Model Calibration Research Articles

Update of the geographic range of Humboldt's squirrel monkeys (Saimiri cassiquiarensis cassiquiarensis Lesson, 1840) using a model-based approach.

Saimiri cassiquiarensis cassiquiarensis (Cebidae) is a primate subspecies with a wide distribution in the Amazonian region of Brazil, Colombia, and Venezuela. However, the boundaries of its geographic range remain poorly defined. This study presents new occurrence localities for this subspecies and updates its distribution using a compiled data set of 140 occurrence records based on literature, specimens vouchered in scientific collections, and new field data to produce model-based range maps. After cleaning our data set, we updated the subspecies' extent of occurrence, which was used in model calibration. We then modeled the subspecies' range using a maximum entropy algorithm (MaxEnt). The final model was adjusted using a fixed threshold, and we revised this polygon based on known geographic barriers and parapatric congeneric ranges. Our findings indicate that this subspecies is strongly associated with lowland areas, with consistently high daily temperatures. We propose modifications to all range boundaries and estimate that 3% of the area of occupancy (AOO,as defined by IUCN) has already been lost due to deforestation, resulting in a current range of 224,469 km2. We also found that 54% of their AOO is currently covered by protected areas (PAs). Based on these results, we consider that this subspecies is currently properly classified as Least Concern, because it occupies an extensive range, which is relatively well covered by PAs, and is currently experiencing low rates of deforestation.

Can prediction models for hospital readmission be improved by incorporating patient-reported outcome measures? A systematic review and narrative synthesis.

To investigate the roles, challenges, and implications of using patient-reported outcome measures (PROMs) in predicting the risk of hospital readmissions. We systematically searched four bibliometric databases for peer-reviewed studies published in English between 1 January 2000 and 15 June 2023 and used validated PROMs to predict readmission risks for adult populations. Reported studies were analysed and narratively synthesised in accordance with the CHARMS and PRISMA guidelines. Of the 2858 abstracts reviewed, 23 studies met predefined eligibility criteria, representing diverse geographic regions and medical specialties. Among those, 19 identified the positive contributions of PROMs in predicting readmission risks. Seven studies utilised generic PROMs exclusively, eleven used generic and condition-specific PROMs, while 5 focussed solely on condition-specific PROMs. Logistic regression was the most used modelling approach, with 13 studies aiming at predicting 30-day all-cause readmission risks. The c-statistic, ranging from 0.54 to 0.84, was reported in 22/23 studies as a measure of model discrimination. Nine studies reported model calibration in addition to c-statistic. Thirteen studies detailed their approaches to dealing with missing data. Our study highlights the potential of PROMs to enhance predictive accuracy in readmission models, while acknowledging the diversity in data collection methods, readmission definitions, and model evaluation approaches. Recognizing that PROMs serve various purposes beyond readmission reduction, our study supports routine data collection and strategic integration of PROMs in healthcare practices to improve patient outcomes. To facilitate comparative analysis and broaden the use of PROMs in the prediction framework, it is imperative to consider the methodological aspects involved.

Rational Kriging

This article proposes a new kriging that has a rational form. It is shown that the generalized least squares estimator of the mean from rational kriging is much more well behaved than that of ordinary kriging. Parameter estimation and uncertainty quantification for rational kriging are proposed using a Gaussian process framework. A generalized version of rational kriging is also proposed, which includes ordinary and rational kriging as special cases. Extensive simulations carried out over a wide class of functions show that the generalized rational kriging performs on par or better than both ordinary and rational kriging in terms of prediction and uncertainty quantification. The only extra step needed for generalized rational kriging over ordinary kriging is the computation of Perron eigenvector of an augmented correlation matrix which can be computed in near linear time and therefore, its overall computational complexity is no more than that of ordinary kriging. The potential applications of the new kriging methods in the emulation of computationally expensive models and model calibration problems are illustrated with real and simulated examples. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

A systematic approach to manual calibration and validation of building energy simulation

