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The prediction of the chronological age of a deceased individual at time of death can provide important information in case of unidentified bodies. The methodological possibilities in these cases depend on the availability of tissues, whereby bones are preserved for a long time due to their mineralization under normal environmental conditions. Age-dependent changes in DNA methylation (DNAm) as well as the accumulation of pentosidine (Pen) and D-aspartic acid (D-Asp) could be useful molecular markers for age prediction. A combination of such molecular clocks into one age prediction model seems favorable to minimize inter- and intra-individual variation. We therefore developed (I) age prediction models based on the three molecular clocks, (II) examined the improvement of age prediction by combination, and (III) investigated if samples with signs of decomposition can also be examined using these three molecular clocks. Skull bone from deceased individuals was collected to obtain a training dataset (n = 86), and two independent test sets (without signs of decomposition: n = 44, with signs of decomposition: n = 48). DNAm of 6 CpG sites in ELOVL2, KLF14, PDE4C, RPA2, TRIM59 and ZYG11A was analyzed using massive parallel sequencing (MPS). The D-Asp and Pen contents were analyzed by high performance liquid chromatography (HPLC). Age prediction models based on ridge regression were developed resulting in mean absolute errors (MAEs)/root mean square errors (RMSE) of 5.5years /6.6 years (DNAm), 7.7 years /9.3 years (Pen) and 11.7 years /14.6 years (D-Asp) in the test set. Unsurprisingly, a general lower accuracy for the DNAm, D-Asp, and Pen models was observed in samples from decomposed bodies (MAE: 7.4–11.8 years, RMSE: 10.4–15.4 years). This reduced accuracy could be caused by multiple factors with different impact on each molecular clock. To acknowledge general changes due to decomposition, a pilot model for a possible age prediction based on the decomposed samples as training set improved the accuracy evaluated by leave-one-out-cross validation (MAE: 6.6–12 years, RMSE: 8.1–15.9 years). The combination of all three molecular age clocks did reveal comparable MAE and RMSE results to the pure analysis of the DNA methylation for the test set without signs of decomposition. However, an improvement by the combination of all three clocks was possible for the decomposed samples, reducing especially the deviation in case of outliers in samples with very high decomposition and low DNA content. The results demonstrate the general potential in a combined analysis of different molecular clocks in specific cases. [ABSTRACT FROM AUTHOR]

AbstractObjectiveMethodResultsConclusionsThe study was designed to evaluate the performance validity module of Advanced Clinical Solutions (ACS) against external criterion measures and compare two alternative aggregation methods for its five components.The ACS was evaluated against psychometrically defined criterion groups in a sample of 93 outpatients with TBI. In addition to the default method, the component performance validity tests (PVTs) were either dichotomized along a single cutoff (VI-ACS) or recoded to capture various degrees of failure (EI-ACS).The standard ACS model correctly classified 75–83% of the sample. The alternative aggregation methods produced superior overall correct classification: 80–91% (VI-ACS) and 86–91% (EI-ACS). Mild TBI was associated with higher failure rates than moderate/severe TBI. Failing just one of the five ACS components resulted in a 3- to 8-fold increase in the likelihood of failing criterion PVTs.Results support the use of the standard PVT module for ACS: it is an effective measure of performance validity that is robust to moderate-to-severe TBI. Post-publication research on individual ACS components and methodological advances in PVT research provide an opportunity to enhance the overall classification accuracy of the ACS model. Passing stringent multivariate PVT cutoffs does not indicate valid performance. [ABSTRACT FROM AUTHOR]

Biomechanical and orthopaedic studies frequently encounter complex datasets that encompass both circular and linear variables. In most cases (i) the circular and linear variables are considered in isolation with dependency between variables neglected and (ii) the cyclicity of the circular variables is disregarded resulting in erroneous decision making. Given the inherent characteristics of circular variables, it is imperative to adopt methods that integrate directional statistics to achieve precise modelling. This paper is motivated by the modelling of biomechanical data, that is, the fracture displacements, that is used as a measure in external fixator comparisons. We focus on a dataset, based on an Ilizarov ring fixator, comprising of six variables. A modelling framework applicable to the six-dimensional joint distribution of circular-linear data based on vine copulas is proposed. The pair-copula decomposition concept of vine copulas represents the dependence structure as a combination of circular-linear, circular-circular and linear-linear pairs modelled by their respective copulas. This framework allows us to assess the dependencies in the joint distribution as well as account for the cyclicity of the circular variables. Thus, a new approach for accurate modelling of mechanical behaviour for Ilizarov ring fixators and other data of this nature is imparted. [ABSTRACT FROM AUTHOR]

Landslide susceptibility maps (LSMs) can play a bigger role in promoting the understanding of future landslides. This paper explores and compares the capability of three state-of-the-art bivariate models, namely the frequency ratio (FR), statistical index (SI), and weights of evidence (WoE), with ensembles of multivariate logistic regression (LR), for LSM in part of Tibet. Firstly, a landslide inventory map with 829 landslide records is obtained from field surveys and interpretation. Secondly, 15 landslide conditioning factors (LCFs) are considered and prepared from multi-data sources. Subsequently, a multicollinearity analysis is conducted to calculate the independence between different factors. Then, the Information Gain Ratio method (IGR) is performed to confirm the predictive ability of the LCFs. Finally, LSMs are constructed by, SI, WoE, LR and their combination through 12 preferred LCFs. The performance of different methods are validated and compared in term of areas under the receiver operating characteristic curve (AUC) and statistical measures. The results from this study indicate the hybrid models FR-LR, WoE-LR and SI-LR achieved higher AUC value than all corresponding single methods. The ensemble frameworks are well in line with the distribution pattern of historical landslides in the research area. Therefore, the proposed high-performance ensemble frameworks are expected to provide a useful reference for landslide hazard prevention in similar areas.

