Absolute Residuals Research Articles

Comparing the allometric model to machine learning algorithms for aboveground biomass estimation in tropical forests

Estimating aboveground biomass (AGB) pools is critical for understanding the structural characteristics of forest ecosystems, as the quantitative analysis of biomass contributes to a better understanding of carbon sequestration and in studying the productivity within forest ecosystems. A more precise estimation of AGB is necessary for the estimation of the amount of carbon stored in an ecosystem that is needed for the valuation of the ecosystem services. Though regression models have been used previously for both tree-level and stand-level biomass estimations, supervised machine learning models have rarely been put to test in field-based estimations of AGB. This study was done in the tropical forests of Northeast India (two reserve forests in Tripura) to compare the allometric performance of weighted nonlinear regression model (NLM) to random forest regression (RFR), extreme gradient boost regression (XGB), and support vector machine regression (SVR) in estimating tree-level AGB. Due to their ease of measurement in field conditions and ensuring practical use, tree diameter at breast height (DBH) and tree height (H) were used as the independent variables to train the models. 506 trees representing 21 local tree species were inventoried. Then, the four models (NLM, RFR, XGB, and SVM) were trained separately on the data and compared to find an optimal method to estimate AGB using modeling efficiency of fit (MEfit) and Root Mean Squared Error (RMSE). The models were then validated using the predicted residual error sum of squares (PRESS) statistics. The best fit model in our study was using RFR, with a high MEfit (0.95–0.99), low RMSE (63.10–132.39 kg), and outperforming all other methods in the PRESS statistics, with the highest modeling efficiency of prediction (0.94–0.99) and lowest mean of absolute PRESS residuals (34.99–78.23). The results from Northeast India presented here provide preliminary evidence that RFR could be a reliable method for determining the field-level carbon stock and understanding the biomass dynamics for evergreen and moist deciduous tropical forests. The model accuracy may be constrained by a limited number of variables chosen to suit practical use in the field, and incorporating additional tree-level or stand-level variables such as crown diameter and stand basal area could enhance the robustness and generalizability of AGB estimation.

A novel method of fuel consumption prediction for wing-diesel hybrid ships based on high-dimensional feature selection and improved blending ensemble learning method

Establishing an accurate fuel consumption prediction model is essential to optimize energy efficiency in wing-diesel hybrid ships. This study proposes an improved Blending ensemble learning method for predicting fuel consumption. Initially, the original data is cleaned using artificial experience and Interquartile Range (IQR) method. A high-dimensional feature selection method, variance analysis- Pearson correlation-mutual information-permutation importance (VA-PC-MI-PI), is then proposed to identify 16 key features from the initial 111 features. Furthermore, 9 algorithms are employed for prediction analysis under different temporal resolution data conditions. Based on the results, optimal time step and algorithm models are determined to construct the Blending ensemble model (BEM). Subsequently, the feature data of “sailing speed” and “bow draft”, which account for the highest permutation importance, are imported into the metadata set to alleviate insufficient BEM data utilization. Additionally, Extra Trees (ET) model is chosen as the meta-learner for adaptation and Bayesian method is employed for global optimization. The resulting improved Blending ensemble model (IBEM) exhibits optimal prediction accuracy and generalization ability with an RMSE of 66.04 kg/h, representing a reduction of 16.99% and 13.86% compared to the Support Vector Machine (SVM) model and BEM, respectively. Additionally, the IBEM exhibits significantly smaller residuals and absolute residuals, demonstrating superior predictive stability and consistency. This study provides an important reference and basis for energy efficiency prediction, control, and optimization of wing-diesel hybrid ships.

Optimized deep neural network to estimate orientation angles for solar photovoltaics intelligent systems

Using a single hidden layer neural network in estimating orientation angles for solar photovoltaics lacks the complexity required to model nonlinear relationships between input variables and the optimal orientation angles for solar photovoltaics. It struggles to generalize well to new and unseen data. More sophisticated neural network architectures such as deep learning with multi-hidden perceptron (MLP) can solve these issues by changing the architecture by deepening the network. Deepening the network will increase complexity, energy consumption, and time complexity. The study uses a novel approach to outperform traditional MLP models with two, three, four, and five hidden layers. An innovative approach was proposed by enhancing a single hidden layer MLP with a quadratic polynomial function, utilizing two robust methodologies, Least Absolute Residuals (LAR) and Bisquare methods. The results demonstrate that these approaches yield significant improvements in Root Mean Square Error (RMSE) and coefficient of determination (R squared). LAR-based MLP showed superiority over both bisquare-based and conventional MLPs methods in R2 and RMSE, ranging from 1.13 to 1.18 and 2.53 to 3.06, respectively. The study outperformed conventional MLP architectures with five hidden layers regarding accuracy and efficiency. The proposed model offers a more effective and less complex solution for data prediction tasks.