PurposeThis study aims to create an efficient approach to validate building energy simulation models amidst challenges from time-intensive data collection. Emphasizing precision in model calibration through strategic short-term data acquisition, the systematic framework targets critical adjustments using a strategically captured dataset. Leveraging metrics like Mean Bias Error (MBE) and Coefficient of Variation of Root Mean Square Error (CV(RMSE)), this methodology aims to heighten energy efficiency assessment accuracy without lengthy data collection periods.Design/methodology/approachA standalone school and a campus facility were selected as case studies. Field investigations enabled precise energy modeling, emphasizing user-dependent parameters and compliance with standards. Simulation outputs were compared to short-term actual measurements, utilizing MBE and CV(RMSE) metrics, focusing on internal temperature and CO2 levels. Energy bills and consumption data were scrutinized to verify natural gas and electricity usage against uncertain parameters.FindingsDiscrepancies between initial simulations and measurements were observed. Following adjustments, the standalone school 1’s average internal temperature increased from 19.5 °C to 21.3 °C, with MBE and CV(RMSE) aiding validation. Campus facilities exhibited complex variations, addressed by accounting for CO2 levels and occupancy patterns, with similar metrics aiding validation. Revisions in lighting and electrical equipment schedules improved electricity consumption predictions. Verification of natural gas usage and monthly error rate calculations refined the simulation model.Originality/valueThis paper tackles Building Energy Simulation validation challenges due to data scarcity and time constraints. It proposes a strategic, short-term data collection method. It uses MBE and CV(RMSE) metrics for a comprehensive evaluation to ensure reliable energy efficiency predictions without extensive data collection.

Impact of the model structure and calibration strategy on baseflow modeling in the German low mountain range

ABSTRACT Baseflow is a vital component of the water balance. The fractured hard rock aquifers of the German low mountain range are in danger of increased water stress due to climate change because they react rapidly to deficits in precipitation and groundwater tables decline sharply. Therefore, simulation software must be able to model baseflow accurately. Three soil moisture simulation and two monthly factor-based baseflow models are evaluated using two calibration strategies. Models were calibrated to total flow (S1) or stepwise to baseflow and then total flow (S2). Results were not significantly different for total flow. Regarding baseflow, S2 proved significantly better with median values (S1 calibration, validation | S2 calibration, validation) of SSE (20.3, 20.3 | 13.5, 13.8), LnNSE (0.15, 0.17 | 0.47, 0.34), and PBIAS (27.8, 21.6 | 2.5, −0.8). Parallel linear reservoir proved best at modeling baseflow with a median SSE (S2: 6.1, 5.9), LnNSE (S2: 0.64, 0.71), and PBIAS (S2: 3.8, 3.8). The new modified monthly factor approach is a simple and robust alternative with SSE (13.0, 13.3), LnNSE (0.61, 0.61), and PBIAS (9.8, −8.6). The results are useful regarding selection of baseflow model structure and calibration strategy in low mountain ranges with fractured hard rock aquifers.

Local Modeling by Adapting Source Calibration Models to Analyte Shifted Target Domain Samples Without Reference Values.

Spectral multivariate calibration aims to derive models characterizing mathematical relationships between sample analyte amounts and corresponding spectral responses. These models are effective at predicting target domain sample analyte amounts when target samples are within the analyte and spectral calibration source domain. Models fail when target samples shift (analyte amounts and/or spectra) from the original calibration domain model. A total recalibration solution requires acquisition of new sample reference values and spectra. However, obtaining enough reference values to distinguish the target domain may be challenging or expensive. A simpler approach adapts the original model to the target domain using target sample spectra without analyte reference values (unlabeled). Analytical chemists have developed several machine learning algorithms using unlabeled regression domain adaptation processes. Unfortunately, prediction accuracy declines for these methods depending on how much the target domain analyte distribution has shifted from the calibration distribution, and regression transfer learning methods are instead needed. Regression domain adaptation and transfer learning are often referred to as model updating in analytical chemistry, but regression domain adaptation only applies to spectral shifts. The regression transfer learning method presented in this paper named null augmentation regression constant analyte (NARCA) leverages unlabeled repeat spectra of a single target sample to update an original calibration model to the shifted target domain sample. With sample repeat spectra, the analyte amount can be assumed constant or nearly constant for NARCA and because models are formed for one sample, NARCA operates as a local modeling method. The performance of NARCA as a regression transfer learning method is evaluated using five near-infrared data sets.

