Random Regression Research Articles

Dual-core silver-coated plasmonic sensor modeling with machine learning

Plasmonic sensors utilizing surface plasmon resonance (SPR) have emerged as powerful tools for sensitive and label-free detection across a wide range of applications. This study introduces a new dual-core silver-coated plasmonic sensor designed to significantly enhance sensitivity and resolution, making it particularly effective for precise analyte detection in complex environments. A key innovation of this sensor lies in its dual-core architecture, which achieves the highest wavelength sensitivity reported at 30,000 nm/RIU and resolution of 3.33×10−6 RIU, covering a broad refractive index (RI) range from 1.34 to 1.41. Furthermore, the integration of machine learning (ML) algorithms, including multiple-variable linear regression (MLR), support vector regression (SVR), and random forest regression (RFR), marks a significant advancement in sensor design. These algorithms enable dynamic adaptation and the extraction of data-driven insights, enhancing the sensor's performance in predicting confinement loss and wavelength across various analytes. The innovative combination of a dual-core design and ML integration positions this plasmonic sensor as a highly sensitive and versatile tool well-suited for advanced bio-sensing applications.

Internal Control and Firms’ Financial Performance in Nigeria: A Study of Selected Manufacturing Firms

This study was carried out for the purpose of understanding the impact of internal control on firm performance. This research wanted to prove that internal control played an important role on the performance of ten selected manufacturing firm in Nigeria. Internal control factors like board size, audit committee size, and board independence were investigated to see how they impact performance. Secondary data gotten from the ten manufacturing firms’ financial statement were analyzed using the panel data regression analysis. Although the fixed effect and the random effect regression were carried out, the Hausman test pointed to the fixed effect regression as significant, hence it was focused on. Findings from the fixed effect revealed that board size, audit committee size, and board independence were all significant in impacting firm performance measured by equity returns. The study therefore recommended among others that the Board of directors should be more diverse in the composition of board members as this would boost their independence.

Evaluation of crop water stress index of wheat by using machine learning models.

The Crop Water Stress Index (CWSI), a pivotal indicator derived from canopy temperature, plays a crucial role in irrigation scheduling for water conservation in agriculture. This study focuses on determining CWSI (by empirical method) for wheat crops in the semi-arid region of western Uttar Pradesh, India, subjected to varying irrigation treatments across two cropping seasons (2021-2022 and 2022-2023). The aim is to investigate further the potential of four machine learning (ML) models-support vector regression (SVR), random forest regression (RFR), artificial neural network (ANN), and multiple linear regression (MLR) to predict CWSI. The ML models were assessed based on determination coefficient (R2), mean absolute error (MAE), and root mean square error (RMSE) under diverse scenarios created from eight distinct input combinations of six variables: air temperature (Ta), canopy temperature (Tc), vapor pressure deficit (VPD), net solar radiation (Rn), wind speed (U), and soil moisture depletion (SD). SVR emerges as the top-performing model, showcasing superior results over ANN, RFR, and MLR. The most effective input combination for SVR includes Tc, Ta, VPD, Rn, and U (R2 = 0.997, MAE = 0.901%, RMSE = 2.223%). Meanwhile, both ANN and MLR achieve optimal results with input combinations involving Tc, Ta, VPD, Rn, U, and SD (R2 = 0.992, MAE = 2.031%, RMSE = 3.705%; R2 = 0.759, MAE = 13.95%, RMSE = 19.98%, respectively). For RFR, the ideal input combination comprises Tc, Ta, VPD, and U (R2 = 0.951, MAE = 5.023%, RMSE = 9.012%). The study highlights the considerable promise of ML models in predicting CWSI, proposing their future application in integration into an irrigation decision support system (IDSS) for crop stress mitigation and efficient water management in agriculture.

Machine Learning Approach to Investigate High Temperature Corrosion of Critical Infrastructure Materials

AbstractDegradation of coatings and structural materials due to high temperature corrosion in the presence of molten salt environment is a major concern for critical infrastructure applications to meet its commercial viability. The choice of high value coatings and structural (construction parts) materials comes with challenges, and therefore data centric approach may accelerate change in discovery and data practices. This research aims to use machine learning (ML) approach to estimate corrosion rates of materials when operated at high temperatures conditions (e.g., nuclear, geothermal, oxidation (dry/wet), solar applications) but geared towards nuclear thermochemical cycles. Published data related to materials (structural and coatings materials), their composition and manufacturing, including corrosion environment were gathered and analysed. Analysis demonstrated that random forest regression model is highly precise compared to other models. Assessment indicates that very limited sets of materials are likely to survive high temperature corrosive environment for extended period of exposure. While a higher quality and larger dataset are required to accurately predict the corrosion rate, the findings demonstrated the value of ML’s regression and data mining capabilities for corrosion data analysis. With the research gap in material selection strategies, proposed research will be critical to advancing data analytics approach exploiting their properties for high temperature corrosion applications. Graphical Abstract

