Model Estimates Research Articles

Numerical and experimental investigation of floating wick solar still with a porous-media system

Wick-based solar desalination and solar-driven interfacial evaporation have attracted significant attention for their high efficiency in evaporation and salt removal. This study presents both experimental and numerical models for wick solar desalination. Cotton fabric, coated with black carbon for enhanced radiation absorption, was used as the evaporation medium in experiments due to its superior desalination, capillary, and salt rejection properties. Experimental results showed an evaporation rate of 1.57 kg/m2·h and efficiency of 81 % with freshwater. For saltwater (3.6 % salinity), the evaporation rate and efficiency were 1.24 kg/m2·h and 71 %, under 1000 W/m2 radiation. After validating the numerical model, the effects of porosity, capillary pressure, wick thickness, and thickness of the fabric’s upper (solar absorber) layer were examined. Results showed that reducing the solar absorber fabric thickness (from 9 mm to 0.6 mm), lowering porosity (from 0.97 to 0.3), and increasing wick thickness (from 1.5 mm to 10.5 mm) improved evaporation rates by 58.6 %, 27 %, and 28.4 %, respectively. Capillary pressure and fabric submerged length had minimal effects on evaporation. The model estimates that in real conditions at Jask coastal port (Iran), a configuration with greater wick thickness (6 mm vs. 3 mm), reduced solar absorber fabric thickness (1 mm vs. 1.5 mm), and lower porosity (0.75 vs. 0.93) could increase annual freshwater output by 26.6 % (from 796.6 to 1008.4 kg/m2). The cost per liter of produced water in the best-case scenario is estimated to be $0.0045.

A Feedback Control Strategy to Regulate the Temperature in a Nonlinear Solar Receiver

Abstract The substantial energy provided by the sun is a promising substitute for traditional heat sources in various industrial applications. However, the transient nature of solar energy still poses a significant challenge to its widespread utilization. This work presents a methodology for regulating the temperature within a solar receiver by dynamically adjusting incoming sunlight through the aperture using a controlled iris mechanism. The performance of this technique is experimentally compared with the gas flowrate control method, which is typically used in industry. The proposed control system, grounded in the physical model of the solar receiver, underwent experimental testing under varying conditions, including different gas flowrates, simulator power levels, and aperture sizes. The collected data were then analyzed to estimate a simplified model of the solar receiver. A model predictive controller (MPC) is implemented using the model estimations, and its performance was assessed by tracking two set points (335 and 325 °C) over a period of 2 h. The experimental testing of both control systems indicates the superiority of iris mechanism over gas flowrate controller in terms of robustness, settling time, and smoothness. A hybrid control system utilizing both aperture size and gas flowrate is also developed and tested during the operation of the solar receiver via computer simulations.

A reassessment of graduation modeling for policy design

A vast and diverse literature estimates graduation chances using logistic models set in an arbitrary timeframe, where a graduation indicator is checked at a conventional point in time and associated with covariates measured at some date. Survival models emerged over time as a robust alternative, for being able to estimate time-to-degree and time-varying effects of predictors. This paper reconsiders the effectiveness of both modeling approaches in addressing policy-relevant questions, particularly in light of the increasingly automated and algorithm-based educational policies. We find that both methods exhibit blind spots and limitations, but that adopting a simple pragmatic approach logistic models can achieve a comparable level of effectiveness at depicting graduation dynamics while also being capable of answering questions that are problematic for survival models. We exploit a unique dataset and the nature of discrete-time survival models as combinations of logistic regressions run at different times to illustrate how arbitrary timeframes impact the estimates of a logistic model of graduation. Conversely, we illustrate how separately running and analyzing all the distinct logistic regressions provides insights that are unlikely to come from a survival model.

The Impact of the Approach to Accounting for Age and Sex in Economic Models on Predicted Quality-Adjusted Life-Years.

