VEGETATION SUCCESSIONAL STRATA CLASSIFICATION IN THE AMAZON ESTUARY THROUGH QUANTILE REGRESSION

ABSTRACTParkia timoriana (DC.) Merr. is an economically important agroforestry tree species grown in home gardens and shifting cultivation areas throughout the North-eastern states of India. This study aimed to establish a best fit volume model to facilitate estimation of biomass and carbon stock of the species without destructive sampling. Stem volumes of 360 trees representing 12 populations were estimated, and the data were used to develop 19 volume models involving diameter at breast height (D) and height (H). These models were then subjected to statistical test, and then cross-validated to select the best fitting model. The coefficient of determination (R2), root mean square error (RMSE), mean absolute deviation (MAD) and Akaike’s Information Criterion (AIC) were used for model selection, and scattered plot with residuals along with paired t-test were used for validation of the resultant models. Among the models compared, V = 0.12500–2.40265D + 21.43248D2 was found to be the best using single independent variable (D), while V = 0.03177 + 1.032D2H was the best model among the models with two independent variables (D and H). These two models gave high accuracy and hence can be used for future referencing.

In this study, the ability of eight different infiltration models (i.e. Green and Ampt, Philip, SCS (US-Soil Conservation Service), Kostiakov, Horton, Swartzendruber, Modified Kostiakov (MK) and Revised Modified Kostiakov (RMK) models) were evaluated by least-squares fitting to measured infiltration data. Six comparison criteria including coefficient of determination (R2), mean root mean square error (MRMSE), root mean square error (RMSE), the F-statistic (F), Cp statistic of Mallows (Cp) and Akaike information criterion (AIC) were used to determine the best performing model with the least number of fitting parameters. Results indicated that R2 and MRMSE were not suitable for model selection. A more valid comparison was achieved by F, Cp, AIC and RMSE statistics. The RMK model including four parameters had the best performance with the majority of soils studied. RMK was better than the MK model in approximately 51.6, 57, 68.5 and 70.6% of soils, when using F, Cp, AIC and RMSE statistics, respectively, and for the other models, a higher per cent of soils was obtained. The RMK model was the best for loam, clay loam and silty clay loam soils, but the MK model was the best for silty loam soils.

BackgroundThe Functional Assessment of Cancer Therapy-Breast (FACT-B) is the most commonly used scale for assessing quality of life in patients with breast cancer. The lack of preference-based measures limits the cost-utility of breast cancer in China. The goal of this study was to explore whether a mapping function can be established from the FACT-B to the EQ-5D-5 L when the EQ-5D health-utility index is not available.MethodsA cross-sectional survey of adults with breast cancer was conducted in China. All patients included in the study completed the EQ-5D-5 L and the disease-specific FACT-B questionnaire, and demographic and clinical data were also collected. The Chinese tariff value was used to calculate the EQ-5D-5 L utility scores. Five models were evaluated using three different modelling approaches: the ordinary least squares (OLS) model, the Tobit model and the two-part model (TPM). Total scores, domain scores, squared terms and interaction terms were introduced into models. The goodness of fit, signs of the estimated coefficients, and normality of prediction errors of the model were also assessed. The normality of the prediction error is determined by calculating the root mean squared error (RMSE), the mean absolute deviation (MAD), and the mean absolute error (MAE). Akaike information criteria (AIC) and Bayes information criteria (BIC) were also used to assess models and predictive performances. The OLS model was followed by simple linear equating to avoid regression to the mean.ResultsThe performance of the models was improved after the introduction of the squared terms and the interaction terms. The OLS model, including the squared terms and the interaction terms, performed best for mapping the EQ-5D-5 L. The explanatory power of the OLS model was 70.0%. The AIC and BIC of this model were the smallest (AIC = -705.106, BIC = -643.601). The RMSE, MAD and MAE of the OLS model, Tobit model and TPM were similar. The MAE values of the 5-fold cross-validation of the multiple models in this study were 0.07155~0.08509; meanwhile, the MAE of the TPM was the smallest, followed by that of the OLS model. The OLS regression proved to be the most accurate for the mean, and linearly equated scores were much closer to observed scores.ConclusionsThis study establishes a mapping algorithm based on the Chinese population to estimate the EQ-5D-5 L index of the FACT-B and confirms that OLS models have higher explanatory power and that TPMs have lower prediction error. Given the accuracy of the mean prediction and the simplicity of the model, we recommend using the OLS model. The algorithm can be used to calculate EQ-5D scores when EQ-5D data are not directly collected in a study.