Abstract A multivariate spectrophotometric method is a potential approach that enables discrimination of spectra of components in complex matrices (e.g., pharmaceutical formulation) serving as a substitution method for chromatography. Four green smart multivariate spectrophotometric models were proposed and validated, including principal component regression (PCR), partial least-squares (PLS), multivariate curve resolution-alternating least squares (MCR-ALS), and artificial neural networks (ANN). The developed chemometric models were compared to resolve highly overlapping spectra of Paracetamol (PARA), Chlorpheniramine maleate (CPM), Caffeine (CAF), and Ascorbic acid (ASC). The four multivariate calibration models were assessed via recoveries percent, and root mean square error of prediction. Hence, the proposed models were efficiently applied with no need for any preliminary separation step. The models were utilized to analyze the studied components in their combined pharmaceutical formulation (Grippostad® C capsules). Analytical GREEnness Metric Approach (AGREE) and eco-scale tools were applied to assess the greenness of the established models and found to be 0.77 and 85, respectively. Moreover, the proposed models have been compared to official ones showing no considerable variations in accuracy and precision. Therefore, these models can be highly advantageous for conducting standard pharmaceutical analysis of the substances researched within product testing laboratories.

A multivariate spectrophotometric method is a potential approach that enables discrimination of spectra of components in complex matrices (e.g., pharmaceutical formulation) serving as a substitution method for chromatography. Four green smart multivariate spectrophotometric models were proposed and validated, including principal component regression (PCR), partial least-squares (PLS), multivariate curve resolution-alternating least squares (MCR-ALS), and artificial neural networks (ANN). The developed chemometric models were compared to resolve highly overlapping spectra of Paracetamol (PARA), Chlorpheniramine maleate (CPM), Caffeine (CAF), and Ascorbic acid (ASC). The four multivariate calibration models were assessed via recoveries percent, and root mean square error of prediction. Hence, the proposed models were efficiently applied with no need for any preliminary separation step. The models were utilized to analyze the studied components in their combined pharmaceutical formulation (Grippostad® C capsules). Analytical GREEnness Metric Approach (AGREE) and eco-scale tools were applied to assess the greenness of the established models and found to be 0.77 and 85, respectively. Moreover, the proposed models have been compared to official ones showing no considerable variations in accuracy and precision. Therefore, these models can be highly advantageous for conducting standard pharmaceutical analysis of the substances researched within product testing laboratories. [ABSTRACT FROM AUTHOR]

Abstract Background Situational engagement in science is often described as context-sensitive and varying over time due to the impact of situational factors. But this type of engagement is often studied using data that are collected and analyzed in ways that do not readily permit an understanding of the situational nature of engagement. The purpose of this study is to understand—and quantify—the sources of variability for learners’ situational engagement in science, to better set the stage for future work that measures situational factors and accounts for these factors in models. Results We examined how learners' situational cognitive, behavioral, and affective engagement varies at the situational, individual learner, and classroom levels in three science learning environments (classrooms and an out-of-school program). Through the analysis of 12,244 self-reports of engagement collected using intensive longitudinal methods from 1173 youths, we found that the greatest source of variation in situational engagement was attributable to individual learners, with less being attributable to—in order—situational and classroom sources. Cognitive engagement varied relatively more between individuals, and affective engagement varied more between situations. Conclusions Given the observed variability of situational engagement across learners and contexts, it is vital for studies targeting dynamic psychological and social constructs in science learning settings to appropriately account for situational fluctuations when collecting and analyzing data.

Introduction: Drought stress or water deficit has been known as one of the most important abiotic stresses so it considerably is decreased crop production. Barley (Hordeum vulgare L.) is the fourth most important cereal crop in the world after wheat, rice and corn. Among cereals, barley is the most tolerant crop against abiotic stresses and due to this capability is cultivated in a wide range of climates. The objective of the current study was to identify the superior drought-tolerant genotypes using grain yield and several yield-based indices of tolerance and susceptibility by applying various multivariate selection models. Materials and Methods: In the present study a set of promising genotypes of barley including 17 new genotypes along with a Jolge cultivar (as a check) was investigated through two separate experiments based on a randomized complete block design (RCBD) with three replications at the Cereal research station, Seed and Plant Improvement Institute, Karaj, Iran during two consecutive growing seasons (2019-2020 and 2020-2021) cropping seasons. After sowing, the number of irrigations was one time in autumn and five times in spring. Drought stress treatment was applied after anthesis, and irrigation was stopped for all stressed plot until seed repining stage. After collecting experimental data and estimating grain yield, several yield-based drought tolerance and susceptible indices were calculated. A heatmap-based correlation method was used to investigate association among calculated indices and grain yield data. Then three selection indices such as multi-trait genotype-ideotype distance index (MGIDI), factor analysis and ideotype design via best linear unbiased prediction (FAI-BLUP), and Smith-Hazel (SH) were exploited to identify the most tolerantgenotypes. All statistical analyses were computed using iPASTIC and R softwares. Results and Discussion: Based on combined analysis of variance for grain yield data showed significant differences for year, environment, and genotype main effects, as well as, the interaction effects for year × genotype, and year × environment × genotype. The result obtained from screening barley genotypes using drought tolerance and susceptible indices revealed good repeatability so that some of the investigated genotypes appeared in the same pattern in each year of experiments. Based on the Spearman's correlation coefficients, grain yields (Yp and Ys) positively and significantly correlated with MP, GMP, and STI indices in the first year. In the second year, a positive and significant correlation was observed between grain yields with STI, MP, GMP, and HM indices. Based on the averaged two-year data, grain yields significantly and positively correlated with HM, STI, MP, and GMP indices, supporting the repeatability of our findings. To identify the most tolerant genotypes based on multi-indices, we used three multi-trait selection indices such as MGIDI, FAIBLUP, and SH. Accordingly, genotypes numbers G7, G9, and G16 for the first year, G4, G13, and G17 for the second year, and three genotypes G7, G13, and G16 over two years were selected as superior genotypes using the MGIDI index. Based on the FAI-BLUP index, the following genotypes were identified as the most tolerant genotypes: G7, G9, and G17 in the first year; G4, G9, and G13 in the second year; G7, G13, and G16 in over two years. The result of screening genotypes using the Smith-Hazel index showed that three sets of genotypes including G4/G7/G13, G13/G14/G16, and G2/G3/G18 were identified as the high-yielding and most tolerant genotypes in each year and averaged two years, respectively. The venn-plot rendered based on three selection indices revealed that genotype numbers G7 and G13 were superior genotypes in the first and second years. Conclusion: In conclusion, our results indicated that G13 "Comp.Cr229//As46/Pro/3/Srs/4/Express/5/D10*2" with the highest grain yield in both control and drought stress conditions as well as the best ranking pattern for all drought tolerance indices can be a candidate as a superior drought-tolerant genotype for further studies before commercial introduction. [ABSTRACT FROM AUTHOR]