The method used to model general population mortality estimates in cohort models can make a meaningful difference in appraisals; particularly in scenarios involving potentially curative treatments where a prior National Institute for Health and Care Excellence (NICE) appraisal demonstrated that this assumption alone could make a difference of ~£10,000 to the incremental cost-effectiveness ratio. Our objective was to evaluate the impact of different methods for calculating general population mortality estimates on the predicted total quality-adjusted life expectancy (QALE) as well as absolute and proportional quality-adjusted life year (QALY) shortfall calculations. We employed three distinct methods for deriving general population mortality estimates: firstly, utilizing the population mean age at baseline; secondly, modelling the distribution of mean age at baseline by fitting a parametric distribution to patient-level data sourced from the Health Survey for England (HSE); and thirdly, modelling the empirical age distribution. Subsequently, we simulated patient age distributions to explore the effects of mean starting age and variance levels on the predicted QALE and applicable severity modifiers. Provided sample code in R and Visual Basic for Applications (VBA) facilitates the utilization of individual patient age and sex data to generate weighted average survival and health-related quality of life (utility) outputs. We observed differences of up to 10.4% (equivalent to a difference of 1.01 QALYs in quality-adjusted life-expectancy) between methods using the HSE dataset. In our simulation study, increasing variance in baseline age diminished the accuracy of predictions relying solely on mean age estimation. Differences of -0.30 to 2.24 QALYs were found at a standard deviation of 20%; commonly observed in trials. For potentially curative treatments this would represent a difference in economically justifiable price of -£4,500-+£33,600 at a cost-effectiveness threshold of £30,000 per QALY for a treatment with a 50% cure rate. For lower baseline ages, the population mean method tended to overestimate QALE, whereas for higher baseline ages, it tended to underestimate QALE compared with individual patient age-based approaches. The severity modifier assigned did not vary, however, apart from simulations with means at the extremes of the age distribution or with very high variance. Our analysis underscores the necessity of accounting for the distribution of mean age at baseline, as failure to do so can lead to inaccurate QALE estimates, thereby affecting calculations of incremental costs and QALYs in models, which base survival and quality of life predictions on general population expectations. We would recommend that patient age and sex distribution should be accounted for when incorporating general population mortality in economic models. Provided sufficient sample size, utilizing the observed empirical distribution for the expected population in clinical practice is likely to yield the most accurate results. However, in the absence of patient-level data, selecting a suitable parametric distribution is recommended.

Normalization of timed measures in bilingualism research

Abstract The time it takes an individual to respond to a probe (e.g., a word, picture, or question) or to read a word or phrase provides useful insights into cognitive processes. Consequently, timed measures are a staple in bilingualism research. However, timed measures usually violate assumptions of linear models, one being normal distribution of the residuals. Power transformations are a common solution but which of the many possible transformations to apply is often guesswork. Box and Cox (1964) developed a procedure to estimate the best-fitting normalizing transformation, coefficient lambda (λ), that is easy to run using standard R packages. This practical primer demonstrates how to perform the Box-Cox transformation in R using as a testbed the distractor items from a recent eye-tracking study on sentence reading in speakers of Spanish as a majority and a heritage language. The analyses show (a) that the exponents selected via the Box-Cox procedure reduce positive skewness as well as or better than the natural log; (b) that the best-fitting value of λ varies based on factors such as group and, in the case of eye-movement data, the measure of interest; and (c) that the choice of transformation sometimes impacts p values for model estimates.

Nonparametric Test for Logistic Regression with Application to Italian Enterprises’ Propensity for Innovation

In this work, a nonparametric method is proposed to jointly test the significance of the regression coefficient estimates in a logistic regression model and identify which explanatory variables are effective in predicting the binary response. The motivating example is related to the factors affecting the propensity of Italian Small Medium Enterprises (SMEs) to innovate. The explanatory variables of the model represent firms’ characteristics, such as size and age, and the possible effect of the sector of economic activity is taken into account by including a set of binary variables as control factors. The dependent variable indicates whether a company, in the period under study, introduced at least one product or process innovation. Therefore, it is also dichotomous, and the logistic regression model is appropriate for representing the relationship between explanatory variables and dependent variables. Specifically, the logit transformation of the firm’s propensity to innovate, i.e., the probability that a company randomly chosen from the population of Italian SMEs has introduced an innovation or, equivalently, the proportion of innovative companies among the Italian SMEs, is expressed as a linear function of the predictors (explanatory and control variables). The proposed test is based on the permutation approach and satisfies important statistical properties, proved in a simulation study. The test is more flexible and robust than the classic parametric approach, and is preferable to typical stepwise regression procedures for the selection of a parsimonious and effective model.

