Random Coefficient Regression Research Articles

Coping with intensive training demands: a longitudinal investigation of the relationships between appraisal, emotion, coping effectiveness and engagement among elite fencers

ABSTRACT Training is, by essence, very challenging, both physically and mentally. The way athletes stay fully engaged in their intensive training programme and achieve their performance goals may depend on their capacity to deal effectively with such daily training demands. This study therefore aimed to further the understanding of the relationships between appraisal, emotion, coping effectiveness and engagement among elite fencers in relation to the demands of intensive training over the course of a competitive season. Twenty-three elite fencers (15 women, 8 men; age: 23.04 ± 3.54 years; fencing experience: 15.04 ± 3.74 years) completed an online questionnaire once a week, measuring perceived effort, perceived difficulty, pleasant and unpleasant emotions, athlete engagement, and coping effectiveness (i.e., 36 time-points). The results of the random coefficient regression models revealed the mediating role played by emotions in the dynamic relationships (a) between perceived difficulty and coping effectiveness (pleasant emotions: Sobel Test = −2.39, p < .05; unpleasant emotions: Sobel Test = −2.47, p < .05), and (b) between perceived effort and coping effectiveness (pleasant emotions: Sobel Test = 2.15, p < .05). Results also highlighted the partial mediating role played by coping effectiveness in the dynamic relationships (a) between perceived effort and engagement (Sobel Test = 2.27, p < .05), and (b) between perceived difficulty and engagement (Sobel Test = −2.16, p < .05). This study underlines the need to consider subjective appraisal during training over the course of a competitive season and its influence on psychological adaptation and engagement in intensive training settings.

Psychiatric predictors of quality of life in Parkinson's disease: A three-year longitudinal study

IntroductionParkinson's disease (PD) is associated with worsened quality of life (QOL) over time. Few longitudinal studies exist investigating the relationship of psychiatric comorbidities with QOL in people with PD (PwP). We sought to determine specific psychiatric symptoms associated with decreasing QOL in PwP over time. MethodsWe recruited PwP without dementia from a movement disorders clinic at an academic medical center. Participants were evaluated annually with motor and neuropsychological assessments at each visit. QOL was measured using the Parkinson's Disease Questionnaire-39 (PDQ-39). We assessed psychiatric symptoms, including depression (Beck Depression Inventory II, BDI-II), anxiety (Beck Anxiety Index, BAI), and apathy (Apathy Scale). Psychosis and impulse control disorders (ICDs) were recorded as present or absent. Using random coefficient regression, we analyzed psychiatric features associated with worsened QOL in PwP over three years. ResultsFrom the 105 participants enrolled at baseline, 67 completed three years of follow up. Mean PDQ-39 scores increased from 16.0 at baseline to 19.8 at year three. In multivariate analysis, higher BDI-II scores, BAI scores, and apathy scores were uniquely associated with worsened QOL over time (p < 0.001 for all measures), while presence of ICDs (p = 0.18) or psychosis (p = 0.10) were not. Changes in the BAI score and the BDI-II score exerted similar effects on the overall PDQ-39 score. ConclusionDepression, anxiety, and apathy are all associated with worsening quality of life over time in PwP, while presence of ICDs and psychosis are not. Treatment of these symptoms may lead to improved QOL in PwP.

Genomic and phenotypic selection indices for decreased birth weight, shortening fattening period, and regulating total weight gain in beef cattle.

To develop genomic and phenotypic indices for beef cattle selection that afford progeny with reduced birth weight and fattening period. The indices should also allow regulating total weight gain during the fattening period and avoid marked increases of inbreeding. Whether addition of constraint on total weight gain to constraints on body weight gain at up to four specific time points during the fattening process was effective for the weight gain until the end of the fattening period was examined in two selection indices with the selection trait being a phenotypic or a genomic breeding value random regression coefficient. Both indices afforded cattle with desired weight gains at specific time points and desired total weight gains. One cycle of index-based selection made it possible to shorten the fattening period two weeks compared with that before selection while maintaining the same final fattening weight as before selection. The impact of constraining total weight gain was smallest when the number of weight gain constraints at specific time points was three or four. However, constraining total weight gain was necessary to avoid poor total weight gain when the number of weight gain constraints at specific time points was only one or two. The developed indices make it possible to regulate total weight gain during the fattening process and to achieve desired weight gains at specific time points. Importantly, the indices bring about genetic improvement without an excessive increase of inbreeding. Thus, we expect that these indices will contribute to sustainable genetic improvement in cattle while maintaining genetic diversity.