Abstract Background Winter wheat grain samples from 185 sites across southern Shanxi region were processed and analyzed using a non-destructive approach. For this purpose, spectral data and protein content of grain and grain powder were obtained. After combining six types of preprocessed spectra and four types of multivariate statistical models, a relationship between hyperspectral datasets and grain protein is presented. Results It was found that the hyperspectral reflectance of winter wheat grain and powder was positively correlated with the protein contents, which provide the possibility for hyperspectral quantitative assessment. The spectral characteristic bands of protein content in winter wheat extracted based on the SPA algorithm were proved to be around 350–430 nm; 851–1154 nm; 1300–1476 nm; and 1990–2050 nm. In powder samples, SG-BPNN had the best monitoring effect, with the accuracy of R v 2 = 0.814, RMSEv = 0.024 g/g, and RPDv = 2.318. While in case of grain samples, the SG-SVM model exhibited the best monitoring effect, with the accuracy of R v 2 = 0.789, RMSEv = 0.026 g/g, and RPDv = 2.177. Conclusions Based on the experimental findings, we propose that a combination of spectral pretreatment and multivariate statistical modeling is helpful for the non-destructive and rapid estimation of protein content in winter wheat. Graphical Abstract

Background: Situational engagement in science is often described as context-sensitive and varying over time due to the impact of situational factors. But this type of engagement is often studied using data that are collected and analyzed in ways that do not readily permit an understanding of the situational nature of engagement. The purpose of this study is to understand—and quantify—the sources of variability for learners' situational engagement in science, to better set the stage for future work that measures situational factors and accounts for these factors in models. Results: We examined how learners' situational cognitive, behavioral, and affective engagement varies at the situational, individual learner, and classroom levels in three science learning environments (classrooms and an out-of-school program). Through the analysis of 12,244 self-reports of engagement collected using intensive longitudinal methods from 1173 youths, we found that the greatest source of variation in situational engagement was attributable to individual learners, with less being attributable to—in order—situational and classroom sources. Cognitive engagement varied relatively more between individuals, and affective engagement varied more between situations. Conclusions: Given the observed variability of situational engagement across learners and contexts, it is vital for studies targeting dynamic psychological and social constructs in science learning settings to appropriately account for situational fluctuations when collecting and analyzing data. [ABSTRACT FROM AUTHOR]

For many sample surveys, researchers, policymakers, and other stakeholders are interested in obtaining estimates for various domains, such as for geographic levels, for demographic groups, or a cross‐classification of both. Often, the demand for estimates at a disaggregated level exceeds what the sample size and survey design can support when estimation is done by traditional design‐based estimation methods. Small area estimation involves exploiting relationships among domains and borrowing strength from multiple sources of information to improve inference relative to direct survey methods. This typically involves the use of models whose success depends heavily on the quality and predictive ability of the sources of information used. Possible sources of auxiliary information include administrative records, Censuses, big data such as traffic or cell phone data, or previous vintages of the same survey. One rich source of information is that of other surveys, especially in countries like the United States, where multiple surveys exist that cover related topics. We will provide an introduction to the topic of combining information from multiple surveys in small area estimation using area‐level models, including practical advice and a technical introduction, and illustrating with applications. We will discuss reasons to combine surveys and give an overview of some of the most common types of models. This article is categorized under:Statistical Models > Multivariate Models [ABSTRACT FROM AUTHOR]

Researchers are often interested in understanding the relationship between a set of covariates and a set of response variables. To achieve this goal, the use of regression analysis, either linear or generalized linear models, is largely applied. However, such models only allow users to model one response variable at a time. Moreover, it is not possible to directly calculate from the regression model a correlation measure between the response variables. In this article, we employed the Multivariate Generalized Linear Mixed Models framework, which allows the specification of a set of response variables and calculates the correlation between them through a random effect structure that follows a multivariate normal distribution. We used the maximum likelihood estimation framework to estimate all model parameters using Laplace approximation to integrate out the random effects. The derivatives are provided by automatic differentiation. The outer maximization was made using a general-purpose algorithm such as PORT and Broyden–Fletcher–Goldfarb–Shanno algorithm (BFGS). We delimited this problem by studying count response variables with the following distributions: Poisson, negative binomial, Conway-Maxwell-Poisson (COM-Poisson), and double Poisson. While the first distribution can model only equidispersed data, the second models equi and overdispersed, and the third and fourth models all types of dispersion (i.e. including underdispersion). The models were implemented on software R with package TMB, based on C++ templates. Besides the full specification, models with simpler structures in the covariance matrix were considered (fixed and common variance, and ρ set to 0) and fixed dispersion. These models were applied to a dataset from the National Health and Nutrition Examination Survey, where two response variables are underdispersed and one can be considered equidispersed that were measured at 1281 subjects. The double Poisson full model specification overcame the other three competitors considering three goodness-of-fit measures: Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC), and maximized log-likelihood. Consequently, it estimated parameters with smaller standard error and a greater number of significant correlation coefficients. Therefore, the proposed model can deal with multivariate count responses and measures the correlation between them taking into account the effects of the covariates. [ABSTRACT FROM AUTHOR]

