Random Regression Research Articles

Genetic parameters for methane production, intensity, and yield predicted from milk mid-infrared spectra throughout lactation in Holstein dairy cows

Genetic selection to reduce methane (CH4) emissions is a promising solution for reducing the environmental impact of dairy cattle production. Before such a selection program can be implemented, however, it is necessary to have a better understanding of the genetic determinism of CH4 emissions and how this might influence other traits of interest. In this study, we performed a genetic analysis of 6 CH4 traits predicted from milk mid-infrared spectra. We predicted 4 CH4 traits in g/d (MeP, calculated using different prediction equations), one in g/kg of fat- and protein-corrected milk (MeI), and one in g/kg of dry matter intake (MeY). Using an external data set, we determined these prediction equations to be applicable in the range of 70 to 200 DIM. We then estimated genetic parameters in this DIM range using random regression models on a large data set of 829,025 spectra collected between January 2013 and February 2023 from 167,514 first- and second-parity Holstein cows. The 6 CH4 traits were found to be genetically stable throughout and across lactations, with average genetic correlations within a lactation ranging from 0.93 to 0.98, and those between lactations ranging from 0.92 to 0.98. All 6 CH4 traits were also found to be heritable, with average heritability ranging from 0.24 to 0.45. The average pairwise genetic correlations between the 6 CH4 traits ranged from -0.15 to 0.77, revealing that they are genetically distinct, including the 4 measurements of MeP. Of the 6 traits, 2 measures of MeP and MeI did not present antagonistic genetic correlations with milk yield, fat and protein contents, and SCS, and can probably be included in breeding goals with limited impact on other traits of interest.

Marker weighting improves single-step genomic prediction reliabilities of udder health traits in Nordic Red and Jersey dairy cattle populations

The standard single-step genomic prediction model assumes that all SNP markers explain an equal amount of genetic variance, which, however, may not be true. This is because SNPs are located in or near different genes with different functions. Therefore, it seems logical to consider SNP marker-specific weights when predicting genomic breeding values. We hypothesized that allowing differences in the amount of genetic variance explained by each SNP marker will improve prediction reliability and response to selection. To investigate this hypothesis, we first developed multi-trait standard single-step genomic models based on the current multi-trait random regression evaluation models for udder health traits of the Nordic Red (RDC) and Jersey (JER) dairy cattle populations. The models included 4 clinical mastitis (CM) traits, 3 test-day somatic cell score (SCS) traits, and the conformation traits fore udder attachment and udder depth. In the second step, we investigated the effect of applying different SNP marker weighting scenarios in the single-step genomic prediction models, for which a single-step SNP best linear unbiased prediction model was applied. We investigated the prediction reliability of the different models by forward prediction, where the last 4 years of the data were removed to estimate breeding values for validation candidates. In addition, genetic trends of the pedigree-based estimated breeding values (PEBV) and genomic enhanced breeding values (GEBV) were examined. The data sets for RDC and JER included 6.9 and 1.2 million animals of which 5.6 and 0.9 million cows had records, respectively. The number of genotyped animals was 125,789 and 64,777 for RDC and JER, respectively. Cows had repeated SCS observations but only single observations for all other traits and breeding values for all traits were modeled by one covariance function. This required modeling 12 eigenvalue breeding value coefficients for each cow and developing SNP marker weights for the principal components rather than for the biological traits. We investigated 3 SNP marker weighting scenarios: 1) a nonlinear method similar to BayesA, 2) using the classical formula 2pqû2 that accounts for allele heterozygosity, and 3) applying a mean SNP weight calculated by 2pqû2 for every 20 adjacent SNP markers. Bias, dispersion, and prediction reliability were calculated using PEBV or GEBV from the evaluation based on the full data set on those using the reduced data set. We found that the recent favorable genetic trend in CM and SCS has been accelerated since the introduction of genomic selection. The study also shows that a significant increase in prediction reliability, i.e., 0.74 vs. 0.48 for RDC and 0.72 vs. 0.41 for JER cows for CM, can be achieved with a standard single-step genomic prediction model compared with a pedigree-based prediction model. Almost all scenarios with SNP marker weighting further improved the prediction reliability between 0.5% and 12.7%. The highest improvement was achieved by weighing the SNP markers based on the 2pqû2 formula.

