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Lane change has a potential significance in road safety. Gap acceptance phenomena serves as a primary and critical phase in lane change maneuver. This study aims to investigate the gap acceptance behaviour of drivers during lane changes on expressways, with a focus on understanding how various factors influence drivers' decisions to change lanes. An extensive dataset collected through various sensors tailored for expressway driving, known as the ‘Expressway Drive: Instrumented Vehicle (EDIV) Dataset’ is utilized. Driving data from 59 drivers covering a distance of around 4000 km was used in the current study. Total 2578 lane changing events are identified through computing lateral deviations measured through 3D LiDAR sensor. Substantial differences are observed within the groups in primary analysis which suggest that lane-change direction significantly affect gap acceptance. To effectively manage both intra- and inter-cluster variances, this study employs two separate three levels mixed-effects linear models. These models account for the interdependence of gap acceptance characteristics within individual drivers and for different directions of lane changes by incorporating random effects. Furthermore, these models examine relationships between lead/ lag gap acceptance and the various influencing factors as fixed effects. It was found that factors such as speed of the subject vehicle, gap position, relative speeds, and surrounding vehicle types had influence on gap acceptance during lane changes on expressways. The insights gained from this study could inform the development of advanced driver assistance systems (ADAS) as well as development of autonomous vehicles, contributing to improved road safety and traffic flow management in high-speed environments.

As an important branch characteristic factor, the quantity of branches could influence crown structure, tree growth, and wood quality. Taking Larix kaempferi plantation in Dagujia Forest Farm, Qingyuan County, Liaoning Province as the research object, we developed a mixed effect prediction model of the first-order branches quantity of L. kaempferi including sprouting branches based on the negative binomial distribution model, and a mixed effect prediction model of the first-order branches density of L. kaempferi including sprouting branches based on the negative exponential model. The results showed that the mixed effect model considering sample level as the random effect effectively decreased the heteroscedasticity and autocorrelation. The fitting goodness was better than the traditional model. The quantity of the first-order branches increased with increasing crown ratio. The mixed effect model with the basic model intercept of the first-order branches quantity as the random effect parameter was determined as the optimal model, with R.t2 = 0.552 and the RMSE = 7.242. As for the density of the first-order branches, the heteroscedasticity and autocorrelation were also reduced when the random effect was added. The density of the first-order increased with increasing crown ratio. The mixed effect model with the basic model intercept of the first-order branches density model and branch depth as random effects was determined as the optimal model, with Ra2 = 0.792 and the RMSE = 4.447. The model for branch quantity and density of L. kaempferi constructed would lay an important foundation for making scientific forest management plans and improving wood quality [ABSTRACT FROM AUTHOR]

Endocrine-disrupting chemicals (EDCs) exhibited the detriment in female reproductive health. Our objective was to investigate the individual and mixture effects of EDCs present in follicular fluid, the environment in which oocytes grow and develop, on early reproductive outcomes. We recruited 188 women seeking reproduction examination from the Study of Exposure and Reproductive Health (SEARCH) cohort between December 2020 and November 2021. We assessed the concentrations of 7 categories of 64 EDCs in follicular fluid, and measured early reproductive outcomes, including retrieved oocytes, mature oocytes, normal fertilized oocytes, and high-quality embryos. In this study Monomethyl phthalate (MMP) (2.17 ng/ml) were the compounds found in the highest median concentrations in follicular fluid. After adjusting for multiple testing, multivariate regression showed that multiple EDCs were significantly negatively associated with early assisted reproduction outcomes. For example, MMP showed a significant negative correlation with the number of high quality embryos (β: −0.1, 95 % CI: −0.15, −0.04). Specifically, eight types of EDCs were significantly negatively associated with four early assisted reproductive outcomes (β range: −0.2 ∼ -0.03). In the mixed exposure model, we found that mixtures of EDC were significantly negatively correlated with all four outcomes. In the quantile g-computation (QGCOMP) model, for each interquartile range increase in the concentration of EDC mixtures, the number of oocytes retrieved, mature oocytes, normally fertilized oocytes, and high-quality embryos decreased by 0.46, 0.52, 0.77, and 1.2, respectively. Moreover, we identified that phthalates (PAEs) predominantly contributed to the negative effects. Future research should validate our findings.

Abstract Background Per- and polyfluoroalkyl substances (PFAS) are known environmental contaminants with immunosuppressive properties. Their connection to rheumatoid arthritis (RA), a condition influenced by the immune system, is not well studied. This research explores the association between PFAS exposure and RA prevalence. Methods This research utilized data from the NHANES, encompassing a sample of 10,496 adults from the 2003–2018 cycles, focusing on serum levels of several PFAS. The presence of RA was determined based on self-reports. This study used multivariable logistic regression to assess the relationship between individual PFAS and RA risk, adjusting for covariates to calculate odds ratios (ORs). The combined effects of PFAS mixtures were evaluated using BKMR, WQS regression, and quantile g-computation. Additionally, sex-specific associations were explored through stratified analysis. Results Higher serum PFOA (OR = 0.88, 95% CI: 0.79, 0.98), PFHxS (OR = 0.91, 95% CI: 0.83, 1.00), PFNA (OR = 0.87, 95% CI: 0.77, 0.98), and PFDA (OR = 0.89, 95% CI: 0.81, 0.99) concentration was related to lower odds of RA. Sex-specific analysis in single chemical models indicated the significant inverse associations were only evident in females. BKMR did not show an obvious pattern of RA estimates across PFAS mixture. The outcomes of sex-stratified quantile g-computation demonstrated that an increase in PFAS mixture was associated with a decreased odds of RA in females (OR: 0.76, 95% CI: 0.62, 0.92). We identified a significant interaction term of the WQS*sex in the 100 repeated hold out WQS analysis. Notably, a higher concentration of the PFAS mixture was significantly associated with reduced odds of RA in females (mean OR = 0.93, 95% CI: 0.88, 0.98). Conclusions This study indicates potential sex-specific associations of exposure to various individual PFAS and their mixtures with RA. Notably, the observed inverse relationships were statistically significant in females but not in males. These findings contribute to the growing body of evidence indicating that PFAS may have immunosuppressive effects.