Accounting for plume rise of aircraft emissions and shoreline meteorology enhances AERMOD’s description of concentrations measured around Los Angeles airport

ABSTRACT The Aviation Environmental Design Tool (AEDT), developed by the FAA, is used to analyze the environmental impact of airport activities on air quality and noise near airports. AEDT incorporates AERMOD to estimate concentrations resulting from aircraft emissions, which possess horizontal momentum as well as buoyancy. The current version (v23132) of AERMOD incorporates plume dynamics associated with such emissions as an ALPHA option. AERMET, AERMOD’s meteorological processor does not account for the meteorology of the land-water interface that is likely to be important for airports located on the shorelines of lakes or oceans. An approach to include these effects in AERMOD was previously developed. This study examines the impact of including plume rise and shoreline effects in AERMOD by evaluating model estimates of NOX and SO2 with corresponding measurements made during the Los Angeles Airport Air Quality Source Apportionment Study (AQSAS) in the winter and summer of 2012. The performance statistics resulting from this model evaluation suggest that the inclusion of plume rise of aircraft emissions and shoreline effects on meteorological inputs is likely to improve AERMOD’s ability to estimate the impact of airport emissions on surrounding air quality. Implications: Because airport emissions, particularly those from aircraft, affect local air quality, the National Environmental Policy Act (NEPA) requires the use of dispersion models such as AERMOD to assess compliance of air quality regulations when potential expansions of airport activity are planned. The current regulatory version of AERMOD does not include aircraft-specific plume rise and shoreline-related meteorological processes, which affect the dispersion of airport emissions. The preliminary evidences presented in our previous work suggest that the incorporation of these effects will enhance AERMOD’s ability to estimate NOX and SO2 concentrations associated with airport emissions. These enhancements are beneficial not only for policy-making and regulatory compliance but also for promoting sustainable development near airports and protecting public health.

Satellite-based measurements of temporal and spatial variations in C fluxes of irrigated and rainfed cotton grown in India

The small number of carbon dioxide (CO2) observation networks and the prohibitively high equipment cost restrict the estimation of net ecosystem CO2 exchange (NEE). Satellite-based remote sensing techniques have made it possible to obtain NEE and component carbon (C) fluxes. One-third of the world's cotton area is in India, but the information on NEE is limited. We used the Level 4 Carbon (L4_C) product from the Soil Moisture Active Passive (SMAP) mission to estimate C fluxes based on satellite-derived soil moisture, weather, and vegetation data. For our study (2018-19 to 2020-21), we chose two ecosystems (rainfed central India vs. irrigated northern India). Seasonal variations were observed in C fluxes. Gross primary productivity was the highest during the boll formation phase. This phase was the strongest sink and coincided with the highest CO2 uptake, followed by the flowering and square formation phases. The cotton crop was a C source during the initial vegetative phase and after the boll opening. Overall, the cotton crop was a sink for atmospheric CO2 with an average NEE value of −189.6 g C m−2 under irrigated and −245.6 g C m−2 in rainfed cotton. Higher ecosystem respiration in irrigated cotton resulted in lower C sink strength than rainfed cotton. Our studies indicate that the SMAP L4_C product model estimates can be used to obtain information on C fluxes in real-world situations. Moreover, such satellite-based remote sensing techniques will enable large-scale environmental monitoring with different cropping systems and support policymaking.

Online eurythmy therapy for cancer-related fatigue: a prospective repeated-measures observational study exploring fatigue, stress, and mindfulness.

