Model Validation Studies Research Articles

Prediction of Mortality Risk After Ischemic Acute Kidney Injury With a Novel Prognostic Model: A Multivariable Prediction Model Development and Validation Study.

Background and Objectives:Acute kidney injury (AKI) that results from ischemia is a common clinical syndrome and correlates with high morbidity and mortality among hospitalized patients. However, a clinical tool to predict mortality risk of ischemic AKI is not available. In this study, we aimed to develop and validate models to predict the 30-day and 1-year mortality risk of hospitalized patients with ischemic AKI.MethodsA total of 1,836 admissions with ischemic AKI were recruited from 277,898 inpatients admitted to three affiliated tertiary general hospitals of Central South University in China between January 2015 and December 2015. Patients in the final analysis were followed up for 1 year. Study patients were randomly divided in a 7:3 ratio to form the training cohort and validation cohort. Multivariable regression analyses were used for developing mortality prediction models.ResultsHepatorenal syndrome, shock, central nervous system failure, Charlson comorbidity index (≥2 points), mechanical ventilation, renal function at discharge were independent risk factors for 30-day mortality after ischemic AKI, while malignancy, sepsis, heart failure, liver failure, Charlson comorbidity index (≥2 points), mechanical ventilation, and renal function at discharge were predictors for 1-year mortality. The area under the receiver operating characteristic curves (AUROCs) of 30-day prediction model were 0.878 (95% confidence interval (CI): 0.849-0.908) in the training cohort and 0.867 (95% CI: 0.820–0.913) in the validation cohort. The AUROCs of the 1-year mortality prediction in the training and validation cohort were 0.803 (95% CI: 0.772–0.834) and 0.788 (95% CI: 0.741–0.835), respectively.ConclusionOur easily applied prediction models can effectively identify individuals at high mortality risk within 30 days or 1 year in hospitalized patients with ischemic AKI. It can guide the optimal clinical management to minimize mortality after an episode of ischemic AKI.

Evaluating methodological quality of prognostic prediction models on patient reported outcome measurements after total hip replacement and total knee replacement surgery: a systematic review protocol

BackgroundPrediction models for poor patient-reported surgical outcomes after total hip replacement (THR) and total knee replacement (TKR) may provide a method for improving appropriate surgical care for hip and knee osteoarthritis. There are concerns about methodological issues and the risk of bias of studies producing prediction models. A critical evaluation of the methodological quality of prediction modelling studies in THR and TKR is needed to ensure their clinical usefulness. This systematic review aims to (1) evaluate and report the quality of risk stratification and prediction modelling studies that predict patient-reported outcomes after THR and TKR; (2) identify areas of methodological deficit and provide recommendations for future research; and (3) synthesise the evidence on prediction models associated with post-operative patient-reported outcomes after THR and TKR surgeries.MethodsMEDLINE, EMBASE, and CINAHL electronic databases will be searched to identify relevant studies. Title and abstract and full-text screening will be performed by two independent reviewers. We will include (1) prediction model development studies without external validation; (2) prediction model development studies with external validation of independent data; (3) external model validation studies; and (4) studies updating a previously developed prediction model. Data extraction spreadsheets will be developed based on the CHARMS checklist and TRIPOD statement and piloted on two relevant studies. Study quality and risk of bias will be assessed using the PROBAST tool. Prediction models will be summarised qualitatively. Meta-analyses on the predictive performance of included models will be conducted if appropriate. A narrative review will be used to synthesis the evidence if there are insufficient data to perform meta-analyses.DiscussionThis systematic review will evaluate the methodological quality and usefulness of prediction models for poor outcomes after THR or TKR. This information is essential to provide evidence-based healthcare for end-stage hip and knee osteoarthritis. Findings of this review will contribute to the identification of key areas for improvement in conducting prognostic research in this field and facilitate the progress in evidence-based tailored treatments for hip and knee osteoarthritis.Systematic review registrationPROSPERO registration number CRD42021271828.

