Accurate Estimation Research Articles

Real-Time Automated Geosteering Interpretation Combining Log Interpretation and 3D Horizon Tracking

Existing methods for estimating formation boundaries from well-log data only analyze the formation along the wellbore, failing to capture changes in the 3D formation structure around it. This paper presents a method for real-time 3D formation boundary interpretation using readily available well logs and seismic image data. In the proposed workflow, the mean formation boundary is estimated as a curve following the well path. 3D surfaces are then fitted through this boundary curve, aligning with the slopes and features in the seismic image data. The proposed method is tested on both synthetic and field datasets and illustrates the capabilities of accurate boundary estimation near the well path and precise representation of boundary shape changes further away from the well trajectory. With this fully automated geological interpretation workflow, human bias and interpretation uncertainty can be minimized. Subsurface conditions can be continually updated while drilling to optimize drilling decisions and further automate the geosteering process.

Approaches to estimate bidirectional causal effects using Mendelian randomization with application to body mass index and fasting glucose.

Mendelian randomization (MR) is an epidemiological framework using genetic variants as instrumental variables (IVs) to examine the causal effect of exposures on outcomes. Statistical methods based on unidirectional MR (UMR) are widely used to estimate the causal effects of exposures on outcomes in observational studies. To estimate the bidirectional causal effects between two phenotypes, investigators have naively applied UMR methods separately in each direction. However, bidirectional causal effects between two phenotypes create a feedback loop that biases the estimation when UMR methods are naively applied. To overcome this limitation, we proposed two novel approaches to estimate bidirectional causal effects using MR: BiRatio and BiLIML, which are extensions of the standard ratio, and limited information maximum likelihood (LIML) methods, respectively. We compared the performance of the two proposed methods with the naive application of UMR methods through extensive simulations of several scenarios involving varying numbers of strong and weak IVs. Our simulation results showed that when multiple strong IVs are used, the proposed methods provided accurate bidirectional causal effect estimation in terms of median absolute bias and relative median absolute bias. Furthermore, compared to the BiRatio method, the BiLIML method provided a more accurate estimation of causal effects when weak IVs were used. Therefore, based on our simulations, we concluded that the BiLIML should be used for bidirectional causal effect estimation. We applied the proposed methods to investigate the potential bidirectional relationship between obesity and diabetes using the data from the Multi-Ethnic Study of Atherosclerosis cohort. We used body mass index (BMI) and fasting glucose (FG) as measures of obesity and type 2 diabetes, respectively. Our results from the BiLIML method revealed the bidirectional causal relationship between BMI and FG in across all racial populations. Specifically, in the White/Caucasian population, a 1 kg/m2 increase in BMI increased FG by 0.70 mg/dL (95% confidence interval [CI]: 0.3517-1.0489; p = 8.43×10-5), and 1 mg/dL increase in FG increased BMI by 0.10 kg/m2 (95% CI: 0.0441-0.1640; p = 6.79×10-4). Our study provides novel findings and quantifies the effect sizes of the bidirectional causal relationship between BMI and FG. However, further studies are needed to understand the biological and functional mechanisms underlying the bidirectional pathway.

Estimation of Diameter at Breast Height in Tropical Forests Based on Terrestrial Laser Scanning and Shape Diameter Function

