Scale Components Research Articles

Dynamically Balancing Load with Overload Control for Microservices

The microservices architecture simplifies application development by breaking monolithic applications into manageable microservices. However, this distributed microservice “service mesh” leads to new challenges due to the more complex application topology. Particularly, each service component scales up and down independently creating load imbalance problems on shared backend services accessed by multiple components. Traditional load balancing algorithms do not port over well to a distributed microservice architecture where load balancers are deployed client-side. In this article, we propose a self-managing load balancing system, BLOC, which provides consistent response times to users without using a centralized metadata store or explicit messaging between nodes. BLOC uses overload control approaches to provide feedback to the load balancers. We show that this performs significantly better in solving the incast problem in microservice architectures. A critical component of BLOC is the dynamic capacity estimation algorithm. We show that a well-tuned capacity estimate can outperform even join-the-shortest-queue, a nearly optimal algorithm, while a reasonable dynamic estimate still outperforms Least Connection, a distributed implementation of join-the-shortest-queue. Evaluating this framework, we found that BLOC improves the response time distribution range, between the 10th and 90th percentiles, by 2 –4 times and the tail, 99th percentile, latency by 2 times.

Beyond the Hip: Clinical Phenotypes of Hip Osteoarthritis Across the Biopsychosocial Spectrum

Background/Objectives: To identify clinical phenotypes of hip osteoarthritis (OA) within a biopsychosocial framework. Methods: A cross-sectional analysis of 143 individuals with hip OA awaiting total hip arthroplasty (THA) was performed. Phenotyping features included sociodemographic and biomedical information, pain-related cognitions and emotions, mental disorders, traumatic experiences, self-efficacy, social support, perceived stress, and somatosensory function. Outcome measures included the hip disability and osteoarthritis outcome score and the numeric pain-rating scale. Decision tree learning was used to select the most important phenotyping features. K-means clustering analyses were performed to identify clinical phenotypes and a decision tree algorithm was trained to classify individuals in the identified clinical phenotypes. Results: Selected phenotyping features associated with pain and disability included a combination of biomedical, psychological, and social variables. Two distinct clinical phenotypes were identified. Individuals within the maladaptive phenotype (34%) reported more comorbidities, less self-efficacy and higher levels of anxiety, depression, pain-related fear-avoidance, and feelings of injustice. No differences were found regarding social support and somatosensory function. Regarding the outcome measures, individuals within the maladaptive phenotype reported higher levels of pain and disability. Finally, based on the Fear-Avoidance Components Scale (FACS) and the anxiety subscale of the Hospital Anxiety and Depression Scale (HADS-A), individuals could be classified into the clinical phenotypes with 87.8% accuracy. Conclusions: Two clinical phenotypes, an adaptive and a maladaptive phenotype, can be identified in individuals with hip OA using the FACS and HADS-A. The identification of these clinical phenotypes represents a crucial step toward precision medicine, enabling the development of targeted treatment pathways tailored to the distinct biomedical and psychological features of each phenotype.

Subjective social status and health-related quality of life after critical illness: results from a three-year prospective cohort study

ABSTRACT Subjective Social Status (SSS) reflects one’s perceived rank within a given social structure and has been shown to be a unique correlate of physical and mental health. However, no research has been conducted to address populations of (recovering) critically ill patients. To shed light on the relationship between SSS and health in critically ill patients, we focus on survivors of acute respiratory distress syndrome (ARDS). A cohort study with N = 877 ARDS survivors was conducted in 61 intensive care units (ICUs) in Germany between 2014 and 2019. Health-related quality of life (HRQoL, Physical and Mental Component Scale: PCS, MCS of the SF-12) and SSS (MacArthur Scale) were assessed at 12, 24, and 36 months after discharge from ICU. Objective social status (Socioeconomic status [SES]) was assessed once at ICU baseline. Bivariate correlations between SSS and HRQoL (PCS and MCS) remain significant throughout the study period (r = .29 – .50, all p-values < .05). Subsequent hierarchical regression shows that SSS remains predictive for PCS and MCS even after controlling for SES (β = .335 – .486, all p values < .001). The results indicate the importance (and unique contribution) of the subjective localization within the status hierarchy for long-term HRQoL after critical illness.