Abstract Background To determine the effect size of observed factors considering trigger factors based on parallel-serial models and to explore how multiple factors can be related to the result of complex events for low-probability events with binary outcomes. Methods A low-probability event with a true binary outcome can be explained by a trigger factor. The models were based on the parallel-serial connection of switches; causal factors, including trigger factors, were simplified as switches. Effect size values of an observed factor for an outcome were calculated as SAR = (Pe-Pn)/(Pe + Pn), where Pe and Pn represent percentages in the exposed and nonexposed groups, respectively, and SAR represents standardized absolute risk. The influence of trigger factors is eliminated by SAR. Actual data were collected to obtain a deeper understanding of the system. Results SAR values of 0.50 indicate low, medium, and high effect sizes, respectively. The system of data visualization based on the parallel-serial connection model revealed that at least 7 predictors with SAR > 0.50, including a trigger factor, were needed to predict schizophrenia. The SAR of the HLADQB1*03 gene was 0.22 for schizophrenia. Conclusions It is likely that the trigger factors and observed factors had a cumulative effect, as indicated by the parallel-serial connection model for binary outcomes. SAR may allow better evaluation of the effect size of a factor in complex events by eliminating the influence of trigger factors. The efficiency and efficacy of observational research could be increased if we are able to clarify how multiple factors can be related to a result in a pragmatic manner.

Background: Winter wheat grain samples from 185 sites across southern Shanxi region were processed and analyzed using a non-destructive approach. For this purpose, spectral data and protein content of grain and grain powder were obtained. After combining six types of preprocessed spectra and four types of multivariate statistical models, a relationship between hyperspectral datasets and grain protein is presented. Results: It was found that the hyperspectral reflectance of winter wheat grain and powder was positively correlated with the protein contents, which provide the possibility for hyperspectral quantitative assessment. The spectral characteristic bands of protein content in winter wheat extracted based on the SPA algorithm were proved to be around 350–430 nm; 851–1154 nm; 1300–1476 nm; and 1990–2050 nm. In powder samples, SG-BPNN had the best monitoring effect, with the accuracy of Rv2 = 0.814, RMSEv = 0.024 g/g, and RPDv = 2.318. While in case of grain samples, the SG-SVM model exhibited the best monitoring effect, with the accuracy of Rv2 = 0.789, RMSEv = 0.026 g/g, and RPDv = 2.177. Conclusions: Based on the experimental findings, we propose that a combination of spectral pretreatment and multivariate statistical modeling is helpful for the non-destructive and rapid estimation of protein content in winter wheat. [ABSTRACT FROM AUTHOR]

Intensity–duration–frequency (IDF) curves of precipitation are a reference decision support tool used in hydrology. They allow the estimation of extreme precipitation and its return periods. Typically, IDF curves are estimated using univariate frequency analysis of the maximum annual intensities of precipitation for different durations. It is then assumed that the annual maxima of different durations are independent to simplify the parameter estimation. This strong hypothesis is not always verified for every climatic region. This study examines the effects of the independence hypothesis by proposing a multivariate model that considers the dependencies between precipitation intensities of different durations. The multivariate model uses D-vine copulas to explore the intraduration dependencies. The generalized extreme values distribution (GEV) is considered a marginal model that fits a wide range of tail behaviors. An illustration of the proposed approach is made for historical data from Moncton, in the province of New Brunswick (Eastern Canada), with climatic projections made through three scenarios of the Representative Concentration Pathway (RCP). [ABSTRACT FROM AUTHOR]

This study was designed to examine the classification accuracy of the Erdodi Index (EI-5), a novel method for aggregating validity indicators that takes into account both the number and extent of performance validity test (PVT) failures. Archival data were collected from a mixed clinical/forensic sample of 452 adults referred for neuropsychological assessment. The classification accuracy of the EI-5 was evaluated against established free-standing PVTs. The EI-5 achieved a good combination of sensitivity (.65) and specificity (.97), correctly classifying 92% of the sample. Its classification accuracy was comparable with that of another free-standing PVT. An indeterminate range between Pass and Fail emerged as a legitimate third outcome of performance validity assessment, indicating that the underlying construct is an inherently continuous variable. Results support the use of the EI model as a practical and psychometrically sound method of aggregating multiple embedded PVTs into a single-number summary of performance validity. Combining free-standing PVTs with the EI-5 resulted in a better separation between credible and non-credible profiles, demonstrating incremental validity. Findings are consistent with recent endorsements of a three-way outcome for PVTs (Pass, Borderline, and Fail). [ABSTRACT FROM AUTHOR]

A multivariate score-driven filter is developed to extract signals from noisy vector processes. By assuming that the conditional location vector from a multivariate Student's t distribution changes over time, we construct a robust filter which is able to overcome several issues that naturally arise when modeling heavy-tailed phenomena and, more in general, vectors of dependent non-Gaussian time series. We derive conditions for stationarity and invertibility and estimate the unknown parameters by maximum likelihood. Strong consistency and asymptotic normality of the estimator are derived. Analytical formulae are derived which consent to develop estimation procedures based on a fast and reliable Fisher scoring method. An extensive Monte–Carlo study is designed to assess the finite samples properties of the estimator, the impact of initial conditions on the filtered sequence, the performance when some of the underlying assumptions are violated, such as symmetry of the underlying distribution and homogeneity of the degrees of freedom parameter across marginals. The theory is supported by a novel empirical illustration that shows how the model can be effectively applied to estimate consumer prices from home scanner data. [ABSTRACT FROM AUTHOR]