External validation study of endometrial cancer preoperative risk stratification model (ENDORISK).

IntroductionAmong industrialized countries, endometrial cancer is a common malignancy with generally an excellent outcome. To personalize medicine, we ideally compile as much information as possible concerning patient prognosis prior to effecting an appropriate treatment decision. Endometrial cancer preoperative risk stratification (ENDORISK) is a machine learning–based computational Bayesian networks model that predicts lymph node metastasis and 5-year disease-specific survival potential with percentual probability. Our objective included validating ENDORISK effectiveness in our patient cohort, assessing its application in the current use of sentinel node biopsy, and verifying its accuracy in advanced stages.MethodsThe ENDORISK model was evaluated with a retrospective cohort of 425 patients from the University Hospital Brno, Czech Republic. Two hundred ninety-nine patients were involved in our disease-specific survival analysis; 226 cases with known lymph node status were available for lymph node metastasis analysis. Patients were included undergoing either pelvic lymph node dissection (N = 84) or sentinel node biopsy (N =70) to explore the accuracy of both staging procedures.ResultsThe area under the curve was 0.84 (95% confidence interval [CI], 0.77–0.9) for lymph node metastasis analysis and 0.86 (95% CI, 0.79–0.93) for 5-year disease-specific survival evaluation, indicating quite positive concordance between prediction and reality. Calibration plots to visualize results demonstrated an outstanding predictive value for low-risk cancers (grades 1–2), whereas outcomes were underestimated among high-risk patients (grade 3), especially in disease-specific survival. This phenomenon was even more obvious when patients were subclassified according to FIGO clinical stages.ConclusionsOur data confirmed ENDORISK model’s laudable predictive ability, particularly among patients with a low risk of lymph node metastasis and expected favorable survival. For high-risk and/or advanced stages, the ENDORISK network needs to be additionally trained/improved.

A Questionnaire-Based Ensemble Learning Model to Predict the Diagnosis of Vertigo: Model Development and Validation Study.

BackgroundQuestionnaires have been used in the past 2 decades to predict the diagnosis of vertigo and assist clinical decision-making. A questionnaire-based machine learning model is expected to improve the efficiency of diagnosis of vestibular disorders.ObjectiveThis study aims to develop and validate a questionnaire-based machine learning model that predicts the diagnosis of vertigo.MethodsIn this multicenter prospective study, patients presenting with vertigo entered a consecutive cohort at their first visit to the ENT and vertigo clinics of 7 tertiary referral centers from August 2019 to March 2021, with a follow-up period of 2 months. All participants completed a diagnostic questionnaire after eligibility screening. Patients who received only 1 final diagnosis by their treating specialists for their primary complaint were included in model development and validation. The data of patients enrolled before February 1, 2021 were used for modeling and cross-validation, while patients enrolled afterward entered external validation.ResultsA total of 1693 patients were enrolled, with a response rate of 96.2% (1693/1760). The median age was 51 (IQR 38-61) years, with 991 (58.5%) females; 1041 (61.5%) patients received the final diagnosis during the study period. Among them, 928 (54.8%) patients were included in model development and validation, and 113 (6.7%) patients who enrolled later were used as a test set for external validation. They were classified into 5 diagnostic categories. We compared 9 candidate machine learning methods, and the recalibrated model of light gradient boosting machine achieved the best performance, with an area under the curve of 0.937 (95% CI 0.917-0.962) in cross-validation and 0.954 (95% CI 0.944-0.967) in external validation.ConclusionsThe questionnaire-based light gradient boosting machine was able to predict common vestibular disorders and assist decision-making in ENT and vertigo clinics. Further studies with a larger sample size and the participation of neurologists will help assess the generalization and robustness of this machine learning method.

Survival Prediction in Patients Treated Surgically for Metastases of the Appendicular Skeleton-An External Validation of 2013-SPRING Model.