Exploring preservice teachers’ practices and perspectives on Whiteness

ABSTRACT The litany of research confirms Whiteness exists among preservice teachers in teacher education. Yet, much of this research is qualitative, and the few quantitative studies that have been conducted with preservice teachers do not consist of instrumentation assessing cultural, emotional and behavioural facets of Whiteness. The purpose of our study was to explore White preservice teachers’ practices and perceptions of Whiteness by developing the Whiteness Components Scale (WCS) which consists of three factors: White Emotionality (WE), White Ontological Expansiveness (WOE) and White Standardization (WS). Results show preliminary support for the WE factor, whereas more development is needed to better measure the constructs of WOE and WS. Further refinement and psychometric testing of the WE should be conducted, and suggestions for future research to expand the WCS are discussed.

Multi-scenario flexibility requirement analysis of high proportion of new energy access to power system

Abstract To address climate change, the proportion of renewable energy integration into the grid system is gradually increasing, leading to higher demands for flexibility. Current research typically employs methods such as dynamic system modeling, the construction of flexibility indicators, and scenario analysis to measure the flexibility requirements of the power system across different time scales. The use of frequency decomposition algorithms to explore flexibility requirements from the perspective of net load curves is relatively rare. This study utilizes net load data from the Western Inner Mongolia Power Grid to explore the distribution patterns of net load across different time scales under various seasonal and penetration scenarios using frequency decomposition algorithms. The results reveal that renewable energy output exhibits significant fluctuations and distinct diurnal variations, while net load also shows notable patterns and considerable volatility and seasonality. The analysis of flexibility requirements on typical seasonal days highlights the differences in demand distribution at various frequencies, with long-time-scale components primarily reflecting the overall trend of net load, while the medium-to-long time scale components characterize smaller, more frequent fluctuations. Additionally, the uncertainty associated with wind and solar output significantly affects the diurnal fluctuations of net load, with seasonal changes mainly represented in short-time-scale components. The study also emphasizes the impact of different penetration levels on flexibility requirements, indicating that as penetration decreases, the midnight requirement peak diminishes, suggesting differences in flexibility requirements based on renewable energy integration levels. Furthermore, the paper proposes corresponding solution technologies for “generation–grid–load–storage” across different time scales of flexibility requirements, ensuring the stable operation of the grid amidst climate change and rising electricity demand.

Pressure Distributions and Flow Regimes: An Integrated Approach For CO2 Sequestration Project Design and Evaluation in Hydraulically Fractured Shale Reservoirs

This paper introduces an integrated approach for estimating the key parameters of CO2 storage projects in depleted hydraulically fractured shale reservoirs. The approach focuses deep insights into evaluating these projects based on the pressure distribution and flow regimes. The objective is reducing the uncertainties in the design and evaluation criteria of CO2 storage by better understanding the flow mechanisms in the porous media. This understanding helps in perfectly modeling CO2 distribution and characterizing the expected flow regimes.Several analytical models are developed for the pressure behavior of CO2 multi-size scale flow mechanisms. Diffusion flow, slip flow, adsorption flow, and Darcy and non-Darcy flow are all considered. The flow in the nano-size organic particles, the micro-size kerogen particles, and the macro-size matrix is analytically described. The flow inside natural fractures in the stimulated and unstimulated reservoir volume as well as the flow of CO2 in the hydraulic fractures are modeled. Several rectangular reservoir configurations, hydraulic fracture characteristics, and petrophysical properties of the matrix, kerogen, and organic matter are examined. An estimation model for the total volume of CO2 that could be stored in the depleted fractured reservoirs is presented. The contribution of the unstimulated and stimulated porous media as well as the hydraulic fractures to the total volume is determined by characterizing the flow regimes. Different models are proposed for the time at which CO2 reaches the hydraulic fracture tips, the borders between stimulated and unstimulated porous media, and the reservoir boundary. Profound explanations are introduced for the impact of the constrained pressure on the total capacity of the reservoirs for CO2 storage.The study has reached several conclusions. The characteristics of the organic matter, kerogen, and the micro-size scale matrix do not significantly impact the pressure distribution and flow regimes. Conversely, the pressure distribution and flow regimes are significantly impacted by the characteristics of the hydraulic and natural fractures. The hydraulic fractures may offer a reasonable capacity for CO2 storage, meanwhile, the capacity of stimulated and unstimulated reservoir volume is controlled by reservoir configuration and fracture spacing. The constrained pressure may strictly reduce the total capacity of CO2 storage in the reservoir. The total volume of the injected CO2 can be determined from the pressure point when the injection pulse has reached to reservoir boundary. Beyond this point, it is not recommended to inject CO2 as it could increase reservoir pressure more than initial pressure. The pressure of the depleted reservoir before injection is a key parameter in the design and evaluation of the CO2 storage in shale reservoirs.This study introduces a novel approach for the design and evaluation criteria of CO2 storage in depleted shale reservoirs. The approach uses the pressure distribution and flow regimes that could be observed in the multi-size scale components of the reservoirs. The study proposes several models for estimating CO2 geosequestration capacity in the hydraulic fractures, stimulated and unstimulated reservoir volume as well as the total storage capacity of the reservoirs. The approach confidently addresses the concerns regarding the applicability of unconventional resources for CO2 storage and the total capacity offered by these reservoirs for this purpose.