Background: Per- and polyfluoroalkyl substances (PFAS) are known environmental contaminants with immunosuppressive properties. Their connection to rheumatoid arthritis (RA), a condition influenced by the immune system, is not well studied. This research explores the association between PFAS exposure and RA prevalence. Methods: This research utilized data from the NHANES, encompassing a sample of 10,496 adults from the 2003–2018 cycles, focusing on serum levels of several PFAS. The presence of RA was determined based on self-reports. This study used multivariable logistic regression to assess the relationship between individual PFAS and RA risk, adjusting for covariates to calculate odds ratios (ORs). The combined effects of PFAS mixtures were evaluated using BKMR, WQS regression, and quantile g-computation. Additionally, sex-specific associations were explored through stratified analysis. Results: Higher serum PFOA (OR = 0.88, 95% CI: 0.79, 0.98), PFHxS (OR = 0.91, 95% CI: 0.83, 1.00), PFNA (OR = 0.87, 95% CI: 0.77, 0.98), and PFDA (OR = 0.89, 95% CI: 0.81, 0.99) concentration was related to lower odds of RA. Sex-specific analysis in single chemical models indicated the significant inverse associations were only evident in females. BKMR did not show an obvious pattern of RA estimates across PFAS mixture. The outcomes of sex-stratified quantile g-computation demonstrated that an increase in PFAS mixture was associated with a decreased odds of RA in females (OR: 0.76, 95% CI: 0.62, 0.92). We identified a significant interaction term of the WQS*sex in the 100 repeated hold out WQS analysis. Notably, a higher concentration of the PFAS mixture was significantly associated with reduced odds of RA in females (mean OR = 0.93, 95% CI: 0.88, 0.98). Conclusions: This study indicates potential sex-specific associations of exposure to various individual PFAS and their mixtures with RA. Notably, the observed inverse relationships were statistically significant in females but not in males. These findings contribute to the growing body of evidence indicating that PFAS may have immunosuppressive effects. [ABSTRACT FROM AUTHOR]

The decomposition of mangrove litter plays a crucial role in material circulation and energy flow within mangrove forests. Evaluating the decomposition-based variation in biogenic elements in litter is important for improving our understanding about their biogeochemical cycling in ecosystems. The main objective of this study was to examine the interaction effect during the decomposition process of mixed Kandelia obovata and Avicennia marina litter. Variations in C and N were also determined in the decomposing leaf litter mixtures. Our findings revealed that the decomposition rates were faster in summer than in winter, and increased with the proportion of A. marina litter. After 35 days of decomposition in summer, the remaining weights for different proportions of K. obovata (KO) and A. marina (AM) were 22.9% (KO:AM = 1:2), 27.2% (KO:AM = 1:1), and 31.2% (KO:AM = 2:1), respectively. Similarly, after 49 days of decomposition in winter, the remaining weights for the different KO:AM proportions were 27.7%, 35.4%, and 44.0%, respectively. Additionally, the decomposition of mixed K. obovata and A. marina litter had an influence on C content and N release dynamics. These results provide a scientific basis for understanding the decomposition of mixed mangrove litter and its implications for material circulation and energy flow within these ecosystems. [ABSTRACT FROM AUTHOR]

Air pollution is widely acknowledged as a significant risk factor for human health, especially reproductive health. Nevertheless, many studies have disregarded the potentially mixed effects of air pollutants on reproductive outcomes. We performed a retrospective cohort study involving 8048 women with 9445 cycles undergoing In Vitro Fertilization (IVF) and Intracytoplasmic Sperm Injection (ICSI) in China, from 2017 to 2021. A land-use random forest model was applied to estimate daily residential exposure to air pollutants, including sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), ozone (O3), and fine particulate matter (PM2.5). Individual and joint associations between air pollutants and oocyte-related outcomes of ART were evaluated. In 90 days prior to oocyte pick-up to oocyte pick-up (period A), NO2, O3 and CO was negatively associated with total oocyte yield. In the 90 days prior to oocyte pick-up to start of gonadotropin medication (Gn start, period B), there was a negative dose-dependent association of exposure to five air pollutants with total oocyte yield and mature oocyte yield. In Qgcomp analysis, increasing the multiple air pollutants mixtures by one quartile was related to reducing the number of oocyte pick-ups by −2.00 % (95 %CI: −2.78 %, −1.22 %) in period A, −2.62 % (95 %CI: −3.40 %, −1.84 %) in period B, and −0.98 % (95 %CI: −1.75 %, −0.21 %) in period C. During period B, a 1-unit increase in the WQS index of multiple air pollutants exposure was associated with fewer number of total oocyte (−1.27 %, 95 %CI: −2.16 %, −0.36 %) and mature oocyte (−1.42 %, 95 %CI: −2.41 %, −0.43 %). O3 and NO2 were major contributors with adverse effects on the mixed associations. Additionally, period B appears to be the susceptible window. Our study implies that exposure to air pollution adversely affects oocyte-related outcomes, which raises concerns about the potential adverse impact of air pollution on women’s reproductive health.

For the clustered recurrent event data, a multiplicative intensity model with a mixed effect was proposed. The correlation within the same cluster was specified by a gamma frailty variable. The parameters of the model were estimated by maximum likelihood estimation. The inference procedure was carried out by EM algorithm. Some simulations were conducted to evaluate the proposed method, and a real data was illustrated. [ABSTRACT FROM AUTHOR]

The size of the leaf area can have a profound impact on the growth and life activities of the tree, and then it is related to the change of the biomass of the tree and the development trend of the forest ecology. The research on leaf area can play a key role in the correlation analysis of trees. Based on the analytical wood data of 48 Korean pine plantations in Heilongjiang Province and the leaf area data measured by the drainage method, a basic linear regression model was established, and then random effect parameters were introduced to construct a mixed effect regression model to improve the fitting effect and prediction accuracy. When random effects at the tree level were added, the optimal mixed effect model obtained contained three random effect parameters, namely logarithm of branch base diameter(lnBD), logarithm of branch length (lnSL), and tree height logarithm(lnHT). The R ² a was 0. 86, the MAE was 0. 342 8, and the RMSE was 0. 484 1. The statistical indicators were better than the basic linear model that only included fixed effects. The comprehensive analysis showed that the model can better describe the leaf area of Korean pine branches. Then, based on the model, the crown leaf area of the Korean pine tree was calculated and a mixed effect prediction model was obtained. The mixed model R ² a was 0. 83 and the RMSE was 0. 442 9. Through the test results, it can be seen that the model can predict the crown and leaf area of Korean pine trees better, and provide a better guiding direction for the future management of artificial Korean pine in this area. [ABSTRACT FROM AUTHOR]