Human Resource Accounting and Share Price Performance Evidence from Listed Insurance Firms in Nigeria

This study investigates the impact of Human Resource Accounting (HRA) on share price performance among listed insurance firms in Nigeria. Specifically, the study examines the effects of human capital efficiency, structural capital efficiency, capital employed efficiency, and the value-added intellectual capital coefficient (VAIC) on share price performance. This research adopts ex-post facto and descriptive research designs, utilizing data from 13 insurance firms listed on the Nigerian Exchange Group from 2012 to 2021. Random effect regression model was employed to address statistical issues ensuring robust results, after controlling for multicollinearity and heteroscedasticity. The findings reveal that human capital efficiency and capital employed efficiency significantly enhance share price performance, highlighting the importance of these factors in driving market valuation. Conversely, VAIC shows a significant negative impact on share price performance, suggesting that not all aspects of intellectual capital positively influence market outcomes in the insurance sector. These results contribute to a deeper understanding of how human resource investments affect financial performance, particularly in the context of emerging markets like Nigeria. Based on these findings, this study recommends among others that insurance firms should prioritize investments in human capital development through targeted training and continuous professional development. This approach will enhance employee productivity and innovation, which in turn would positively influence share price performance and contribute to the overall financial health and market competitiveness of the firm.

Subjective Well-Being of Parents and Childless People in Older Age in Germany

AbstractThe growing number of older childless individuals in Western societies has raised concerns about their subjective well-being. In this study, we scrutinize the subjective well-being of parents and childless individuals aged 60 years and older. We examine subjective well-being as a multi-dimensional construct encompassing life satisfaction, positive affect, and negative affect. Based on the value-of-children approach, we hypothesize that childless individuals experience lower subjective well-being than parents and that the disparity in well-being differs with age, gender, and the quality of the parent-child relationship. For our analysis, we use data from a nationally representative sample of adults aged 60 years and older (n = 10,682) drawn from the German Ageing Survey. Our findings from random effects regression models show that childless individuals report lower life satisfaction, less positive affect, and more negative affect than parents. These associations are larger for men than for women but do not vary significantly across age groups. Furthermore, our results suggest that the benefits of parenthood strongly depend on the quality of the parent-child relationship. Childless people report lower subjective well-being than parents with a close emotional bond with their children, but similar or even higher subjective well-being than parents with an emotionally distant parent-child relationship. In sum, our study indicates that childless people experience, on average, lower well-being across all three dimensions of subjective well-being in older age. However, under specific circumstances, childless individuals may fare better than parents.

Pengaruh Persepsi Layanan Publik terhadap Migrasi di Indonesia

Population mobility occurs every day. Many factors encourage individuals to make the movement. In addition to economic driving factors, social factors such as public services also influence individual decisions to migrate. Regions with good quality public services will trigger migration growth. The quality of public services is assessed on individual perceptions. This research focuses on 4 (four) perceptions of public services, namely perceptions of transportation services, perceptions of education services, perceptions of health services, and perceptions of business license services. The test was carried out using the Random Effect Probit Regression method. We find that perceptions of transportation services, education and business permits have a significant effect on migration.

Estimation of Cotton SPAD Based on Multi-Source Feature Fusion and Voting Regression Ensemble Learning in Intercropping Pattern of Cotton and Soybean

The accurate estimation of soil plant analytical development (SPAD) values in cotton under various intercropping patterns with soybean is crucial for monitoring cotton growth and determining a suitable intercropping pattern. In this study, we utilized an unmanned aerial vehicle (UAV) to capture visible (RGB) and multispectral (MS) data of cotton at the bud stage, early flowering stage, and full flowering stage in a cotton–soybean intercropping pattern in the Yellow River Delta region of China, and we used SPAD502 Plus and tapeline to collect SPAD and cotton plant height (CH) data of the cotton canopy, respectively. We analyzed the differences in cotton SPAD and CH under different intercropping ratio patterns. It was conducted using Pearson correlation analysis between the RGB features, MS features, and cotton SPAD, then the recursive feature elimination (RFE) method was employed to select image features. Seven feature sets including MS features (five vegetation indices + five texture features), RGB features (five vegetation indices + cotton cover), and CH, as well as combinations of these three types of features with each other, were established. Voting regression (VR) ensemble learning was proposed for estimating cotton SPAD and compared with the performances of three models: random forest regression (RFR), gradient boosting regression (GBR), and support vector regression (SVR). The optimal model was then used to estimate and visualize cotton SPAD under different intercropping patterns. The results were as follows: (1) There was little difference in the mean value of SPAD or CH under different intercropping patterns; a significant positive correlation existed between CH and SPAD throughout the entire growth period. (2) All VR models were optimal when each of the seven feature sets were used as input. When the features set was MS + RGB, the determination coefficient (R2) of the validation set of the VR model was 0.902, the root mean square error (RMSE) was 1.599, and the relative prediction deviation (RPD) was 3.24. (3) When the features set was CH + MS + RGB, the accuracy of the VR model was further improved, compared with the feature set MS + RGB, the R2 and RPD were increased by 1.55% and 8.95%, respectively, and the RMSE was decreased by 7.38%. (4) In the intercropping of cotton and soybean, cotton growing under 4:6 planting patterns was better. The results can provide a reference for the selection of intercropping patterns and the estimation of cotton SPAD.