Estimating forest carbon content typically requires the precise measurement of the trees’ diameter at breast height (DBH), which is crucial for maintaining the health and sustainability of natural forests. Currently, Terrestrial Laser Scanning (TLS) systems are commonly used to acquire forest point cloud data for DBH estimation. However, traditional circular fitting methods face challenges such as a reliance on forest elevation normalization and underfitting of large trees. This study explores a novel approach, the Shape Diameter Function (SDF) algorithm model, leveraging the advantages of three-dimensional point cloud information to replace traditional circular fitting methods. This study employed a parallel approach, combining the Digital Elevation Model (DEM) with Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to segment tree point clouds at breast height. Additionally, a point cloud SDF algorithm based on an octree structure was proposed to accurately estimate individual tree DBH. The research data were obtained from tropical secondary forests located in Cameroon, Peru, Indonesia, and Guyana, with forest ground point cloud data acquired via TLS. The experimental results demonstrated the superior performance of the SDF algorithm in estimating DBH. Compared with the Random Sample Consensus (RANSAC) and Hough transform methods, the Root Mean Square Error (RMSE) decreased by 28.1% and 47.8%, respectively. Particularly in estimating DBH for large trees, the SDF algorithm exhibited smaller errors, indicating a closer alignment between the estimated individual tree DBH values and those obtained from manual measurements. This study presented a more accurate DBH estimation algorithm, contributing to the exploration of improved forest carbon content estimation methods.

State of charge estimation for Li-ion battery during dynamic driving process based on dual-channel deep learning methods and conditional judgement

The accurate estimation of the state of charge (SOC) is crucial for the safe and reliable operation of Li-ion batteries. In this paper, novel dual-channel deep learning methods were proposed for SOC estimation during dynamic driving cycles. For dual-channel deep learning methods, each channel of the deep learning methods could be a single convolutional neural network (CNN) layer, single long short-term memory (LSTM) layer or sequentially combined structure of CNN and LSTM. After evaluation, it could be found that dual-channel deep learning methods could achieve higher accuracy than conventional single-channel method, however, the model size and floating-point operations (FLOPs) could be either reduced or increased. Among all the dual-channel deep learning methods, CNN + LSTM-CNN + LSTM method could achieve the highest accuracy and the shortest training time with sacrifices in computational complexity. CNN–CNN + LSTM method could achieve higher accuracy than the single-channel deep learning method with reduced computational complexity and model size. As the error of SOC estimation is highly correlated with voltage signals, conditional judgement was then integrated with dual-channel deep learning method to obtain higher accuracy. After integrated with conditional judgement, the averaged error of SOC estimation could be reduced by more than 45% without a substantial sacrifice in computational time.

Adaptive Active Disturbance Rejection Control for Vehicle Steer-by-Wire under Communication Time Delays

In this paper, an adaptive active disturbance rejection control is newly designed for precise angular steering position tracking of the uncertain and nonlinear SBW system with time delay communications. The proposed adaptive active disturbance rejection control comprises the following two elements: (1) An adaptive extended state observer and (2) an adaptive state error feedback controller. The adaptive extended state observer with adaptive gains is employed for estimating the unmeasured velocity, acceleration, and compound disturbance which consists of system parameter uncertainties, nonlinearities, exterior disturbances, and time delay in which the observer gains are dynamically adjusted based on the estimation error to enhance estimation performances. Based on the accurate estimations of the adaptive extended state observer, the proposed adaptive full state error feedback controller is equipped with variable gains driven by the tracking error to develop control precision. The integration of the advantages of the adaptive extended state observer and the adaptive full state error feedback controller can improve the dynamic transient and static steady-state effectiveness, respectively. To assess the superior performance of the proposed adaptive active disturbance rejection control, a comparative analysis is conducted between the proposed control scheme and the classical active disturbance rejection control in two different cases. It is worth noting that the active disturbance rejection control serves as a benchmark for evaluating the performance of the proposed control approach. The results from the comparison studies executing two simulated cases validate the superiority of the suggested control, in which estimation, tracking response rate, and steering angle precision are greatly improved by the scheme proposed in this article.

Free-energy landscape and spinodals for the liquid-liquid transition of the TIP4P/2005 and TIP4P/Ice models of water.

Continued increases in computational power now make it possible to evaluate the free-energy landscape associated with the first-order liquid-liquid transition in realistic models of water for which an accurate estimate of the liquid-liquid critical point exists, and to explore its change with pressure near the coexistence line. We report the results of 50 μs-long NPT umbrella sampling simulations for two realistic models for water, TIP4P/2005 and TIP4P/ice, 3-9K below their critical temperatures. The free energy profile at different pressures clearly shows the presence of two well-defined free energy basins and makes it possible to identify the liquid-liquid spinodal points, the limits of stability that define the (temperature dependent) pressure range within which two distinct free energy basins exist. The results show that for temperatures less than 10K below the critical temperature, metastable states are possible across a very limited pressure interval, information that is relevant to the interpretation of experiments probing the metastable phase behavior of deeply supercooled water in the so-called no-man's land.