Rapid quantitative analysis of multiple rare earth elements in NdFeB alloys based on laser-induced breakdown spectroscopy (LIBS) and random forest (RF)

NdFeB material has excellent comprehensive magnetic properties, which plays a crucial role in the field of rare earth magnetic materials, and is one of the important basic materials to support modern high-tech industries. In the production of NdFeB alloys, the control of rare earth elements (REEs) is directly related to the quality and resource utilization efficiency of NdFeB materials. Therefore, the prediction of REEs content in NdFeB alloys is of great research significance and application value for the quality control of products, the enhancement of production efficiency and reduction of energy consumption by the rare earth material industry. In this study, a combination of laser-induced breakdown spectroscopy (LIBS) and random forest (RF) was used to investigate the quantitative analysis of four REEs (Nd, Pr, Tb and Dy) in NdFeB alloys. Firstly, the original LIBS spectra were screened by principal component analysis-mahalanobis distance (PCA-MD). Then, the effects of different data processing methods on the screened LIBS spectra were explored, and next, the feature variables were extracted from the preprocessed spectral data by the variable importance measurement (VIM). In order to further verify the prediction performance of the model, the prediction results of the RF models based on the different methods were compared. Finally, a PCA-VIM-RF calibration model was established on the basis of the optimized spectra, selected feature variables and parameters. Leave-one-out cross validation (LOOCV) was used to optimize the parameters of PCA-MD method, spectral preprocessing method and variable importance thresholds during the construction of the calibration model. The results show that the PCA-VIM-RF model has better prediction performance than the RF calibration model based on the raw spectra. For the PCA-VIM-RF calibration method of Nd, Pr, Tb and Dy elements, the values of R2CV are 0.9991, 0.9998, 0.9986, and 0.9984, respectively, the values of RMSECV are 0.06296%, 0.02788%, 0.04647% and 0.05252%, respectively. The values of R2P are 0.9508, 0.9975, 0.9691 and 0.9457, respectively, and the values of RMSEP are 0.6082%, 0.09205%, 0.5776% and 0.2631%, respectively.The above results indicate that LIBS combined with PCA-VIM-RF algorithm is a promising method for rapid quantitative analysis of REEs in NdFeB alloys without complicated sample preparation, which can provide some new ideas or strategies for the future research, development and quality control of rare earth materials.

Validation of the BOADICEA model in a prospective cohort of BRCA1/2 pathogenic variant carriers

BackgroundNo validation has been conducted for the BOADICEA multifactorial breast cancer risk prediction model specifically in BRCA1/2 pathogenic variant (PV) carriers to date. Here, we evaluated the performance of BOADICEA...

Fast and Reliable History Matching of Channel Reservoirs Using Initial Models Selected by Streamline and Deep Learning

Abstract Ensemble-based methods involve using multiple models for model calibration correct initial models based on observed data. The assimilated ensemble models allow probabilistic analysis of future production behaviors. It is crucial to use good initial models to obtain reliable history matching and prediction of both oil and water productions especially for channel reservoirs having high uncertainty and heterogeneity. In this study, we propose a fast and reliable history matching method by selecting good initial models using streamline and deep learning. The proposed method is applied to two cases of 3D channel reservoir generated by SGeMS and GAN. The proposed method offers predictions with accuracy improvement more than 20% for oil and 10% for water productions compared with two other model selection methods. It also reduces the overall simulation time by 75% compared to the method of using all initial models.