Simple SummaryBone tumour metastases are highly prevalent among cancer patients. In case these have to be treated surgically due to impending or pathological fracture, estimation of patients’ life expectancy is of importance in order to choose the best treatment option possible. In the current study, the 2013-SPRING model, developed to predict prognosis of surgically treated bone metastasis patients, was validated in an external patient cohort of 303 bone metastasis patients. AUC ROCs at all three endpoints assessed (i.e., survival at 3, 6 and 12 months following surgery for bone metastases) were all above 0.780. Furthermore, stratification into type of surgery (endoprosthesis (n = 162) vs. osteosynthesis (n = 141) and metastasis location (upper limb (n = 65) vs. lower limb (n = 238)) revealed a comparable predictive accuracy of the 2013-SPRING model, albeit slightly better performance in the osteosynthesis as compared with endoprosthesis subgroup, as well as upper limb in comparison to lower limb subgroup was observed.Introduction: The aim of this study was to externally validate the 2013-SPRING model, a survival prediction tool for patients treated surgically for bone metastases in a retrospective patient cohort from a single institution. Moreover, subgroup analyses on patients treated with (A) endoprostheses or (B) osteosynthesis, as well as (C) upper limb and (D) lower limb metastases, were performed. Methods: Altogether, 303 cancer patients (mean age: 67.6 ± 11.1 years; 140 males (46.2%)) with bone metastases to the extremities, treated surgically between March 2000 and June 2018 at a single tertiary sarcoma centre, were retrospectively included. Median follow-up amounted to 6.3 (interquartile range (IQR): 2.3–21.8) months, with all patients followed-up for at least one year or until death. The 2013-SPRING model was applied to assess the prognostication accuracy at 3, 6 and 12 months. Models were validated with area under the curve receiver operator characteristic (AUC ROC; the higher the better), as well as Brier score. Results: Of the 303 patients, 141 had been treated with osteosynthesis (46.5%), and the remaining 162 patients with endoprosthesis (53.5%). Sixty-five (21.5%) metastases were located in the upper limbs, and two hundred and thirty-eight (78.5%) in the lower limbs. Using the 2013-SPRING model for the entire cohort, the accuracy of risk of death prediction at 3, 6 and 12 months, determined by the AUC ROC, was 0.782 (95% CI: 0.729–0.843), 0.810 (95% CI: 0.763–0.858) and 0.802 (95% CI: 0.751–0.854), respectively. Corresponding Brier scores were 0.170, 0.178 and 0.169 at 3, 6 and 12 months. In the subgroup analyses, predictive accuracy of the 2013-SPRING model was likewise encouraging, albeit being slightly higher in the osteosynthesis subgroup as compared with the endoprosthesis subgroup, and also higher in the upper limb in comparison to the lower limb metastasis subgroup. Conclusions: The current validation study of the 2013-SPRING model shows that this model is clinically relevant to use in an external cohort, also after stratification for surgical procedure and metastasis location.

Wave Attenuation by Vegetation: Model Implementation and Validation Study

Wave–vegetation interaction is implemented in the WAVEWATCH III (WW3) model. The vegetation sink term followed the early formulations of Dalrymple et al. (Journal of Waterway, Port, Coastal, and Ocean Engineering, 1984, 110, 67–79), which focused on monochromatic waves and vegetation approximated as an array of rigid, vertical cylinders, and was later expanded by Mendez and Losada (Coastal Engineering, 2004, 51, 103–118) for random wave transformations over mildly sloping vegetation fields under breaking and nonbreaking conditions assuming a Rayleigh distribution of wave heights. First, validation is carried out for 63 laboratory cases (Anderson and Smith, 2014) with homogeneous vegetation fields for single and double-peak wave spectra. Then, a field case application is conducted to assess the wave attenuation in a wetland environment with spatially variable vegetation fields during stormy conditions. The case study uses data collected at the Magothy Bay located in the Chesapeake Bay, United States, during Hurricanes Jose and Maria in 2017. The domain decomposition parallelization and the implicit scheme have been used for the simulations to efficiently resolve complex shorelines and high-gradient wave zones, incorporating dominant physics in the complicated coastal zone, including wave breaking, wave–current interaction, bottom friction and scattering, wave–vegetation interaction, and nonlinearity (Abdolali et al., 2020). The lab validation and field application demonstrate that WW3 is an effective tool for evaluating the capacity of wetland natural or nature-based features to attenuate wave energy to achieve coastal flood risk reduction.