Abstract A089: Intersecting burdens: The role of social barriers and perceived stress in moderating cortisol reactivity among African American breast cancer survivors

Abstract Background: Disparities in breast cancer morbidity and mortality persist in African Americans. Social determinants and their subsequent effect on stress reactivity may impact the biological mechanisms associated with the onset and progression of cancer. However, stress reactivity has not been examined specifically among African American breast cancer survivors. Objective: To evaluate within-subject variation in stress reactivity among African American breast cancer survivors and determine whether this reactivity is influenced by social stressors, such as financial strain and social isolation, known to be associated with cancer outcomes. Methods: We conducted a laboratory intervention study using the Trier Social Stress Test on African American breast cancer survivors. We measured stress reactivity using salivary cortisol levels; socioeconomic, social, and psychological stressors were estimated by self-report. We performed multi-level random effect repeated-measures linear regression models to estimate within-subject changes in cortisol levels. We tested the moderating effects of social factors and cumulative social stressors on reactivity outcomes. Results: Sixty African American breast cancer survivors were included in the study. There were significant increases in mean cortisol levels at 30 minutes (beta=0.054, 95%CI=0.026, 0.082; p<0.001) and 40 minutes (beta=0.040, 95% CI=0.015, 0.065; p=.002) post-stress during the recovery period. We identified significant interactions between time and social stressors on cortisol levels for financial strain (χ2/df=15.83/4, p=.003), social isolation (χ2/df=9.67/4, p=.04), negative life events (χ2/df=12.75/4, p=.01), perceived stress (χ2/df=11.93/4, p=.02), and cumulative index of social stressors (χ2/df=11.14/4, p=.03). Conclusion: Social risk factors moderate the effects of stressors on cortisol reactivity among African American breast cancer survivors. There is a need to understand the lived experiences of Black women and develop interventions that mitigate the impact of social stressors on stress responses. Citation Format: Dr. Fatimata Sanogo, Dr. Junhan Cho, Dr. Trista Beard, Dr. Chanita Hughes Halbert. Intersecting burdens: The role of social barriers and perceived stress in moderating cortisol reactivity among African American breast cancer survivors [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr A089.

Tiered maize and wheat nutrient removal coefficients estimated from available data

AbstractEstimates of cropland nutrient budgets at national to global scale generally rely on regional or global mean coefficients for quantifying nutrients removed in crop yield and by-products. Use of such mean values masks the variability in these coefficients. Using maize and wheat as examples, we assessed variation in nutrient removal coefficients, namely harvest index (HI), nitrogen (N), phosphorus (P) and potassium (K) concentrations of crop products (Grain N, Grain P and Grain K respectively) and N, P and K concentrations of crop residues (Residue N, Residue P, and Residue K respectively). Variation in these coefficients was assessed by three categories (Tiers) of estimation. Statistical (mixed-effects) and machine learning (random forest regression) models (Tier 3) were used to predict the coefficients using generally available predictor variables at a global level. Mean prediction accuracies (R2) of the mixed-effects and random forest models were 0.32 for maize coefficients and 0.45 for wheat coefficients when based on a random sub-selection of mainly replicated field experiment data. When predictions were applied to on-farm data only, prediction accuracies were lower (mean R2 values of 0.08 and 0.36 for maize and wheat respectively). Variation in, and dearth of on-farm data for the coefficients contributed to these poor prediction accuracies. Until the limitations of on-farm data are overcome, it is recommended to use Tier 2 (regional) coefficient estimates in country and global cropland nutrient balance and nutrient use efficiency estimates. Where Tier 2 values are not available, then global average (Tier 1) coefficients can be used.