MAPPING AND ASSESSMENT OF THE SPATIAL AND TEMPORAL DISTRIBUTION OF TURBIDITY IN LAKE BUHI FROM SENTINEL-2 IMAGES USING GEOGRAPHICALLY WEIGHTED REGRESSION AND NORMALIZED DIFFERENCE TURBIDITY INDEX

Abstract. Water quality monitoring is vital in ensuring the suitability of Lake Buhi for aquaculture and recreational activities. However, with the limitations of traditional monitoring methods, the Bureau of Fisheries and Aquatic Resources (BFAR) cannot comprehensively characterize the spatial and temporal water quality patterns in the lake. With the development of remote sensing methods, this study aimed to describe the spatial and temporal variability of turbidity in Lake Buhi in 2020 using Geographically Weighted Regression (GWR) and the Normalized Difference Turbidity Index (NDTI) applied to Sentinel-2 images. GWR provided accurate estimates of turbidity, with the results achieving an R2 value of 0.98 for the February image and 0.93 for October. GWR-derived turbidity was 2 used as an alternative to in situ data and then regressed with NDTI values to acquire turbidity models for dry and wet seasons. Upon validation, the best turbidity model for the dry (R2 = 0.59, RMSE = 0.60, MAE = 0.15) and wet (R2 = 0.49, RMSE = 1.22, MAE = 0.25) seasons produced acceptable results, hence, used to assess the spatial and temporal variability of turbidity in the lake throughout 2020. The analysis revealed that Lake Buhi is more turbid during the dry season than the wet season. Turbidity during the dry season is governed by its natural water flow, while it is heavily influenced by precipitation during the wet season.

Accuracy of prenatal and postnatal biomarkers for estimating gestational age: a systematic review and meta-analysis

Knowledge of gestational age (GA) is key in clinical management of individual obstetric patients, and critical to be able to calculate rates of preterm birth and small for GA at a population level. Currently, the gold standard for pregnancy dating is measurement of the fetal crown rump length at 11-14 weeks of gestation. However, this is not possible for women first presenting in later pregnancy, or in settings where routine ultrasound is not available. A reliable, cheap and easy to measure GA-dependent biomarker would provide an important breakthrough in estimating the age of pregnancy. Therefore, the aim of this study was to determine the accuracy of prenatal and postnatal biomarkers for estimating gestational age (GA). Systematic review prospectively registered with PROSPERO (CRD42020167727) and reported in accordance with the PRISMA-DTA. Medline, Embase, CINAHL, LILACS, and other databases were searched from inception until September 2023 for cohort or cross-sectional studies that reported on the accuracy of prenatal and postnatal biomarkers for estimating GA. In addition, we searched Google Scholar and screened proceedings of relevant conferences and reference lists of identified studies and relevant reviews. There were no language or date restrictions. Pooled coefficients of correlation and root mean square error (RMSE, average deviation in weeks between the GA estimated by the biomarker and that estimated by the gold standard method) were calculated. The risk of bias in each included study was also assessed. Thirty-nine studies fulfilled the inclusion criteria: 20 studies (2,050 women) assessed prenatal biomarkers (placental hormones, metabolomic profiles, proteomics, cell-free RNA transcripts, and exon-level gene expression), and 19 (1,738,652 newborns) assessed postnatal biomarkers (metabolomic profiles, DNA methylation profiles, and fetal haematological components). Among the prenatal biomarkers assessed, human chorionic gonadotrophin measured in maternal serum between 4 and 9 weeks of gestation showed the highest correlation with the reference standard GA, with a pooled coefficient of correlation of 0.88. Among the postnatal biomarkers assessed, metabolomic profiling from newborn blood spots provided the most accurate estimate of GA, with a pooled RMSE of 1.03 weeks across all GAs. It performed best for term infants with a slightly reduced accuracy for preterm or small for GA infants. The pooled RMSEs for metabolomic profiling and DNA methylation profile from cord blood samples were 1.57 and 1.60 weeks, respectively. We identified no antenatal biomarkers that accurately predict GA over a wide window of pregnancy. Postnatally, metabolomic profiling from newborn blood spot provides an accurate estimate of GA, however, as this is known only after birth it is not useful to guide antenatal care. Further prenatal studies are needed to identify biomarkers that can be used in isolation, as part of a biomarker panel, or in combination with other clinical methods to narrow prediction intervals of GA estimation. The research was funded by the Bill and Melinda Gates Foundation (INV-000368). ATP is supported by the Oxford Partnership Comprehensive Biomedical Research Centre with funding from the NIHR Biomedical Research Centre funding scheme. The views expressed are those of the authors and not necessarily those of the UK National Health Service, the NIHR, the Department of Health, or the Department of Biotechnology. The funders of this study had no role in study design, data collection, analysis or interpretation of the data, in writing the paper or the decision to submit for publication.