Studies of genetic diversity and genome-wide association for vitamin C content in lettuce (Lactuca sativa L.) using high-throughput SNP arrays.

Lettuce (Lactuca sativa L.) is a source of beneficial compounds though they are generally present in low quantities. We used 40K Axiom and 9K Infinium SNP (single nucleotide polymorphism) arrays to (i) explore the genetic variability in 21 varieties and (ii) carry out genome-wide association studies (GWAS) of vitamin C content in21 varieties and a population of 205 plants from the richest variety in vitamin C ('Lechuga del Pirineo'). Structure and phylogenetic analyses showed that the group formed mainly by traditional varieties was the most diverse, whereas the red commercial varieties clustered together and very distinguishably apart from the rest. GWAS consistently detected, in a region of chromosome 2, several SNPs related to dehydroascorbic acid (a form of vitamin C) content using three different methods to assess population structure, subpopulation membership coefficients, multidimensional scaling, and principal component analysis. The latter detected the highest number of SNPs (17) and the most significantly associated, 12 of them showing a high linkage disequilibrium with the lead SNP. Among the 84 genes in the region, some have been reported to be related to vitamin C content or antioxidant status in other crops either directly, like those encoding long non-coding RNA, several F-box proteins, and a pectinesterase/pectinesterase inhibitor, or indirectly, like extensin-1-like protein and endoglucanase 2 genes. The involvement of other genes identified within the region in vitamin C levels needs to be further studied. Understanding the genetic control of such an important quality trait in lettuce becomes very relevant from a breeding perspective.

Development and validation of the mathematics self-efficacy scale for university students

Background: Academic underperformance in mathematics is widespread across all educational levels. To comprehend its causes, it is crucial to analyze various factors that impact academic advancement in mathematics through their interplay. The present study aimed to develop and validate a mathematics self-efficacy scale for university students. Methods: The research method was correlational and of instrument development type. The statistical population included all undergraduate students of Islamic Azad Universities in Tehran in 2021, of which 300 were selected as a sample using a multi-stage cluster random sampling method. In the process of scale construction, a set of items was initially selected based on a review of the research literature in the field of mathematics self-efficacy and the identification of valid tests in this area. Then, a preliminary version of the questionnaire was administered to a sample of 300 students. Factor analysis, Cronbach’s alpha coefficient, and Pearson correlation coefficient were used to analyze the data. Results: Based on the results of the exploratory factor analysis, the mathematics self-efficacy scale consisted of four components: mathematical achievement and success, general mathematical self-efficacy, mathematical anxiety, and mathematical stress. Cronbach’s alpha for the four components were 0.97, 0.95, 0.95, and 0.93, respectively. The results of the convergent validity evaluation showed that all four components of the mathematics self-efficacy scale were positively correlated with the intrinsic and extrinsic motivation components of the Academic Motivation Scale and negatively correlated with academic motivation. Conclusion: The components of the mathematics self-efficacy scale had acceptable internal consistency and convergent validity. Therefore, it can be concluded that the designed scale can be used as a valid tool to measure the level of mathematics self-efficacy in university students.