Toxic effects in terms of mitochondria and hereditary substances have been characterized in vitro for individual rare earth elements, while, the joint effects of mixed elements exposure in the population remain ambiguous. Based on the Occupational Chromate Exposure Dynamic Cohort of China, this study investigated the relationship between 15 blood rare earth elements (cerium, dysprosium, erbium, europium, gadolinium, holmium, lanthanum, lutetium, neodymium, praseodymium, samarium, terbium, thulium, yttrium, and ytterbium) and mitochondrial DNA copy number (MtDNACN) as well as peripheral blood lymphocyte micronucleus frequency (MNF). The elastic net was used to select elements highly correlated with effect indicators, whose dose-response relationships were further illustrated by restricted cubic splines. Bayesian kernel regression was employed to explore the combined effects of elements and the contributions of single element. The results showed that most rare earth elements were positively correlated with effect indicators, with yttrium showing the strongest association (β (95% CI): 0.139 (0.1089 – 0.189) for MtDNACN, 0.937 (0.345 – 1.684) for MNF). In the mixed exposure model, with the exposure level fixed at the 50th percentile as the reference, the effect estimates on MtDNACN and MNF increased by 0.228 and 0.598 units, respectively, at the 75th percentile. The single effect analysis implied that yttrium, lanthanum and terbium contributed the most to the elevation of MtDNACN, while yttrium posed the highest risk for genetic damage, accordingly, we provided recommendations to prioritize these elements of concern. In addition, we observed a chief mediating effect of MtDNACN on the elevation of MNF caused by lanthanum, whereas further mechanistic exploration is required to confirm this finding. [Display omitted] [ABSTRACT FROM AUTHOR]

(1) Background: In managing ecological tea gardens, litter composed of pruned and fallen tea leaves from companion tree species is an important component of tea garden soil. The decomposition of litter plays a crucial role in regulating nutrient cycling in tea garden ecosystems. (2) Methods: This study employed the litterbag method to investigate chemical stoichiometry characteristics and enzyme activity changes during the decomposition process of pruned and fallen Camellia sinensis leaves from companion tree species in an ecological tea garden located in central Guizhou Province. (3) Results: With decomposition duration, the general trend of changes in the C/N and C/P ratios showed a decrease in the activity of UE (urease), AP (acid phosphatase), and PPO (polyphenol oxidase) followed by an increase, while CAT (catalase) and CEL (cellulase) activity decreased, then increased, and then decreased again. On the other hand, the N/P and the activity of SC (sucrase) first increased and then decreased. The C/N and the activities of UE, PPO, and AP generally reached their maximum values during the late decomposition stage (366–428 d), while the N/P and the CAT activity peaked during the mid-decomposition stage (305 d). In contrast, the activity of SC and CEL reached its maximum value during the early decomposition stage (123 d). The N/P ratios were significantly higher than those of the CS (C. sinensis) litter in the mixed treatment, while C/N and C/P ratios were significantly lower than those in the CS during decomposition for 184–366 days. The UE, CAT, AP, and SC activities of CBL (C. sinensis + B. luminifera) litter were significantly higher than those of the CS litter during decomposition. During the experiment, antagonistic effects were observed in the C/N and C/P ratios of the different litter types. Most mixed litter exhibited additive effects on enzyme activity, while a few showed nonadditive effects. For the nonadditive effects, most were antagonistic effects, mainly in the CPM (C. sinensis + C. glanduliferum) litter. A small portion, mainly observed in the CBL and CCG (C. sinensis + C. glanduliferum) litter, showed synergistic effects. (4) Conclusions: Selecting B. luminifera and C. glanduliferum to be part of the tree species composition in ecological tea gardens can produce positive mixed effects on enzyme activity during litter decomposition, increase nutrient return capacity, maintain tea garden fertility, and achieve the ecological development of tea gardens. [ABSTRACT FROM AUTHOR]

Copyright of Inorganic Chemicals Industry is the property of Editorial Office of Inorganic Chemicals Industry 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.)

Shared frailty models, that is, hazard regression models for censored data including random effects acting multiplicatively on the hazard, are commonly used to analyze time-to-event data possessing a hierarchical structure. When the random effects are assumed to be normally distributed, the cluster-specific marginal likelihood has no closed-form expression. A powerful method for approximating such integrals is the adaptive Gauss-Hermite quadrature (AGHQ). However, this method requires the estimation of the mode of the integrand in the expression defining the cluster-specific marginal likelihood: it is generally obtained through a nested optimization at the cluster level for each evaluation of the likelihood function. In this work, we show that in the case of a parametric shared frailty model including a normal random intercept, the cluster-specific modes can be determined analytically by using the principal branch of the Lambert function, W 0 . Besides removing the need for the nested optimization procedure, it provides closed-form formulas for the gradient and Hessian of the approximated likelihood making its maximization by Newton-type algorithms convenient and efficient. The Lambert-based AGHQ (LAGHQ) might be applied to other problems involving similar integrals, such as the normally distributed random intercept Poisson model and the computation of probabilities from a Poisson lognormal distribution. [ABSTRACT FROM AUTHOR]

The main objective of this study is to examine the effect of health on economic growth based on 719 estimates obtained from 64 studies from all over the world. We find evidence of a publication bias towards a positive estimated effect of health on economic growth. After accounting for heterogeneity of the estimates, we show that health has a genuine positive effect on economic growth. Less developed countries seem to enjoy a higher effect of health on growth driven by the ongoing economic–demographic transition in those countries. The variation of the health effect on economic growth is also influenced by the available data, estimation procedure, model specification, publication channel, and country characteristics in each study. Studies that do not account for endogeneity seem to create an upward bias. Studies with more comprehensive variables seem to increase the estimated effect of health on growth. A higher number of years of compulsory education, longer working experience, and more favourable environmental conditions also increase the effect size. Overall, our results confirm the key role of the health factor in explaining economic growth across countries. [ABSTRACT FROM AUTHOR]

[Display omitted] • A total of 64 EDCs were measured in follicular fluid samples. • OPEs and CPs were found to cross the blood-follicle barrier and reach the follicular fluid. • Mixtures of EDCs were observed to cause significant adverse effects on early reproductive outcomes. • PAEs have been identified as key EDCs that contribute to adverse early reproductive outcomes. Endocrine-disrupting chemicals (EDCs) exhibited the detriment in female reproductive health. Our objective was to investigate the individual and mixture effects of EDCs present in follicular fluid, the environment in which oocytes grow and develop, on early reproductive outcomes. We recruited 188 women seeking reproduction examination from the Study of Exposure and Reproductive Health (SEARCH) cohort between December 2020 and November 2021. We assessed the concentrations of 7 categories of 64 EDCs in follicular fluid, and measured early reproductive outcomes, including retrieved oocytes, mature oocytes, normal fertilized oocytes, and high-quality embryos. In this study Monomethyl phthalate (MMP) (2.17 ng/ml) were the compounds found in the highest median concentrations in follicular fluid. After adjusting for multiple testing, multivariate regression showed that multiple EDCs were significantly negatively associated with early assisted reproduction outcomes. For example, MMP showed a significant negative correlation with the number of high quality embryos (β: −0.1, 95 % CI: −0.15, −0.04). Specifically, eight types of EDCs were significantly negatively associated with four early assisted reproductive outcomes (β range: −0.2 ∼ -0.03). In the mixed exposure model, we found that mixtures of EDC were significantly negatively correlated with all four outcomes. In the quantile g-computation (QGCOMP) model, for each interquartile range increase in the concentration of EDC mixtures, the number of oocytes retrieved, mature oocytes, normally fertilized oocytes, and high-quality embryos decreased by 0.46, 0.52, 0.77, and 1.2, respectively. Moreover, we identified that phthalates (PAEs) predominantly contributed to the negative effects. Future research should validate our findings. [ABSTRACT FROM AUTHOR]