Tandem gait step-width increases more rapidly in more severely affected people with Parkinson's disease

IntroductionTandem gait performance reportedly predicts fall risk in people with Parkinson's disease (PwPD) and help distinguish PwPD from atypical parkinsonism. In a cross-sectional study, we previously showed that tandem gait step-width widens with increasing Hoehn and Yahr (H&Y) staging scores. In this longitudinal study, we aimed to determine if progression in tandem gait deficits is dependent on disease severity in PwPD. MethodsParticipants underwent an instrumented tandem gait measurement every 6 months for at least 2 years. The mean and variability of 4 tandem gait parameters were calculated at each visit: step-width, step-length, step-time, and step-velocity. The change in these parameters over time for 3 H&Y groups (stage 1, 2 and 2.5+) compared to aging controls was determined using a random coefficients regression model. The annual percent change in tandem gait parameters was correlated with initial disease features using Kendall's τB. Results66 participants were analyzed (46 PD, 20 controls). Mean step-width increased over time in an H&Y stage-dependent manner, with H&Y 2 and H&Y 2.5+ experiencing increases of 6% and 10% per year (p = 0.001 and 0.024 respectively). Annual percent-change in step-width was correlated with initial motor Unified Parkinson's Disease Rating Scale (UPDRS) scores (Kendall's τB = 0.229), total UPDRS scores (τB = 0.249), H&Y scores (τB = 0.266) and inversely correlated with Montreal Cognitive Assessment (MoCA) scores (τB = −0.209; ps ≤ 0.019). ConclusionTandem gait step-width widens over time more rapidly in more severely affected PD patients. These results suggest that tandem gait should be routinely clinically evaluated and considered in the management of imbalance in PwPD.

A statistical study for the impact of REMS and nuclear energy on carbon dioxide emissions reductions in G20 countries

Renewable and nuclear energy are seen as clean and eco-friendly substitutes for energy derived from fossil fuels. Which of the two is better, though, is still up for debate. In order to meet their combined global energy objectives and perhaps become carbon neutral or even carbon negative in the near future, a shift to additional renewable energy sources and nuclear energy will be necessary. One such method of improving energy usage through the implementation of a renewable energy management system (REMS) that will help attain a continuous and reliable source of energy, allowing for the proactive and effective management of renewable assets and dependable supply. This article investigates attitudes and opinions about nuclear and renewable energy production technologies by analyzing the dependence of carbon dioxide (CO2) emissions on non-renewable energy sources, renewable energy sources, and nuclear energy in the group of twenty (G20) countries, over the period from 2000 to 2021. This study employs advanced panel data econometric techniques in the presence of outliers in the G20 dataset, utilizing a robust random coefficient regression (RRCR) model via the M-estimation approach to study the impacts of non-renewable, renewable, and nuclear energy use with respect to CO2 emission mitigation in G20 countries. The overall conclusions of the econometric analysis confirm renewable and nuclear energy's importance in reducing CO2 emissions. We can conclude that the robust M-estimate approach is the best estimation technique for the G20 dataset, as compared to the classical estimation methods in the presence of outliers. The results show that for every percentage point of energy consumption that falls into the category of non-renewable energy, CO2 emissions will increase by 2.5737% for all G20 countries. Also, A 1% rise in renewable energy consumption reduces CO2 emissions by 1.0566% for all G20 countries. Similarly, a 1% increase in nuclear energy consumption will reduce CO2 emissions by 5.5703% for all G20 countries. Therefore, we can conclude that energy derived from renewable sources and nuclear energy greatly cuts CO2 emissions in all G20 countries. Total energy consumption, on the other hand, increases CO2 emissions in the long run. According to these findings, it is pivotal for the G20 countries to increase nuclear energy use and employ REMS to reduce the majority of G20 country's reliance on fossil fuels in order to minimize CO2 emissions. These findings are vital for understanding the relationship between these variables and the implementation of new or revised policies and initiatives supporting carbon neutrality at the G20 countries and national levels.