Multitask Learning With Recurrent Neural Networks for Acute Respiratory Distress Syndrome Prediction Using Only Electronic Health Record Data: Model Development and Validation Study

BackgroundAcute respiratory distress syndrome (ARDS) is a condition that is often considered to have broad and subjective diagnostic criteria and is associated with significant mortality and morbidity. Early and accurate prediction of ARDS and related conditions such as hypoxemia and sepsis could allow timely administration of therapies, leading to improved patient outcomes.ObjectiveThe aim of this study is to perform an exploration of how multilabel classification in the clinical setting can take advantage of the underlying dependencies between ARDS and related conditions to improve early prediction of ARDS in patients.MethodsThe electronic health record data set included 40,703 patient encounters from 7 hospitals from April 20, 2018, to March 17, 2021. A recurrent neural network (RNN) was trained using data from 5 hospitals, and external validation was conducted on data from 2 hospitals. In addition to ARDS, 12 target labels for related conditions such as sepsis, hypoxemia, and COVID-19 were used to train the model to classify a total of 13 outputs. As a comparator, XGBoost models were developed for each of the 13 target labels. Model performance was assessed using the area under the receiver operating characteristic curve. Heat maps to visualize attention scores were generated to provide interpretability to the neural networks. Finally, cluster analysis was performed to identify potential phenotypic subgroups of patients with ARDS.ResultsThe single RNN model trained to classify 13 outputs outperformed the individual XGBoost models for ARDS prediction, achieving an area under the receiver operating characteristic curve of 0.842 on the external test sets. Models trained on an increasing number of tasks resulted in improved performance. Earlier prediction of ARDS nearly doubled the rate of in-hospital survival. Cluster analysis revealed distinct ARDS subgroups, some of which had similar mortality rates but different clinical presentations.ConclusionsThe RNN model presented in this paper can be used as an early warning system to stratify patients who are at risk of developing one of the multiple risk outcomes, hence providing practitioners with the means to take early action.

Parametrically-upscaled continuum damage mechanics (PUCDM) model for plain weave woven composites: Part II model validation and parametric studies

In this second of the two-part paper, the two-level parametrically-upscaled continuum damage mechanics models (PUCDM-1 and PUCDM-2) developed in the first part Xiao et al. (2022) are applied for multiscale analysis of plain weave composite structures. The material damage response in the intra-yarn unidirectional microstructures and the woven yarn–matrix mesostructures are respectively represented by level-1 and level-2 PUCDM models. The models bridge material length scales through explicit functions of the lower-scale morphological descriptors (representative aggregated micro/mesostructural parameters or RAMPs) and material properties (Xmat) with high computational efficiency. The paper focuses on model validation with experiments and parametric studies. The model is validated with observations from tensile tests on an hourglass-shaped specimen with good agreement at multiple scales. The validated PUCDM model is then employed for the analysis of multiscale deformation and damage mechanisms in single-edge notched bending (SENB) tests on woven composite beams. Parametric studies are performed to investigate the effect of lower-scale morphology and material properties on the structural damage response of the woven composite beams.