Exploring Air Quality Dynamics and Predictive Modeling by Using Artificial Intelligence During COVID-19 Lock Down Over the Western Part of India

The lockdown period, initially imposed for three months due to the COVID-19 outbreak in India, was later prolonged. Air quality data from eight monitoring sites in Rajasthan was used to calculate the AQI according to the following parameters: Particulate matter (PM2.5 and PM10), Nitrogen Dioxide (NO2), Ammonia (NH3), Sulfur dioxide (SO2), Ozone (O3), and Carbon monoxide (CO), dispersed throughout the state by CPCB. Among the chosen cities, the study found that the AQI percentage dropped the most in Alwar, by 35.6% between pre-lockdown and lockdown. Conversely, it rose the most in Jaipur, by 86.77% between lockdown and post-lockdown. Python deep learning was used to simulate the relationship between Air Quality Index and Air contamination in the study area. Air quality index values ranging from Good (0–50) to Severe (>401) were used to create the AQI class categorization in Python. The study found that PM2.5 and PM10 had the strongest correlation. Metrics such as the coefficient of determination (R2) and the root mean square error (RMSE) were applied to assess the model on the datasets used for training and testing. Random forest, decision trees, and linear regression were worked to verify the precision of the prototype. The author used supervised learning techniques, such as decision tree (DT), extreme gradient boosting (XGBoost), K-nearest neighbor (KNN), logistic regression (LR), and random forest (RF), to determine the model's prediction. These findings suggest that urban areas are characterized by societal, commercial, and cultural aspects that contribute to similar discharge patterns and air quality issues. The study would be advantageous for authorities, as it is clearly apparent that reducing the sources of emissions can improve quality. This will set the stage for safeguarding and improving the environment.

Machine learning-enabled attapulgite/polyimide nanofiber composite aerogels-based colorimetric sensor array for real-time monitoring of balsa fish freshness

This paper presents the development and application of attapulgite/polyimide nanofiber composite aerogels (ATP/PI NFAs) integrated with a range of acid-base indicators, fabricated using electrospinning and freeze-drying technologies. A detailed characterization of the ATP/PI NFAs revealed a 3D multi-level pore structure that enhanced the mass transfer of target gas molecules and their interaction with probe molecules. Utilizing machine learning approaches, we designed an ATP/PI NFAs-based colorimetric sensor array capable of real-time evaluation of balsa fish freshness. Color features sensitive to changes in freshness were selected using principal component analysis and random forest. Partial least squares regression and random forest regression models were established, achieving the prediction of total volatile basic nitrogen content in balsa fish. The system was validated using a national standard method to demonstrate its accuracy and practicality. The combination of advanced ATP/PI NFAs-based colorimetric sensor array with robust machine learning models paves the way for food safety monitoring.

Supervised Machine Learning Techniques for Breeding Value Prediction in Horses: An Example Using Gait Visual Scores.

Gait scores are widely used in the genetic evaluation of horses. However, the nature of such measurement may limit genetic progress since there is subjectivity in phenotypic information. This study aimed to assess the application of machine learning techniques in the prediction of breeding values for five visual gait scores in Campolina horses: dissociation, comfort, style, regularity, and development. The dataset contained over 5000 phenotypic records with 107,951 horses (14 generations) in the pedigree. A fixed model was used to estimate least-square solutions for fixed effects and adjusted phenotypes. Variance components and breeding values (EBV) were obtained via a multiple-trait model (MTM). Adjusted phenotypes and fixed effects solutions were used to train machine learning models (using the EBV from MTM as target variable): artificial neural network (ANN), random forest regression (RFR) and support vector regression (SVR). To validate the models, the linear regression method was used. Accuracy was comparable across all models (but it was slightly higher for ANN). The highest bias was observed for ANN, followed by MTM. Dispersion varied according to the trait; it was higher for ANN and the lowest for MTM. Machine learning is a feasible alternative to EBV prediction; however, this method will be slightly biased and over-dispersed for young animals.

Mapping Field-Level Maize Yields in Ethiopian Smallholder Systems Using Sentinel-2 Imagery

Remote sensing offers a low-cost method for estimating yields at large spatio-temporal scales. Here, we examined the ability of Sentinel-2 satellite imagery to map field-level maize yields across smallholder farms in two regions in Oromia district, Ethiopia. We evaluated how effectively different indices, the MTCI, GCVI, and NDVI, and different models, linear regression and random forest regression, can be used to map field-level yields. We also examined if models improved by adding weather and soil data and how generalizable our models were if trained in one region and applied to another region, where no data were used for model calibration. We found that random forest regression models that used monthly MTCI composites led to the highest yield prediction accuracies (R2 up to 0.63), particularly when using only localized data for training the model. These models were not very generalizable, especially when applied to regions that had significant haze remaining in the imagery. We also found that adding soil and weather data did little to improve model fit. Our results highlight the ability of Sentinel-2 imagery to map field-level yields in smallholder systems, though accuracies are limited in regions with high cloud cover and haze.