Crown width is a critical variable in reflecting the individual tree growth status and in developing forest growth and yield models. With the crown width base model as reference, we developed the crown width quantile regression models for different quantiles (0.50, 0.90, 0.93, 0.95, 0.96, 0.99) based on the data of 2763 Korean pines in 66 permanent plots from the 10-55 years old plantations in Dabiangou forest farm, mountainous areas of eastern Liaoning Province. We used the reparameterization method by introducing the single tree competition index (Rd) and used the dummy variable method by introducing stand density and forest layer variables. We then selected optimal quantile of maximum crown width in the stand by comparing our model developed routine to the traditional methods. The final crown width linear mixed effect quantile regression model was developed based on the optimal quantile at the plot level. The influence of each variable on crown width was analyzed to reflect the difference of crown width among individual trees in the stand. The models with different stand densities and forest layers had significant difference based on F statistical test: the Ra2 of the model increased by 0.0104, the root mean square error decreased by 0.0115 and the mean square error reduction was 7.4%, after the variables of forest layer, forest density, and competition being incorporated into the basic model. The developed quantile regression model performed better than that of the ordinary least square method in simulating the maximum crown width of a single tree in the forest stand. The selected best quantile of the quantile regression model for the upper forest layer and lower forest layer was 0.96 and 0.93, respectively. The linear quantile regression model with the mixed effect was superior to the traditional quantile regression model in Akaike, Bayesion and HQ information criterion and other evaluation parameters, the standard error for the parameters of estimates was significantly reduced, and the introduced mixed effect well explained differences among different plots. For the upper forest layer and lower forest layer, the maximum crown width decreased with increasing stand density, increased with increasing relative diameters. The influence of stand density on the crown width of the lower forest layer was greater than that of the upper forest layer. The crown width would increase first and then decrease with the increases of DBH when the stand density was large enough. The mixed effect of the quantile regression model developed here could significantly improve the fitting stability of the model. The sustainable development of Korean pine plantation in the mountainous area of eastern Liaoning Province should be realized by adjusting stand density and moderate tending and thinning in the future. [ABSTRACT FROM AUTHOR]

Aim: Explore the optimal cut-off values for plasma nicotine and its metabolites in assessing smoking status and quantify the association between individual tobacco exposure and pulmonary nodules (PNs). Materials & methods: A total of 2245 plasma samples were included for the determination of nicotine (Nic), cotinine (Cot) and trans -3'-hydroxycotinine (OHCot) concentrations. The receiver operating characteristic curve was used to determine the optimal biomarkers reflecting smoking status. Binary logistic regression, restricted cubic spline and generalized linear model were used to analyze the association of nicotine and its metabolites with PNs. Quantile g-computation was used to investigate the mixed effects between them. Results: Cot was found to be the best biomarker of self-reported active-passive smoking, with optimal thresholds of 9.06 and 1.26 ng/ml, respectively. Except for OHCot, increased concentrations of Cot, Nic, total nicotine equivalent (TNE2) and TNE3 were significantly positively associated with the risk of PNs, whereas nicotine metabolite ratio presented a negative association. The mixed effects of OHCot, Cot and Nic were associated with PNs, with an odds ratio of 1.17 and a 95% CI of 1.05-1.30. Conclusion: Nicotine and its metabolites as potential biomarkers of tobacco exposure were significantly associated with PNs.

The economic reports on income inequality, poverty, and other welfare indicators are relevant if it was supported by the evidence of income traps because rural households in developing countries were frequently hit by several shocks. This study intended to investigate the households' vulnerability to covariate and idiosyncratic shocks in Ethiopia using three waves of the household panel survey for the period of 2011–2016. The study applied a three-level mixed effect model for analysis. The result of the three-level mixed effect model points out that both covariate and idiosyncratic shocks) were found to influence household consumption and covariate shocks are a relatively larger and more significant effect on households' consumption than idiosyncratic shocks. Based on the finding of this study, it is suggested that the implementation of different policies that address factors contributing vulnerability to covariate and idiosyncratic shocks of rural households in Ethiopia.

(1) Background: In managing ecological tea gardens, litter composed of pruned and fallen tea leaves from companion tree species is an important component of tea garden soil. The decomposition of litter plays a crucial role in regulating nutrient cycling in tea garden ecosystems. (2) Methods: This study employed the litterbag method to investigate chemical stoichiometry characteristics and enzyme activity changes during the decomposition process of pruned and fallen Camellia sinensis leaves from companion tree species in an ecological tea garden located in central Guizhou Province. (3) Results: With decomposition duration, the general trend of changes in the C/N and C/P ratios showed a decrease in the activity of UE (urease), AP (acid phosphatase), and PPO (polyphenol oxidase) followed by an increase, while CAT (catalase) and CEL (cellulase) activity decreased, then increased, and then decreased again. On the other hand, the N/P and the activity of SC (sucrase) first increased and then decreased. The C/N and the activities of UE, PPO, and AP generally reached their maximum values during the late decomposition stage (366–428 d), while the N/P and the CAT activity peaked during the mid-decomposition stage (305 d). In contrast, the activity of SC and CEL reached its maximum value during the early decomposition stage (123 d). The N/P ratios were significantly higher than those of the CS (C. sinensis) litter in the mixed treatment, while C/N and C/P ratios were significantly lower than those in the CS during decomposition for 184–366 days. The UE, CAT, AP, and SC activities of CBL (C. sinensis + B. luminifera) litter were significantly higher than those of the CS litter during decomposition. During the experiment, antagonistic effects were observed in the C/N and C/P ratios of the different litter types. Most mixed litter exhibited additive effects on enzyme activity, while a few showed nonadditive effects. For the nonadditive effects, most were antagonistic effects, mainly in the CPM (C. sinensis + C. glanduliferum) litter. A small portion, mainly observed in the CBL and CCG (C. sinensis + C. glanduliferum) litter, showed synergistic effects. (4) Conclusions: Selecting B. luminifera and C. glanduliferum to be part of the tree species composition in ecological tea gardens can produce positive mixed effects on enzyme activity during litter decomposition, increase nutrient return capacity, maintain tea garden fertility, and achieve the ecological development of tea gardens.