HIFAST: An HI Data Calibration and Imaging Pipeline for FAST II. Flux Density Calibration

Abstract Accurate flux density calibration is essential for precise analysis and interpretation of observations across different observation modes and instruments. In this research, we introduce the flux calibration model incorporated in HIFAST pipeline, designed for processing HI 21-cm spectra. Furthermore, we investigate different calibration techniques and assess the dependence of the gain parameter on the time and environmental factors. A comparison is carried out in various observation modes (e.g. tracking and scanning modes) to determine the flux density gain (G), revealing insignificant discrepancies in G among different methods. Long-term monitoring data shows a linear correlation between G and atmospheric temperature. After subtracting the G - Temperature dependence, the dispersion of G is reduced to <3% over a one-year time scale. The stability of the receiver response of FAST is considered sufficient to facilitate HI observations that can accommodate a moderate error in flux calibration (e.g., >~5%) when utilizing a constant G for calibration purposes. Our study will serve as a useful addition to the results provided by Jiang et al (2020). Detailed measurements of G for the 19 beams of FAST, covering the frequency range 1000 MHz - 1500 MHz can be found on the HIFAST homepage: https://hifast.readthedocs.io/fluxgain

Rational gaze: Presenting the open-source HYPEtools R package for analysis, visualization, and interpretation of hydrological models and datasets

The open-source HYPEtools R package aims to address current challenges in hydrological modeling and data analysis by providing a suite of tools for data management, visualization, interpretation, and exploration. Originally developed as a supporting toolbox for the HYPE hydrological model, HYPEtools has since evolved into a standalone package with features ranging from diverse aggregation and evaluation routines to interactive apps and mapping. Specifically, functions are included for data manipulation and conversion, data summarization, plotting and mapping, and interactive data exploration. Two case studies are provided to highlight how HYPEtools can simplify hydrological workflows and analyses both independently of and within HYPE modeling contexts. First, HYPEtools is used for data exploration and mapping of water transfers in South Africa. Then, a case study for the Baltic Sea region of a pan-European HYPE model setup illustrates how HYPEtools can assist model calibration and validation.

The present state of low-cost air quality sensors in Japan and their accuracy

ABSTRACT The Sustainable Development Goals (SDGs) emphasise the importance of clean air (SDG 3 and SDG 11.6). Japan faces challenges in air quality prediction, especially in rural areas, because of the absence of a climate-specific calibration model and limited deployment of low-cost sensors. Our study compared three sensor types: low-cost pre-assembled and pre-calibrated sensors (PurpleAir); self-assembled low-cost sensors (Citizen Science Project); and highly accurate weather stations, focusing on PM2.5 values. Linear and generalised linear mixed models show potential for low-cost sensor calibration.

Calibration of soil and water assessment model for its potential impact on climate change

Calibration of soil and water assessment model for its potential impact on climate change

Parameter calibration of discrete element model for gluten densification molding.

The densified powder material is convenient for storage and transportation, with broad market application prospects. In this study, the discrete element model parameters required for simulating gluten densification were calibrated using the Hertz-Mindlin with JKR contact model. Initially, physical testing techniques were utilized to assess the size distribution, density, and angle of repose (AoR) of gluten particles. Following this, the Plackett-Burman test, the steepest ascent test, and the Box-Behnken test were conducted, and the significant factors were obtained: The coefficient of rolling friction (P-P) was 1.038, the coefficient of static friction (P-P) was 0.071, and the surface energy (P-P) was 0.047. Finally, the AoR and densification simulations were performed under the optimal parameter combination, along with validation tests. The results showed that the relative error between the simulated and tested AoR was 0.52%. The compression ratio and compression force curves of simulated and actual were similar.

Accelerated aging among survivors of childhood leukemia and lymphoma: Estimates of early onset and excess morbidity from the COMPASS model.