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

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

Exploring Mitochondrial Autophagy Dysregulation in Osteosarcoma: Its Implications for Prognosis and Targeted Therapy

Objective: This study aimed to investigate the differential expression of mitophagyrelated genes in osteosarcoma patients with distinct prognostic outcomes and explore potential molecular regulatory mechanisms. Methods: We analyzed microarray data from metastatic and nonmetastatic osteosarcoma patients using the UCSC dataset. Differential gene screening and intersection of mitophagy-related genes were performed using NetworkAnalyst. Random forest and LASSO regression were employed to screen selected genes and establish a risk prediction model. Functional enrichment analysis, protein- protein interaction (PPI) networks, immunoassays, and in vitro experiments were conducted to validate the findings. Results: Seven differentially expressed genes were identified, and a robust risk prediction model was developed (AUC=0.886). PPI and functional enrichment analyses provided insights into relevant molecules and regulatory pathways. The immunoassay results revealed differences in the immune environment between the metastatic and nonmetastatic groups. Immunohistochemistry demonstrated significant downregulation of EPHA3 expression in the metastatic group, and in vitro experiments indicated that inhibiting EPHA3 increased the proliferative activity and migration ability of osteosarcoma cells. Conclusion: Our study suggests that the downregulation of EPHA3 may contribute to mitochondrial autophagy dysfunction, thereby increasing the risk of osteosarcoma metastasis.

Spatial variations of annual net ecosystem productivity and its trend over Chinese terrestrial ecosystems based on spatial downscaling.

Annual net ecosystem productivity (NEP), the amount of net carbon sequestration during a year, serves as the basis of terrestrial carbon sink. Quantifying the spatial variations of NEP and its trend would enhance our understandings on the response and adaption of ecosystems to environmental change, which also serves for the regional carbon management targeting at carbon neutrality. Based on process-based model and data-driven model simulating NEP, we selected the optimal simulating NEP mostly representing NEP spatial variations with multiple site eddy covariance measurements to develop the spatial downscaling method and generate high resolution NEP data of China, which was used to examine the spatial variations of NEP and its trend and driving factors during 2000-2017. Compared with process-based model results, data-driven model simulating NEP could mostly represent the spatial variation of site measurements. The random forest regression based on climate, soil, and biological data combining with the simple scaling could successfully downscale NEP to a high spatial resolution. From 2000 to 2017, the total amount of NEP in China was (1.30±0.03) Pg C·a-1, showing a decreasing-increasing pattern with the inflection point in 2009. Chinese NEP decreased from southeast to northwest, showing a descending latitudinal distribution and an ascending longitudinal distribution, with the combined effects of climate and biotic factors. NEP trend decreased from east towards west, which was only accompanied with a slightly ascending longitudinal distribution, while photosynthetically active radiation and soil organic carbon content dominated the spatial variations of NEP trend. Therefore, the spatial patterns of generated NEP obviously differed from those of NEP trend, suggesting the obvious difference between the responses and adaptions of ecosystems to environmental changes.

Vegetation restoration enhancing soil carbon sequestration in karst rocky desertification ecosystems: A meta-analysis

Vegetation restoration measures have been increasingly employed to alleviate rocky desertification in karst ecosystems. However, the comprehensive effects of these interventions on soil properties and soil organic carbon (SOC) remain poorly understood. Herein, we gathered 644 paired observations from 68 studies and conducted a meta-analysis to quantify the performance of different vegetation restoration measures including moss (MS), grassland (GL), cash crop (CP), shrub (SH), and secondary forest (SF) through soil properties and SOC. Our results demonstrated significant effects of MS, GL, CP, SH, and SF on soil biotic and abiotic factors, each with distinct response characteristics. Particularly, MS significantly enhanced all soil properties (excluding a slight decrease in soil pH by 10.8%). Moreover, MS, GL, CP, SH, and SF could elevate SOC by 32.1%, 17.6%, 24.9%, 59.2%, and 48.7% respectively. Utilizing random forest and linear regression models, we identified primary drivers for SOC in MS, GL, CP, SH, and SF as soil moisture content, arbuscular mycorrhizal fungi, soil microbial phosphorus, total nitrogen, and β-1,4-glucosidase, respectively. This meta-analysis underlined the varied effects of vegetation restoration measures on soil properties and advocates for restoration measures that prioritize plant productivity and reduce soil temperature during the karst rocky desertification restoration process. Additionally, this study underscores the pivotal role of vegetation rehabilitation in environmental conservation and carbon sequestration of ecologically vulnerable regions.