This study was designed to evaluate the susceptibility of various performance validity tests (PVTs) to limited English proficiency (LEP). A battery of free-standing and embedded PVTs was administered to 95 undergraduate students at a Romanian university, randomly assigned to the control (n = 65) or experimental malingering group (n = 30). Overall correct classification (OCC) at the first cutoff to clear.90 specificity (with group membership as criterion) was used as the main metric to compare PVTs. Mean OCC for free-standing PVTs (.784) was comparable to mean OCC for embedded PVTs (.780). Cutoffs on embedded PVTs often had to be adjusted (more conservative) to meet the specificity standard. Contrary to our predictions, embedded PVTs with high verbal mediation outperformed those with low verbal mediation (mean OCC.807 versus.719). Although multivariate models of PVTs performed very well (mean OCC =.892), several individual freestanding and embedded PVTs produced comparable mean OCC (.863-.895). Other embedded PVTs had trivial sensitivity (.03-.13) at ≥.90 specificity. PVTs administered in both languages (English and Romanian) provided conclusive evidence of both the deleterious effects of LEP and the cross-cultural validity of existing methods of performance validity testing. Results defied most of our a priori predictions: level of verbal mediation was an influential, but not a decisive factor in the classification accuracy of PVTs; free-standing PVTs were not necessarily superior to embedded PVTs; multivariate models of performance validity assessment outperformed most, but not all their individual components. Our findings suggest that some PVTs may be inherently unfit to be used with examinees with LEP. The multiple unexpected findings signal a fundamental uncertainty about the psychometric properties of instruments developed and validated in North America when applied to examinees outside the US or Canada. Although several existing PVTs have the potential to be useful in examinees with LEP, their relevant psychometric properties should be independently verified in new target populations to ensure the validity of their clinical interpretation. The classification accuracy observed in native speakers of English cannot be assumed to transfer to members of linguistically and culturally different communities – doing so risks potentially consequential errors in performance validity assessment. Of course, the abundance of counterintuitive findings also serves as a note of caution: our findings may not generalize to different samples. [ABSTRACT FROM AUTHOR]

Introduction: During COVID‐19 pandemic, artificial neural network (ANN) systems have been providing aid for clinical decisions. However, to achieve optimal results, these models should link multiple clinical data points to simple models. This study aimed to model the in‐hospital mortality and mechanical ventilation risk using a two step approach combining clinical variables and ANN‐analyzed lung inflammation data. Methods: A data set of 4317 COVID‐19 hospitalized patients, including 266 patients requiring mechanical ventilation, was analyzed. Demographic and clinical data (including the length of hospital stay and mortality) and chest computed tomography (CT) data were collected. Lung involvement was analyzed using a trained ANN. The combined data were then analyzed using unadjusted and multivariate Cox proportional hazards models. Results: Overall in‐hospital mortality associated with ANN‐assigned percentage of the lung involvement (hazard ratio [HR]: 5.72, 95% confidence interval [CI]: 4.4–7.43, p < 0.001 for the patients with >50% of lung tissue affected by COVID‐19 pneumonia), age category (HR: 5.34, 95% CI: 3.32–8.59 for cases >80 years, p < 0.001), procalcitonin (HR: 2.1, 95% CI: 1.59–2.76, p < 0.001, C‐reactive protein level (CRP) (HR: 2.11, 95% CI: 1.25–3.56, p = 0.004), glomerular filtration rate (eGFR) (HR: 1.82, 95% CI: 1.37–2.42, p < 0.001) and troponin (HR: 2.14, 95% CI: 1.69–2.72, p < 0.001). Furthermore, the risk of mechanical ventilation is also associated with ANN‐based percentage of lung inflammation (HR: 13.2, 95% CI: 8.65–20.4, p < 0.001 for patients with >50% involvement), age, procalcitonin (HR: 1.91, 95% CI: 1.14–3.2, p = 0.14, eGFR (HR: 1.82, 95% CI: 1.2–2.74, p = 0.004) and clinical variables, including diabetes (HR: 2.5, 95% CI: 1.91–3.27, p < 0.001), cardiovascular and cerebrovascular disease (HR: 3.16, 95% CI: 2.38–4.2, p < 0.001) and chronic pulmonary disease (HR: 2.31, 95% CI: 1.44–3.7, p < 0.001). Conclusions: ANN‐based lung tissue involvement is the strongest predictor of unfavorable outcomes in COVID‐19 and represents a valuable support tool for clinical decisions. [ABSTRACT FROM AUTHOR]

Background: To determine the effect size of observed factors considering trigger factors based on parallel-serial models and to explore how multiple factors can be related to the result of complex events for low-probability events with binary outcomes. Methods: A low-probability event with a true binary outcome can be explained by a trigger factor. The models were based on the parallel-serial connection of switches; causal factors, including trigger factors, were simplified as switches. Effect size values of an observed factor for an outcome were calculated as SAR = (Pe-Pn)/(Pe + Pn), where Pe and Pn represent percentages in the exposed and nonexposed groups, respectively, and SAR represents standardized absolute risk. The influence of trigger factors is eliminated by SAR. Actual data were collected to obtain a deeper understanding of the system. Results: SAR values of < 0.25, 0.25–0.50, and > 0.50 indicate low, medium, and high effect sizes, respectively. The system of data visualization based on the parallel-serial connection model revealed that at least 7 predictors with SAR > 0.50, including a trigger factor, were needed to predict schizophrenia. The SAR of the HLADQB1*03 gene was 0.22 for schizophrenia. Conclusions: It is likely that the trigger factors and observed factors had a cumulative effect, as indicated by the parallel-serial connection model for binary outcomes. SAR may allow better evaluation of the effect size of a factor in complex events by eliminating the influence of trigger factors. The efficiency and efficacy of observational research could be increased if we are able to clarify how multiple factors can be related to a result in a pragmatic manner. [ABSTRACT FROM AUTHOR]

Abstract Traffic speed prediction is an integral part of an Intelligent Transportation System (ITS) and the Internet of Vehicles (IoV). Advanced knowledge of average traffic speed can help take proactive preventive steps to avoid impending problems. There have been studies for traffic speed prediction in which data has been decomposed into components using various decomposition techniques such as empirical mode decomposition, wavelets, and seasonal decomposition. As far as the authors are aware, no research has used additively decomposed components as input features. In this study, we used additive decomposition on 21,843 samples of traffic speed data. We implemented two statistical techniques designed for double seasonality (i) Double Seasonal Holt‐Winter, and (ii) Trigonometric seasonality, Box‐Cox transformation, autoregressive integrated moving average errors, trend, and Seasonal components (TBATS), and five machine learning (ML) techniques, (i) Multi‐Layer Perceptron, (ii) Convolutional‐Neural Network, (iii) Long Short‐Term Memory, (iv) Gated Recurrent Unit and (v) Convolutional‐Neural Network‐LSTM. Machine learning techniques are used in univariate mode with raw time series as features and then with decomposed components as features in multivariate mode. This study demonstrates that using decomposed components (trend, seasonal, and residual), as features, improves prediction results for multivariate ML techniques. This becomes a significant advantage when no other features are available.