This study compares the performance of standalone adaptive neuro-fuzzy inference system (ANFIS) and its hybrid with particle swarm optimisation (PSO) in predicting the energy consumption from climatic factors for a multi-campus institution in South Africa. Monthly weather condition datasets (average wind speed, average maximum temperature, average minimum temperature, average dew point and average relative humidity) for 36 months were mapped with the corresponding monthly energy consumption for each campus as the model inputs and output respectively. The ANFIS and ANFIS-PSO models were trained and tested with 70% and 30% of the dataset respectively. The root mean square error (RMSE), mean absolute deviation (MAD), mean absolute percentage error (MAPE) and the computational time were used to evaluate the model for the four campuses. The ANFIS standalone model developed for campus C outperforms other standalone models for campus A, B and D with the following performance indices: RMSE = 1.27, MAD = 1.01, MAPE = 13.34, computational time = 14.61 secs. On the contrary, the ANFIS-PSO model developed for campus D outperforms both the standalone and the hybrid models for campus A, B and C with the following values of performance indices except for computational time: RMSE = 0.147, MAD = 0.125, MAPE = 2.89, computational time = 95.60 secs. This study concludes that tuning ANFIS parameters with PSO offers a better prediction accuracy, which is reliable for strategic energy planning, though at a higher computational time.

Biodiesel as an alternative to diesel fuel produced from vegetable oils or animal fats has attracted more and more attention because it is renewable and environmentally friendly. Compared to conventional diesel fuel, biodiesel has slightly lower performance in engine combustion due to the lower calorific value that leads to lower power generated. This study investigates the effect of multi-walled carbon nanotubes (MWCNTs) as an additive to the rice bran methyl ester (RBME). Artificial neural network (ANN) and response surface methodology (RSM) was used for predicting the calorific value. The interaction effects of parameters such as dosage of MWCNTs, size of MWCNTs and reaction time on the calorific value of RBME were studied. Comparison of RSM and ANN performance was evaluated based on the correlation coefficient (R2), the root mean square error (RMSE), the mean absolute percentage error (MAPE), and the average absolute deviation (AAD) showed that the ANN model had better performance (R2 = 0.9808, RMSE = 0.0164, MAPE = 0.0017, AAD = 0.173) compare to RSM (R2 = 0.9746, RMSE = 0.0170, MAPE = 0.0028, AAD = 0.279). The optimum predicted of RBME calorific value that is generated using the cuckoo search (CS) via lévy flight optimization algorithm is 41.78 (MJ/kg). The optimum value was obtained using 64 ppm of < 7 nm MWCNTs blending for 60 min. The predicted calorific value was validated experimentally as 41.05 MJ/kg. Furthermore, the experimental results have shown that the addition of MWCNTs was significantly increased the calorific value from 36.87 MJ/kg to 41.05 MJ/kg (11.6%). Also, the addition of MWCNTs decreased flashpoint (−18.3%) and acid value (−0.52%). As a conclusion, adding MWCNTs as an additive had improved the physicochemical properties characteristics of RBME. To our best knowledge, no research has yet been performed on the effect of multi-walled carbon nanotubes-additive in physicochemical property of rice brand methyl ester application so far.