Design Method of a Novel Interface Connection Device for Multiscale Test Model Considering Multiparametric Similarity of Internal Forces

ABSTRACTThe multiscale model of building structures, as a balanced solution between accuracy and cost, has been widely used in the analysis of structural seismic performance. A reasonable interface connection method can accurately ensure load transfer and motion coordination between models of different scales. In this paper, a novel interface connection device and the corresponding design method for a multiscale test model of building structures were proposed, in which the upper structure with smaller sized components was replaced by a simplified story‐scale model, and the lower structure was adopted as a component‐scale model. The overall and local equations of motion for this multiscale model were established. For the interface connection between different scale models, a design method considering multiparametric similarity of shear force, axial force, and bending moment was proposed. In this method, the internal nodes at the interface of the component scale model were decomposed, and the coupling relationship of internal force between two adjacent nodes was established. The axial force of each node was decoupled into the interstory shear force and bending moment provided together. Additionally, the overturning moment is provided by adding the overlapping domain. According to the equilibrium relationships of the nodes at the interface, the corresponding transfer matrix was provided, and the design method of the interface connection device was proposed. The accuracy and feasibility of the method were validated by static and shaking table tests on a frame structure.

Pangolin scales as adaptations for innate immunity against pathogens

BackgroundPangolins are the only mammals that have overlapping scales covering most of their bodies, and they play a crucial role in the ecosystem, biological research, and human health and disease. Previous studies indicated pangolin scale might provide an important mechanical defense to themselves. The origin and exact functions of this unique trait remain a mystery. Using a multi-omics analysis approach, we report a novel functional explanation for how mammalian scales can provide host–pathogen defense.ResultsOur data suggest that pangolin scales have a sophisticated structure that could potentially trap pathogens. We identified numerous proteins and metabolites exhibiting antimicrobial activity, which could suggest a role for scales in pathogen defense. Notably, we found evidence suggesting the presence of exosomes derived from diverse cellular origins, including mesenchymal stem cells, immune cells, and keratinocytes. This observation suggests a complex interplay where various cell types may contribute to the release of exosomes and antimicrobial compounds at the interface between scales and viable tissue. These findings indicate that pangolin scales may serve as a multifaceted defense system, potentially contributing to innate immunity. Comparisons with human nail and hair revealed pangolin-specific proteins that were enriched in functions relating to sensing, immune responses, neutrophil degranulation, and stress responses. We demonstrated the antimicrobial activity of key pangolin scale components on pathogenic bacteria by antimicrobial assays.ConclusionsThis study identifies a potential role of pangolin scales and implicates scales, as possible determinants of pathogen defense due to their structure and contents. We indicate for the first time the presence of exosomes in pangolin scales and propose the new functions of scales and their mechanisms. This new mechanism could have implications for multiple fields, including providing interesting new research directions and important insights that can be useful for synthesizing and implementing new biomimetic antimicrobial approaches.

Quality of life under treatment with the immune checkpoint inhibitors ipilimumab and nivolumab in melanoma patients. Real-world data from a prospective observational study at the Skin Cancer Center Kiel

PurposeCombined immunotherapy (ipilimumab + nivolumab) has improved survival in stage IV melanoma patients, making Health-related Quality of Life (HrQoL) crucial due to potential immune-related adverse events (irAEs). Previous studies treated HrQoL as secondary/explorative endpoint, and no specific HrQoL questionnaire for melanoma patients on immune checkpoint inhibitor (ICI) therapy exists. This study aimed to gather specific HrQoL data during combined ICI therapy, tracking changes during and after treatment, and examining associations with gender, irAEs, and treatment response.Methods35 melanoma patients (22 males, 13 females) undergoing combined ICI were surveyed using the Short-form 36 questionnaire (SF-36), the Inflammatory Bowel Disease Questionnaire – Deutsch (IBDQ-D), and the distress thermometer (DT). HrQoL was evaluated during treatment, after six months, and at the onset of autoimmune colitis.ResultsirAEs occurred in 51.4% of patients, with colitis being the most common (26.1%). 45.7% had progressive disease. SF-36 showed stable HrQoL during treatment and follow-up. Women had worse HrQoL on the physical component scale than men (p = 0.019). Patients with progression showed worse HrQoL over time in physical (p = 0.015) and mental health scales (p = 0.04). IBDQ-D showed constant HrQoL throughout treatment and follow-up. Distress on DT remained constant, with women reporting higher levels of distress.ConclusionHrQoL remained stable during and after therapy. Female gender and disease progression negatively impacted HrQoL. The development of irAEs was not associated with HrQoL, though this may not apply to severe irAEs like colitis, which were not assessed.