Cancer is a debilitating disease with an often chronic course. One of the most taxing and prevalent sequelae in this context is cancer-related fatigue (CRF) resulting from the disease and/or associated treatments. Over the last years mindfulness-based interventions such as eurythmy therapy (ERYT), a mindful-movement therapy from anthroposophic medicine, have emerged as promising adjunct therapies in oncology. This prospective study investigated an online implementation of ERYT for CRF using a single arm repeated-measures design based on two consecutive studies. Study 1 consisted of an initial assessment before, during, after, and at follow up of a 6-week online ERYT-based program in a mixed sample of N = 165 adults with or without cancer diagnosis. Study 2 involved a similar design with an adapted 8-week online ERYT-based program in a sample of N = 125 adults who had been diagnosed with cancer. Outcomes were assessed using the Functional Assessment of Chronic Illness Therapy-Fatigue, Perceived Stress Scale, Mindful Attention Awareness Scale, and Insomnia Severity Index (for Study 1 all, for Study 2 only the former three). We additionally performed an exploratory analysis regarding practice frequency and duration. Data were analyzed using Linear Mixed-Effect Models per outcome; ANOVA was used for practice times. For Study 1, mixed-effects model estimates showed no significant effect on fatigue, but pointed to significantly improved emotional and physical well-being, reduced stress, as well as increased mindfulness (mixed subjects). Functional and social well-being or sleep quality did not change significantly. Study 2 model estimates on the other hand showed significantly improved CRF in conjunction with the ERYT-based online intervention, as well as improved stress and mindfulness scores (cancer-diagnosed subjects). Taken together, while our results should be interpreted with caution given the single-arm design and relatively high dropout, they suggest online ERYT may be associated with a reduction in fatigue for individuals diagnosed with cancer, an increase in mindfulness, and benefits for stress and certain well-being indicators. The online group format is advantageous in view of affordability and accessibility, the latter being particularly relevant for individuals who due to high symptom severity cannot leave their homes. Randomized-controlled studies will be needed to confirm these findings.

Minimum profile Hellinger distance estimation for single-index models

Single-index models are the most commonly used covariate models and are widely used in statistical applications. Such models include linear models and generalised linear models as special cases. This article investigates efficient and robust estimates for single-index models. For this purpose, we employ the minimum distance approach which in general is automatically robust with respect to the stability of the quantity being estimated. In particular, the minimum Hellinger distance approach introduced by Beran [(1977), ‘Minimum Hellinger Distance Estimators for Parametric Models’, Annals of Statistics, 5, 445–463] produces estimators that are asymptotically efficient at the model density and simultaneously possess excellent robustness properties. In this paper, we construct a minimum profile Hellinger distance estimator (MPHDE) for single-index models. We prove the consistency of the proposed MPHDE and examine its finite-sample performance and robustness properties via Monte Carlo simulation studies and real data analysis.

Systematically missing data in distributed data networks: multiple imputation when data cannot be pooled

Systematically missing data in distributed data networks presents practical and methodological challenges. Failure to handle it appropriately can bias statistical inference. Multiple imputations can be used to address systematic missingness. However, when data from different study sites cannot be pooled into a unified file, conventional imputation approaches become unavailable due to the absence of a basis for imputation. To address such challenges, we introduce an imputation method based on conditional quantiles – conditional quantile imputation (CQI) – which involves four steps: (i) estimating 99 quantiles for the systematically missing variable in studies with observed data; (ii) deriving a weighted average of regression coefficients across studies and transmitting it to sites with systematically missing data; (iii) imputing the systematically missing values based on observed data and the set of regression coefficients from step ii; and (iv) combining estimates of the substantive outcome model across imputations using Rubin's rules. We evaluate CQI in different simulation scenarios and illustrate it with an applied data example. We conclude that CQI can be a suitable approach for the imputation of systematically missing data when data from multiple studies cannot be pooled.

Automated abdominal organ segmentation algorithms for non-enhanced CT for volumetry and 3D radiomics analysis