Adjusting Incidence Estimates with Laboratory Test Performances: A Pragmatic Maximum Likelihood Estimation-Based Approach.

Understanding the incidence of disease is often crucial for public policy decision-making, as observed during the COVID-19 pandemic. Estimating incidence is challenging, however, when the definition of incidence relies on tests that imperfectly measure disease, as in the case when assays with variable performance are used to detect the SARS-CoV-2 virus. To our knowledge, there are no pragmatic methods to address the bias introduced by the performance of labs in testing for the virus. In the setting of a longitudinal study, we developed a maximum likelihood estimation-based approach to estimate laboratory performance-adjusted incidence using the expectation-maximization algorithm. We constructed confidence intervals (CIs) using both bootstrapped-based and large-sample interval estimator approaches. We evaluated our methods through extensive simulation and applied them to a real-world study (TrackCOVID), where the primary goal was to determine the incidence of and risk factors for SARS-CoV-2 infection in the San Francisco Bay Area from July 2020 to March 2021. Our simulations demonstrated that our method converged rapidly with accurate estimates under a variety of scenarios. Bootstrapped-based CIs were comparable to the large-sample estimator CIs with a reasonable number of incident cases, shown via a simulation scenario based on the real TrackCOVID study. In more extreme simulated scenarios, the coverage of large-sample interval estimation outperformed the bootstrapped-based approach. Results from the application to the TrackCOVID study suggested that assuming perfect laboratory test performance can lead to an inaccurate inference of the incidence. Our flexible, pragmatic method can be extended to a variety of disease and study settings.

Simplified equations for wet bulb globe temperature estimation in Bangladesh

AbstractThe increasing temperatures and shifts in meteorological conditions have heightened the vulnerability of Bangladesh's densely populated regions, known for their high heat and humidity, to potential health hazards. While the Wet Bulb Globe Temperature (WBGT) is a widely acknowledged and robust measure for evaluating heat stress in various occupational and outdoor environments, its widespread application is impeded by the complexity of calculations, the substantial computational resources required, and the need for specialized expertise, particularly in developing nations. Therefore, this study aimed to develop simplified equations for estimating WBGT in Bangladesh using meteorological variables. This study applied Liljegren's model on high‐resolution reanalysis data of the European Centre for Medium‐Range Weather Forecasts (ERA5) to calculate WBGT from 1979 to 2021. Subsequently, linear and nonlinear regressions were used to derive simplified equations for estimating Liljegren WBGT in Bangladesh. The quadratic regression models offer simplified equations for WBGT estimation. The model with only temperature (Ta) as an input estimates WBGT with an R2 of 0.967 and an RMSE of 0.716, effectively capturing a significant portion of WBGT variability. The inclusion of solar radiation (SR) with Ta improved the performance, with an R2 of 0.996 and an RMSE of 0.242. The best parsimonious model, with an R2 of 0.986 and an RMSE of 0.471, is derived when wind speed is considered with Ta and SR inputs, achieving an R2 of 0.993 and an RMSE of 0.331. Comprehensive graphical and statistical analyses confirm the high accuracy of all models. The three‐input model notably demonstrates exceptional performance, attaining optimal values for critical metrics, including extreme WBGT. The results affirm the practical suitability of the derived models for accurate estimations of WBGT in Bangladesh. These equations provide simplified tools for assessing heat stress conditions, contributing to public health initiatives, occupational safety guidelines, and climate change adaptation strategies.