Dynamics of rain-triggered lahars and destructive power inferred from seismo-acoustic arrays and time-lapse camera correlation at Volcán de Fuego, Guatemala

AbstractLahars, or volcanic mudflows, are one of the most devastating natural, volcanic hazards. Deadly lahars, such as the one that occurred after the Nevado del Ruiz, Columbia eruption in 1985, in which at least 23,000 people tragically lost their lives, threaten the safety and well-being of humans, the economy, and the infrastructure of many of the communities living in the vicinity of volcanoes. Due to their complex flow behaviors, lahars remain a major challenge to those studying them. We present an analysis of several rain-triggered lahar events at Volcán Fuego in Guatemala using both seismic and infrasound monitoring to quantify both ground vibrations and low-frequency atmospheric sound waves associated with these mudflows. Geophysical data collected over this field campaign quantifies flow parameters such as velocities, stage and the frequency of these rain-triggered lahars. Time-lapse imagery of lahar flows is compared with filtered seismo-acoustic signal characteristics to ascertain stage predictions and relationship to stage fluxes. Using random forest regression models, we establish moderate correlations (correlation coefficient modes 0.48–0.53) with statistical significance (p value = 0.01–0.02) between signal energetics and respective stage. Compiling a catalog of rain-triggered lahar events in Volcán de Fuego’s drainages over a season permits a dataset amenable to statistical analysis. Our goal is the development of new-generation geophysical monitoring tools that will be capable of remote and real-time estimation of flow parameters.

Interpretation of delayed suspended sediment transport during flood events from the perspective of movement characteristics

Different velocities of flood and suspended sediment peaks during flood events result in the delayed transport of suspended sediment, which is exacerbated by reservoir impoundment. The diversity of flood events, the asynchronous pattern of suspended sediment peaks during flood formation and human activities lead to notable differences in the propagation of flood peaks and suspended sediment peaks and subsequently affect the suspended sediment lag, which poses certain problems and challenges for watershed governance, especially reservoir management. In this paper, the Three Gorges Reservoir (TGR) is selected as the research object, and we adopts the analysis of measured data, K-means cluster analysis, 1-D mathematical model and random forest regression. First, the flood process of the TGR from 2003 to 2020 is selected, and then flood event types with nine flood indices are identified. The influences of different flood types on flood and suspended sediment peak propagation are subsequently clarified. We then systematically analyse the factors influencing flood and suspended sediment peak propagation, explore the contributions of different influencing factors to the suspended sediment transport lag, and assess the reasons for the recent changes in the suspended sediment transport lag. The results show that (1) flood events entering the TGR can be approximately classified into six different types, the long-duration and dwarf-fat type (Type 1, 6.59 %), the sharp-thin and early-peak type (Type 2, 20.88 %), the medium-duration and conventional type (Type 3, 27.47 %), the short-duration and sharp-thin type (Type 4, 25.27 %), the long-duration and dramatic-change type (Type 5, 5.49 %), and the medium-duration and multiple-peaks type (Type 6, 14.29 %). (2) Different flood event types result in different effects on the propagation of both the flood and suspended sediment peak. Under the condition of a reservoir flood limit level of 145 m, the flood types for which Qp propagates are as follows from slowest to fastest: Type 1 > Type 5 > Type 2 > Type 3 > Type 6 > Type 4. Moreover, the flood types for which SSCp propagates are as follows from slowest to fastest: Type 4 > Type 2 > Type 3 > Type 6 > Type 1 > Type 5. (3) The initial SSCp asynchronous relationship is the most dominant factor influencing the lagged transport of suspended sediment in the reservoir area, accounting for 32.80 % to 89.40 % of the variation in suspended sediment transport lag, followed by the impacts of differences in flood types (8.2 % to 52.20 %) and reservoir scheduling (2.4 % to 15 %). (4) Human activities, particularly the damming of the cascade reservoirs in the upper reaches of the TGR area and the adjustment of the water level from 145 to 155 m in the TGR due to reservoir scheduling protocols, lead to an increase in the lagged transport time of suspended sediment peak. These findings reveal the asynchronous and differential propagation characteristics and motion mechanisms of flood and suspended sediment peaks in the reservoir area, thereby providing a solid theoretical basis for refining sediment peak discharge scheduling in the TGR.