Copyright of Chinese Journal of Applied Ecology / Yingyong Shengtai Xuebao is the property of Chinese Journal of Applied Ecology 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.)

Air pollution is widely acknowledged as a significant risk factor for human health, especially reproductive health. Nevertheless, many studies have disregarded the potentially mixed effects of air pollutants on reproductive outcomes. We performed a retrospective cohort study involving 8048 women with 9445 cycles undergoing In Vitro Fertilization (IVF) and Intracytoplasmic Sperm Injection (ICSI) in China, from 2017 to 2021. A land-use random forest model was applied to estimate daily residential exposure to air pollutants, including sulfur dioxide (SO 2), nitrogen dioxide (NO 2), carbon monoxide (CO), ozone (O 3), and fine particulate matter (PM 2.5). Individual and joint associations between air pollutants and oocyte-related outcomes of ART were evaluated. In 90 days prior to oocyte pick-up to oocyte pick-up (period A), NO 2 , O 3 and CO was negatively associated with total oocyte yield. In the 90 days prior to oocyte pick-up to start of gonadotropin medication (Gn start, period B), there was a negative dose-dependent association of exposure to five air pollutants with total oocyte yield and mature oocyte yield. In Qgcomp analysis, increasing the multiple air pollutants mixtures by one quartile was related to reducing the number of oocyte pick-ups by −2.00 % (95 %CI: −2.78 %, −1.22 %) in period A, −2.62 % (95 %CI: −3.40 %, −1.84 %) in period B, and −0.98 % (95 %CI: −1.75 %, −0.21 %) in period C. During period B, a 1-unit increase in the WQS index of multiple air pollutants exposure was associated with fewer number of total oocyte (−1.27 %, 95 %CI: −2.16 %, −0.36 %) and mature oocyte (−1.42 %, 95 %CI: −2.41 %, −0.43 %). O 3 and NO 2 were major contributors with adverse effects on the mixed associations. Additionally, period B appears to be the susceptible window. Our study implies that exposure to air pollution adversely affects oocyte-related outcomes, which raises concerns about the potential adverse impact of air pollution on women's reproductive health. [Display omitted] • A LURF model was used to evaluate air pollution among women underwent ART. • The mixture effect of air pollutants on early ART outcomes was assessed. • The higher exposure level of air pollutants, fewer number of oocytes retrieved. [ABSTRACT FROM AUTHOR]

The relationship between perfluoroalkyl and polyfluoroalkyl substances (PFAS) and cognitive function are inconsistent, and the mixed effects of PFAS on cognitive function are still unclear. We aimed to evaluate the joint effects of PFAS on cognitive function assessed using four tests as follows: the Consortium to Establish a Registry for Alzheimer's Disease Immediate Recall Test (IRT), Delayed Recall Test (DRT), the Animal Fluency Test (AFT), and the Digit Symbol Substitution Test (DSST) in the US elderly. A total of 777 individuals aged ≥ 60 from the National Health and Nutrition Examination Survey (NHANES) 2011–2014 were included in this study. Multivariable logistic regression and Bayesian kernel machine regression (BKMR) were constructed to estimate the overall and the individual effects of PFAS exposure on cognitive function. There were 21.36%, 22.65%, 21.62%, and 21.24% participants with cognitive decline in IRT, DRT, AFT, and DSST, respectively. After multivariable adjustment, perfluorooctanoic acid (PFOA) was inversely associated with cognitive decline in IRT, DRT, and AFT, while no significant association was observed between any other PFAS and cognitive decline. Compared with the lowest quartile, the adjusted odds ratio of cognitive decline with a 95% confidence interval (CI) for the highest quartile of PFOA was 0.33 (95% CI: 0.15–0.69) in IRT, 0.50 (0.26–0.96) in DRT, and 0.45 (0.21–0.95) in AFT. In BKMR analysis, the overall effect of mixtures was significantly protective on cognitive decline in IRT, of which PFOA made the greatest contribution. The consistent protective effect in DRT and DSST was observed when all the chemicals were at their 50th percentile or below it. No significant interaction was observed among PFAS for cognitive function. These findings suggested that PFAS mixture at a low level of current exposure of the US population may have a protective effect on cognitive function. [ABSTRACT FROM AUTHOR]

Copyright of Bulletin of Botanical Research is the property of Bulletin of Botanical Research Editorial Department 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.)

As an important branch characteristic factor, the quantity of branches could influence crown structure, tree growth, and wood quality. Taking Larix kaempferi plantation in Dagujia Forest Farm, Qingyuan County, Liao-ning Province as the research object, we developed a mixed effect prediction model of the first-order branches quantity of L. kaempferi including sprouting branches based on the negative binomial distribution model, and a mixed effect prediction model of the first-order branches density of L. kaempferi including sprouting branches based on the negative exponential model. The results showed that the mixed effect model considering sample level as the random effect effectively decreased the heteroscedasticity and autocorrelation. The fitting goodness was better than the traditional model. The quantity of the first-order branches increased with increasing crown ratio. The mixed effect model with the basic model intercept of the first-order branches quantity as the random effect parameter was determined as the optimal model, with R =0.552 and the RMSE=7.242. As for the density of the first-order branches, the heteroscedasticity and autocorrelation were also reduced when the random effect was added. The density of the first-order increased with increasing crown ratio. The mixed effect model with the basic model intercept of the first-order branches density model and branch depth as random effects was determined as the optimal model, with a 2 =0.552 and the RMSE=7.242. As for the density of the first-order branches, the heteroscedasticity and autocorrelation were also reduced when the random effect was added. The density of the first-order increased with increasing crown ratio. The mixed effect model with the basic model intercept of the first-order branches density model and branch depth as random effects was determined as the optimal model, with R a 2 =0.792 and the RMSE=4.447. The model for branch quantity and density of L. kaempferi constructed would lay an important foundation for making scientific forest management plans and improving wood quality.