Semi-Analytical Formula for Estimating Variance Response of Megawatt Wind Turbine Tower Under Parked Condition

As the supporting structure of the wind turbine, the tower is important for the safety of the wind turbine. The accurate estimation of tower response is critical for the wind turbine tower design. In the preliminary design, lots of calculations are necessary to determine design parameters. The traditional time domain calculation suffers its low efficiency and hinders the design progress. To overcome this difficulty, the frequency domain analysis should be developed, especially for the large wind turbine which may be significantly affected by the aerodynamic damping. In this study, a semi-analytical formula for the variance of the tower base bending moment is developed from the frequency domain analysis at parked condition. First, based on the quasi-steady theory and the three-component (mean, background and resonant components) method commonly used in the wind-resistant design of structure, the analytical formula of the variance for the tower base bending moment is presented considering the three components of the wind velocity. Second, the analytical formula is corrected based on the comparison with the calculation result by FAST. In the correction, the empirical aerodynamic damping is adopted. Finally, the applicability of the developed semi-analytical formula is verified by 5[Formula: see text]MW, 10[Formula: see text]MW and 15[Formula: see text]MW wind turbines.

Lithium-Ion Battery Capacity Estimation Based on Incremental Capacity Analysis and Deep Convolutional Neural Network

Accurate estimation of Li-ion battery capacity is critical for a battery management system (BMS). This paper proposes an innovative method which combines a convolutional neural network and incremental capacity analysis (ICA). In the present approach, the voltage and temperature, which significantly affect the ICA curve during the discharging process, are adopted as the inputs for CNN. Rather than extracting feature parameters of an IC curve, as is carried out in the available research, the present method uses the whole ICA curve as the input to avoid complicated feature extraction and correlation analysis. The results show that the maximum error of capacity estimation is less than 4.7%, the rectified mean squared error is less than 1.3% for each battery, and the overall RMSE is below 1.12%.

The case for a national breast implant registry in Canada.

SummaryThe House of Commons Standing Committee on Health proposed in 2022 to start a national registry for breast implants. Why, and what requirements are needed, will be outlined. Breast implant products are not always in compliance with international norms and standards, and several scandals have occurred because of industry fraud. To trace which patients have defective breast implants, a good registry is an absolute must. Furthermore, some diseases, such as lymphomas, autoimmune diseases, and so-called breast implant illness, are believed to be associated with breast implants. An accurate estimation of how often these diseases occur in patients with breast implants is lacking. A registry in which not only surgical data but also patient-reported outcome measurements are recorded will result in a better understanding of patient outcomes and device performance. The registry should not be a voluntary ("opt-in") registry but a mandatory ("opt-out") registry, in which only the patient (and not the surgeon) has the choice whether to participate.