Evaluation of serum concentrations of total protein and gamma-globulin as an indicator of serum immunoglobulin G concentration in dairy calves

The aim of this study was to evaluate precision of estimating serum immunoglobulin G (IgG) concentration from total protein (TP) or gamma-globulin (γGLB) concentration as an alternative approach, and to compare morbidity of pre-weaned dairy calves differing in serum γGLB concentration. In trial 1, blood was sampled from 129 Holstein calves in the first week after birth, and serum concentrations of TP, γGLB, and IgG were measured. Spearman's correlation coefficient (rs) between serum IgG and TP concentrations was 0.89, and rs between serum IgG and γGLB concentrations was 0.96. Absolute residual (observed – predicted) serum IgG concentrations were smaller when estimated by serum γGLB concentration than by serum TP concentration, and differences in the absolute residuals were smaller for calves fed colostrum replacer (1.68 vs. 4.29 g/L) than those fed whole colostrum (2.41 vs. 3.48 g/L). In trial 2, blood was sampled from 740 Holstein heifer calves during the first week of age, and serum γGLB concentration was measured. The calves were divided into 4 categories based on their serum γGLB concentration; ≥ 1.0 g/dL (Excellent), 0.7 ≤ γGLB <1.0 g/dL (Good), 0.4 ≤ γGLB <0.7 g/dL (Fair) and <0.4 g/dL (Poor). Morbidity for diarrhea and respiratory disease in pre-weaned dairy calves was determined for the first 28 and 56 d of age, and compared among the 4 categories based on serum γGLB concentration. Calves with serum γGLB concentration higher than 0.7 g/dL (Good and Excellent) had less diarrhea during the first 28 d of age than those with lower serum γGLB concentration (Fair and Poor). Calves with serum γGLB concentration higher than 1.0 g/dL (Excellent) had less respiratory diseases for the first 56 d of age than those with lower serum γGLB concentration (Good, Fair, and Poor). These results suggest that serum IgG concentration can be estimated more precisely from concentration of γGLB than TP particularly for calves fed colostrum replacer, and that γGLB concentrations in the first week of age are associated with morbidity of calves. Transfer of passive immunity in dairy calves can be assessed effectively by serum γGLB concentration.

Bayesian physical–statistical retrieval of snow water equivalent and snow depth from X- and Ku-band synthetic aperture radar – demonstration using airborne SnowSAr in SnowEx'17

Abstract. A physical–statistical framework to estimate snow water equivalent (SWE) and snow depth from synthetic aperture radar (SAR) measurements is presented and applied to four SnowSAR flight-line data sets collected during the SnowEx'2017 field campaign in Grand Mesa, Colorado, USA. The physical (radar) model is used to describe the relationship between snowpack conditions and volume backscatter. The statistical model is a Bayesian inference model that seeks to estimate the joint probability distribution of volume backscatter measurements, snow density and snow depth, and physical model parameters. Prior distributions are derived from multilayer snow hydrology predictions driven by downscaled numerical weather prediction (NWP) forecasts. To reduce the signal-to-noise ratio, SnowSAR measurements at 1 m resolution were upscaled by simple averaging to 30 and 90 m resolution. To reduce the number of physical parameters, the multilayer snowpack is transformed for Bayesian inference into an equivalent one- or two-layer snowpack with the same snow mass and volume backscatter. Successful retrievals meeting NASEM (2018) science requirements are defined by absolute convergence backscatter errors ≤1.2 dB and local SnowSAR incidence angles between 30 and 45∘ for X- and Ku-band VV-pol backscatter measurements and were achieved for 75 % to 87 % of all grassland pixels with SWE up to 0.7 m and snow depth up to 2 m. SWE retrievals compare well with snow pit observations, showing strong skill in deep snow with average absolute SWE residuals of 5 %–7 % (15 %–18 %) for the two-layer (one-layer) retrieval algorithm. Furthermore, the spatial distributions of snow depth retrievals vis-à-vis lidar estimates have Bhattacharya coefficients above 94 % (90 %) for homogeneous grassland pixels at 30 m (90 m resolution), and values up to 76 % in mixed forest and grassland areas, indicating that the retrievals closely capture snowpack spatial variability. Because NWP forecasts are available everywhere, the proposed approach could be applied to SWE and snow depth retrievals from a dedicated global snow mission.