Covid-19 vaccine dosages and government factors role on the global variation in covid-19 mortality: A statistical and regression analysis

The objective of our study was to explore the influence of the current vaccination program and other relevant government factors to explain the variation in Covid-19 mortality in the world. The study involves a cross-sectional survey of Covid-19 related and government factors from 161 countries. We retrieved and processed publically available coronavirus pandemic data (July 17, 2021) from several online databases, excluding countries' data violating correlation and regression analysis assumptions. In addition, partial correlations studies and multivariate analysis were performed to explore the influence current vaccination program and other relevant government factors on the relationship between the explanatory variable and the total deaths due to Covid-19. The partial-correlation studies revealed that controlling for a complete dosage of Covid-19 vaccine per 100 people in the population had a significant (P<0.001) impact on the strength of the relationship between some explanatory variables and the response variable (total Covid-19 mortality). Furthermore, the Stepwise Linear Regression (SLR) model shows that the covariates, namely total_cases, hospital patients per million, hospital beds per thousand, male smokers, and people fully vaccinated per hundred, added significantly (P<0.001) to the prediction of the response variable. Our SLR model validation study revealed that the observed total Covid-19 mortality was highly correlated with the predicted total Covid-19 mortality in various countries (r = 0.977, P<0.001). Our Stepwise Linear Regression model performs significantly better with an R-squared value of 0.958 and adjusted R-squared value of 0.956 than other related regression models built to predict COVID-19 mortality. Based on our current findings, we conclude that governments with better hospital infrastructure and people with complete dosages of the Covid-19 vaccine will have minimal Covid-19 fatalities.

Potential Cost-Effectiveness of Risk-Based Pancreatic Cancer Screening in Patients With New-Onset Diabetes.

There are no established methods for pancreatic cancer (PAC) screening, but the NCI and the Pancreatic Cancer Action Network (PanCAN) are investigating risk-based screening strategies in patients with new-onset diabetes (NOD), a group with elevated PAC risk. Preliminary estimates of the cost-effectiveness of these strategies can provide insights about potential value and inform supplemental data collection. Using data from the Enriching New-Onset Diabetes for Pancreatic Cancer (END-PAC) risk model validation study, we assessed the potential value of CT screening for PAC in those determined to be at elevated risk, as is being done in a planned PanCAN Early Detection Initiative trial. We created an integrated decision tree and Markov state-transition model to assess the cost-effectiveness of PAC screening in patients aged ≥50 years with NOD using CT imaging versus no screening. PAC prevalence, sensitivity, and specificity were derived from the END-PAC validation study. PAC stage distribution in the no-screening strategy and PAC survival were derived from the SEER program. Background mortality for patients with diabetes, screening and cancer care expenditure, and health state utilities were derived from the literature. Life-years (LYs), quality-adjusted LYs (QALYs), and costs were tracked over a lifetime horizon and discounted at 3% per year. Results are presented in 2020 US dollars, and we took a limited US healthcare perspective. In the base case, screening resulted in 0.0055 more LYs, 0.0045 more QALYs, and $293 in additional expenditures for a cost per QALY gained of $65,076. In probabilistic analyses, screening resulted in a cost per QALY gained of <$50,000 and <$100,000 in 34% and 99% of simulations, respectively. In the threshold analysis, >25% of screen-detected PAC cases needed to be resectable for the cost per QALY gained with screening to be <$100,000. We found that risk-based PAC screening in patients with NOD is likely to be cost-effective in the United States if even a modest fraction (>25%) of screen-detected patients with PAC are resectable. Future studies should reassess the value of this intervention once clinical trial data become available.

Segregation and intermixing in polydisperse liquid–solid fluidized beds: A multifluid model validation study

AbstractMultifluid model (MFM) simulations have been carried out on liquid–solid fluidized beds (LSFB) consisting of binary and higher‐order polydisperse particle mixtures. The role of particle–particle interactions was found to be as crucial as the drag force under laminar and homogenous LSFB flow regimes. The commonly used particle–particle closure models are designed for turbulent and heterogeneous gas–solid flow regimes and thus exhibit limited to no success when implemented for LSFB operating under laminar and homogenous conditions. A need is perceived to carry out direct numerical simulations of liquid–solid flows and extract data from them to develop rational closure terms to account for the physics of LSFB. Finally, a recommendation flow regime map signifying the performance of the MFM has been proposed. This map will act as a potential guideline to identify whether or not the bed expansion characteristics of a given polydisperse LSFB can be correctly simulated using MFM closures tested.