Comparison-Transfer Learning Based State-of-Health Estimation for Lithium-Ion Battery

Abstract Rapid and accurate estimation of the state of health of lithium-ion batteries is of great significance. This paper aims to address two issues faced when applying deep learning methods to estimate the health status of lithium-ion batteries: high data quality requirements and poor model generalizability. This paper proposes a comparison-transfer learning approach with cyclic synchronization to estimate the state of health of lithium-ion batteries. First, a cyclic synchronization method based on the Bezier curve fitting algorithm is introduced to synchronize the data obtained at different charge–discharge cycles of the lithium-ion battery, facilitating input to the model. Second, a comparison-transfer network using the Pearson correlation coefficient is proposed to transfer knowledge from the source dataset to predict the target dataset under different environmental temperatures. By training a pre-trained model on the source dataset and utilizing the correlation coefficient to analyze the similarity between the source and target datasets, the accumulated knowledge in the source dataset can be effectively utilized to enhance prediction performance on the target dataset. In the experiments, the proposed method is validated using the lithium-ion battery aging public datasets. The experimental results demonstrate that the proposed approach achieves superior prediction performance in the case of small-sample sizes, exhibiting higher accuracy and stability compared to traditional deep learning methods.

Molecular mechanisms that govern infection and antifungal resistance in Mucorales.

SUMMARYThe World Health Organization has established a fungal priority pathogens list that includes species critical or highly important to human health. Among them is the order Mucorales, a fungal group comprising at least 39 species responsible for the life-threatening infection known as mucormycosis. Despite the continuous rise in cases and the poor prognosis due to innate resistance to most antifungal drugs used in the clinic, Mucorales has received limited attention, partly because of the difficulties in performing genetic manipulations. The COVID-19 pandemic has further escalated cases, with some patients experiencing the COVID-19-associated mucormycosis, highlighting the urgent need to increase knowledge about these fungi. This review addresses significant challenges in treating the disease, including delayed and poor diagnosis, the lack of accurate global incidence estimation, and the limited treatment options. Furthermore, it focuses on the most recent discoveries regarding the mechanisms and genes involved in the development of the disease, antifungal resistance, and the host defense response. Substantial advancements have been made in identifying key fungal genes responsible for invasion and tissue damage, host receptors exploited by the fungus to invade tissues, and mechanisms of antifungal resistance. This knowledge is expected to pave the way for the development of new antifungals to combat mucormycosis. In addition, we anticipate significant progress in characterizing Mucorales biology, particularly the mechanisms involved in pathogenesis and antifungal resistance, with the possibilities offered by CRISPR-Cas9 technology for genetic manipulation of the previously intractable Mucorales species.

Exploring the dependability of Combined Ratio Estimators in Stratified Ranked Set Sampling: Insights from COVID-19 data

In this study, we introduce a novel combined ratio type estimator within the framework of Stratified Ranked Set Sampling to estimate the population mean of the study variable by incorporating bivariate auxiliary information. We conduct a comprehensive comparative analysis, including traditional combined ratio, combined regression, Shabbir and Khan [13], and Bhushan and Kumar [37] estimators. We assess the bias and mean squared error of the proposed estimator under the initial degree of approximation. The data source consists of COVID-19 data up to July 2023. Through empirical investigation and simulation studies, our proposed estimator consistently demonstrates superior performance compared to its counterparts, exhibiting the highest relative efficiency. These findings underscore the practical significance of our research in public health and decision-making, emphasizing the potential of this estimator to provide more accurate and reliable estimates in various applications involving ranked set sampling and auxiliary information.

