest

In recent years, with the acceleration of urbanization and the frequent occurrence of extreme weather globally, the risk of urban flood disasters has gradually increased, and its potential consequences are immeasurable. Therefore, conducting risk assessment of urban flood disasters is of great significance, as it is one of the foundations and decision-making means for Disaster Prevention and Mitigation, and has become a hot topic and trend in current research. This paper starts by exploring the concept and formation mechanism of urban flood disasters, taking Hazard Factors, Disaster-prone Environment sensitivity, Vulnerability of Exposed Bodies, and Disaster Prevention and Mitigation Capabilities as primary indicators. Based on this, a risk assessment index system is established with 14 secondary indicators, such as annual average rainfall, distance to water systems, elevation, and terrain undulation. The spatialization of each indicator data point is processed through ArcGIS10.7, and the importance of hazard and sensitivity indicators is ranked using the Random Forest algorithm. The indicators are then weighted using a combination of the Analytic Hierarchy Process (AHP) and the entropy method, and the combined weights of each assessment indicator are calculated. Taking Wuhan City as the research area, the weights of each indicator are input into the established risk assessment model. ArcGIS spatial analysis techniques and raster calculation functions are utilized to solve the fuzzy comprehensive evaluation of the assessment model, obtaining zoning maps of risk levels for hazard, sensitivity, vulnerability, disaster prevention, and mitigation capabilities, as well as the distribution of comprehensive risk levels. The validity and rationality of the model results are verified by actual disaster data, providing important reference for urban flood disaster prevention in the future.

Factors Affecting Customer Satisfaction in the Retail Formats in Rupandehi

This paper aims to investigate the Factors Affecting Customer Experience in the Retail Formats In Rupandehi district of Nepal. Primary data have been collected through structured questionnaire using online method i.e. Google Forms out of 384 different retail format users district of Nepal. A descriptive Research and causal comparative design was used and sample was selected by purposive sampling method. Smart PLS 4 is used as a data analysis tool and various tests like descriptive analysis, Measurement Model Assessment, Convergent Validity and Discriminant Validity, Model Fit Assessment, Structural Model Assessment and Importance-Performance Map Analysis (IMPA) tests were conducted as analysis tool to assess the quantitative raw data. The study concludes that Price and Assortment has positive significant relationship with Customer satisfaction whereas social environment and Retail atmosphere has positive insignificant relationship with customer satisfaction.

Granitoids of the western Himalaya and Karakoram as potential geothermal reservoirs – A petrological, geochemical and petrophysical study

Hot springs in various granitoid and gneissic complexes in northern Pakistan indicate elevated geothermal gradients, which warrants their evaluation for geothermal applications. Evaluation of such prospects requires extensive knowledge of rock properties and their interaction with reservoir fluids. Our contribution provides first-order information on petrophysical, geochemical, and petrographic descriptions derived from outcrop analogs. About seventy samples of (mostly) granitoids and gneisses from the Nanga Parbat Massif (NPM), the Kohistan-Ladakh Batholith (KLB), and the Karakoram Batholith (KB) were collected. Biotite, K-feldspar, and plagioclase in the granitoid and gneiss show weak to moderate alteration, which increases in shear zones, suggesting fracture-assisted fluid interaction. Geochemical results indicate that the gneisses and granites of the NPM are mostly peraluminous S-type and show enrichment in most LREEs and depletion in HREEs. In addition, depleted Sr and Ba and enriched Rb and U in granites indicate partial melting and high fractionation. On the contrary, granitoids from the KLB are of calc-alkaline I-type and characterized by the depletion of REEs and the enrichment in Ba and Sr. The granitoids of the KB, due to their different magmatic histories, are more diverse, ranging from older I-type calc-alkaline granodiorite, and younger I-type alkaline syenite and S-type calc-alkaline granite. They show an overall enrichment in HREEs along with Ba, Th, Ta, Sr, and Lu. Allanites from syenite of the KB show significant concentrations of Th and U.Petrophysical measurements reveal low matrix porosities (0.6–3.5 %), with primarily average thermal conductivities (1.48–3.37 W m−1K −1) and thermal diffusivities (0.68–1.95 ∙10–6 m2 s−1), with minor variation in specific heat capacities (744–767 J kg−1 K−1). The NPM displays higher average thermal conductivity, thermal diffusivity, and lower porosity than the KB, while the petrophysical properties of the KLB range between these two domains.The geothermal systems in the area operate due to the thickening of a granitoid-dominant crust, which is enriched with radiogenic elements. The fault zones provide channels for meteoric water to access deeply these hot and thermally conductive rocks in the subsurface. After heat exchange, the water is discharged back to the surface as hot springs. The present data set provides a better understanding of regional geothermal regimes from petrological, geochemical, and petrophysical perspectives and assists in numerical modeling for potential geothermal assessment.