Disentangling the impact of nested sources of variability on species growth processes: A mixture of multilevel mixed model approach.

SummaryThe understanding of tree growth processes is crucial for promoting sustainable forest management strategies. This is a challenging task in highly biodiverse ecosystems where many tree species are observed on very few individuals and the small sample sizes hinder a good fit of species‐specific models. We propose the use of finite mixture of random coefficient regression models with multilevel nested random effects to infer guild specific fixed and random effects while evaluating the relative importance of the nested sources of variability on goodness‐of‐fit. This approach extends finite mixture of linear mixed model used for longitudinal or single group structured data contexts. A dedicated expectation–maximisation algorithm is introduced for parameter estimation. Simulations are performed for the evaluation of the misspecification of nested‐grouping structures. This work has been motivated by data collected biennially in Central African rainforests from 1986 to 2010. We show the accuracy of the proposed approach in successfully reproducing individual growth processes and classifying tree species into well‐differentiated clusters with clear ecological interpretations. Moreover, results confirm that interindividual variability appears as the most important factor to explain tropical tree species growth process variability from Central Africa forests.

Approximation of reliabilities for random-regression single-step genomic best linear unbiased predictor models

Random-regression models (RRM) are used in national genetic evaluations for longitudinal traits. The outputs of RRM are an index based on random regression coefficients and its reliability. The reliabilities are obtained from the inverse of the coefficient matrix of mixed model equations (MME). The reliabilities must be approximated for large data sets because it is impossible to invert the MME. There is no extensive literature on methods to approximate the reliabilities of RRM when genomic information is included by single-step GBLUP. We developed an algorithm to approximate such reliabilities. Our method combines the reliability of the index without genomic information with the reliability of a GBLUP model in terms of effective record contributions. We tested our algorithm in the 3-lactation model for milk yield from the Czech Republic. The data had 30 million test-day records, 2.5 million animals in the pedigree, and 54,000 genotyped animals. The correlation between our approximation and the reliabilities obtained from the inversion of the MME was 0.98, and the slope and intercept of the regression were 0.91 and 0.02, respectively. The elapsed time to approximate the reliabilities for the Czech data was 21 min.

BOUNDED SUPPORT IN LINEAR RANDOM COEFFICIENT MODELS: IDENTIFICATION AND VARIABLE SELECTION

We consider linear random coefficient regression models, where the regressors are allowed to have a finite support. First, we investigate identification, and show that the means and the variances and covariances of the random coefficients are identified from the first two conditional moments of the response given the covariates if the support of the covariates, excluding the intercept, contains a Cartesian product with at least three points in each coordinate. We also discuss identification of higher-order mixed moments, as well as partial identification in the presence of a binary regressor. Next, we show the variable selection consistency of the adaptive LASSO for the variances and covariances of the random coefficients in finite and moderately high dimensions. This implies that the estimated covariance matrix will actually be positive semidefinite and hence a valid covariance matrix, in contrast to the estimate arising from a simple least squares fit. We illustrate the proposed method in a simulation study.

Low Field MRI Surveillance 6-24 Months Post-acute COVID-19 Pneumonia: Factors Influencing Severity and Evolution of Lung Opacities

To determine factors influencing low-field MRI lung opacity severity 6-24 months after acute Covid-19 pneumonia. 104 post-acute Covid-19 patients with 167 MRI exams were included. 32 patients had more than one exam, and 63 exams were serial exams. Pulmonary findings were graded on a scale of 0-4 by quadrant, total score ranging from 0 (no opacity) to 16 (opacity > 75%), and score >8 considered moderate and >12 severe opacity. Kruskal-Wallis, Mann-Whitney, and Spearman rank correlation was used to assess the association of clinical and demographic factors with MR opacity severity at time intervals from acute infection. Random coefficients regression was used to assess whether opacity score changed over time. Severity of initial illness was associated with increased MR opacity score at timeframes up to 24 months (p<.05). Among the 167 exams, moderate to severe MR opacities (total opacity score >8) were identified in 33% of exams beyond 6 months: 37% at 6 - <12 months (n=23/63); 31% at 12- <18 months (n=13/42); 25% at 18- <24 months (n=6/24); and 50% at >24 months (n=3/6). No significant change in total opacity score over time was identified by random coefficients regression. Among the 32 patients with serial exams, 11 demonstrated no change in opacity score from initial to final exam, 10 decrease in score (mean 2.3, stdev 1.25, range 1-4), and 11 increase in score (average 2.8, stdev 1.48, range 1-7). Initial Covid-19 disease severity was associated with increased MRI total opacity score at time intervals up to 24 months, and moderate to severe opacities were commonly identified by low-field MRI beyond 6 months from acute illness.