A novel SARCIMA model based on central difference and its application in solar power generation of China

Due to the influence of exogenous factors such as weather, solar power generation shows the characteristics of instability and volatility, so the prediction of solar power generation has become especially important. In this paper, a new seasonal central difference autoregressive moving average model abbreviate as SARCIMA is proposed to accurately predict solar power generation. Firstly, the non-stationary time series is subjected to central difference until the difference series is stationary. Then, by analyzing autocorrelation images and partial autocorrelation images respectively, the number of the autoregressive term p and the moving average term q are obtained, so as to obtain the ARCIMA model. Next, the seasonal difference is combined with the ARCIMA model, thus establishing the SARCIMA model. Finally, the new model is applied to the prediction of solar power generation data exhibiting periodic characteristics and compared with five competing models. Mean absolute percentage error and residuals are separately calculated as indicators to evaluate the accuracy and stability of the models. In addition, the prediction of solar power generation in the next three months shows that the new model has better effectiveness, which provides a scientific basis for predicting solar power generation in the future.

СОВЕРШЕНСТВОВАНИЕ АЛГОРИТМА ТОЧНОГО ОЦЕНИВАНИЯ МОДУЛЬНЫХ ЛИНЕЙНЫХ РЕГРЕССИЙ С ПОМОЩЬЮ МЕТОДА НАИМЕНЬШИХ МОДУЛЕЙ

This article is devoted to the study of the algorithm for exact estimation of modular linear regression models using the least absolute deviations. Such models contain a mathematical operation module and are linear in factors, but nonlinear in parameters, regressions. Previously, a special algorithm was developed to exact estimate them using the least absolute deviations. Its essence is that by manually going through all possible combinations of absolute values signs and solving a mixed integer 0-1 linear programming problem for each case, the best model in terms of the sum of absolute residuals is selected. To implement this algorithm, the MODULIR-1 software package was previously developed. The disadvantage of the algorithm is that, in total, quite a lot of time is spent on its implementation. This paper proposes a new and improved algorithm for exact estimation of modular regressions. In it, instead of solving a series of mixed integer 0-1 linear programming problems, you only need to solve one single problem. The developed algorithm is implemented in the new version of the MODULIR-1 program. To compare the effectiveness of the old and new algorithms using the example of chemical data processing, computational experiments were carried out. In six out of seven cases, the new algorithm turned out to be more effective than the existing one. When constructing the most computationally complex modular regression, the estimation time decreased by 74.3%. The modular regression constructed as a result of the experiments based on the sum of absolute residuals turned out to be approximately 2.3 times better than the classical linear regression. At the same time, the number of zero residues in modular regression also turned out to be greater.

Hybrid modeling for grassland productivity prediction: A parametric and machine learning technique for grazing management with applicability to digital twin decision systems

CONTEXTMonitoring Aboveground Net Primary Production (ANPP) is critical to assess not only the current ecosystem status but also its long-term dynamics. In rangelands, the seasonal dynamics of ANPP determines forage availability, stock density, and livestock productivity. OBJECTIVETo develop a hybrid model to be used as a prediction engine for ANPP in the native grasslands of Uruguay. The model combines a parametric component based on the seasonal dynamics of ANPP, and an artificial neural network (ANN) component used to model the remaining non-linearities, which are mainly related to precipitation and temperature variability. The output of hybrid model is proposed as the “virtual entity” of a digital twin support decision system where the “physical entity” is characterized by a collection of bi-weekly (fortnight) ANPP estimates. METHODSFortnight ANPP data were calculated from MODIS EVI for the 2001–2020 period. A sigmoidal functional response, having three parameters with an explicit biological interpretation, was fitted to the accumulated ANPP as a function of time. Forecasts were generated by extrapolating the sigmoidal functional response fit up to four fortnights ahead. From these fits, we obtained the fortnight ANPP values by differentiating the accumulated fortnight ANPP. Predictions (up to four fortnights) were generated for each fortnight and year. The residuals from these fits were modeled using a multilayer perceptron trained by backpropagation using climate variables as independent variables. RESULTS AND CONCLUSIONSThe sigmoidal functional response model fit was highly significant for the accumulated ANPP profile. This model also had a high explanatory power for the accumulated ANPP curve. The median of the percentage absolute residuals for forecasts made 1 to 4 fortnights ahead ranged from 17% to 18%. The ANN significantly reduced this unexplained variability in ANPP, showing a median reduction in residuals of 35%, 31%, 30%, and 30% for 1 to 4 fortnights ahead forecasts, respectively, when compared to predictions from the sigmoidal functional response fit. SIGNIFICANCEBy integrating both parametric and machine learning techniques, the hybrid model developed can make accurate predictions in a way that is both efficient and dependable. The hybrid model not only represents an advantage in terms of predictive power, but it also allows for a deeper understanding of the basic ecological processes involved in forage production.