10028 Background: Cohort studies have detected an increased risk of accelerated aging among survivors of childhood cancer but the timing and magnitude are not known. Methods: To predict the clinical course of diseases of aging— cancer and cardiovascular disease — among survivors of childhood leukemia and lymphoma compared to the general population, we developed the Cancer Outcomes Microsimulation: Pediatric and Adolescent SurvivorShip (COMPASS) model. The model simulates 8 severe, disabling or life-threatening chronic health conditions (CHCs), including 4 subsequent cancers (breast, colorectal, glial tumors, sarcomas) and 4 cardiovascular diseases (heart failure, myocardial infarction/coronary artery disease, valvular disease, stroke), and excess mortality risks among survivors diagnosed between 1970 and 1999 over the course of their lifetimes. Treatment-related risks varied by patient characteristics (sex, age at diagnosis, diagnosis) and treatment exposures (chemotherapy, radiation dose) and were based on data from the Childhood Cancer Survivor Study (CCSS). Age-related risks and competing mortality were based on national databases (SEER, NHLBI, CDC Wonder). We used model calibration to identify parameter sets that generated outcomes consistent with observed data. Model outcomes included cumulative CHC risk. For comparisons to the general population, age-, sex, and diagnosis year-matched individuals who faced only age-related risks were simulated. Results: Among survivors representative of CCSS participants, the model estimated that 45% of leukemia and 65% of lymphoma survivors will develop at least 1 of the 8 CHCs by age 65. Compared to the estimated 20% cumulative risk for the general population, this represented a two- to three-fold excess morbidity risk among survivors. The age at which 20% of survivors developed at least 1 CHC was 51 years for leukemia and 42 years for lymphoma, suggesting an early onset of 14 and 21 years compared to the general population. Outcomes varied by diagnosis and treatment era due to competing risk changes. Among leukemia survivors diagnosed in the 1990s vs. 1970s, cumulative late recurrence mortality by age 40 (11% to 1%) declined. This combined with a reduction in radiation exposure (79% to 24%) resulted in a greater proportion living into adulthood and developing CHCs by age 65 (38% to 48%). In contrast, among lymphoma survivors, the proportion projected to develop a CHC by age 65 remained stable during the same period (62% to 65%) as late recurrence mortality risk declined (8% to 1%), but almost half still received radiation (90% to 49%). Conclusions: Despite improvements in therapy, leukemia and lymphoma survivors are projected to experience CHC early onset and excess morbidity, underscoring the importance of prevention-focused survivorship care and continued efforts to develop more targeted therapies.

Estimating risk for pancreatic cancer among 9.4 million veterans in care.

10544 Background: Screening for pancreatic ductal adenocarcinoma (PDAC) has focused on individuals with a hereditary component, accounting for only 10% of PDAC events. Using the electronic health record, we developed a PDAC risk prediction model in a general population in the Veterans Health Administration (VA), the largest integrated health system in the US. Methods: We included all individuals from 2002- 2021 with ≥1 inpatient or outpatient VA encounter and recorded race, starting follow-up 1.5 years after first VA encounter. We excluded those who developed PDAC before start of follow-up. We used the multivariable Cox proportional hazards model in the derivation dataset (random 70% of cohort) to estimate the coefficients associated with each predictor for our outcome, time to PDAC (3 and 10 years), censoring at loss to follow-up or 12/31/2021. Candidate predictors included age, gender, race/ethnicity, sex, BMI, smoking status, alcohol use (alcohol use/related disorders and AUDIT-C), diabetes, pancreatitis, Hepatitis C, HIV, liver disease, cholecystectomy, pancreatic cysts, Charlson comorbidity index and common laboratory values. Selection of predictors was based on clinical reasoning, extent of missing values and model fit (Akaike Information Criterion and Harrell’s c–statistic). We evaluated model discrimination using c – statistic and model calibration comparing observed Kaplan – Meier estimated incidence vs. predicted risk score. Results: Among 9.4 million individuals, we observed 19,839 cases of PDAC within 10 years (6,022 cases within 3 years). Median baseline age was 58.5 years, 75.0% were White, and 16.3% were Black. Final predictors included age, gender, smoking, alcohol use (alcohol use/related disorders and AUDIT-C), BMI, diabetes, pancreatitis, pancreatic cyst, and history of cancer (Table, c-statistic in the derivation cohort 3 years: 0.79; 10 years: 0.77). Ten-year risk ranged maximally from 0-5.5%. Preliminary studies suggest labs do not clinically improve model performance. Conclusions: We developed a novel prediction model using available clinical data in a general population that identifies PDAC with high accuracy. Future work will try to identify additional predictors to improve the range of risk predicted. We will validate our findings in the testing set and in external cohorts. [Table: see text]

Comparative Study of Low Flow Frequency Analysis Using Bivariate Copula Model at Soyanggang Dam and Chungju Dam