OS TESTES SITUACIONAIS DE INTELIGÊNCIA EMOCIONAL COMO UM TESTE ADAPTATIVO COMPUTADORIZADO

The present study aimed to assess whether an emotional understanding test (STEU) and an emotional management test (STEM) could benefit from being administered as computer-adaptive tests (CATs) without impacting the validity of the test scores. To this end, 11 item selection algorithms (ISAs) were benchmarked for their bias and efficiency. Two simulation studies were run using the same response patterns from the 688 participants used in the original validation study, the same 11 ISAs, but differed in their stopping rules (SRs). For the first study, one simulation was run for each ISA with the SR being standard error lower than (ΔSE < ), the most commonly used stopping rule criterion. For the second study, k simulations were run for each ISA, for each test, with the SR being a fixed number of items between 1 and k, where k was the total number of items of the relevant test (32 for the STEU and 30 for the STEM). Results of the first simulation showed that testing with all ISAs resulted in accurate ability estimates, all of them having r > .98 between their estimates and the estimates calculated with the entire test. The first simulation also showed that, using the best performing ISA, 368 (53.5%) participants needed to answer at least one fewer item without loss of validity. The second study showed that the STEU stood to benefit the most, with the mean standard error (MSE) being minimized six items before the end of the test, though ISAs based on the Kullback–Leibler information performed worse. However, these ISAs also displayed slightly less bias, , than the Fisher information-based ones . For the STEM, no ISA minimized MSE levels before the end of the test, but up to six fewer items for the STEM and 15 fewer items for the STEU could be administered with a slightly higher tolerance of ΔSE < . These results indicate that the use of CAT methodology to administer these tests is viable, and EI testing stands to gain from using CAT tests. Future studies should test ISA performance with additional testing constraints.

Exploring the Limits of a Deflection-Based Method for the Estimation of Dynamic Stress in Beams

Estimation of the dynamic stress in structures, such as beams and plates, has previously been made using the relationship between stress and velocity spatial maxima based on farfield assumptions. This paper presents a method for the estimation of dynamic stress in a beam using Euler-Bernoulli beam theory, where deflection data from a grid of measurement points on the surface of the beam is used to estimate the dynamic bending stress in the structure. The limitations of the method are investigated via response data provided by a numerical model of a free-free beam. A non-dimensional wavenumber analysis is used to determine the number of points required for an accurate estimate of stress. Beams with a range of material and geometric parameters are modelled in order to explore the limits of the estimation method, and parameters representative of several real-world materials are used to assess the suitability of the method for practical applications.

Communication-Efficient Nonparametric Quantile Regression via Random Features

This article introduces a refined algorithm designed for distributed nonparametric quantile regression in a reproducing kernel Hilbert space (RKHS). Unlike existing nonparametric approaches that primarily address homogeneous data, our approach uses kernel-based quantile regression to effectively model heterogeneous data. Moreover, we integrate the concepts of random features (RF) and communication-efficient surrogate likelihood (CSL) to ensure accurate estimation and enhance computational efficiency in distributed settings. Specifically, we employ an embedding technique to map the original data into RF spaces, enabling us to construct an extended surrogate loss function. This function can be locally optimized using an iterative alternating direction method of multipliers (ADMM) algorithm, minimizing the need for extensive computation and communication within the distributed system. The article thoroughly investigates the asymptotic properties of the distributed estimator and provides convergence rates of the excess risk. These properties are established under mild technical conditions and are comparable to state-of-the-art results in the literature. Additionally, we validate the effectiveness of the proposed algorithm through a comprehensive set of synthetic examples and a real data study, effectively highlighting its advantages and practical utility. Supplementary materials for this article are available online.

Growth unveiled: decoding the start of grassland seasons in Austria

ABSTRACT The start of the growing season (SOS) in grasslands is a critical factor that significantly affects grassland dynamics, production and quality. In the context of grassland fodder production, the exact detection of the start of the growing season allows farmers and land managers to precisely plan harvest schedules for hay production or silage making. However, the NDVI time-series based detection of the SOS for grassland poses challenges due to the gradual onset of greenness and the wide variety of environmental conditions. These challenges emphasize the need for novel approaches to improve satellite-based SOS detection accuracy. This study establishes a new methodology for identifying the SOS specifically tailored to grassland regions in Austria. To accomplish this, a synergistic approach combining remote sensing (MODIS NDVI at 250 m) and weather data analysis was adopted. Results indicate that the integrated approach outperforms alternative methods in estimating SOS. The study demonstrated a more robust and accurate estimates of SOS that were generally consistent with the phenological observations of a reference species (Forsythia suspensa) and in agreement with the observed SOS dates based on visual interpretation of 10 m resolution Sentinel-2 images. For future studies, the integration of additional validation techniques is crucial.