Cardiovascular vulnerability predicts hospitalisation in primary care clinically suspected and confirmed COVID-19 patients: A model development and validation study.

Cardiovascular conditions were shown to be predictive of clinical deterioration in hospitalised patients with coronavirus disease 2019 (COVID-19). Whether this also holds for outpatients managed in primary care is yet unknown. The aim of this study was to determine the incremental value of cardiovascular vulnerability in predicting the risk of hospital referral in primary care COVID-19 outpatients. Analysis of anonymised routine care data extracted from electronic medical records from three large Dutch primary care registries. Primary care. Consecutive adult patients seen in primary care for COVID-19 symptoms in the 'first wave' of COVID-19 infections (March 1 2020 to June 1 2020) and in the 'second wave' (June 1 2020 to April 15 2021) in the Netherlands. A multivariable logistic regression model was fitted to predict hospital referral within 90 days after first COVID-19 consultation in primary care. Data from the 'first wave' was used for derivation (n = 5,475 patients). Age, sex, the interaction between age and sex, and the number of cardiovascular conditions and/or diabetes (0, 1, or ≥2) were pre-specified as candidate predictors. This full model was (i) compared to a simple model including only age and sex and its interaction, and (ii) externally validated in COVID-19 patients during the 'second wave' (n = 16,693). The full model performed better than the simple model (likelihood ratio test p<0.001). Older male patients with multiple cardiovascular conditions and/or diabetes had the highest predicted risk of hospital referral, reaching risks above 15-20%, whereas on average this risk was 5.1%. The temporally validated c-statistic was 0.747 (95%CI 0.729-0.764) and the model showed good calibration upon validation. For patients with COVID-19 symptoms managed in primary care, the risk of hospital referral was on average 5.1%. Older, male and cardiovascular vulnerable COVID-19 patients are more at risk for hospital referral.

Removal of Pb2+ from water using the carbon nanotube-g-poly[(sodium methacrylate)-co- 2-(methacryloyloxy)ethyl acetoacetate]: experimental investigation and modeling.

A solid polymer, poly[(sodium methacrylate)-co-2-(methacryloyloxy)ethyl acetoacetate], p(MAA-co-MEAA) was synthesized and then grafted onto carbon nanotubes to prepare poly(MAA-co-MEAA)-grafted carbon nanotubes [CNT-g-p(MAA-co-MEAA)]. NMR, TGA, and FT-IR characterized the synthesized polymers and adsorbents. SEM-EDX was used to investigate the surface characteristics of the adsorbents. Pb2+ was removed from the aqueous solution using the CNT-g-p(MAA-co-MEAA). A batch adsorption experiment was performed at different Pb2+ concentrations (1, 10, 25, 50 mg/L), pH (4 and 6.75), temperature (25 and 35 °C), and contact periods (1, 5, 20, 60, and 1440 min) to study the adsorption kinetics and isotherm. The adsorbent dose of 2.5 g/L could effectively lower the initial Pb2+ concentration of 1000 to 2 ppb. The maximum adsorption capacity of the adsorbent was found to be 1178 mg/g. In addition, the adsorbents have been shown to effectively reduce the coexisting metal ion concentrations from industrial wastewater, which indicated the potential of the proposed adsorbent in removing metal ions from coexisting metals containing wastewater. To predict the adsorption efficiency of Pb2+, various linear, non-linear, and neural network models were established. An additional data set, not incorporated in model training, was used to validate the models. A number of models showed excellent performance with R2 in the range of 0.89-0.98. In model validation studies, the correlation coefficients (r) ranged from 0.94 to 0.99. The novel adsorbent and models will most likely aid in the development of a robust treatment technique for removing Pb2+ ions from water and wastewater.