Quantile Regression Model and Its Application Research

Quantile regression is a regression analysis method that estimates the parameters of a model by minimizing the weighted sum of absolute residuals. It was introduced by Roger Koenker in 1978. As a complementary and extended approach to the traditional regression method, namely least squares method, quantile regression addresses the limitations of least squares method in the presence of heteroscedasticity and ensures the robustness of quantile regression through its robustness to outliers, which compensates for the weakness of least squares method in dealing with outlier data. In practical applications, quantile regression can provide a more comprehensive reflection of data information and capture the tails of the distribution of the dependent variable, thus overcoming the limitation of least squares method that can only estimate the central tendency of the dependent variable distribution. Moreover, quantile regression provides more reasonable interpretations and prevents biased or even erroneous estimations that can occur when using least squares method. Overall, quantile regression offers a novel regression method that addresses many shortcomings of least squares method. By combining quantile regression with least squares method, we can understand both the central tendency and tail behavior of the dependent variable distribution. The fitting results obtained from quantile regression can also provide insights into the suitability of least squares estimation. Briefly, the combination of both methods yields better results in statistical problems. This paper introduces the principles of quantile regression and further discusses its scope and application, aiming to provide a preliminary summary for a better understanding of quantile regression.

Geospatial prediction of total soil carbon in European agricultural land based on deep learning

Accurate geospatial prediction of soil parameters provides a basis for large-scale digital soil mapping, making efficient use of the expensive and time-consuming process of field soil sampling. To date, few studies have used deep learning for geospatial prediction of soil parameters, but there is evidence that it may provide higher accuracy compared to machine learning methods. To address this research gap, this study proposed a deep neural network (DNN) for geospatial prediction of total soil carbon (TC) in European agricultural land and compared it with the eight most commonly used machine learning methods based on studies indexed in the Web of Science Core Collection. A total of 6209 preprocessed soil samples from the Geochemical mapping of agricultural and grazing land soil (GEMAS) dataset in heterogeneous agricultural areas covering 4,899,602 km2 in Europe were used. Prediction was performed based on 96 environmental covariates from climate and remote sensing sources, with extensive comprehensive hyperparameter tuning for all evaluated methods. DNN outperformed all evaluated machine learning methods (R2 = 0.663, RMSE = 9.595, MAE = 5.565), followed by Quantile Random Forest (QRF) (R2 = 0.635, RMSE = 25.993, MAE = 22.081). The ability of DNN to accurately predict small TC values and thus produce relatively low absolute residuals was a major reason for the higher prediction accuracy compared to machine learning methods. Climate parameters were the main factors in the achieved prediction accuracy, with 23 of the 25 environmental covariates with the highest variable importance being climate or land surface temperature parameters. These results demonstrate the superiority of DNN over machine learning methods for TC prediction, while highlighting the need for more recent soil sampling to assess the impact of climate change on TC content in European agricultural land.

Testing symmetry of model errors for non linear multiplicative distortion measurement error models

To study the symmetry and asymmetry of the model error under multiplicative distortion measurement errors setting, we propose a correlation coefficient-based measure between the distribution function and the root of density function. The unknown distribution function and density function are estimated from four kinds of residuals: the conditional mean calibration-based residuals, the conditional absolute mean calibration-based residuals, the conditional variance calibration-based residuals, and the conditional absolute logarithmic calibration-based residuals. We study the asymptotic results of the estimators of correlation coefficient-based measure under four calibrations. Next, we consider statistical inference of the correlation coefficient-based measure by using the empirical likelihood method. The empirical likelihood statistics are shown to be an asymptotically standard chi-squared distribution. Simulation studies demonstrate the performance of the proposed estimators and test statistics. A real example is analyzed to illustrate its practical usage.