Hyperspectral Imaging for Phenotyping Plant Drought Stress and Nitrogen Interactions Using Multivariate Modeling and Machine Learning Techniques in Wheat

Accurate detection of drought stress in plants is essential for water use efficiency and agricultural output. Hyperspectral imaging (HSI) provides a non-invasive method in plant phenotyping, allowing the long-term monitoring of plant health due to sensitivity to subtle changes in leaf constituents. The broad spectral range of HSI enables the development of different vegetation indices (VIs) to analyze plant trait responses to multiple stresses, such as the combination of nutrient and drought stresses. However, known VIs may underperform when subjected to multiple stresses. This study presents new VIs in tandem with machine learning models to identify drought stress in wheat plants under varying nitrogen (N) levels. A pot wheat experiment was set up in the glasshouse with four treatments: well-watered high-N (WWHN), well-watered low-N (WWLN), drought-stress high-N (DSHN) and drought-stress low-N (DSLN). In addition to ensuring that plants were watered according to the experiment design, photosynthetic rate (Pn) and stomatal conductance (gs) (which are used to assess plant drought stress) were taken regularly, serving as the ground truth data for this study. The proposed VIs, together with known VIs, were used to train three classification models: support vector machines (SVM), random forest (RF), and deep neural networks (DNN) to classify plants based on their drought status. The proposed VIs achieved more than 0.94 accuracy across all models, and their performance further increased when combined with known VIs. The combined VIs were used to train three regression models to predict the stomatal conductance and photosynthetic rates of plants. The random forest regression model performed best, suggesting that it could be used as a stand-alone tool to forecast gs and Pn and track drought stress in wheat. This study shows that combining hyperspectral data with machine learning can effectively monitor and predict drought stress in crops, especially in varying nitrogen conditions.

English digital reading achievement for East Asian students: identifying the key predictors using a machine learning approach

ABSTRACT Students’ digital reading literacy has attracted increasing attention in the current digital era; however, few studies have been conducted to explore how factors at different levels influence students’ digital reading achievement. Grounded by socio-ecological theory, the current comprehensively explored the relative importance of 24 individual, microsystem, and mesosystem variables in predicting English digital reading achievement through the machine learning approach (i.e. random forest regression). The secondary data were retrieved from the Program for International Student Assessment (PISA) 2018, including 7,703 15-year-old students from Macao, Hong Kong, and Singapore. Our study identified 12 key factors that best predicted East Asian students’ English digital reading achievement. Among them, students’ socioeconomic status, subject-related ICT use during lessons, and interest in ICT ranked as the top three factors. The disparities in the roles played by disciplinary climate, gender, being bullied, immigrant status, and home language among the three economies were discussed.

Determination of sodium, chlorine, and bromine concentrations in breast milk using neutron activation: Correlation, regression, and machine learning predictions

Breast milk plays a vital role in infant health, providing essential nutrients that directly impact nutrition and development. Understanding its elemental composition, as well as the relationships among these elements, is crucial for assessing its nutritional adequacy. This study focused on determining the concentrations of sodium (Na), chlorine (Cl), and bromine (Br) in the breast milk of lactating mothers in Tehran, Iran, during early lactation. The primary objectives were to analyze the concentrations of these elements, investigate their interrelationships, and employ machine-learning techniques to predict their concentrations in breast milk. Neutron activation analysis (NAA) was used to precisely measure the levels of Na, Cl, and Br, while statistical methods, including correlation and regression analyses, were applied to further explore these relationships. Machine-learning models, specifically Random Forest and Linear Regression, were utilized to predict the concentrations of these elements based on their interdependencies. The study revealed mean concentrations of 5.12 mg/g for Na, 8.14 mg/g for Cl, and 11.84 mg/kg for Br in the breast milk samples. A strong positive correlation was observed between Na and Cl (r = 0.976, p < 0.001), while moderate positive correlations were found between Na and Br (r = 0.558, p < 0.001) and Cl and Br (r = 0.606, p < 0.001). Multiple regression models showed that 94.7 % of the variance in Na concentration could be explained by Cl and Br levels (R-squared = 0.947), with a strong positive association between Cl and Na, and a slight inverse relationship between Br and Na. A similar model for Cl concentrations showed strong predictive power, with Na being a significant predictor. However, the model for Br concentrations explained a smaller proportion of variance (R-squared = 0.318), suggesting that additional factors influencing Br levels were not captured in this study. Furthermore, multicollinearity was observed between Na and Cl (VIF = 20.675), indicating potential interactions between these elements. This study highlights the significant correlations between Na, Cl, and Br in breast milk, with particularly strong predictive models for Na and Cl concentrations. The findings also suggest the need for further investigation into the factors affecting Br concentrations. Overall, this research provides valuable insights into the elemental composition of breast milk and its implications for infant nutrition and health.