This article deals with the optimality aspects of directional neighbor balanced block design under the assumption of randomness of block effects, both under additive and neighbor × direct effects non-additive model. Some classes of designs have been shown to be universally optimal for the estimation of direct effects of treatments as well as neighbor effects for the above situations. [ABSTRACT FROM AUTHOR]

Abstract Background Birth control is the conscious control of the birth rate by methods which temporarily prevent conception by interfering with the normal process of ovulation, fertilization, and implantation. High contraceptive prevalence rate is always expected for controlling births for those countries that are experiencing high population growth rate. The factors that influence contraceptive prevalence are also important to know for policy implication purposes in Bangladesh. This study aims to explore the socio-economic, demographic and others key factors that influence the use of contraception in Bangladesh. Methods The contraception data are extracted from the 2014 Bangladesh Demographic and Health Survey (BDHS) data which were collected by using a two stage stratified random sampling technique that is a source of nested variability. The nested sources of variability must be incorporated in the model using random effects in order to model the actual parameter effects on contraceptive prevalence. A mixed effect logistic regression model has been implemented for the binary contraceptive data, where parameters are estimated through generalized estimating equation by assuming exchangeable correlation structure to explore and identify the factors that truly affect the use of contraception in Bangladesh. Results The prevalence of contraception use by currently married 15–49 years aged women or their husbands is 62.4%. Our study finds that administrative division, place of residence, religion, number of household members, woman’s age, occupation, body mass index, breastfeeding practice, husband’s education, wish for children, living status with wife, sexual activity in past year, women amenorrheic status, abstaining status, number of children born in last five years and total children ever died were significantly associated with contraception use in Bangladesh. Conclusions The odds of women experiencing the outcome of interest are not independent due to the nested structure of the data. As a result, a mixed effect model is implemented for the binary variable ‘contraceptive use’ to produce true estimates for the significant determinants of contraceptive use in Bangladesh. Knowing such true estimates is important for attaining future goals including increasing contraception use from 62 to 75% by 2020 by the Bangladesh government’s Health, Population & Nutrition Sector Development Program (HPNSDP).

Growth and inactivation regression equations were developed to describe the effects of temperature on Salmonella concentration on chicken meat for refrigerated temperatures (⩽10 °C) and for thermal treatment temperatures (55–70 °C). The main objectives were: (i) to compare Salmonella growth/inactivation in chicken meat versus laboratory media; (ii) to create regression equations to estimate Salmonella growth in chicken meat that can be used in quantitative risk assessment (QRA) modeling; and (iii) to create regression equations to estimate D-values needed to inactivate Salmonella in chicken meat. A systematic approach was used to identify the articles, critically appraise them, and pool outcomes across studies. Growth represented in density (Log10 CFU/g) and D-values (min) as a function of temperature were modeled using hierarchical mixed effects regression models. The current meta-analysis analysis found a significant difference (P ⩽ 0.05) between the two matrices – chicken meat and laboratory media – for both growth at refrigerated temperatures and inactivation by thermal treatment. Growth and inactivation were significantly influenced by temperature after controlling for other variables; however, no consistent pattern in growth was found. Validation of growth and inactivation equations against data not used in their development is needed.

Copyright of Chinese Journal of Applied Ecology / Yingyong Shengtai Xuebao is the property of Chinese Journal of Applied Ecology 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.)

Background: The risk of a woman in a developing country dying from a maternalrelated cause is higher compared to a woman living in a developed country. Despite the fact that delivery care service utilization is essential for further improvement of mothers and newborns, the coverage of delivery service in Ethiopia is still near to the ground. This study aimed to identify factors associated with home delivery among women in Ethiopia at their last birth. Methods: The data was obtained from 2016 Ethiopia Demographic and Health Survey which is the fourth survey. The sample was selected using a stratified, two-stage cluster sampling design and the data was analyzed using mixed effect logistic regression model. Results: A total of 10,622 women were considered in this study and 67.2% of them gave birth at home. The percentage of home delivery at their last birth was high in Afar and Somali region (89.6% and 81.7%, respectively) while only 3.3% women who lived in Addis Ababa delivered at home. Living in rural areas, being uneducated, older age, not watching TV, and being poor are predictors of home delivery at 5% level of significance. Conclusion: There is a need of giving special attention to women living in rural area, women from poor families and uneducated women to decrease home delivery. [ABSTRACT FROM AUTHOR]

Background: Birth control is the conscious control of the birth rate by methods which temporarily prevent conception by interfering with the normal process of ovulation, fertilization, and implantation. High contraceptive prevalence rate is always expected for controlling births for those countries that are experiencing high population growth rate. The factors that influence contraceptive prevalence are also important to know for policy implication purposes in Bangladesh. This study aims to explore the socio-economic, demographic and others key factors that influence the use of contraception in Bangladesh.Methods: The contraception data are extracted from the 2014 Bangladesh Demographic and Health Survey (BDHS) data which were collected by using a two stage stratified random sampling technique that is a source of nested variability. The nested sources of variability must be incorporated in the model using random effects in order to model the actual parameter effects on contraceptive prevalence. A mixed effect logistic regression model has been implemented for the binary contraceptive data, where parameters are estimated through generalized estimating equation by assuming exchangeable correlation structure to explore and identify the factors that truly affect the use of contraception in Bangladesh.Results: The prevalence of contraception use by currently married 15-49 years aged women or their husbands is 62.4%. Our study finds that administrative division, place of residence, religion, number of household members, woman's age, occupation, body mass index, breastfeeding practice, husband's education, wish for children, living status with wife, sexual activity in past year, women amenorrheic status, abstaining status, number of children born in last five years and total children ever died were significantly associated with contraception use in Bangladesh.Conclusions: The odds of women experiencing the outcome of interest are not independent due to the nested structure of the data. As a result, a mixed effect model is implemented for the binary variable 'contraceptive use' to produce true estimates for the significant determinants of contraceptive use in Bangladesh. Knowing such true estimates is important for attaining future goals including increasing contraception use from 62 to 75% by 2020 by the Bangladesh government's Health, Population & Nutrition Sector Development Program (HPNSDP). [ABSTRACT FROM AUTHOR]

Linear mixed effects models with general skew normal-symmetric (SNS) error are considered and several properties of the SNS distributions are obtained. Under the SNS settings, ANOVA-type estimates of variance components in the model are unbiased, the ANOVA-type F-tests are exact F-tests in SNS setting, and the exact confidence intervals for fixed effects are constructed. Also the power of ANOVA-type F-tests for components are free of the skewing function if the random effects normally distributed. For illustration of the main results, simulation studies on the robustness of the models are given by comparisons of multivariate skew-normal, multivariate skew normal-Laplace, multivariate skew normal-uniform, multivariate skew normal-symmetric, and multivariate normal distributed errors. A real example is provided for the illustration of the proposed method. [ABSTRACT FROM AUTHOR]