Safety performance functions for Two-Lane urban arterial segments

This paper presents the calibration and development process of the safety performance function for the undivided two-lane urban and suburban arterial segments in New Jersey. Data requirements, the availability of required data, and the data processing and extraction methods are presented, along with detailed results of the calibration and development process. Negative binomial, Poisson, zero-inflated Poisson and Hurdle models were generated using the development database. The best model fit was based on likelihood ratio test, AIC and BIC statistics, Vuong test and rootograms. The test database was used to calculate the calibration factor for U2 segments. The predictions of the location-specific count models were then evaluated and compared to those of calibrated Highway Safety Manual model, using the test dataset. The validation test results showed that the negative binomial and hurdle models exhibited better performance in terms of absolute residuals and absolute Pearson residual statistics. This paper also shows the impact of crash location information on analyses results, and underlines that efforts made to manually extract the missing required data can easily be offset by the inaccuracies in crash frequency databases, and the thresholds used to identify intersection related crashes.

Multi-sensor and multi-platform retrieval of water chlorophyll a concentration in karst wetlands using transfer learning frameworks with ASD, UAV, and Planet CubeSate reflectance data

China has one of the widest distributions of carbonate rocks in the world. Karst wetland is a special and important ecosystem of carbonate rock regions. Chlorophyll-a (Chla) concentration is a key indicator of eutrophication, and could quantitatively evaluate water quality status of karst wetland. However, the spectral reflectance characteristics of the water bodies of karst wetland are not yet clear, resulting in remote sensing retrieval of Chla with great challenges. This study is a pioneer in utilizing field-based full-spectrum hyperspectral data to reveal the spectral response characteristics of karst wetland water body and determine the sensitive spectral bands of Chla. We further evaluated the Chla retrieval performance of multi-platform spectral data between Analytical Spectral Device (ASD), Unmanned aerial vehicle (UAV), and PlanetScope (Planet). We proposed two multi-sensor weighted integration strategies and two transfer learning frameworks for estimating water Chla from the largest karst wetland in China by combing a partial least square with adaptive ensemble algorithms. The results showed that: (1) In the range of 500–850 nm, the spectral reflectance of water bodies in the karst wetland was overall 0.001–0.105 higher than the inland water bodies, and the sensitive spectral ranges of water Chla focus on 603–778 nm; (2) UAV images outperformed ASD and Planet data, and produced the highest inversion accuracy (R2 = 0.670) for water Chla in karst wetland; (3) Multi-sensor weighted integration retrieval methods improved the Chla estimation accuracy (R2 = 0.716). Integration retrieval methods with the different weights produced the better Chla estimation accuracy than that of methods with the equal weights; (4) The transfer learning methods from ASD to UAV platform provided the better retrieval performance (the average R2 = 0.669) than that of methods from UAV to Planet platform. The transfer learning methods obtained the highest estimation accuracy of Chla (R2 = 0.814) when the ratio of the training and test data in the target domain was 7:3. The transfer learning methods produced the higher estimation accuracies with the distribution of the absolute residuals between predicted and measured values <20.957 mg/m3 compared to the multi-sensor weighted integration retrieval methods, which demonstrated that transfer learning is more suitable for estimating Chla in karst wetland water bodies using multi-platform and multi-sensor data. The results provide a scientific basis for the protection and sustainable development of karst wetlands.

Predicting global ionospheric TEC maps using Gaussian process regression

The integration of machine learning technology into ionospheric prediction has emerged as a burgeoning field of research. However, several challenges still exist, such as the disregard of delays in real-world data acquisition, variations in accuracy due to different input data and methods. In this study, we present an ionospheric Gaussian process regression (GPR) model with multiple input parameters to forecast global ionospheric total electron content (TEC). We assess two solutions based on GPR model that leverage different ionospheric data, namely the vertical TEC (VTEC) and spherical harmonic coefficients (SHC) prediction solutions. Our findings demonstrate that both GPR models exhibit strong accuracy and stability. Specifically, for 1-day-ahead-predicted global ionospheric maps (GIMs) in 2015 year and 2019 year, the root mean square error (RMSE) of the SHC prediction solution is 4.343 TECU and 1.702 TECU, respectively, while the VTEC prediction solution has RMSE values of 4.321 TECU and 1.673 TECU. Moreover, in high and low solar activity, over 29% and 68% of the absolute residuals are within 1.0 TECU, respectively. Furthermore, we compare the GPR model with conventional methods (such as Adaptive Autoregressive Model (AAR)) and observe that the RMSE of the GPR model is lower than that of the AAR model for GIMs predicted under different solar and geomagnetic activities, with a difference ranging from 0.2 to 0.4 TECU. In addition, the GPR model can provide prediction intervals for the predicted values. These intervals are typically represented using the mean and standard deviation, where the mean represents the predicted value and the standard deviation represents the uncertainty associated with the prediction. Prediction intervals can assist users in understanding the uncertainty of the model's predictions.