Formal volunteering holds great importance for the recipients of volunteer services, individuals who volunteer, and the wider society. However, how much recent birth cohorts volunteer in middle and late adulthood compared with earlier birth cohorts is not well understood. Even less well-known are the age and cohort trends in informal helping provided to friends and neighbors in later adulthood. Using longitudinal data from the Health and Retirement Study, we estimated age and cohort trends in formal volunteering and informal helping from 1998 to 2018 for a wide range of birth cohorts born between 1909 and 1958. We used multivariate, multilevel models based on Bayesian generalized modeling methods to estimate the probabilities of volunteering and informal helping simultaneously in a single model. Despite having advantages in human and health capital, recent birth cohorts showed volunteering levels in late adulthood that are similar to those of their predecessors. Moreover, more recent birth cohorts were consistently less engaged in informal helping than earlier birth cohorts throughout the observation period. More research is needed to illuminate the sociocultural drivers of changes in helping behaviors and overall prosocial and civic engagement. [ABSTRACT FROM AUTHOR]

Copyright of Muhasebe ve Vergi Uygulamalari Dergisi (MUVU) / Journal of Accounting & Taxation Studies (JATS) is the property of Ankara Serbest Muhasebeci Mali Musavirler Odasi and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The BGLR-R package implements various types of single-trait shrinkage/variable selection Bayesian regressions. The package was first released in 2014, since then it has become a software very often used in genomic studies. We recently develop functionality for multitrait models. The implementation allows users to include an arbitrary number of random-effects terms. For each set of predictors, users can choose diffuse, Gaussian, and Gaussian-spike-slab multivariate priors. Unlike other software packages for multitrait genomic regressions, BGLR offers many specifications for (co)variance parameters (unstructured, diagonal, factor analytic, and recursive). Samples from the posterior distribution of the models implemented in the multitrait function are generated using a Gibbs sampler, which is implemented by combining code written in the R and C programming languages. In this article, we provide an overview of the models and methods implemented BGLR's multitrait function, present examples that illustrate the use of the package, and benchmark the performance of the software. [ABSTRACT FROM AUTHOR]

Reliable prediction of solar thermal power is essential for optimal operation and control of renewable energy driven distributed power systems. This paper presents a Convolutional Neural Networks (CNNs) based multivariate approach for forecasting power generation from solar thermal collectors over multiple horizons simultaneously. It also demonstrates an application of solar thermal power generation forecasting in a building cooling system as part of a predictive central controller. Historical data from an evacuated collector field and a single axis tracking collector field have been used to develop the prediction models and assess the performance of the proposed approach. Experimental results show that the proposed approach provides accurate prediction for multiple forecast horizons: MAPE is 2.99%–4.18% for 30 min to 24 h ahead prediction. The proposed approach utilising both historical and predicted future weather data achieves 25%–37% improvements of accuracy compared to its univariate counterpart that uses only lagged power data as input. It also outperforms existing data driven approaches based on NNs, LSTM, and RF, and achieves 5.46%–21.28% statistically significant improvements compared to them. ● Application of Convolutional Neural Networks to forecast solar thermal power output. ● Data cleaning based on an unsupervised ML method (called local outlier factor). ● Input variables selection by applying mutual information. ● Evaluation of prediction model using data for two types of solar thermal collectors. ● 5–21% improvement of prediction accuracy over existing data-driven approaches. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the associations of childhood adversities (CAs) with lifetime onset and transitions across suicidal thoughts and behaviors (STB) among incoming college students. Methods: Web-based self-report surveys administered to 20,842 incoming college students from nine countries (response rate 45.6%) assessed lifetime suicidal ideation, plans and attempts along with seven CAs: parental psychopathology, three types of abuse (emotional, physical, sexual), neglect, bully victimization, and dating violence. Logistic regression estimated individual- and population-level associations using CA operationalizations for type, number, severity, and frequency. Results: Associations of CAs with lifetime ideation and the transition from ideation to plan were best explained by the exact number of CA types (OR range 1.32–52.30 for exactly two to seven CAs). Associations of CAs with a transition to attempts were best explained by the frequency of specific CA types (scaled 0–4). Attempts among ideators with a plan were significantly associated with all seven CAs (OR range 1.16–1.59) and associations remained significant in adjusted analyses with the frequency of sexual abuse (OR = 1.42), dating violence (OR = 1.29), physical abuse (OR = 1.17) and bully victimization (OR = 1.17). Attempts among ideators without plan were significantly associated with frequency of emotional abuse (OR = 1.29) and bully victimization (OR = 1.36), in both unadjusted and adjusted analyses. Population attributable risk simulations found 63% of ideation and 30–47% of STB transitions associated with CAs. Conclusion: Early-life adversities represent a potentially important driver in explaining lifetime STB among incoming college students. Comprehensive intervention strategies that prevent or reduce the negative effects of CAs may reduce subsequent onset of STB. [ABSTRACT FROM AUTHOR]

Forecasting future heat load in smart district heating networks is a key problem for utility companies that need such predictions for optimizing their operational activities. From the statistical learning viewpoint, this problem is also very interesting because it requires to integrate multiple time series about weather and social factors into a dynamical model, and to generate models able to explain the relationships between weather/social factors and heat load. Typical questions in this context are: "Which variables are more informative for the prediction?" and "Do variables have different influence in different contexts (e.g., time instant or situations)?" We propose a methodology for generating simple and interpretable models for heat load forecasting, then we apply this methodology to a real dataset, and, finally, provide new insight about this application domain. The methodology merges multi-equation multivariate linear regression and forward variable selection. We generate a (sparse) equation for each pair day-of-the-week/hour-of-the-day (for instance, one equation concerns predictions of Monday at 0.00, another predictions of Monday at 1.00, and so on). These equations are simple to explain because they locally approximate the prediction problem in specific times of day/week. Variable selection is a key contribution of this work. It provides a reduction of the prediction error of 2.4% and a decrease of the number of parameters of 49.8% compared to state-of-the-art models. Interestingly, different variables are selected in different equations (i.e., times of the day/week), showing that weather and social factors, and autoregressive variables with different delays, differently influence heat predictions in different times of the day/week. [ABSTRACT FROM AUTHOR]