Development and comparison of forensic interval age prediction models by statistical and machine learning methods based on the methylation rates of ELOVL2 in blood DNA

Modeling Techniques for Fitting Healthcare Resource Use Costs Derived From Patient Reported Counts of Hospital, ER, And PCP Visits

Introduction: Quantile regression is a valuable alternative for survival data analysis, enabling flexible evaluations of covariate effects on survival outcomes with intuitive interpretations. It offers practical computation and reliability. However, challenges arise when applying quantile regression to censored data, particularly for upper quantiles. The minimum distance approach, utilizing dual-kernel estimation and the inverse cumulative distribution function, shows promise in addressing these challenges, especially with higher-dimensional covariates. Methods: This study contrasts two methods within the realm of quantile linear regression for survival analysis: check-based modeling and the minimum distance approach. Effectiveness is assessed across various scenarios through comprehensive simulation. Results: The simulation results showed that using the quantile regression model with the minimum distance approach reduces the percentage of root mean square error in parameter estimation compared to the quantile regression models based on the check loss function. Additionally, a larger sample size and reduced censoring percentage led to decreased root mean square error in parameter estimation. Conclusion: The research highlights the benefits of using the minimum distance approach for quantile regression. It reduces errors, improves model predictions, captures patterns, and optimizes parameters even with complete data. However, this approach has limitations. The accuracy of estimated quantiles can be influenced by the choice of distance metric and weighting scheme. The assumption of independence between censoring mechanism and survival time may not hold in real-world scenarios. Additionally, dealing with large datasets can be computationally complex.

Site index curves were created for natural Aleppo pine (Pinus halepensis Mill.) stands in the Ebro Valley (northeastern Spain). Data were obtained from 54 felled dominant trees. The Generalized Algebraic Difference Approach (GADA) was used to fit 11 equations with a longitudinal data structure that included all possible growth intervals. Three statistical criteria were used for model comparison: root mean square error (RMSE), adjusted coefficient of determination (R2adj) and Akaike’s information criterion (AIC). Graphical evaluation of the data (plots of observed against predicted values and of residuals against predicted values) was also conducted. In addition, the root mean square error (RMSE) was plotted against age. Finally, fitted site index curves for different site qualities were superimposed on the profile plots of the stems analysed. The best reference age (60 years) was calculated from the relative error in the dominant height prediction. The Hossfeld IV model proved the most suitable for representing site index in the study area. Furthermore, only three site index curves (6, 10 and 14 m at 60 years) were suitable for classifying the entire study area because of the low productivity of the stands. These were compared with other site index curves developed for Spain and other countries, for the natural range of distribution of the species. Specific site index curves must be created for the study area, because the existing qualities and growth patterns are not well-represented in other models.

The prediction of reservoir parameters is the most important part of reservoir evaluation, and porosity is very important among many reservoir parameters. In order to accurately measure the porosity of the core, it is necessary to take cores for indoor experiments, which is tedious and difficult. To solve this problem, this paper introduces machine learning models to estimate porosity through logging parameters. In this paper, gated recurrent unit neural network based on quantile regression method is introduced to predict porosity. Porosity measurement is implemented by taking cores for indoor experiments. The data is divided into training set and test set. The logging parameters are used as the input parameters of the prediction model, and the porosity parameters measured in the laboratory are used as the output parameters. Experimental results show that the quantile regression method improves the accuracy of the gated recurrent unit neural network, and the RMSE (Root Mean Square Error) of the unoptimized GRU neural network is 0.1774, after optimization, the RMSE is 0.1061. By comparing with the most widely used BP neural network, the accuracy of the method proposed in this paper is much higher than that of BP neural network. This shows that the gated recurrent neural network method based on quantile regression is excellent in predicting reservoir parameters.

Height and diameter are two factors that are considered when developing (volume and yield) tables, as well as for determining site quality and site index. Diameter is easily measured using precise and affordable instruments. However, height measurement is complex in terms of time, skill, and resource. So, developing allometric equation of height-diameter is useful to predict height from diameter to calculate tree volume, biomass, and carbon storage and survival analysis. The study was carried out in Nepal. The study area comprised of a total of 664 unique plots of <i>Pinus roxburghii</i>. Data was obtained from Forest Resource Assessment, 2018 undertaken by Forest Research and Training Centre (then Department of Forest Research and survey). Diameter was measured with a diameter tape at 1.3 m height above the ground level and total height was measured with a Vertex IV and Transponder. A two-phase cluster sampling was applied during data collection. Statistical software R and MS-Excel were used for data analysis. Correlation analysis showed significant positive correlation (r = 0.86) between DBH (diameter at breast height) and Height. The relationship between height as dependent variable to diameter was established through regression analysis, different suggested models were tested accordingly. Different forms of candidate models including linear, polynomial, logarithmic, and inverse were fitted to select the best height prediction model. The Akaike Information Criterion (AIC), Root Mean Square Error (RMSE), and Adjusted Coefficient of Determination (R<sup>2</sup> adj.) were used to evaluate the model. Polynomial degree 2 form of equation (height=1.1052804+0.6252304*dbh−0.0021242*dbh<sup>2</sup>) resulted as the best model with values of adj. R<sup>2</sup> RMSE, and AIC; 0.720, 3.639 and 2735.253 respectively.