Discovery of optimal silver nanowires synthesis conditions using machine learning

Silver nanowires (AgNWs) are essential nanomaterials for diverse applications, including medical devices. Their morphology, like length and diameter, significantly affects conductivity, which is crucial for effective electrical signal transmission. Traditional trial-and-error approaches to adjust synthesis conditions for morphology control are time consuming. To overcome the limitation, this study integrates machine learning (ML) with experimental approaches to investigate how nucleants affect AgNWs synthesis. Random forest regression models are developed to analyze the effect of varying nucleant concentrations on morphology. Our approach builds a new framework to optimize synthesis conditions for morphology control, accelerating advancements in manufacturing capabilities.

Enhanced root zone soil moisture monitoring using multitemporal remote sensing data and machine learning techniques

Accurate root zone soil moisture (RZSM) estimation using remote sensing (RS) in areas with dense vegetation is essential for real-time field monitoring and precise irrigation scheduling. Traditional methods often face challenges due to the dense crop cover and the complexity of soil and climate interactions. These challenges include the coarse spatial resolution of available soil moisture products, the influence of vegetation and surface roughness, and the difficulty of estimating RZSM from surface data. Aiming to overcome these limitations, two RZSM estimation methods were developed by combining synthetic aperture radar (SAR) data from Sentinel-1 (VV and VH polarizations) and optical and thermal RS data from Landsat-8. These data sources were used in conjunction with various machine learning (ML) models such as M5-pruned (M5P), support vector regression (SVR), extreme gradient boosting (XGBoost), and random forest regression (RFR) to improve the accuracy of soil moisture estimation. In addition to RS data, soil physical and hydraulic properties, meteorological variables, and topographical parameters were selected as inputs to the ML models for estimating the RZSM of sugarcane crops in Khuzestan, Iran. This study identified the temperature vegetation dryness index (TVDI) as a critical parameter for estimating RZSM in combination with the Sentinel-1 SAR data under high vegetation conditions. In both methods, the RFR algorithm outperformed, with similar performance, the XGBoost, SVR, and M5P algorithms in estimating soil surface moisture (R2 = 0.89, RMSE = 0.04 cm3cm−3). However, the accuracy of the RFR algorithm decreased with increasing depth for both the optical-thermal and combined SAR and optical-thermal RS data. This decrease was more pronounced in the combined approach, particularly for the root zone, where the RMSE reached approximately 0.073 cm3cm−3. Accordingly, the key findings demonstrated that the optical-thermal RS data outperformed the SAR RS data for retrieving RZSM in high-vegetated areas. However, combining TVDI with SAR data is a substantial improvement that opens a new path in radar-based RZSM estimation methods under high vegetation conditions.

Antecedents of physical sickness presenteeism during the COVID-19 pandemic

ABSTRACT Physical sickness presenteeism––the behaviour of going to work despite being ill––constitutes a threat to organizations because it puts the health of their members at risk. Herein, we shed light on potential antecedents of physical sickness presenteeism during COVID-19. Specifically, we test several pre-registered hypotheses suggesting relations of (inter-individual) personality dimensions (e.g. Honesty-Humility, Emotionality, Empathy) and COVID-19-related situational perceptions (e.g. Perceived Risk, Trust in Government) with physical sickness presenteeism. To test our hypotheses, we use both panel (N = 660) and cross-sectional (N = 1,959) survey data collected between mid-2020 and early 2021 in Denmark. Results from logistic random effects regression analyses show that individuals with higher levels of Emotionality engaged more often in physical sickness presenteeism. Further, individuals with higher levels of Honesty-Humility engaged less often in physical sickness presenteeism in times with COVID-19-related workplace restrictions. In general, the findings provide insights into what organizations and policy makers should focus on in order to mitigate physical sickness presenteeism in pandemic times.