China's first Mars exploration mission, named Tianwen-1, landed on Mars on 15 May 2021. The Mars Surface Composition Detector (MarSCoDe) payload onboard the Zhurong rover applied the laser-induced breakdown spectroscopy (LIBS) technique to acquire chemical compositions of Martian rocks and soils. The quantitative interpretation of MarSCoDe-LIBS spectra needs to establish a LIBS spectral database that requires plenty of terrestrial geological standards. In this work, we selected 316 terrestrial standards including igneous rocks, sedimentary rocks, metamorphic rocks, and ores, whose chemical compositions, rock types, and chemical weathering characteristics were comparable to those of Martian materials from previous orbital and in situ detections. These rocks were crushed, ground, and sieved into powders less than <38 μm and pressed into pellets to minimize heterogeneity at the scale of laser spot. The chemical compositions of these standards were independently measured by X-ray fluorescence (XRF). Subsequently, the LIBS spectra of MAL standards were acquired using an established LIBS system at Shandong University (SDU-LIBS). In order to evaluate the performance of these standards in LIBS spectral interpretation, we established multivariate models using partial least squares (PLS) and least absolute shrinkage and selection (LASSO) algorithms to predict the abundance of major elements based on SDU-LIBS spectra. The root mean squared error (RMSE) values of these models are comparable to those of the published models for MarSCoDe, ChemCam, and SuperCam, suggesting these PLS and LASSO models work well. From our research, we can conclude that these 316 MAL targets are good candidates to acquire geochemistry information based on the LIBS technique. These targets could be regarded as geological standards to build a LIBS database using a prototype of MarSCoDe in the near future, which is critical to obtain accurate chemical compositions of Martian rocks and soils based on MarSCoDe-LIBS spectral data. [ABSTRACT FROM AUTHOR]

This article discusses two methods to measure the concept of local competitiveness: Principal Component Analysis (PCA) and Analytical Hierarchy Process (AHP). The goal of this analysis is to determine whether these two methods used in social sciences research lead to comparable model results. By non-parametric tests we show that there is a significant correlation between the PCA and AHP local competitiveness indexes. Thereafter, a developed mixed method examination of whether the methods can be used interchangeably is presented and illustrated with detailed examples of two mixed approaches. The mixed method confirms the correlation between the PCA and AHP models. However, the mixed modelling results indicate the utility of the PCA in the situation of a multicriteria local competitiveness data examination. [ABSTRACT FROM AUTHOR]

This study investigated risk factors for school absenteeism in a sample of 983 children in elementary and lower secondary schools in Denmark, using administrative data on absenteeism measured in the year following risk factor measurement. Risk factors were measured by survey (children and teachers) and register data. Two methods of determining importance of risk factors were compared: individual risk factors versus four domains of risk factors (psychological problems, physical problems, school factors, and demographic and family factors). Significant individual risk factors were found in all four domains. When teacher-reports of the children's psychological problems were used, psychological problems was the risk factor domain that predicted school absenteeism best and the school-related factors domain predicted worst. The results highlight the need to distinguish between single risk factors that identify groups of individuals with elevated risk of school absence and the detection of risk factor domains that better predict school absence. [ABSTRACT FROM AUTHOR]

The aim of this study was to select traditional accessions, compose a core collection of common bean, and assess the representativeness of the collection in relation to the base collection accommodated in the BAG of Embrapa using analysis strategies for multivariate models. We used data characterizing 2903 accessions from collections representing all geographic areas of Brazil regarding three morphologic descriptors (seed color, growth habit type, and seed size) and four ecogeographic descriptors (geographical areas, states, altitudes, and soil classes). A set of 400 accessions were selected using multivariate models applied to the data transformed in multibinary values. The accessions sampled had maximum similarity (100%) to the traditional collection, phenotypic diversity, and representative heterogeneity in relation to the traditional collection. In the core collection, the accessions represented 9.5% of the traditional accessions and were equivalent to 3% of the accessions of the base collection. Thus, it is possible to form a core collection that is representative of the base collection regarding genetic diversity and the conservation of rare alleles. [ABSTRACT FROM AUTHOR]

The expanding human activities in coastal areas increase the need for developing solutions to limit impacts on the marine environment. Sea disposal affects the marine environment, but despite the growing knowledge of potential impacts, there are still no standardized leaching tests for sea disposal. The aim of this study was to contribute to the development of leaching tests, exemplified using mine tailings, planned for submarine disposal in the Repparfjord, Norway. The mine tailings had elevated concentrations of Ba, Cr, Cu, Mn and Ni compared to background concentrations in the Repparfjord. Variables known to affect metal leaching in marine environments (DOC, pH, salinity, temperature, aerated/anoxic) were studied, as was the effect of flocculant (Magnafloc10), planned to be added prior to discharge. Stirred/non-stirred setups simulated the resuspension and disposal phases. Leaching of metals was below 2% in all experiments, with the highest rate observed for Cu and Mn. Multivariate analysis revealed a different variable importance for metals depending on their association with minerals. Higher leaching during resuspension than disposal, and lower leaching with the addition of Magnafloc10, especially for Cu and Mn, was observed. The leaching tests performed in this study are transferable to other materials for sea disposal. [ABSTRACT FROM AUTHOR]