Abstract Purpose To develop fully-automated abdominal organ segmentation algorithms from non-enhanced abdominal CT and low-dose chest CT and assess their feasibility for automated CT volumetry and 3D radiomics analysis of abdominal solid organs. Methods Fully-automated nnU-Net-based models were developed to segment the liver, spleen, and both kidneys in non-enhanced abdominal CT, and the liver and spleen in low-dose chest CT. 105 abdominal CTs and 60 low-dose chest CTs were used for model development, and 55 abdominal CTs and 10 low-dose chest CTs for external testing. The segmentation performance for each organ was assessed using the Dice similarity coefficients, with manual segmentation results serving as the ground truth. Agreements between ground-truth measurements and model estimates of organ volume and 3D radiomics features were assessed using the Bland–Altman analysis and intraclass correlation coefficients (ICC). Results The models accurately segmented the liver, spleen, right kidney, and left kidney in abdominal CT and the liver and spleen in low-dose chest CT, showing mean Dice similarity coefficients in the external dataset of 0.968, 0.960, 0.952, and 0.958, respectively, in abdominal CT, and 0.969 and 0.960, respectively, in low-dose chest CT. The model-estimated and ground truth volumes of these organs exhibited mean differences between − 0.7% and 2.2%, with excellent agreements. The automatically extracted mean and median Hounsfield units (ICCs, 0.970–0.999 and 0.994–0.999, respectively), uniformity (ICCs, 0.985–0.998), entropy (ICCs, 0.931–0.993), elongation (ICCs, 0.978–0.992), and flatness (ICCs, 0.973–0.997) showed excellent agreement with ground truth measurements for each organ; however, skewness (ICCs, 0.210–0.831), kurtosis (ICCs, 0.053–0.933), and sphericity (ICCs, 0.368–0.819) displayed relatively low and inconsistent agreement. Conclusion Our nnU-Net-based models accurately segmented abdominal solid organs in non-enhanced abdominal and low-dose chest CT, enabling reliable automated measurements of organ volume and specific 3D radiomics features.

Precision estimation of winter wheat crop height and above-ground biomass using unmanned aerial vehicle imagery and oblique photoghraphy point cloud data.

Crop height and above-ground biomass (AGB) serve as crucial indicators for monitoring crop growth and estimating grain yield. Timely and accurate acquisition of wheat crop height and AGB data is paramount for guiding agricultural production. However, traditional data acquisition methods suffer from drawbacks such as time-consuming, laborious and destructive sampling. The current approach to estimating AGB using unmanned aerial vehicles (UAVs) remote sensing relies solely on spectral data, resulting in low accuracy in estimation. This method fails to address the ill-posed inverse problem of mapping from two-dimensional to three-dimensional and issues related to spectral saturation. To overcome these challenges, RGB and multispectral sensors mounted on UAVs were employed to acquire spectral image data. The five-directional oblique photography technique was utilized to construct the three-dimensional point cloud for extracting crop height. This study comparatively analyzed the potential of the mean method and the Accumulated Incremental Height (AIH) method in crop height extraction. Utilizing Vegetation Indices (VIs), AIH and their feature combinations, models including Random Forest Regression (RFR), eXtreme Gradient Boosting (XGBoost), Gradient Boosting Regression Trees (GBRT), Support Vector Regression (SVR) and Ridge Regression (RR) were constructed to estimate winter wheat AGB. The research results indicated that the AIH method performed well in crop height extraction, with minimal differences between 95% AIH and measured crop height values were observed across various growth stages of wheat, yielding R2 ranging from 0.768 to 0.784. Compared to individual features, the combination of multiple features significantly improved the model's estimate accuracy. The incorporation of AIH features helps alleviate the effects of spectral saturation. Coupling VIs with AIH features, the model's R2 increases from 0.694-0.885 with only VIs features to 0.728-0.925. In comparing the performance of five machine learning algorithms, it was discovered that models constructed based on decision trees were superior to other machine learning algorithms. Among them, the RFR algorithm performed optimally, with R2 ranging from 0.9 to 0.93. In conclusion, leveraging multi-source remote sensing data from UAVs with machine learning algorithms overcomes the limitations of traditional crop monitoring methods, offering a technological reference for precision agriculture management and decision-making.

Dementia risk prediction using decision-focused content selection from medical notes

Several general-purpose language model (LM) architectures have been proposed with demonstrated improvement in text summarization and classification. Adapting these architectures to the medical domain requires additional considerations. For instance, the medical history of the patient is documented in the Electronic Health Record (EHR) which includes many medical notes drafted by healthcare providers. Direct processing of these notes may not be possible because the computational complexity of LMs imposes a limit on the length of input text. Therefore, previous applications resorted to content selection using truncation or summarization of the text. Unfortunately, these text processing techniques may lead to information loss, redundancy or irrelevance. In the present paper, a decision-focused content selection technique is proposed. The objective of this technique is to select a subset of sentences from the medical notes of a patient that are relevant to the target outcome over a predefined observation period. This decision-focused content selection methodology is then used to develop a dementia risk prediction model based on the Longformer LM architecture. The results show that the proposed framework delivers an AUC of 78.43 when the summary is restricted to 1024 tokens, outperforming previously proposed content selection techniques. This performance is notable given that the model estimates dementia risk with a one year prediction horizon, relies on an observation period of only one year and solely uses medical notes without other EHR data modalities. Moreover, the proposed techniques overcome the limitation of machine learning models that use a tabular representation of the text by preserving contextual content, enable feature engineering from raw text and circumvent the computational complexity of language models.