Impact of Six Months of Three Different Modalities of Exercise on Stress in Post-Treatment Breast Cancer Survivors

Abstract: Background/Objectives: Extensive evidence suggests that exercise is physically and mentally beneficial for cancer survivors. This study reports on changes in self-reported stress, physiological biomarkers for stress (salivary cortisol), and HR-QOL constructs for fifty breast cancer survivors participating in one of three different exercise programs over 6 months. Methods: Fifty post-treatment breast cancer survivors were randomized to either therapeutic yoga-based exercise (YE), comprehensive exercise (CE) (aerobic, resistance, flexibility), or choosing (C) their own exercise activities. Participants completed the Perceived Stress Scale (PSS), Medical Outcomes Short-Form 36® (SF-36), and the Pittsburgh Sleep Quality Index (PSQI). Five samples of salivary cortisol were collected on two consecutive days. The 10 samples were used to calculate the diurnal rhythm slope. Outcome measures were repeated after six months. Results: All groups improved in HR-QOL measures of PSS; PSQI sleep quality components of latency and daytime functioning; and five of the ten SF-36 scales (Mental Component Scale, Social Functioning subscale, Mental Health subscale, Physical Component Scale, Physical Functioning subscale). Although the CE group observed the most favorable change in cortisol (−0.183), where cortisol slope changes approached significance (p = 0.057), but no significant decrease in cortisol between groups were noted. Conclusion: Our results suggest that it is the engagement of, rather than the specific type of exercise, which is associated with improved HR-QOL. However, longer-term studies with better adherence monitoring and larger sample sizes are needed to better determine clinical impact.

Ability of Heart Rate Recovery and Gait Kinetics in a Single Wearable to Predict Frailty: Quasiexperimental Pilot Study.

Aging is a risk factor for falls, frailty, and disability. The utility of wearables to screen for physical performance and frailty at the population level is an emerging research area. To date, there is a limited number of devices that can measure frailty and physical performance simultaneously. The aim of this study is to evaluate the accuracy and validity of a continuous digital monitoring wearable device incorporating gait mechanics and heart rate recovery measurements for detecting frailty, poor physical performance, and falls risk in older adults at risk of falls. This is a substudy of 156 community-dwelling older adults ≥60 years old with falls or near falls in the past 12 months who were recruited for a fall prevention intervention study. Of the original participants, 22 participants agreed to wear wearables on their ankles. An interview questionnaire involving demographics, cognition, frailty (FRAIL), and physical function questions as well as the Falls Risk for Older People in the Community (FROP-Com) was administered. Physical performance comprised gait speed, timed up and go (TUG), and the Short Physical Performance Battery (SPPB) test. A gait analyzer was used to measure gait mechanics and steps (FRAIL-functional: fatigue, resistance, and aerobic), and a heart rate analyzer was used to measure heart rate recovery (FRAIL-nonfunctional: weight loss and chronic illness). The participants' mean age was 74.6 years. Of the 22 participants, 9 (41%) were robust, 10 (46%) were prefrail, and 3 (14%) were frail. In addition, 8 of 22 (36%) had at least one fall in the past year. Participants had a mean gait speed of 0.8 m/s, a mean SPPB score of 8.9, and mean TUG time of 13.8 seconds. The sensitivity, specificity, and area under the curve (AUC) for the gait analyzer against the functional domains were 1.00, 0.84, and 0.92, respectively, for SPPB (balance and gait); 0.38, 0.89, and 0.64, respectively, for FRAIL-functional; 0.45, 0.91, and 0.68, respectively, for FROP-Com; 0.60, 1.00, and 0.80, respectively, for gait speed; and 1.00, 0.94, and 0.97, respectively, for TUG. The heart rate analyzer demonstrated superior validity for the nonfunctional components of frailty, with a sensitivity of 1.00, specificity of 0.73, and AUC of 0.83. Agreement between the gait and heart rate analyzers and the functional components of the FRAIL scale, gait speed, and FROP-Com was significant. In addition, there was significant agreement between the heart rate analyzer and the nonfunctional components of the FRAIL scale. The gait and heart rate analyzers could be used in a screening test for frailty and falls in community-dwelling older adults but require further improvement and validation at the population level.