Locally optimal tests against periodic linear regression in short panels

This paper aims to detect the periodic coefficients of a Linear Regression model with Panel Data. Nonparametric locally and asymptotically optimal tests are proposed for testing the null hypothesis of a traditional linear regression model, against an alternative of a Periodic Coefficient Regression model. The Uniform Local Asymptotic Normality (ULAN) property is established for Periodic Linear Regression (PLR) model, to construct Locally asymptotically optimal tests. The rank-based version of the optimal parametric test is provided in particular cases. The performance of the proposed tests is valid with numerical simulations using the Monte Carlo study. Real data was studied for applying the periodic linear regression model which compared to both traditional regression and random coefficient regression models.

Statistical innovations in the API ADAD Colombia trial: a randomized, double‐blind, placebo‐controlled, parallel‐group study in cognitively unimpaired PSEN1 E280A mutation carriers evaluating efficacy and safety of crenezumab

AbstractBackgroundA number of extended families in Colombia with early‐onset ADAD have a PSEN1 mutation at codon 280 (E280A), comprise the largest ADAD kindred in the world. Trial participants have been treated with crenezumab (an experimental anti‐amyloid monoclonal antibody) or placebo. Topline results were presented in August, 2022 at AAIC. Although the trial did not demonstrate a statistically significant clinical benefit in either of its co‐primary endpoints, multiple statistical innovations were incorporated into this trial.MethodThe API ADAD Colombia trial is a randomized, double‐blind, placebo‐controlled, parallel‐group phase 2b study evaluating the efficacy, safety, and tolerability of crenezumab in cognitively unimpaired ADAD PSEN1 E280A mutation carriers in Colombia (NCT01998841). Due to under enrollment and concerns with the sensitivity of the primary endpoint, the protocol was amended mid‐trial to change from 260 weeks of treatment to a common‐close design whereby treatment continued in the double‐blind portion until the last enrolled participant received 260 weeks of treatment. Because of the change in trial design and power considerations, the primary analysis was adapted from a traditional mixed model with repeated measures (MMRM) based analysis to a random coefficient regression model (RCRM) based analysis. To quantitatively examine the adequacy of the RCRM assumptions, a robust variance estimator (Huber 1967; White 1982) was prespecified as a sensitivity analysis to calculate the standard errors and p‐values of the co‐primary endpoints. This study used a type‐I‐error‐controlled statistical procedure to identify and elevate the Free and Cued Selective Reminding Test (FCSRT) cueing index to a co‐primary endpoint. To comprehensively assess the crenezumab effect across primary and secondary endpoints, the prespecified relative reduction point estimates along with the bootstrapped 95% confidence intervals were generated.ResultInnovative statistical methods in this trial will be presented.ConclusionAlthough the API Colombia study did not meet its co‐primary endpoints, statistical learnings from this trial can benefit development of future AD trials. To further maximize the value of this unique trial dataset, participant‐level data will be made publicly available, including clinical, imaging, and biomarker data.Funding: NIA grants RF1‐AG041705, R01‐AG055444, Genentech, Roche, philanthropic contributions to Banner Alzheimer’s Foundation. FlorbetapirPET was partly supported by Avid and Lilly.