Approximate cross-validated mean estimates for Bayesian hierarchical regression models

We introduce a novel procedure for obtaining cross-validated predictive estimates for Bayesian hierarchical regression models (BHRMs). BHRMs are popular for modeling complex dependence structures (e.g., Gaussian processes and Gaussian Markov random fields) but can be computationally expensive to run. Cross-validation (CV) is, therefore, not a common practice to evaluate the predictive performance of BHRMs. Our method circumvents the need to re-run computationally costly estimation methods for each cross-validation fold and makes CV more feasible for large BHRMs. We shift the CV problem from probability-based sampling to a familiar and straightforward optimization problem by conditioning on the variance-covariance parameters. Our approximation applies to leave-one-out CV and leave-one-cluster-out CV, the latter of which is more appropriate for models with complex dependencies. In many cases, this produces estimates equivalent to full CV. We provide theoretical results, demonstrate the efficacy of our method on publicly available data and in simulations, and compare the model performance with several competing methods for CV approximation. Code and other supplementary materials available online.

Machine Learning for the Prediction of Surgical Morbidity in Placenta Accreta Spectrum.

We sought to create a machine learning (ML) model to identify variables that would aid in the prediction of surgical morbidity in cases of placenta accreta spectrum (PAS). A multicenter analysis including all cases of PAS identified by pathology specimen confirmation, across five tertiary care perinatal centers in New York City from 2013 to 2022. We developed models to predict operative morbidity using 213 variables including demographics, obstetrical information, and limited prenatal imaging findings detailing placental location. Our primary outcome was prediction of a surgical morbidity composite defined as including any of the following: blood loss (>1,500 mL), transfusion, intensive care unit admission, vasopressor use, mechanical ventilation/intubation, and organ injury. A nested, stratified, cross-validation approach was used to tune model hyperparameters and estimate generalizability. Gradient boosted tree classifier models incorporated preprocessing steps of standard scaling for numerical variables and one-hot encoding for categorical variables. Model performance was evaluated using area under the receiver operating characteristic curve (AUC), positive and negative predictive values (PPV, NPV), and F1 score. Variable importance ranking was also determined. Among 401 PAS cases, 326 (81%) underwent hysterectomy. Of the 401 cases of PAS, 309 (77%) had at least one event defined as surgical morbidity. Our predictive model had an AUC of 0.79 (95% confidence interval: 0.69, 0.89), PPV 0.79, NPV 0.76, and F1 score of 0.88. The variables most predictive of surgical morbidity were completion of a hysterectomy, prepregnancy body mass index (BMI), absence of a second trimester ultrasound, socioeconomic status zip code, BMI at delivery, number of prenatal visits, and delivery time of day. By identifying social and obstetrical characteristics that increase patients' risk, ML models are useful in predicting PAS-related surgical morbidity. Utilizing ML could serve as a foundation for risk and complexity stratification in cases of PAS to optimize surgical planning. · ML models are useful models are useful in predicting PAS-related surgical morbidity.. · Optimal management for PAS remains unclear.. · Utilizing ML can serve as a foundation for risk and complexity stratification in cases of PAS..

Estimating community-wide indirect effects of influenza vaccination: triangulation using mathematical models and bias analysis.