Characterizing Inter-Seasonal Meteorological Drought Using Random Effect Logistic Regression

Sustainable watershed development focuses on building resilience to drought through better water resource management, ecosystem protection, and adaptation strategies. In this study, the spatiotemporal dynamics and inter-seasonal characteristics of meteorological drought across Ankara Province, Turkey, were investigated and compared using a conditional fixed effect logistic regression model (CFELogRM) and a random effect logistic regression model (RELogRM). To assess the statistical validity and effectiveness of these models, we conducted significance tests, including the log-likelihood ratio chi-square, and Wald chi-square tests. The obtained p-values associated with both the RELogRM and CFELogRM models for the selected seasons demonstrate their statistical significance. Additionally, we conducted the Hausman test (HT) to compare the efficiency of the RELogRM and CFELogRM models. Remarkably, the results of the HT suggest that RELogRM is the optimal model for modeling fall-to-winter season drought dynamics across the study area. Notably, the significant coefficient derived from RELogRM indicates a statistically significant negative correlation between spring moisture conditions and the probability of summer droughts. Specifically, the odds ratio of 0.2416 reflects a 24.16% reduction in the likelihood of transitioning to a higher drought category, emphasizing the crucial role of antecedent moisture conditions in influencing drought propensity.

Random forest regression kriging modeling for soil organic carbon density estimation using multi-source environmental data in central Vietnamese forests

Random forest regression kriging modeling for soil organic carbon density estimation using multi-source environmental data in central Vietnamese forests

Digital color analysis and machine learning for ballpoint pen ink clustering and aging investigation

Fraudulent activities often involve document manipulation, which poses a significant challenge to forensic science. To address this issue, a novel method was developed that combines intended artificial UV pre-degradation, digital color analysis (DCA) of stroke images, and various machine learning (ML) models. This method can cluster blue ballpoint pen inks and predict their photodegradation time. The results of the study indicate that the k-shape clustering method is highly effective in differentiating between inks based on their degradation curve patterns and HSV or RBS color features, aligning well with results from chromatography analyses. Furthermore, the random forest regression model demonstrated superior performance in predicting age, exhibiting the highest coefficients of determination. The DCA-ML method is a straightforward, cost-effective, and highly accurate solution for clustering blue pen inks. Using photodegradation curves to predict document age could eliminate the need for conventional physicochemical analysis techniques.