Poverty Mapping Under Area-Level Random Regression Coefficient Poisson Models

Abstract Under an area-level random regression coefficient Poisson model, this article derives small area predictors of counts and proportions and introduces bootstrap estimators of the mean squared errors (MSEs). The maximum likelihood estimators of the model parameters and the mode predictors of the random effects are calculated by a Laplace approximation algorithm. Simulation experiments are implemented to investigate the behavior of the fitting algorithm, the predictors, and the MSE estimators with and without bias correction. The new statistical methodology is applied to data from the Spanish Living Conditions Survey. The target is to estimate the proportions of women and men under the poverty line by province.

Global Hunger Index: A multistage coefficient estimation analysis using machine learning techniques for a hunger free society

The Global Hunger Index (GHI) is one of the most prominent indices for calculating and describing the severity of hunger level in various countries. Nutrition, mortality, and food supply are the basic parameters defining the hunger index value. Using a single method for coefficient estimation of hunger index parameters is not a viable option. It is necessary to design a new framework which can help us to explore more possibilities for coefficient estimation and for identifying the critical features as well. In this paper, we have proposed a new coefficient estimation technique called Multi-Stage Coefficient Estimation (MSCE) strategy that enhances the prediction rate. MSCE is an aggregation of multiple methods for generating the coefficient values of the associated features group. Decision Tree Regression, Random Forest regression, Logistic Regression (comprising of lbfgs, saga and sag problem solvers), Simple Regression and Multiple Regression coefficient computing methods are used in the proposed framework to estimate the coefficient values. The obtained coefficients were then used to build the max, avg and min regression equations for prediction. The introduced methodology is applied on the global hunger index data collected from the GHI repository. The results of the proposed MSCE methodology were compared with other popular approaches like Simple Linear Regression, and Multiple Linear Regression in terms of Mean Absolute Error, Mean Square Error, Root Mean Square Error, R- Squared and Adjusted R-Squared metrics. The proposed MSCE strategy achieved better results when compared with existing methods.

Emotion experience and regulation in undergraduates following social rejection: A daily diary study

We used a 30-day daily diary assessment method to examine the within-person associations between social rejection, emotional experiences, and emotion regulation strategies in a sample of 34 college students. Taking emotional experience as the dependent variable, we explored and analyzed cumulative and hysteresis effects using a random regression coefficient model. The results showed that situations of social rejection tended to induce negative emotional experiences, for which college students mostly adopted attention transfer strategies. In contrast, positive emotional experience increased in situations of social acceptance, and college students mostly adopted cognitive reappraisal strategies in this setting. Further, cognitive reappraisal strategies had time accumulation and overlapping effects on individual positive emotional experiences, and attention transfer strategies had a lag effect on individual emotional experiences. These findings advance understanding of the negative affect–emotion regulation association among individuals exposed to social rejection.

The marker effects of a single-step random regression model for 4 test-day traits in German Holsteins

The single-step genomic model has become the golden standard for routine evaluation in livestock species, such as Holstein dairy cattle. The single-step genomic model with direct estimation of marker effects has been proven to be efficient in accurately accounting for millions of genotype records. For diverse applications including frequent genomic evaluation updates on a weekly basis, estimates of the marker effects from the single-step evaluations play a central role in genomic prediction. In this study we focused on exploring the marker effect estimates from the single-step evaluation. Phenotypic, genotypic, and pedigree data were taken from the official evaluation for German dairy breeds in April 2021. A multilactation random regression test-day model was applied to more than 242 million test-day records separately for 4 traits: milk, fat, and protein yields, and somatic cell scores (SCS). Approximately one million genotyped Holstein animals were considered in the single-step genomic evaluations including ∼21 million animals in pedigree. Deregressed multiple across-country breeding values of Holstein bulls having daughters outside Germany were integrated into the national test-day data to increase the reliability of genomic breeding values. To assess the stability and bias of the marker effects of the single-step model, test-day records of the last 4 yr were deleted, and the integrated bulls born in the last 4 yr were truncated from the complete phenotypic dataset. Estimates of the marker effects were shown to be highly correlated, with correlations ∼0.9, between the full and truncated evaluations. Regression slope values of the marker-effect estimates from the full on the truncated evaluations were all close to their expected value, being ∼1.03. Calculated using random regression coefficients of the marker effect estimates, drastically different shapes of the genetic lactation curve were seen for 2 markers on chromosome 14 for the 4 test-day traits. The contribution of individual chromosomes to the total additive genetic variances seemed to follow the polygenic inheritance mode for protein yield and SCS. However, chromosome 14 was found to make an exceptionally large contribution to the total additive genetic variance for milk and fat yields because of markers near the major gene DGAT1. For the first lactation test-day traits, we obtained ∼0 correlations of chromosomal direct genomic values between any pair of the chromosomes; no spurious correlations were found in our analysis, thanks to the large reference population. For trait milk yield, chromosomal direct genomic values appeared to have a large variation in the between-lactation correlations among the chromosomes, especially between first and second or third lactations. The optimal features of the random regression test-day model and the single-step marker model allowed us to track the differences in the shapes of genetic lactation curves down to the individual markers. Furthermore, the single-step random regression test-day model enabled us to better understand the inheritance mode of the yield traits and SCS (e.g., variable chromosomal contributions to the total additive genetic variance and to the genetic correlations between lactations).