This study proposes a time-varying effect model that can be used to characterize gender-specific trajectories of health behaviors and conduct hypothesis testing for gender differences. The motivating examples demonstrate that the proposed model is applicable to not only multi-wave longitudinal studies but also short-term studies that involve intensive data collection. The simulation study shows that the accuracy of estimation of trajectory functions improves as the sample size and the number of time points increase. In terms of the performance of the hypothesis testing, the type I error rates are close to their corresponding significance levels under all combinations of sample size and number of time points. Furthermore, the power increases as the alternative hypothesis deviates more from the null hypothesis, and the rate of this increasing trend is higher when the sample size and the number of time points are larger. [ABSTRACT FROM AUTHOR]

The City of New York negotiated a dispute over the performance of new garbage trucks purchased from a vehicle manufacturer. The dispute concerned the fulfillment of a specification in the purchase contract that the trucks load a minimum full-load of 12.5 tons of household refuse. On behalf of the City, but in cooperation with the manufacturer, the City's Department of Sanitation and consulting statisticians tested fulfillment of the contract specification, employing a Latin Square design for routing trucks. We present the classical analysis using a linear model and analysis of variance. We also show how fixed, mixed, and random effect models are useful in analyzing the results of the test. Finally, we take a Bayesian perspective to demonstrate how the information from the data overcomes the difference between the prior densities of the city and the manufacturer for the load capacities of the trucks to result in much closer posterior densities. This procedure might prove useful in similar negotiations. Supplementary material including the data and R code for computations in the article are available online. [ABSTRACT FROM PUBLISHER]

Adequate allometric equations are needed for estimating carbon pools of fast growing tree species in relation to international reporting of CO2emissions and for assessing their possible contribution to increasing forest biomass resources. We developed models for predicting biomass, stem basic density and expansion factors of stem to above-ground biomass for five fast growing conifers. Data included destructive measurements of 236 trees from 14 sites, covering a wide range of growth conditions. To ensure model efficiency, models for predicting stem, crown and total above-ground biomass for the five species were estimated simultaneously using a linear, mixed effects model that allowed contemporaneous correlations between the different tree components. Models differed among species and included dbh and tree height. The models explained more than 98% of the variation in above-ground biomass and reflected differences in the allometry between tree species. Stem density differed among species but generally declined with increasing site index and dbh. The overall model for predicting stem basic density included dbh,H100and site index and explained 66% of the total variation. Expansion factors decreased from 1.8–2.0 in small trees (dbh < 10 cm) to 1.1–1.2 for large trees (dbh > 25 cm), but differed among species. The overall model explained 86% of the variation and included quadratic mean diameter and dbh. [ABSTRACT FROM PUBLISHER]

CCHP (combined cooling heating and power) system based on ICE (internal combustion engine) has been widely used. A key issue is to efficiently recover the jacket water and exhaust gas waste heat for refrigeration. In this work, a mixed effect absorption chiller (AC), which couples single effect and double effect processes together, is investigated to recover these two kinds of waste heat simultaneously. The high pressure generator is powered by exhaust gas while one low pressure generator is powered by jacket water waste heat. Thermodynamic characteristics and off-design performance are simulated. Considering thermodynamic constraints, the start point temperature in low pressure generator should be 77°Cor lower. For a 16 kW ICE, the cooling output can reach 34.4 kW with COP of 0.96 and exergy efficiency of 0.186. Comparing with double effect or single effect AC, it can make a better use of different waste heat in CCHP system. [ABSTRACT FROM AUTHOR]

Biological processes such as fishery selectivity, natural mortality, and somatic growth can vary over time, but it is challenging to estimate the magnitude of time-variation of demographic parameters in population dynamics models, particularly when using penalized-likelihood estimation approaches. Random-effect approaches can estimate the variance, but are computationally infeasible or not implemented for many models and software packages. We show that existing models and software based on penalized-likelihood can be used to calculate the Laplace approximation to the marginal likelihood of parameters representing variability over time, and specifically demonstrate this approach via application to Stock Synthesis. Using North Sea cod and Pacific hake models as case studies, we show that this method has little bias in estimating variances for simulated data. It also provides a similar estimate of variability in hake recruitment (log-SD = 1.43) to that obtained from Markov chain Monte Carlo (MCMC) methods (log-SD = 1.68), and the method estimates a non-trivial magnitude (log-SD = 0.07) of variation in growth for North Sea cod. We conclude by discussing the generality of the proposed method and by recommending future research regarding its performance relative to MCMC, particularly when estimating multiple variances simultaneously. [ABSTRACT FROM PUBLISHER]

Small area estimation has been extensively studied under linear mixed effects models. However, when the functional form of the relationship between the response and the covariates is not linear, it may lead to biased estimators of the small area parameters. In this paper, we relax the assumption of linear regression for the fixed part of the model and replace it by using the underlying concept of support vector quantile regression. This makes it possible to express the nonparametric small area estimation problem as mixed or fixed effects model regression. Through numerical studies we compare the efficiency of different models in estimating small area mean. [ABSTRACT FROM AUTHOR]

Perfluoroalkyl and polyfluoroalkyl substances (PFASs) are widely used in consumer products. However, the role of PFAS in infertility is still poorly understood. A total of 788 women from the 2013-2016 nationally representative NHANES were included to explore the association between PFAS exposure and self-reported infertility. Six PFAS, including PFDE, PFNA, PFHxS, n-PFOA, n-PFOS, and Sm-PFOS, were detected by online SPE-HPLC-TIS-MS/MS. We used the generalized linear regression model (GLM), generalized additive models (GAM), and Bayesian kernel machine regression (BKMR) to assess the single effects, non-linear relationships, and mixed effects on women's infertility, respectively. The prevalence of self-reported infertility was 15.54% in this study. In GLM, n-PFOA showed a negative association with self-reported infertility in women for the Q3 (OR: 0.396, 95% CI: 0.119, 0.788) and Q4 (OR: 0.380, 95% CI: 0.172-0.842) compared with Q1 ( p for trend = 0.013). A negative trend was also observed in n-PFOS and ∑PFOS ( p for trend < 0.05). In GAM, a non-linear relationship was revealed in Sm-PFOS, which exhibits a U-shaped relationship. The BKMR model indicated that there might be a joint effect between PFAS and women's infertility, to which PFNA contributed the highest effect (PIP = 0.435). Moreover, age stratification analysis showed a different dose-response curve in under and above 35 years old. Women under the age of 35 have a more noticeable U-shaped relationship with infertility. Therefore, the relatively low level of mixed PFAS exposure was negatively associated with self-reported infertility in women in general, and the impact of PFAS on infertility may vary among women of different age groups. Further studies are needed to determine the etiological relationship.