The dairy industry in Nigeria faces chronic low productivity, with indigenous Bunaji cattle yielding only 0.5–3.0 L/day and national output meeting just 35–40% of demand. Crossbreeding with Holstein-Friesian has shown heterosis, particularly in the Jos Plateau’s cool highland climate, yet the absence of individual-level test-day records limits genetic evaluation and herd management. This study compared the predictive performance of three parametric non-linear models (Wood, Wilmink, and Ali and Schaeffer) and an artificial neural network (ANN) in modelling lactation curves of Holstein-Friesian × Bunaji crossbred dairy cows under intensive management. The dataset comprised 21 complete lactations of Holstein-Friesian × Bunaji crossbred cows, each with 35 weekly milk yield observations, resulting in 735 potential test-day records used for lactation curve modelling. Derived parameters included peak yield, time to peak, persistency index, and average yield. Model efficiency was assessed using adjusted R2, root mean square error (RMSE), Akaike information criterion (AIC), and Bayesian information criterion (BIC). The Ali and Schaeffer model provided the superior statistical fit (Adj. R2 = 0.97, RMSE = 0.25, AIC = −87.5, BIC = −79.7), followed by Wood’s model. The ANN yielded the highest biological realism (peak yield = 8.40 L at week 4; persistency index = 0.54) but the weakest goodness-of-fit. All models produced similar average daily yields (≈6.49 L), with peaks ranging 7.72–8.40 L and persistency indices 0.47–0.54. These findings demonstrate that classical parametric models such as Ali and Schaeffer remain the most parsimonious and accurate for reconstructing lactation curves from limited intensive data in Nigeria. ANN offers complementary value for early prediction of key parameters. Accurate curve modelling will support context-specific breeding programmes, genetic evaluation, and sustainable dairy intensification on the Jos Plateau. Precise lactation curve modeling enhances estimation of key parameters (peak yield, persistency, and total lactation milk production), supporting improved herd management, genetic evaluation, and breeding strategies to boost productivity in crossbred cattle under similar production systems.

BackgroundThe early warning model of infectious diseases plays a key role in prevention and control. This study aims to using seasonal autoregressive fractionally integrated moving average (SARFIMA) model to predict the incidence of hemorrhagic fever with renal syndrome (HFRS) and comparing with seasonal autoregressive integrated moving average (SARIMA) model to evaluate its prediction effect.MethodsData on notified HFRS cases in Weifang city, Shandong Province were collected from the official website and Shandong Center for Disease Control and Prevention between January 1, 2005 and December 31, 2018. The SARFIMA model considering both the short memory and long memory was performed to fit and predict the HFRS series. Besides, we compared accuracy of fit and prediction between SARFIMA and SARIMA which was used widely in infectious diseases.ResultsModel assessments indicated that the SARFIMA model has better goodness of fit (SARFIMA (1, 0.11, 2)(1, 0, 1)12: Akaike information criterion (AIC):-631.31; SARIMA (1, 0, 2)(1, 1, 1)12: AIC: − 227.32) and better predictive ability than the SARIMA model (SARFIMA: root mean square error (RMSE):0.058; SARIMA: RMSE: 0.090).ConclusionsThe SARFIMA model produces superior forecast performance than the SARIMA model for HFRS. Hence, the SARFIMA model may help to improve the forecast of monthly HFRS incidence based on a long-range dataset.

Weather Variability and the Incidence of Cryptosporidiosis: Comparison of Time Series Poisson Regression and SARIMA Models

Gamma frailty model for survival risk estimation: an application to cancer data