Urethral tissue characterization using multiparametric ultrasound imaging

A decrease in urethral closure pressure is one of the primary causes of stress urinary incontinence in women. Atrophy of the urethral muscles is a primary factor in the 15 % age-related decline in urethral closure pressure per decade. Incontinence not only affects the well-being of women but is also a leading cause of nursing home admission. The objective of this research was to develop a noninvasive test to assess urethral tissue microenvironmental changes using multiparametric ultrasound (mpUS) imaging technique. Transperineal B-scan ultrasound (US) data were captured using clinical scanners equipped with curvilinear or linear transducers. Imaging was performed on volunteers from our institution medical center (n = 15, 22 to 76 y.o.) during Valsalva maneuvers. After expert delineation of the region of interest in each frame, the central axis of the urethra was automatically defined to determine the angle between the urethra and the US beam for further analysis. By integrating angle-dependent backscatter with radiomic texture feature analysis, a mpUS technique was developed to identify biomarkers that reflect subtle microstructural changes expected within the urethral tissue. The process was repeated when the urethra and US beam were at a fixed angle. Texture selection was conducted for both angle-dependent and angle-independent results to remove redundancies. Ultimately, a distinct biomarker was derived using a random forest regression model to compute the urethra score based on features selected from both processes. Angle-dependent backscatter analysis shows that the calculated slope of US mean image intensity decreased by 0.89 (±0.31) % annually, consistent with the expected atrophic disorganization of urethral tissue structure and the associated reduction in urethral closure pressure with age. Additionally, textural analysis performed at a specific angle (i.e., 40 degrees) revealed changes in gray level nonuniformity, skewness, and correlation by 0.08 (±0.04) %, −2.16 (±1.14) %, and −0.32 (±0.35) % per year, respectively. The urethra score was ultimately determined by combining data selected from both angle-dependent and angle-independent analysis strategies using a random forest regression model with age, yielding an R2 value of 0.96 and a p-value less than 0.001. The proposed mpUS tissue characterization technique not only holds promise for guiding future urethral tissue characterization studies without the need for tissue biopsies or invasive functional testing but also aims to minimize observer-induced variability. By leveraging mpUS imaging strategies that account for angle dependence, it provides more accurate assessments. Notably, the urethra score, calculated from US images that reflect tissue microstructural changes, serves as a potential biomarker providing clinicians with deeper insight into urethral tissue function and may aid in diagnosing and managing related conditions while helping to determine the causes of incontinence.

Influence of different environmental challenges on the expression of reproductive traits in Holstein cattle in Southern Brazil.

The aim of this study was to assess the impact of genotype-environment interaction (GEI) on the manifestation of traits such as age at first calving (AFC), age at first service (AFS), and calving interval (CI) through the application of the reaction norm model in Holstein cattle raised in Paraná state, Brazil. Utilizing data from the milk testing service of the Paraná Association of Holstein Cattle Breeders (APCBRH), this study analyzed records from 179,492 animals undergoing their first, second, and third lactations from the years 2012 to 2022. These animals were part of 513 herds spread across 72 municipalities in Paraná. The environmental gradient was established by normalizing contemporary group solutions, derived from the animal model, with the 305-day-corrected milk yield serving as the dependent variable. Subsequently, reaction norms were determined utilizing a Random Regression Model. Spearman's correlation was then applied to compare the estimates of breeding values across different environmental gradients for the studied traits. The highest EG (+ 4) indicates the least challenging environments, where animals experience better environmental conditions. Conversely, lower EG (-4) values represent the most challenging environments, where animals endure worse conditions. The only trait that exhibited a moderate heritability magnitude was AFC (0.23) in the least challenging environmental condition. The other traits were classified as having low heritability magnitudes regardless of the evaluated environmental gradient. While minimal evidence was found for the influence of GEI on CI, a clear GEI effect was observed for AFC and AFS across all environmental gradients examined. A reversal in genotype ranking occurred under extreme environmental conditions. The findings suggest that the best-performing genotype under one environmental gradient may not necessarily excel under another.

Genetic Parameter Estimates for Growth of Hāpuku (Groper, Polyprion oxygeneios) in Land-Based Aquaculture Using Random Regression Models

Hāpuku (Polyprion oxygeneios) is a promising candidate for aquaculture production in New Zealand. Methods for spawning, juvenile production, and growout to harvest entirely on land, where water quality, pathogens, environmental impacts, and genetic “pollution” can be tightly controlled, have been developed, and genetic improvement to optimise land-based production is the obvious next step. However, estimates of genetic parameters are required to design a rigorous, disciplined, and effective selective breeding program. By using existing data consisting of irregularly spaced repeated measurements of fork length and live body weight collected on wild-collected founders and two generations of captively reared progeny, we evaluated the species’ genetic potential for improvement in growth. We first tested a range of univariate random regression models to identify the best-fitting models for these data. Subsequently, using a bivariate model, we estimated variance components for growth trajectories of fork length and whole body weight. With one to six records available per fish, the best-fitting univariate models included only a fixed effect for contemporary groups and fixed and random genetic third-order Legendre polynomials. More complex models that included full-sib family and/or permanent environmental effects produced unacceptable constrained and/or non-positive-definite solutions. Both traits are moderately heritable at all stages of the growout phase (~0.4–0.5), and the genetic correlation patterns between daily breeding values estimated via the covariance function are different for length and weight. Genetic correlations for length between all pairs of age-specific breeding values are positive and strong (>0.7) and change gradually and smoothly with increasing temporal separation. For weight, these correlations deteriorate more rapidly with increasing time lags between measurements and become negative for some age pairings. We conclude that random regression analyses are a valuable tool for extracting genetic information from irregularly spaced repeated measurements of fish size, speculate that emerging technologies for high-throughput genotyping and phenotyping will add to the value of this approach in the near future, and reason that a breeding strategy that rigorously takes into account the potentially unfavourable genetic correlations between breeding values for weight at some ages will further adapt hāpuku to land-based systems and enhance the profitability commercial-scale production.