The economic reports on income inequality, poverty, and other welfare indicators are relevant if it was supported by the evidence of income traps because rural households in developing countries were frequently hit by several shocks. This study intended to investigate the households' vulnerability to covariate and idiosyncratic shocks in Ethiopia using three waves of the household panel survey for the period of 2011-2016. The study applied a three-level mixed effect model for analysis. The result of the three-level mixed effect model points out that both covariate and idiosyncratic shocks) were found to influence household consumption and covariate shocks are a relatively larger and more significant effect on households' consumption than idiosyncratic shocks. Based on the finding of this study, it is suggested that the implementation of different policies that address factors contributing vulnerability to covariate and idiosyncratic shocks of rural households in Ethiopia., Competing Interests: The authors declare no conflict of interest., (© 2022 The Author(s).)

Crown width is a critical variable in reflecting the individual tree growth status and in developing forest growth and yield models. With the crown width base model as reference, we developed the crown width quantile regression models for different quantiles (0.50, 0.90, 0.93, 0.95, 0.96, 0.99) based on the data of 2763 Korean pines in 66 permanent plots from the 10-55 years old plantations in Dabiangou forest farm, mountainous areas of eastern Liaoning Province. We used the reparameterization method by introducing the single tree competition index ( R ) and used the dummy variable method by introducing stand density and forest layer variables. We then selected optimal quantile of maximum crown width in the stand by comparing our model developed routine to the traditional methods. The final crown width linear mixed effect quantile regression model was developed based on the optimal quantile at the plot level. The influence of each variable on crown width was analyzed to reflect the difference of crown width among individual trees in the stand. The models with different stand densities and forest layers had significant difference based on d ) and used the dummy variable method by introducing stand density and forest layer variables. We then selected optimal quantile of maximum crown width in the stand by comparing our model developed routine to the traditional methods. The final crown width linear mixed effect quantile regression model was developed based on the optimal quantile at the plot level. The influence of each variable on crown width was analyzed to reflect the difference of crown width among individual trees in the stand. The models with different stand densities and forest layers had significant difference based on F of the model increased by 0.0104, the root mean square error decreased by 0.0115 and the mean square error reduction was 7.4%, after the variables of forest layer, forest density, and competition being incorporated into the basic model. The developed quantile regression model performed better than that of the ordinary least square method in simulating the maximum crown width of a single tree in the forest stand. The selected best quantile of the quantile regression model for the upper forest layer and lower forest layer was 0.96 and 0.93, respectively. The linear quantile regression model with the mixed effect was superior to the traditional quantile regression model in Akaike, Bayesion and HQ information criterion and other evaluation para-meters, the standard error for the parameters of estimates was significantly reduced, and the introduced mixed effect well explained differences among different plots. For the upper forest layer and lower forest layer, the maximum crown width decreased with increasing stand density, increased with increasing relative diameters. The influence of stand density on the crown width of the lower forest layer was greater than that of the upper forest layer. The crown width would increase first and then decrease with the increases of DBH when the stand density was large enough. The mixed effect of the quantile regression model developed here could significantly improve the fitting stability of the model. The sustainable development of Korean pine plantation in the mountainous area of eastern Liaoning Pro-vince should be realized by adjusting stand density and moderate tending and thinning in the future.R a 2 of the model increased by 0.0104, the root mean square error decreased by 0.0115 and the mean square error reduction was 7.4%, after the variables of forest layer, forest density, and competition being incorporated into the basic model. The developed quantile regression model performed better than that of the ordinary least square method in simulating the maximum crown width of a single tree in the forest stand. The selected best quantile of the quantile regression model for the upper forest layer and lower forest layer was 0.96 and 0.93, respectively. The linear quantile regression model with the mixed effect was superior to the traditional quantile regression model in Akaike, Bayesion and HQ information criterion and other evaluation para-meters, the standard error for the parameters of estimates was significantly reduced, and the introduced mixed effect well explained differences among different plots. For the upper forest layer and lower forest layer, the maximum crown width decreased with increasing stand density, increased with increasing relative diameters. The influence of stand density on the crown width of the lower forest layer was greater than that of the upper forest layer. The crown width would increase first and then decrease with the increases of DBH when the stand density was large enough. The mixed effect of the quantile regression model developed here could significantly improve the fitting stability of the model. The sustainable development of Korean pine plantation in the mountainous area of eastern Liaoning Pro-vince should be realized by adjusting stand density and moderate tending and thinning in the future.

Fire is an important influencing factor in forest ecosystems. Establishing an accurate forest fire forecasting model is important for forest fire management. We used different meteorological factors as predictors to construct a forest fire prediction model in Fujian Province, based on Logistic regression and generalized linear mixed effect model. We compared the fitness and prediction accuracy of the two models, judged the applicability of the mixed effect model in forest fire forecasting. The results showed that the AUC and accuracy values of the Logistic base model were 0.664 and 60.4%, respectively. Models considering random effects gave better fitting and validating statistics. Among them, the two-level mixed model containing both area and altitude difference effects performed best, with increases of 0.057 and 6.0% for the AUC and accuracy values, respectively. By applying the model to predict the probability of forest fires in Fujian Province, we found that the middle-incidence and high-incidence areas of forest fires distributed in northwest and south Fujian, whereas the low-incidence areas of forest fires distributed in southwest and east Fujian, which was consistent with the observed data. The data fitting and forest fire prediction of the mixed effects model was better than those of the Logistic basic model. Therefore, it could be used as an important tool for forest fire prediction and management.

Abstract: Growth and inactivation regression equations were developed to describe the effects of temperature on Salmonella concentration on chicken meat for refrigerated temperatures (⩽10°C) and for thermal treatment temperatures (55–70°C). The main objectives were: (i) to compare Salmonella growth/inactivation in chicken meat versus laboratory media; (ii) to create regression equations to estimate Salmonella growth in chicken meat that can be used in quantitative risk assessment (QRA) modeling; and (iii) to create regression equations to estimate D-values needed to inactivate Salmonella in chicken meat. A systematic approach was used to identify the articles, critically appraise them, and pool outcomes across studies. Growth represented in density (Log10 CFU/g) and D-values (min) as a function of temperature were modeled using hierarchical mixed effects regression models. The current meta-analysis analysis found a significant difference (P ⩽0.05) between the two matrices – chicken meat and laboratory media – for both growth at refrigerated temperatures and inactivation by thermal treatment. Growth and inactivation were significantly influenced by temperature after controlling for other variables; however, no consistent pattern in growth was found. Validation of growth and inactivation equations against data not used in their development is needed. [Copyright &y& Elsevier]