A Machine Learning Approach to Predict the Outcome of Urinary Calculi Treatment Using Shock Wave Lithotripsy: Model Development and Validation Study.

BackgroundShock wave lithotripsy (SWL), ureteroscopy, and percutaneous nephrolithotomy are established treatments for renal stones. Historically, SWL has been a predominant and commonly used procedure for treating upper tract renal stones smaller than 20 mm in diameter due to its noninvasive nature. However, the reported failure rate of SWL after one treatment session ranges from 30% to 89%. The failure rate can be reduced by identifying candidates likely to benefit from SWL and manage patients who are likely to fail SWL with other treatment modalities. This would enhance and optimize treatment results for SWL candidates.ObjectiveWe proposed to develop a machine learning model that can predict SWL outcomes to assist practitioners in the decision-making process when considering patients for stone treatment.MethodsA data set including 58,349 SWL procedures performed during 31,569 patient visits for SWL to a single hospital between 1990 and 2016 was used to construct and validate the predictive model. The AdaBoost algorithm was applied to a data set with 17 predictive attributes related to patient demographics and stone characteristics, with success or failure as an outcome. The AdaBoost algorithm was also applied to a training data set. The generated model’s performance was compared to that of 5 other machine learning algorithms, namely C4.5 decision tree, naïve Bayes, Bayesian network, K-nearest neighbors, and multilayer perceptron.ResultsThe developed model was validated with a testing data set and performed significantly better than the models generated by the other 5 predictive algorithms. The sensitivity and specificity of the model were 0.875 and 0.653, respectively, while its positive predictive value was 0.7159 and negative predictive value was 0.839. The C-statistics of the receiver operating characteristic (ROC) analysis was 0.843, which reflects an excellent test.Conclusions We have developed a rigorous machine learning model to assist physicians and decision-makers to choose patients with renal stones who are most likely to have successful SWL treatment based on their demographics and stone characteristics. The proposed machine learning model can assist physicians and decision-makers in planning for SWL treatment and allow for more effective use of limited health care resources and improve patient prognoses.

The Greenland Firn Compaction Verification and Reconnaissance (FirnCover) dataset, 2013–2019

Abstract. Assessing changes in the density of snow and firn is vital to convert volume changes into mass changes on glaciers and ice sheets. Firn models simulate this process but typically rely upon steady-state assumptions and geographically and temporally limited sets of field measurements for validation. Given rapid changes recently observed in Greenland's surface mass balance, a contemporary dataset measuring firn compaction in a range of climate zones across the Greenland ice sheet's accumulation zone is needed. To fill this need, the Firn Compaction Verification and Reconnaissance (FirnCover) dataset comprises daily measurements from 48 strainmeters installed in boreholes at eight sites on the Greenland ice sheet between 2013 and 2019. The dataset also includes daily records of 2 m air temperature, snow height, and firn temperature from each station. The majority of the FirnCover stations were installed in close proximity to automated weather stations that measure a wider suite of meteorological measurements, allowing the user access to auxiliary datasets for model validation studies using FirnCover data. The dataset can be found here: https://doi.org/10.18739/A25X25D7M (MacFerrin et al., 2021).

Practical Improvements to Generator Parameter Validation Using Stator-Decrement Test

This article discusses the accuracy issue in applying the commonly used stator-decrement test (load rejection) method for validating generator d-axis parameters. Theoretical analysis and actual examples are provided to illustrate how the accuracy issue is related to the rotor field winding current as well as hysteresis, which has not been analyzed in detail for the second-order generator models widely used in power system studies. A generic, practical method for validating generator synchronous inductance is proposed and proven with actual field measurements and model validation study results using the GENQEC model. The generator field current compensation factor of the GENQEC model improves the field current accuracy independently of magnetic saturation. Its advantages are demonstrated in model parameter validation using field measurement results from in-service salient-pole and round-rotor synchronous generators. The proposed method with the GENQEC dynamic model can be used to establish a baseline for accurately validating generator direct-axis parameters using stator-decrement test results. The innovative method of validating generator synchronous d-axis reactance proposed in this article can be a practical and reliable alternative to the test methods provided by IEEE standards.