Prediction of phases and mechanical properties of magnesium-based high-entropy alloys using machine learning

ObjectivesTo predict phases and mechanical properties of Mg-Al-Cu-Mn-Zn alloys and to validate the results. MethodsIn this study, 29 predictor features of the alloys were examined based on dataset drawn from relevant publications. The correlation of selected predictor features with mechanical properties of Mg-Al-Cu-Mn-Zn alloys were evaluated. New features specific to vehicle and aerospace applications were used. Feature selection schemes involving four machine learning (ML) classifiers that included artificial neural networks (ANN), linear discriminant analysis (LDA), random forest regression (RF) and k-nearest neighbours (k-NN) were adopted. Tensile test was carried out based on ASTM E8 standard. ResultsResults of correlation of features showed that specific strengths and specific modulus of the alloys were strongly and positively correlated with composition of alloying elements but strongly and negatively correlated with composition of magnesium. The results also revealed that homogenization temperatures and time were weakly correlated with the mechanical properties and phases while electronegativity difference and VEC had significant positive correlation. ANN was the best performing classifier followed by k-NN, LDA, and lastly RF with prediction accuracy on test data of 98.7%, 98.1%, 97.9% and 97.8%, respectively. The validity and applicability of the model was tested with three magnesium-based alloys: Mg-80-Al-10-Cu-5-Mn-5-Zn-0, Mg-80-Al-5-Cu-5-Mn-5-Zn-5 and Mg-91.2-Al-8.3-Cu-0-Mn-0.15-Zn-0.35 and compared with findings in literature. The model had higher prediction accuracies compared to previous ML models used on magnesium alloys. The model was then used to predict phases in the Mg-89.43-Al-8.16-Cu-0.34-Mn-0.25-Zn-1.81 alloy and it accurately predicted presence of Mg17Al12, Mg2Si, MgZn and MgZn2. Results of simulation in MatCalc version 6.04 also verified presence of the phases. The phases were further confirmed through SEM/EDS analysis.Conclusions.Dominant strengthening phases were Mg17Al12, Mg2Si, MgZn and MgZn2. Predicted yield strength, ultimate tensile strength and Young’s modulus were within the range of experimental results. Specific strengths and specific modulus were also within the range.

Inversion Modeling of Chlorophyll Fluorescence Parameters in Cotton Canopy via Moisture Data and Spectral Analysis

The study of chlorophyll fluorescence parameters is very important for understanding plant photosynthesis. Monitoring cotton chlorophyll fluorescence parameters via spectral technology can aid in understanding the photosynthesis, growth, and stress of cotton fields in real time and provide support for cotton growth regulation and planting management. In this study, cotton plot experiments with different water treatments were set up to obtain the spectral reflectance of the cotton canopy, the maximum photochemical quantum yield (Fv/Fm), and the photochemical quenching coefficient (qP) of leaves at different growth stages. Support vector machine regression (SVR), random forest regression (RFR), and artificial neural network regression (ANNR) were used to establish a fluorescence parameter inversion model of the cotton canopy leaves. The results show that the original spectrum was transformed by multivariate scattering correction (MSC), the standard normal variable (SNV), and continuous wavelet transform (CWT), and the model constructed with Fv/Fm passed accuracy verification. The SNV-SVR model at the budding stage, the MSC-SVR model at the early flowering stage, the SNV-SVR model at the full flowering stage, the MSC-SVR model at the flowering stage, and the CWT-SVR model at the full boll stage had the highest estimation accuracy. The accuracies of the three spectral preprocessing and qP models were verified, and the MSC-SVR model at the budding stage, SNV-SVR model at the early flowering stage, MSC-SVR model at the full flowering stage, SNV-SVR model at the flowering stage, and CWT-SVR model at the full boll stage presented the highest estimation accuracies.