Derivation of the Penman–Monteith Equation with the Thermodynamic Approach. II: Numerical Solutions and Evaluation

A review of the derivation of the Penman–Monteith equation with the thermodynamic approach of Monteith is presented in a companion manuscript. The resultant set of equations (expressed in terms of latent heat flux, lf, sensible heat flux, qf, final air temperature, Ta, and the slope parameter related to the saturation vapor pressure curve, Δ) represents a coupled system. Thus, a pair of alternative numerical solutions, with different levels of complexity, were developed and evaluated in the study reported here. Results showed that the alternative models (labeled as model 1 and 2) produced outputs that are essentially identical and also in close agreement with a reference solution. Intercomparison of the alternative models based on the criteria of numerical efficiency and robustness suggests that each model represents a comparable alternative to the other to estimate evaporation. However, owing to its simplicity, model 1 was selected for further consideration. A comparison of the outputs of model 1 with those of the conventional model (i.e., the approach widely used to evaluate the Penman–Monteith set of equations), based on data sets covering a range of evaporation conditions, showed that the difference in the approaches implemented in the two models has a significant effect on estimates of qf, a limited effect on lf, and a negligible effect on Ta. Notably, the results also showed that the mean absolute residual for latent heat flux, lf (i.e., the mean of the absolute residuals between estimates obtained with model 1 and the conventional model) is relatively small (only about 8.2%), suggesting that differences between lf estimates computed with model 1 and the conventional model should generally be within the margin of error of the conventional model.

Bootstrapping the transformed goodness-of-fit test on heavy-tailed GARCH models

We study the bootstrap inference on the goodness-of-fit test for generalized autoregressive conditional heteroskedastic (GARCH) models. Note that the commonly-used portmanteau tests for model adequacy checking necessarily impose moment conditions on innovations, we hence construct the test on the sample autocorrelations of a bounded transformation of absolute residuals, which are obtained by the least absolute deviation estimation from a fitted GARCH model. Specifically, we employ the empirical distribution function of absolute residuals as the transformation. Thus the corresponding portmanteau tests are applicable for very heavy-tailed innovations with only finite fractional moments. We bootstrap both the estimation equation and sample autocorrelations of transformed residuals to approximate the test statistics. The asymptotic validity of the bootstrap procedure is established. Monte Carlo experiments compare the finite-sample performance of the proposed bootstrap-based test with other existing tests. An empirical analysis of modeling exchange rates illustrates its usefulness.

Grape Heterogeneity Index: Assessment of Overall Grape Heterogeneity Using an Aggregation of Multiple Indicators

Uniform grape maturity can be sought by producers to minimise underripe and/or overripe proportions of fruit and limit any undesirable effects on wine quality. Considering that grape heterogeneity is a multifaceted phenomenon, a composite index summarising overall grape heterogeneity was developed to benefit vineyard management and harvest date decisions. A grape heterogeneity index (GHI) was constructed by aggregating the sum of absolute residuals multiplied by the range of values from measurements of total soluble solids, pH, fresh weight, total tannins, absorbance at 520 nm (red colour), 3-isobutyl-2-methoxypyrazine, and malic acid. Management of grape heterogeneity was also studied, using Cabernet Sauvignon grapes grown under four viticultural regimes (normal/low crop load, full/deficit irrigation) during the 2019/2020 and 2020/2021 seasons. Comparisons of GHI scores showed grape variability decreased throughout ripening in both vintages, then significantly increased at the harvest time point in 2020, but plateaued on sample dates nearing the harvest date in 2021. Irrigation and crop load had no effect on grape heterogeneity by the time of harvest in both vintages. Larger vine yield, leaf area index, and pruning weight significantly increased GHI score early in ripening, but no significant relationship was found at the time of harvest. Differences in the Ravaz index, normalised difference vegetation index, and soil electrical conductivity did not significantly change the GHI score.