Analysis of Early Growth of Piglets from Hyperprolific Sows Using Random Regression Coefficient.

Management of hyperprolific sows is challenging when it comes to controlling birth weight variability and piglet survival in large litters. The growth of low birth weight piglets can be compromised and have a negative impact on production efficiency. The objective of the study was to apply a random regression coefficient model to estimate the main effects of the growth of piglets of highly prolific sows. The dataset contained growth data for 360 piglets from 25 Pen Ar Lan Naima sows. In addition to routine procedures after farrowing, piglets were weighed five times: on day 1 after farrowing, on day 14 of life, at weaning on day 28, on day 30 of nursery period, and at the end of the nursery period when piglets were 83 days old. Data were treated as longitudinal, with body weight as the dependent variable. Fitting age as a quadratic regression within piglets in the random part of the model helped to determine the significant effect of birth weight, litter size, and parity on the growth of the piglets. Since the piglets from large litters often have non-uniform birth weights and this can affect further growth, the use of a random regression coefficient model is practical for analysing the growth of such piglets due to the ability to describe the individual growth pattern of every individual.

Promotive Voice, Leader-member Exchange, and Creativity Endorsement: The Role of Supervisor-Attributed Motives

Employee voice is perceived as an approach with both significant advantages and disadvantages. The current study aims to analyse the behaviours of supervisors when endorsing the ideas of employees who raise promotive voices and consider the employees positively for the development of quality relationships. Specifically, 970 employees were recruited from various small and medium enterprises (SMEs), including manufacturing and electronics companies, in Pakistan for the current study participation. Data collection was conducted in two stages through two sources (supervisors and subordinates) before examining the collected data on the Mplus software for random coefficient regression analyses. Subordinates’ promotive voice was significantly and positively correlated to the Leader-Member-Exchange (LMX) quality and creative idea endorsement when supervisors interpreted the voice as driven more by performance enhancement motives instead of impression management motives. The findings suggested that performance enhancement motives and impression management motives would produce contrasting material consequences for subordinates. The present study limitations and future research directions were also discussed.

Evaluation of Dual-Band Near-Infrared Spectroscopy and Chemometric Analysis for Rapid Quantification of Multi-Quality Parameters of Soy Sauce Stewed Meat.