A simple mortality risk prediction score for viper envenoming in India (VENOMS): A model development and validation study.

Snakebite is a neglected problem with a high mortality in India. There are no simple clinical prognostic tools which can predict mortality in viper envenomings. We aimed to develop and validate a mortality-risk prediction score for patients of viper envenoming from Southern India. We used clinical predictors from a prospective cohort of 248 patients with syndromic diagnosis of viper envenoming and had a positive 20-minute whole blood clotting test (WBCT 20) from a tertiary-care hospital in Puducherry, India. We applied multivariable logistic regression with backward elimination approach. External validation of this score was done among 140 patients from the same centre and its performance was assessed with concordance statistic and calibration plots. The final model termed VENOMS from the term "Viper ENvenOming Mortality Score included 7 admission clinical parameters (recorded in the first 48 hours after bite): presence of overt bleeding manifestations, presence of capillary leak syndrome, haemoglobin <10 g/dL, bite to antivenom administration time > 6.5 h, systolic blood pressure < 100 mm Hg, urine output <20 mL/h in 24 h and female gender. The lowest possible VENOMS score of 0 predicted an in-hospital mortality risk of 0.06% while highest score of 12 predicted a mortality of 99.1%. The model had a concordance statistic of 0·86 (95% CI 0·79-0·94) in the validation cohort. Calibration plots indicated good agreement of predicted and observed outcomes. The VENOMS score is a good predictor of the mortality in viper envenoming in southern India where Russell's viper envenoming burden is high. The score may have potential applications in triaging patients and guiding management after further validation.

Parallel-Computing Two-Way Grid-Nested Storm Surge Model with a Moving Boundary Scheme and Case Study of the 2013 Super Typhoon Haiyan

This study presents a numerical tool for calculating storm surges from offshore, nearshore, and coastal regions using the finite-difference method, two-way grid-nesting function in time and space, and a moving boundary scheme without any numerical filter adopted. The validation of the solitary wave runup on a circular island showed the perfect matches between the model results and measurements for the free surface elevations and runup heights. After the benchmark problem validation, the 2013 Super Typhoon Haiyan event was selected to showcase the storm surge calculations with coastal inundation and flood depths in Tacloban. The catastrophic storm surges of about 8 m and wider, storm-induced inundation due to the Super Typhoon Haiyan were found in the Tacloban Airport, corresponding to the findings from the field survey. In addition, the anti-clockwise, storm-induced currents were explored inside of Cancabato Bay. Moreover, the effect of the nonlinear advection terms with the fixed and moving shoreline and the parallel efficiency were investigated. By presenting a storm surge model for calculating storm surges, inundation areas, and flood depths with the model validation and case study, this study hopes to provide a convenient and efficient numerical tool for forecasting and disaster assessment under a potential severe tropical storm with climate change.

Image Analysis of Particle Flow in Centrifugal Solar Particle Receiver

Abstract Particle solar receivers promise economical and operational advantages compared to the molten salt-based solar receivers. In this study, an experiment is designed to observe the particle flow characteristics in the Centrifugal Particle Solar Receiver. A set of experiments for various receiver rotational speeds and particle mass flowrates is conducted, and experimentally obtained raw results are post-processed by means of an Image Processing Routine based on 4BestEstimate algorithm. The axial advance of the particles in one receiver rotation, the particle film thickness, and the ratio of the length of the stationary zone to the receiver circumference is measured to be later used in the validation study of the discrete element method-based numerical model.

Real-Time Comprehensive Energy Analysis of the LHD 811MK-V Machine with Mathematical Model Validation and Empirical Study of Overheating: An Experimental Approach

Real-Time Comprehensive Energy Analysis of the LHD 811MK-V Machine with Mathematical Model Validation and Empirical Study of Overheating: An Experimental Approach