Establishing a telehealth model addressing paediatric sleep health in remote and rural Northern Territory Australia: Overcoming the distance barrier.

This study examined the outcomes of a telehealth model for sleep health assessment among Indigenous and non-Indigenous children residing in remote and regional communities at the Top End Northern Territory (NT) of Australia. Video telehealth consultation, that included clinical history and relevant physical findings assessed virtually with an interstate paediatric sleep physician was conducted remotely. Polysomnography (PSG) and therapeutic interventions were carried out locally at Darwin, NT. The study participants were children referred between 2015 and 2020. Of the total 812 children referred for sleep assessment, 699 underwent a diagnostic PSG. The majority of patients were female (63%), non-Indigenous (81%) and resided in outer regional areas (88%). Indigenous children were significantly older and resided in remote or very remote locations (22% vs. 10%). Referral patterns differed according to locality and Indigenous status - (non-Indigenous via private (53%), Indigenous via public system (35%)). Receipt of referrals to initial consultation was a median of 16 days and 4 weeks from consult to PSG. Remote children had slightly longer time delay between the referral and initial consult (32 vs. 15 days). Fifty one percent were diagnosed to have OSA, 27% underwent adenotonsillectomy and 2% were prescribed with CPAP therapy. This study has demonstrated that a telehealth model can be an effective way in overcoming logistical barriers and in providing sleep health services to children in remote and regional Australia. Further innovative efforts are needed to improve the service model and expand the reach for vulnerable children in very remote communities.

Quantum sensing with tunable superconducting qubits: optimization and speed-up

Abstract Sensing and metrology are crucial in both fundamental science and practical applications. They meet the constant demand for precise data, enabling more dependable assessments of theoretical models’ validity. Sensors, now a common feature in many fields, play a vital role in applications like gravity imaging, geology, navigation, security, timekeeping, spectroscopy, chemistry, magnetometry, healthcare, and medicine. The advancements in quantum technologies have sparked interest in employing quantum systems as sensors, offering enhanced capabilities and new possibilities. This article describes the optimization of the quantum-enhanced sensing&amp;#xD;of magnetic fluxes with a Kitaev phase estimation algorithm based on frequency tunable transmon qubits. It provides the optimal flux biasing point for sensors with different qubit transition frequencies and gives an estimation of decoherence rates and achievable sensitivity. The use of 2- and 3-qubit entangled states are compared in simulation with the single-qubit case. The flux sensing accuracy reaches 10-8·Φ0 and scales inversely with time, which proves the speed-up of sensing with high ultimate accuracy.

Validation Assessment of Turbulent Reacting Flow Model Using the Area-Validation Metric on Medium-Scale Methanol Pool Fire Results

Abstract Accident analysis and ensuring power plant safety are pivotal in the nuclear energy sector. Significant strides have been achieved over the past few decades regarding fire protection and safety, primarily centered on design and regulatory compliance. Yet, after the Fukushima accident a decade ago, the imperative to enhance measures against fire, internal flooding, and power loss has intensified. Hence, a comprehensive, multilayered protection strategy against severe accidents is needed. Consequently, gaining a deeper insight into pool fires and their behavior through extensive validated data can greatly aid in improving these measures using advanced validation techniques. A model validation study was performed at Sandia National Laboratories (SNL) in which a 30-cm diameter methanol pool fire was modeled using the SIERRA/Fuego turbulent reacting flow code. This validation study used a standard validation experiment to compare model results against, and conclusions have been published. The fire was modeled with a large eddy simulation (LES) turbulence model with subgrid turbulent kinetic energy closure. Combustion was modeled using a strained laminar flamelet library approach. Radiative heat transfer was accounted for with a model utilizing the gray-gas approximation. In this study, additional validation analysis is performed using the area validation metric (AVM). These activities are done on multiple datasets involving different variables and temporal/spatial ranges and intervals. The results provide insight into the use of the area validation metric on such temporally varying datasets and the importance of physics-aware use of the metric for proper analysis.