Understanding whether influenza vaccine promotion strategies produce community-wide indirect effects is important for establishing vaccine coverage targets and optimizing vaccine delivery. Empirical epidemiologic studies and mathematical models have been used to estimate indirect effects of vaccines but rarely for the same estimand in the same dataset. Using these approaches together could be a powerful tool for triangulation in infectious disease epidemiology because each approach is subject to distinct sources of bias. We triangulated evidence about indirect effects from a school-located influenza vaccination program using two approaches: a difference-in-difference (DID) analysis, and an age-structured, deterministic, compartmental model. The estimated indirect effect was substantially lower in the mathematical model than in the DID analysis (2.1% (95% Bayesian credible intervals 0.4 - 4.4%) vs. 22.3% (95% CI 7.6% - 37.1%)). To explore reasons for differing estimates, we used sensitivity analyses and probabilistic bias analyses. When we constrained model parameters such that projections matched the DID analysis, results only aligned with the DID analysis with substantially lower pre-existing immunity among school-age children and older adults. Conversely, DID estimates corrected for potential bias only aligned with mathematical model estimates under differential outcome misclassification. We discuss how triangulation using empirical and mathematical modelling approaches could strengthen future studies.

Thermal coal futures trading volume predictions through the neural network

Purpose Predicting commodity futures trading volumes represents an important matter to policymakers and a wide spectrum of market participants. The purpose of this study is to concentrate on the energy sector and explore the trading volume prediction issue for the thermal coal futures traded in Zhengzhou Commodity Exchange in China with daily data spanning January 2016–December 2020. Design/methodology/approach The nonlinear autoregressive neural network is adopted for this purpose and prediction performance is examined based upon a variety of settings over algorithms for model estimations, numbers of hidden neurons and delays and ratios for splitting the trading volume series into training, validation and testing phases. Findings A relatively simple model setting is arrived at that leads to predictions of good accuracy and stabilities and maintains small prediction errors up to the 99.273th quantile of the observed trading volume. Originality/value The results could, on one hand, serve as standalone technical trading volume predictions. They could, on the other hand, be combined with different (fundamental) prediction results for forming perspectives of trading trends and carrying out policy analysis.

Driving Under the Influence of Alcohol and Cannabis by Sexual Identity, Race, Ethnicity, and Gender: A Nationwide Analysis Using the 2016 to 2019 National Survey on Drug Use and Health.

Sexual minority populations experience higher rates of substance use and related problems, but little is known about their specific involvement in driving under the influence (DUI) of alcohol (DUIA) and cannabis (DUIC) incidents. Using data from the 2016 to 2019 National Survey on Drug Use and Health, we used logistic regression models to estimate the interactive effects of sexual identity, race/ethnicity, and gender on past-year DUIA among adults who used alcohol and DUIC among adults who used cannabis, accounting for covariates. Using model estimates and linear combinations, we calculated the predicted probabilities of each outcome and compared sexual identity differences within and across race/ethnicity and gender. With few exceptions, the predicted probabilities of DUIA and DUIC were significantly higher among sexual minority women than heterosexual women of similar race/ethnicity. The results were more variable among men with the probabilities of DUIA and DUIC being significantly higher for some groups of sexual minority men and some groups having probabilities equal to or lower than similar heterosexual men. Some of the largest sexual minority gaps in DUIA and DUIC were observed among Hispanic and Other lesbian women and Black gay men. Sexual minority individuals are more likely to report DUI than their heterosexual counterparts; however, the risk of DUI among sexual minority populations varies by racial/ethnic and gender subgroup. Our findings indicate the importance of applying an intersectional framework when addressing substance-use-related disparities and when designing effective DUI prevention interventions for sexual minority populations.

A calibration method for telecentric line-scan cameras with an enhanced dynamic imaging model and initial estimates derived from direct linear transformation

This study presents a calibration method for telecentric line-scan cameras, incorporating a dynamic imaging model and initial estimates derived from direct linear transformation. The enhanced dynamic imaging model includes an inclination parameter instead of velocities in three orthogonal spatial directions. It is applicable to any direction of motion, considers lens distortion, and accommodates the line sensor at an arbitrary position relative to the optical axis. Employing the enhanced camera model, the initial values of telecentric line-scan camera parameters are determined derived from direct linear transformation. Sensitivity of our method with respect to the noise level of image points and the number of pattern planes are assessed through numerical simulations, while effectiveness and robustness are validated through experiments using real-world data, achieving a reprojection error of 0.6386 pixels and a standard deviation of 0.0160 pixels. The versatility of the proposed method can be extended to measurement applications utilizing flatbed scanners.