Efficient Modeling of Spatial Extremes over Large Geographical Domains

Various natural phenomena exhibit spatial extremal dependence at short spatial distances. However, existing models proposed in the spatial extremes literature often assume that extremal dependence persists across the entire domain. This is a strong limitation when modeling extremes over large geographical domains, and yet it has been mostly overlooked in the literature. We here develop a more realistic Bayesian framework based on a novel Gaussian scale mixture model, with the Gaussian process component defined by a stochastic partial differential equation yielding a sparse precision matrix, and the random scale component modeled as a low-rank Pareto-tailed or Weibull-tailed spatial process determined by compactly-supported basis functions. We show that our proposed model is approximately tail-stationary and that it can capture a wide range of extremal dependence structures. Its inherently sparse structure allows fast Bayesian computations in high spatial dimensions based on a customized Markov chain Monte Carlo algorithm prioritizing calibration in the tail. We fit our model to analyze heavy monsoon rainfall data in Bangladesh. Our study shows that our model outperforms natural competitors and that it fits precipitation extremes well. We finally use the fitted model to draw inference on long-term return levels for marginal precipitation and spatial aggregates.

A Priori Test of Scale-Similarity Parameterizations for Subgrid-Scale Fluxes in Convective Storms

Abstract This study evaluates a parameterization scheme for subgrid-scale (SGS) fluxes based on the scale-similarity assumption and employing a large-eddy simulation of an idealized back-building convective system. In this parameterization, the SGS fluxes are decomposed into the “Leonard term,” which depends only on the resolved scale components, the “Reynolds term,” which depends only on the SGS components, and the “cross term,” which corresponds to the interaction between the resolved scale and SGS components. Assuming a linear relationship between the Leonard term and the Reynolds and cross terms, SGS fluxes are expressed as the product of an empirical coefficient and the Leonard term. The Leonard term reasonably represents the SGS flux derived by a smooth filter operation, including the countergradient vertical SGS transport of potential temperature, which cannot be represented by a traditional eddy-diffusivity model. The dependence of the empirical coefficient on filter width is also evaluated. This dependence is related mainly to the Reynolds term, the magnitude of which varies widely with the filter width. The estimation based on the spectral decomposition of the Reynolds term explains the obtained dependence of the empirical coefficient for the vertical flux on filter width. In contrast, the variation of the empirical coefficient with filter width is not required to obtain the horizontal flux. For the parameterization of SGS fluxes in kilometer-scale models that use finite difference or volume methods, the Leonard term is expressed by the horizontal gradient of variables on a discrete grid. The Leonard term on a discrete grid also accurately represents the amplitude and spatial pattern of the SGS flux. Significance Statement Kilometer-scale numerical weather prediction models can reproduce deep convection explicitly. However, they cannot reproduce smaller inner structures within this deep convection. Therefore, a parameterization scheme that can express the nature of the transport associated with such structures is required. Using a large-eddy simulation with a horizontal resolution sufficient to express such inner structures, we evaluated a parameterization based on the scale similarity assumption, including an empirical coefficient. Based on spectral decomposition, we quantitatively estimated the relationship between the empirical coefficient and grid spacing. Our results provide guidance in selecting the value of the empirical coefficient in this parameterization.

The MOST (Mortality Score for TBI): A Novel Prediction Model Beyond CRASH-Basic and IMPACT-Core for Isolated Traumatic Brain Injury

BackgroundDue to significant injury heterogeneity, outcome prediction following traumatic brain injury (TBI) is challenging. This study aimed to develop a simple model for high-accuracy mortality risk prediction after TBI. Study DesignData from the American College of Surgeons (ACS) Trauma Quality Program (TQP) from 2019 to 2021 was used to develop a summary score based on age, the Glasgow Coma Scale (GCS) component subscores, and pupillary reactivity data. We then compared the predictive accuracy to that of the Corticosteroid Randomisation After Significant Head Injury Trial (CRASH)-Basic and International Mission for Prognosis and Analysis of Clinical Trial in TBI (IMPACT)-Core models. Two separate series of sensitivity analyses were conducted to further assess our model's generalizability. We evaluated predictive performance of the models with discrimination [the area under the receiver-operating characteristic curves (AUC), sensitivity, specificity] and calibration (Brier score). Discriminative ability was compared with DeLong tests. Results259,404 patients were included in the present study (mean age, 60 years; 93,495 (36%) female). The mortality score after TBI (MOST) model (AUC = 0.875) had better discrimination (DeLong test p values < 0.00001) than CRASH-Basic (AUC = 0.837) and IMPACT-Core (AUC = 0.821) models, and superior calibration (MOST = 0.02729, CRASH-Basic = 0.02962, IMPACT-Core = 0.02962) in predicting in-hospital mortality. The MOST model similarly outperformed in predicting 3-, 7-, 14-, and 30-day mortality. ConclusionThe MOST model can be rapidly calculated and outperforms two widely used models for predicting mortality in TBI patients. It utilizes a larger, contemporaneous dataset that reflects modern trauma care.