Parasite Abundance‐Occupancy Relationships Across Biogeographic Regions: Joint Effects of Niche Breadth, Host Availability and Climate

ABSTRACTAimChanging biodiversity and environmental conditions may allow multi‐host pathogens to spread among host species and affect prevalence. There are several widely acknowledged theories about mechanisms that may influence variation in pathogen prevalence, including the controversially debated dilution effect and abundance‐occupancy relationship hypotheses. Here, we explore such abundance‐occupancy relationships for unique lineages of three vector‐borne avian blood parasite genera (the avian malaria parasite Plasmodium and the related haemosporidian parasites Parahaemoproteus and Leucocytozoon) across biogeographical regions.LocationNearctic‐Neotropical and Palearctic‐Afrotropical regions.MethodsWe compiled a cross‐continental dataset of 17,116 bird individuals surveyed from 46 bird assemblages across the Nearctic‐Neotropical and Palearctic‐Afrotropical regions and explored relationships between local parasite lineage prevalence and host assemblage metrics in a Bayesian random regression framework.ResultsMost lineages from these three genera infected ≥ 5 host species and exhibited clear phylogenetic or functional host specificity. Lineage prevalence from all three genera increased with host range, but also with higher degrees of specialisation to phylogenetically or functionally related host species. Local avian community features were also found to be important drivers of prevalence. For example, bird species richness was positively correlated with lineage prevalence for Plasmodium and Leucocytozoon, whereas higher relative abundances of the main host species were associated with lower prevalence for Plasmodium and Parahaemoproteus but higher prevalence for Leucocytozoon.ConclusionsOur results broadly support several of the leading hypotheses about mechanisms that influence pathogen prevalence, including the niche breadth hypothesis in that higher avian host species diversity and broader host range amplify prevalence through increasing ecological opportunities and the trade‐off hypotheses in that specialisation among subsets of available host species may increase prevalence. Furthermore, the three studied avian haemosporidian genera exhibited different abundance‐occupancy relationships across the major global climate gradients and in relation to host availability, emphasising that these relationships do not strictly follow common rules for vector‐borne parasites with different life histories.

Machine Learning Modelling for Soil Moisture Retrieval from Simulated NASA-ISRO SAR (NISAR) L-Band Data

Soil moisture is a critical factor that supports plant growth, improves crop yields, and reduces erosion. Therefore, obtaining accurate and timely information about soil moisture across large regions is crucial. Remote sensing techniques, such as microwave remote sensing, have emerged as powerful tools for monitoring and mapping soil moisture. Synthetic aperture radar (SAR) is beneficial for estimating soil moisture at both global and local levels. This study aimed to assess soil moisture and dielectric constant retrieval over agricultural land using machine learning (ML) algorithms and decomposition techniques. Three polarimetric decomposition models were used to extract features from simulated NASA-ISRO SAR (NISAR) L-Band radar images. Machine learning techniques such as random forest regression, decision tree regression, stochastic gradient descent (SGD), XGBoost, K-nearest neighbors (KNN) regression, neural network regression, and multilinear regression were used to retrieve soil moisture from three different crop fields: wheat, soybean, and corn. The study found that the random forest regression technique produced the most precise soil moisture estimations for soybean fields, with an R2 of 0.89 and RMSE of 0.050 without considering vegetation effects and an R2 of 0.92 and RMSE of 0.042 considering vegetation effects. The results for real dielectric constant retrieval for the soybean field were an R2 of 0.89 and RMSE of 6.79 without considering vegetation effects and an R2 of 0.89 and RMSE of 6.78 with considering vegetation effects. These findings suggest that machine learning algorithms and decomposition techniques, along with a semi-empirical technique like Water Cloud Model (WCM), can be effective tools for estimating soil moisture and dielectric constant values precisely. The methodology applied in the current research contributes essential insights that could benefit upcoming missions, such as the Radar Observing System for Europe in L-band (ROSE-L) and the collaborative NASA-ISRO SAR (NISAR) mission, for future data analysis in soil moisture applications.