A univariate analysis that relies solely on precipitation data in low flow frequency analysis is a technique to express meteorological drought, so it is limited to analyzing the characteristics of hydrological drought related to available water resources. In addition, if the data for the model calibration are insufficient, the uncertainty of a single variable limits the construction of a reliable model. To improve this problem, a frequency analysis was performed by constructing a bivariate copula model as a multivariate model with a high correlation between variables targeting reservoir inflows. The methodology utilizes the theory of runs to identify low flow events, establishing a threshold based on the mandatory regional water supply plan, and determining the low flow duration and cumulative water deficit. The Gumbel copula function, effective in capturing correlations between hydrological variables, was applied to derive a joint bivariate probability distribution, facilitating the calculation of combined low flow event return periods. This study compared low flow frequencies at Soyanggang dam (’74–’22) and Chungju dam (’86–‘22), which are in the same Han River basin but have different capacities and water demands, using a bivariate copula model. The top four extreme low flow events for the two adjacent dam basins did not occur in the same year and, in the years of the extreme low flow events at one of the two dam basins, there was an insignificant magnitude at the remaining dam basin. This result is noteworthy because it shows that the possibility of extreme low flow events appearing simultaneously in both watersheds is not as high as expected. The operational efficiency can be improved by setting the coordinated operation rules of the two reservoirs using the copula dependency structure.

A lightweight holographic imager for cloud microphysical studies from an untethered balloon

Abstract. We describe the construction and testing of an in situ cloud particle imager based on digital holography. The instrument was designed to be low cost and lightweight for vertical profiling of clouds with an untethered weather balloon. This capability is intended to address the lack of in situ cloud microphysical observations that are required for improving the understanding of cloud processes, calibration of climate and weather models, and validation of remote sensing observation methods. From a balloon sounding through multiple bands of cloud, we show that we can retrieve shape information and size distributions of the cloud particles as a function of altitude. Microphysical retrievals from an imaging satellite are compared to these in situ observations, and significant differences are identified, consistent with those identified in prior evaluation campaigns.

Vertical load distribution in precast hollow core floors: State of the art and future perspectives

AbstractPrecast prestressed hollow core (HC) floors are widely used in various applications within the construction sector. Such floors are usually designed as single, simply supported elements, although it is known that individual elements forming the floor interact with each other. This article presents the state of the art regarding load redistribution in HC floors in the light of experimental data, current analytical models and code provisions. While this phenomenon is widely known and recognized, only sparse, and often poorly documented experimental data are available, which represent the basis for the assessment and calibration of analytical models. Moreover, even though the available models and code provisions share similar assumptions, their outcomes are in some cases conflicting. Having recognized the existing knowledge gap, the authors outline future perspectives for the development of consistent analytical and numerical approaches supplemented by new experimental data.

POE-Based Error Modeling and Multiple Plane Constraint-Based Parameter Identification for the Kinematic Calibration of a 4-UPS/SPR Parallel External Fixator

Limb deformity is a common complaint in orthopedic surgery. Currently, gradual treatment using parallel external fixator (PEF) has become the preferred option for deformity correction. As the key medical apparatus with the special application properties of temporary assembly and direct utilization, the geometric errors generated during the manufacturing and assembly process of the PEF contribute to the post-correction residual and iatrogenic deformities. However, considering the complexity of clinical scenarios, there is a lack of an applicable solution to reduce the effect of the above error source of PEF. To overcome these problems, taking a 4-UPS/SPR PEF as the research object, this paper proposes a novel kinematic calibration method. First, the error model with the features of completeness, continuity, and minimality is first established based on the product of exponentials (POE) formula. Then, the identifiability analysis is conducted to eliminate the redundant error parameters and ensure the stability and accuracy of the iterative calculation in the process of parameter identification. Furthermore, a multiple plane constraint (MPC)-based parameter identification approach is introduced to the calibration task of the PEF to effectively reduce the difficulty of the pose information measurement in the current cost-effective constraint calibration method. Finally, the simulation and prototype experiments validate that the proposed calibration method (i.e., the combination of the POE-based error model and the MPC-based parameter identification approach) can accomplish the calibration tasks in clinical applications and meet the cost-effectiveness and efficiency requirements.