On the Use of Absolute Residuals as Dependent Variables in Accounting Research

On the Use of Absolute Residuals as Dependent Variables in Accounting Research

Sensitivity correction of fluorescent nuclear track detectors using alpha particles: Determining LET spectra of light ions with enhanced accuracy.

Radiation fields encountered in proton therapy (PT) and ion-beam therapy (IBT) are characterized by a variable linear energy transfer (LET), which lead to a variation of relative biological effectiveness and also affect the response of certain dosimeters. Therefore, reliable tools to measure LET are advantageous to predict and correct LET effects. Fluorescent nuclear track detectors (FNTDs) are suitable to measure LET spectra within the range of interest for PT and IBT, but so far the accuracy and precision have been challenged by sensitivity variations between individual crystals. To develop a novel methodology to correct changes in the fluorescent intensity due to sensitivity variations among FNTDs. This methodology is based on exposing FNTDs to alpha particles in order to derive a detector-specific correction factor. This will allow us to improve the accuracy and precision of LET spectra measurements with FNTDs. FNTDs were exposed to alpha particles. Afterward, the detectors were irradiated to monoenergetic protons, 4 He-, 12 C-, and 16 O-ions. At each step, the detectors were imaged with a confocal laser scanning microscope. The tracks were reconstructed and analyzed using in-house developed tools. Alpha-particle tracks were used to derive a detector-specific sensitivity correction factor ( ). Proton, 4 He-, 12 C-, and 16 O-ion tracks were used to establish a traceable calibration curve that relates the fluorescence intensity with the LET in water ( ). FNTDs from a second batch were exposed and analyzed following the same procedures, to test if can be used to extend the applicability of the calibration curve to detectors from different batches. Finally, a set of blind tests was performed to assess the accuracy of the proposed methodology without user bias. Throughout all stages, the main sources of uncertainty were evaluated. Based on a sample of 100 FNTDs, our findings show a high sensitivity heterogeneity between FNTDs, with having values between 0.57 and 2.55. The fitting quality of the calibration curve, characterized by the mean absolute percentage residuals and correlation coefficient, was improved when was considered. Results for detectors from the second batch show that, if the fluorescence signal is corrected by , the differences in the predicted with respect to the reference set are reduced from 55%, 141%, 41%, and 186% to 4.2%, 6.5%, 5.0%, and 11.0%, for protons, 4 He-, 12 C-, and 16 O-ions, respectively. The blind tests showed that it is possible to measure the track- and dose-average with an accuracy of 0.3%, 16%, and 9.6% and 1.7%, 28%, and 30% for protons, 12 C-ions and mixed beams, respectively. On average, the combined uncertainty of the measured was 11%, 13%, 21%, and 26% for protons, 4 He-, 12 C-, and 16 O-ions, respectively. These values were increased by a mean factor of 2.0 when was not applied. We have demonstrated for the first time that alpha particles can be used to derive a detector-specific sensitivity correction factor. The proposed methodology allows us to measure LET spectra using FNTD-technology, with a degree of accuracy and precision unreachable before with sole experimental approaches.

Vs30 Structure of Almeria City (SE Spain) Using SPAC and MASW Methods and Proxy Correlations

The topographic slope method is an innovative, fast and very low-cost technique for estimating the average S-wave velocity in the upper 30 m (Vs30) based on the relationship between this quantity and the slope of the ground, obtained using a Digital Elevation Model (DEM). The method is based on the good linear correlations log(Vs30)–log(slope) found experimentally, which, ideally, should be determined for each region. If measured Vs30 data are not available to carry out this fitting for the study area, correlations from other areas could be used, although the reliability of the estimated Vs30 results would be lower. In this article, Vs30 observations are made for the city of Almeria, using Spatial Autocorrelation Surveys (SPAC) and Multichannel Analysis of Surface Waves (MASW), obtaining two types of fitting: (a) linear relationship log(Vs30)–log(slope); and (b) considering additional dependence on geological units. The reliability, evaluated by Multiple R-Squared (MRS), varies between 79.2% in the first case and 87.0% in the second, lowering the mean absolute values of the residuals at the observation points in the first case from 40.0 m/s to 29.0 m/s. Using a more generic correlation obtained for other areas of the world, the mean absolute residuals increase to 74.7 m/s.