Genomic prediction models that capture genotype-by-environment interaction are useful for predicting site-specific performance by leveraging information among related individuals and correlated environments, but implementing such models is computationally challenging. This study describes the algorithm of these scalable approaches, including two models with latent representations of genotype-by-environment interactions, namely MegaLMM and MegaSEM, and an efficient multivariate mixed model solver, namely PEGS, fitting different covariance structures (unstructured, XFA, HCS). Accuracy and runtime are benchmarked on simulated scenarios with varying numbers of genotypes and environments. MegaLMM and PEGS-based XFA and HCS models provided the highest accuracy under sparse testing with 100 testing environments. PEGS-based unstructured model was orders of magnitude faster than REML-based multivariate GBLUP while providing the same accuracy. MegaSEM provided the lowest runtime, fitting a model with 200 traits and 20,000 individuals in approximately 5 minutes, and a model with 2,000 traits and 2,000 individuals in less than 3 minutes. With the G2F data, the most accurate predictions were attained with the univariate model fitted across environments and by averaging environment-level GEBVs from models with HCS and XFA covariance structures., (© The Author(s) 2024. Published by Oxford University Press on behalf of The Genetics Society of America.)

This study examined associations of individual characteristics on perceived workplace conditions and safety in a volunteer sample of 254 employees from businesses in New York City's World Trade Center (WTC) towers and other area workplaces who completed structured diagnostic and disaster-specific interviews an average of 35 months after the September 11, 2001 (9/11) terrorist attacks. WTC workplace employees perceived greater workplace responsiveness to their post-9/11 needs relative to employees of other workplaces, independent of individual demographic and other disaster-related variables; they also reported lower perceived safety at work. Thus, employee disaster-related workplace location, an organizational-level variable, was a powerful determinant of individual perceptions of the postdisaster workplace and its responsiveness, suggesting the importance of organizational disaster planning and response in helping workers adjust to the postdisaster workplace environment and promoting personal healing and recovery. [ABSTRACT FROM AUTHOR]

Rapid and accurate prediction of soil available S, an important secondary nutrient, is crucial for its site-specific management in a cultivated region. Although traditional chemical analysis of any nutrient is an accurate method, but often costly, time-consuming and destructive in nature. Recently visible near-infrared (VIS-NIR) reflectance spectroscopic technique has gained its popularity for rapid, non-destructive and cost-effective assessment of soil nutrients. Hence, a study was carried out in an intensively cultivated region of Katol block of Nagpur, Maharashtra, during 2018-20 for rapid prediction of soil available S using spectroscopic technique. Both spectroscopic and chemical analyses were carried out using 132 georeferenced surface soil samples (0-15 cm depth). The descriptive statistical analysis showed that the available S content varied from 1.09 to 47.88 mg/kg. Multivariate models namely partial least square regression (PLSR) and random forest (RF) were applied to develop spectral models for S prediction from spectral dataset. Several statistical diagnostics like coefficient of determination (R2), root mean square error (RMSE), ratio of performance deviation (RPD) and ratio of performance to interquartile distance (RPIQ) were used to evaluate the performances of two models. The best prediction of S was achieved from nonlinear RF model (R2 = 0.71, RMSE = 8.86, RPD =1.18, RPIQ = 1.69) as compared to linear PLSR model (R2 = 0.53, RMSE = 9.04, RPD = 1.16, RPIQ = 1.66) datasets. Therefore, the result suggested applying non-linear multivariate model (RF) for obtaining best predictability for S from spectroscopic technique.

Copyright of Formación Universitaria is the property of Centro de Informacion Tecnologica (CIT) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

OBJECTIVE: This study was designed to empirically investigate the signal detection profile of various multivariate models of performance validity tests (MV-PVTs) and explore several contested assumptions underlying validity assessment in general and MV-PVTs specifically. METHOD: Archival data were collected from 167 patients (52.4%male; MAge = 39.7) clinicially evaluated subsequent to a TBI. Performance validity was psychometrically defined using two free-standing PVTs and five composite measures, each based on five embedded PVTs. RESULTS: MV-PVTs had superior classification accuracy compared to univariate cutoffs. The similarity between predictor and criterion PVTs influenced signal detection profiles. False positive rates (FPR) in MV-PVTs can be effectively controlled using more stringent multivariate cutoffs. In addition to Pass and Fail, Borderline is a legitimate third outcome of performance validity assessment. Failing memory-based PVTs was associated with elevated self-reported psychiatric symptoms. CONCLUSIONS: Concerns about elevated FPR in MV-PVTs are unsubstantiated. In fact, MV-PVTs are psychometrically superior to individual components. Instrumentation artifacts are endemic to PVTs, and represent both a threat and an opportunity during the interpretation of a given neurocognitive profile. There is no such thing as too much information in performance validity assessment. Psychometric issues should be evaluated based on empirical, not theoretical models. As the number/severity of embedded PVT failures accumulates, assessors must consider the possibility of non-credible presentation and its clinical implications to neurorehabilitation. [ABSTRACT FROM AUTHOR]

Pair-copula constructions have proven to be a useful tool in statistical modeling, particularly in the field of finance. The copula-based approach can be used to choose a model that describes the dependence structure and marginal behaviour of the data in efficient way, but is usually applied to pairs of securities. In contrast, vine copulas provide greater flexibility and permit the modeling of complex dependency patterns using the rich variety of bivariate copulas which may be arranged and analysed in a tree structure. However, the number of possible configurations of a vine copula grows exponentially as the number of variables increases, making model selection a major challenge in development. So, to learn the best possible model, one has to identify the best possible structure, which necessitates identifying the connections between the variables and selecting between the multiple bivariate copulas for each pair in the structure. This paper features the use of regular vine copulas in analysis of the co-dependencies of four major Russian Stock Market securities such as Gazprom, Sberbank, Rosneft and FGC UES, represented by the RTS index. For these stocks the D-vine structures of bivariate copulas were constructed, which models are described by Gumbel, Student, BB1and BB7 copulas, and estimates of their parameters were obtained. Computer simulations showed a high accuracy of the approximation of the explored data by D-vine structure of bivariate copulas and the effectiveness of our approach in general.