The objective of this study was to evaluate the performance of near-infrared spectroscopy (NIRS) systems operated in dual band for the non-destructive measurement of the fat, protein, collagen, ash, and Na contents of soy sauce stewed meat (SSSM). Spectra in the waveband ranges of 650-950 nm and 960-1660 nm were acquired from vacuum-packed ready-to-eat samples that were purchased from 97 different brands. Partial least squares regression (PLSR) was employed to develop models predicting the five critical quality parameters. The results showed the best predictions were for the fat (Rp = 0.808; RMSEP = 2.013 g/kg; RPD = 1.666) and protein (Rp = 0.863; RMSEP = 3.372 g/kg; RPD = 1.863) contents, while barely sufficient performances were found for the collagen (Rp = 0.524; RMSEP = 1.970 g/kg; RPD = 0.936), ash (Rp = 0.384; RMSEP = 0.524 g/kg; RPD = 0.953), and Na (Rp = 0.242; RMSEP = 2.097 g/kg; RPD = 1.042) contents of the SSSM. The quality of the content predicted by the spectrum of 960-1660 nm was generally better than that for the 650-950 nm range, which was retained in the further prediction of fat and protein. To simplify the models and make them practical, regression models were established using a few wavelengths selected by the random frog (RF) or regression coefficients (RCs) method. Consequently, ten wavelengths (1048 nm, 1051 nm, 1184 nm, 1191 nm, 1222 nm, 1225 nm, 1228 nm, 1450 nm, 1456 nm, 1510 nm) selected by RF and eight wavelengths (1019 nm, 1097 nm, 1160 nm, 1194 nm, 1245 nm, 1413 nm, 1441 nm, 1489 nm) selected by RCs were individually chosen for the fat and protein contents to build multi-spectral PLSR models. New models led to the best predictive ability of Rp, RMSEP, and RPD of 0.812 and 0.855, 1.930 g/kg and 3.367 g/kg, and 1.737 and 1.866, respectively. These two simplified models both yielded comparable performances to their corresponding full-spectra models, demonstrating the effectiveness of these selected variables. The overall results indicate that NIRS, especially in the spectral range of 960-1660 nm, is a potential tool in the rapid estimation of the fat and protein contents of SSSM, while not providing particularly good prediction statistics for collagen, ash, and Na contents.

Random regression for modeling soybean plant response to irrigation changes using time-series multispectral data.

Plant response to drought is an important yield-related trait under abiotic stress, but the method for measuring and modeling plant responses in a time series has not been fully established. The objective of this study was to develop a method to measure and model plant response to irrigation changes using time-series multispectral (MS) data. We evaluated 178 soybean (Glycine max (L.) Merr.) accessions under three irrigation treatments at the Arid Land Research Center, Tottori University, Japan in 2019, 2020 and 2021. The irrigation treatments included W5: watering for 5 d followed by no watering 5 d, W10: watering for 10 d followed by no watering 10 d, D10: no watering for 10 d followed by watering 10 d, and D: no watering. To capture the plant responses to irrigation changes, time-series MS data were collected by unmanned aerial vehicle during the irrigation/non-irrigation switch of each irrigation treatment. We built a random regression model (RRM) for each of combination of treatment by year using the time-series MS data. To test the accuracy of the information captured by RRM, we evaluated the coefficient of variation (CV) of fresh shoot weight of all accessions under a total of nine different drought conditions as an indicator of plant's stability under drought stresses. We built a genomic prediction model () using the genetic random regression coefficients of RRM as secondary traits and evaluated the accuracy of each model for predicting CV. In 2020 and 2021,the mean prediction accuracies of built in the changing irrigation treatments (r = 0.44 and 0.49, respectively) were higher than that in the continuous drought treatment (r = 0.34 and 0.44, respectively) in the same year. When the CV was predicted using the across 2020 and 2021 in the changing irrigation treatment, the mean prediction accuracy (r = 0.46) was 42% higher than that of the simple genomic prediction model (r =0.32). The results suggest that this RRM method using the time-series MS data can effectively capture the genetic variation of plant response to drought.

Remaining useful life prediction of aero-engines based on random-coefficient regression model considering random failure threshold

Remaining useful life (RUL) prediction is one of the most crucial components in prognostics and health management (PHM) of aero-engines. This paper proposes an RUL prediction method of aero-engines considering the randomness of failure threshold. Firstly, a random-coefficient regression (RCR) model is used to model the degradation process of aeroengines. Then, the RUL distribution based on fixed failure threshold is derived. The prior parameters of the degradation model are calculated by a two-step maximum likelihood estimation (MLE) method and the random coefficient is updated in real time under the Bayesian framework. The failure threshold in this paper is defined by the actual degradation process of aeroengines. After that, a expectation maximization (EM) algorithm is proposed to estimate the underlying failure threshold of aeroengines. In addition, the conditional probability is used to satisfy the limitation of failure threshold. Then, based on above results, an analytical expression of RUL distribution of aero-engines based on the RCR model considering random failure threshold (RFT) is derived in a closed-form. Finally, a case study of turbofan engine is used to demonstrate the effectiveness and superiority of the RUL prediction method and the parameters estimation method of failure threshold proposed.