Unraveling the hidden relationships between seismic multiattributes, well-dynamic data, and Brazilian pre-salt carbonate reservoirs productivity: A shallow versus deep machine-learning approach

During the initial phases of an E&P project, the most reliable data on the reservoir’s deliverability are acquired via drill stem tests (DST), which provide the productivity per flow unit whenever production logging tool data are available. However, DSTs are restricted to a few kilometers, whereas seismic data cover large areas. The integration of these data has been challenging, particularly due to the difference in scale between them. So, a new workflow to determine the relationship between poststack seismic attributes and reservoir productivity using classic supervised (shallow) and deep-learning regression algorithms was developed. The DST parameters were predicted over the entire seismic cube, which can be extremely valuable for the decision-making process. The data set is from the Brazilian deep-water presalt carbonate reservoirs of the Mero Field, which is a well-explored area with a plethora of test and production data. It is adjacent to an underexplored Central Libra appraisal plan area, which is covered by the same seismic survey. Thus, any relationships between seismic attributes and well productivity data observed at the Mero Field are extrapolated to the adjacent underexplored area. Ten seismic attributes and DST data from 10 wells of the Mero Field were used to train shallow and deep-learning supervised regression algorithms for the prediction of flow capacity and productivity index seismic cubes. Twenty development wells (blind tests) were used for the assessment of our predictive models. The highest percentage of correct predictions at the blind test wells (85%) was obtained with random forest regression using six attributes derived from a spectrally balanced full-stack volume, and neither amplitude-variation-with-offset nor inversion data were needed. Deep learning provided lower performance (75%) at a higher computational cost. It demonstrated a new reservoir derisking tool that can be used for project optimization in areas covered by the same seismic survey.

A field investigation of 3 masks proposed as respiratory protection for wildland firefighters: a randomized controlled trial in British Columbia, Canada.

Wildland firefighters are exposed through the lungs and skin to particulate matter, fumes, and vapors containing polycyclic aromatic hydrocarbons (PAH). Wearing respiratory protection should reduce pulmonary exposure, but there is uncertainty about the most effective and acceptable type of mask. Firefighters from 6 unit crews working with the British Columbia Wildfire Service were approached and those consenting were randomly allocated within each crew to a "no mask" control group or to use 1 of 3 types of masks: X, half-face respirator with P100/multi gas cartridge; Y, cloth with alpaca filter; Z mesh fabric with a carbon filter. Crews were followed for 3 consecutive firefighting days. The mask allocated was constant for each firefighter throughout. All participants completed a brief questionnaire at the start and end of each day, giving information on mask use, respiratory symptoms, and assessment of mask qualities. Spot urine samples were collected pre and post shift to assess 1-hydroxypyrene (1-HP) concentration as an indicator of total PAH absorption. Skin wipe samples from the hands and throat were collected pre and post shift and analyzed for PAH concentration. On each day monitored, 4 participants carried sampling pumps to measure total particulates and PAHs on particles and in vapor phase. The primary outcome was the concentration of urinary 1-HP at the end of the fire day. Secondary outcomes were changes in respiratory and eye symptoms during the course of the shift, reported mask use, and perception of mask qualities. The analysis used a 3-level random intercept regression model that clustered observations within individuals and crews. We aimed to detect any relation of allocated mask type to the 4 outcomes, having allowed for estimated exposure. Information was collected from 89 firefighters, including 14 women: 49% (37/75) of male firefighters were bearded. Nineteen fire days were monitored for a total of 263 firefighter × days, 64 to 68 for each intervention group. The end of shift 1-HP was higher than the start of the shift. Urinary 1-HP was more strongly related to PAHs on the skin than in the breathing zone. Men with beards had higher end-of-shift urinary log 1-HP/creat (ng/g) than other firefighters. None of the groups allocated a mask had lower 1-HP than the no-mask group, either in the study group overall or when stratified by beard-wearing. Among those without either beards or a failed fit-test, Mask Z reduced at the end of shift 1-HP where airborne PAH concentration was high. End-of-shift symptoms were related to particle mass in the breathing zone but was not mitigated by any of the masks. Hours electing not to wear a mask increased from the first to third shift for all mask types. Mask Z was rated as more comfortable than other types. Mask X was rated highest on fit and perceived protection. Mask Y gained the lowest ratings on fit, comfort and feelings of protection. Allocated masks did not provide protection overall, but the results highlighted the need for a wider understanding of the circumstances in which wearing efficient protection is well-advised.