Void drift in narrow rectangular channels for highly advective bubbly flows: A macroscopic drift-flux model derived from two-fluid local simulations

This article addresses the issue of reduced models to describe turbulent two-phase flows in industrial applications. This work is an application based on Bois (2021) theoretical derivations to connect the spatially-averaged drift-flux model (DFM) to the local Reynolds-averaged two-fluid model (TFM). It presents new opportunities to calibrate closure laws or propose new models based on finer-scale descriptions. Highly convective steady bubbly flows with wall-peaking void fraction profiles are considered. Dispersed bubbles are weakly deformable. A new model dedicated to void fraction dispersion in flat rectangular channels is developed for application into the DFM. The model is calibrated based on reference local Euler-Euler two-fluid simulations of pressurised water and steam mono-dispersed bubbles, in adiabatic conditions. 64 conditions are considered to cover regularly the 3D parameter space in void fraction, Reynolds and Eötvös numbers. The new formulation proposed shows a very good fit with the post-processed predictions of local CFD simulations along with a substantial reduction in grid requirement. The model contains sub-filter correlations between vapour concentration and mixture or relative velocities. This model derived from local simulations is validated a posteriori in an industrial component scale code where the full space-filtered DFM is implemented and resolved. This work proves the benefits and feasibility of one-by-one model development and calibration based on two-fluid simulations.

Upscaling bio-based products: poplar and hemp for soil and building circular synergies

Abstract The paper illustrates the rationale of an ongoing research aimed at identifying innovative applications for poplar- and hemp-based products in constructive systems, focusing on load-bearing poplar plywood elements and hemp blocks and panels for the building envelope. The underlying approach extends the circular-based perspective from the building component scale to the territorial and economic one, adopting a context-wise approach. The potential of these renewable materials is examined beyond the application to reversible building systems, to depict their comprehensive contribution to a carbon-neutral region by examining the links with the local territorial management and a potential short supply chain. The contribution describes poplar and hemp agricultural productions, connecting their outputs to several economic sector. After addressing the environmental benefits, the two cultivations produce in terms of climate change mitigation and adaptation, among which carbon uptake, it contextualises poplar- and hemp-based products within current bio-based solutions in the building sector, exploring possible applications in new and existing constructions through circular constructive systems. The results discussion outlines the sustainable multiscale synergies supported by poplar- and hemp-based building products within the research perimeter.

Psychometric Evaluation of the Emotion Beliefs Questionnaire – Norwegian Version (EBQ-N)

Abstract: The Emotion Beliefs Questionnaire (EBQ) assesses beliefs about the controllability and usefulness of positive and negative emotions. The aim of the present study was to establish the construct validity and reliability of the EBQ within the cultural and linguistic context of Norway. The Norwegian version of the EBQ (EBQ-N) was administered to a sample of university students ( N = 380, 64.2% females, 93.7% aged 25 or younger). Confirmatory factor analyses indicated that the EBQ-N is best represented by three first-order factors and a higher-order general factor, consistent with the findings of the initial validation study. The items displayed both adequate convergence in assessing their intended constructs and effective discrimination among the three first-order factors. The scale components demonstrated acceptable to good internal consistency. In the context of its intrinsic properties, the EBQ-N appears to be a valid and reliable instrument for assessing emotion beliefs in Norwegian research and clinical settings.

Growing simplicial complex with face dimension selection and preferential attachment.

When simplicial complexes are used to represent higher-order systems, information regarding when and how interactions happen may be lost. In this paper, we propose the concept of temporal simplicial complexes, in which simplices with timestamps (or temporal simplices) are used to represent interactions, and faces with weights are used to represent relations. Then, we propose a growing model with two rules, face dimension selection (FDS), and preferential attachment. By properly setting the probability parameter vector q in the FDS rule, one can balance network diameter expansion and network centrality, thus attaining more flexibility in the growing process. Our theoretical analysis and simulations that followed show the generalized degree of faces of any dimension follows a power-law distribution, with a scaling component controlled by q. Our work provides a flexible growing model and can be used to study higher-order systems with temporal properties.