Direct Estimation Research Articles

Prediction of the weighted-mean soil water diffusivity in supporting the falling rate stage of evaporation

Daily soil evaporation from drying soil can be described as isothermal linear diffusion process, resulting in a simple equation for estimating soil evaporation rate. However, determining soil water diffusivity and subsequently the crucial parameter of weighted-mean diffusivity, essential for estimating soil evaporation rate, is challenging and time-consuming. In this paper, the soil water diffusivity was re-evaluated by considering the effect of film flow, a phenomenon often overlooked but recently argued by Wang et al. (2019, https://doi.org/10.1029/2019WR025003) as the dominant process in the falling rate stage of evaporation. Theoretical derivation reveals a universal relationship between soil water diffusivity and matric potential, independent of soil types. Additionally, soil water diffusivity can be expressed in the form of an exponential function, a form commonly assumed empirically in the literature. Testing with various datasets suggests that the theoretically derived soil water diffusivity generally agrees closely with observations for matric potentials less than about −1 m, wherein the diffusivity accounting for capillary flow shows significant underestimation. Furthermore, a simple function is derived for estimating the weighted-mean diffusivity, enabling a direct estimation of soil evaporation rates in the falling rate stage. Testing with various data sets of different soil types and ambient conditions generally demonstrates close agreements between the estimated soil evaporation rates and observations, indicating that the developed method provides a robust estimation of the weighted-mean soil water diffusivity. When the initial matric potential of the linear diffusion process of drying is higher than about −1 m, which usually occurs under very high atmospheric demand conditions or for very coarse-textured soils, however, the effects of capillary flow were found to be important and need consideration. The effects of vapor diffusion were also discussed and found to have a negligible impact on the estimation of weighted-mean soil water diffusivity.

Genetic correlation estimates between calving ease in primiparous cows and economically important traits in Nellore cattle.

This study aimed to estimate (co)variance components and genetic parameters for calving ease (CE) and their genetic correlations with growth, reproductive, carcass, and feed efficiency traits in Nellore cattle. Phenotypes for CE are scored in two categories: normal calving and assisted calving. The traits considered were probability of precocious calving, age at first calving, stayability, adjusted scrotal circumference at 365 days of age, accumulated cow productivity, age at puberty of males, gestation length, birth weight, adjusted weights at 210 and 450 days of age, adult cow weight, frame score, hip height, rib eye area, subcutaneous backfat thickness, rump fat thickness, intramuscular fat percentage, residual feed intake and dry matter intake. The estimation of genetic parameters was performed using a two-trait threshold-linear animal model, except for CE, stayability, and probability of precocious calving, which were evaluated through a two-trait threshold animal model. The direct (0.27) and maternal (0.19) heritability estimates for CE in heifers primiparous Nellore indicated that selecting for this trait is feasible. The selection to improve the female sexual precocity should consider CE during the selection and mating decisions to reduce calving problems. Genetic correlation estimates between CE and BW suggest that selecting low birth weight to reduce calving problems is not an appropriate strategy to improve calving ease in heifers Nellore. Therefore, adopting a multi-trait selection model with CE and BW in the Nellore breed would reduce calving difficulties, particularly in sexually precocious heifers, without impairing the growth, reproductive, feed efficiency conversion, and carcass indicator traits.

Reservoir porosity assessment and anomaly identification from seismic attributes using Gaussian process machine learning

AbstractPorosity, as one of the reservoir properties, is an important parameter to numerous studies, i.e., the reservoir’s oil/gas volume estimation or even the storage capacity measurement in the Carbon Capture Storage (CCS) project. However, an approach to estimate porosity using elastic property from the inversion propagates its error, affecting the result’s accuracy. On the other hand, direct estimation from seismic data is another approach to estimating porosity, but it poses a high non-linear problem. Thus, we propose the non-parametric machine learning approach, Gaussian Process (GP), which draws distribution over the function to solve the high non-linear problem between seismic data with porosity and quantify the prediction uncertainty simultaneously. With the help of Random Forest (RF) as the feature selection method, the GP predictions show excellent results in the blind test, a well that is completely removed from the training data, and comparison with other machine learning models. The uncertainty, standard deviation from GP prediction, can act as a quantitative evaluation of the prediction result. Moreover, we generate a new attribute based on the quartile of the standard deviation to delineate the anomaly zones. High anomaly zones are highlighted and associated with high porosity from GP and low inverted P-impedance from inversion results. Thus, applying the GP using seismic data shows its potential to characterize the reservoir property spatially, and the uncertainty offers insights into quantitative and qualitative evaluation for hydrocarbon exploration and development.

Directional distribution of the pseudo intensity vector in anisotropic late reverberation.

The pseudo intensity vector (PIV) is often used to analyze the directional properties of spatial room impulse responses. In the early part of the response, it is capable of estimating the directions of individual reflections. However, thus far, its behaviour in the late field is unclear. Specifically, it is unknown whether anisotropy, i.e., a direction-dependent energy distribution, is captured by the directional estimates. In this study, a closed-form expression of the directional distribution of the pressure-normalized pseudo intensity vector contingent on a general stochastical model of anisotropic fields was analytically derived. This paper shows that the probability density function of this PIV is a multivariate Cauchy distribution, which does indeed depend on the energy distribution of the field, yet the directional distribution has very limited degrees of freedom. The derived distribution is compared to the results of Monte Carlo simulations and fields captured with a microphone array in a real room. These results facilitate better understanding of the behaviour of parametric spatial room impulse response methods and may enable improved directional estimators for anisotropic fields.

Estimation of Circular Statistics in the Presence of Measurement Bias.

Circular statistics and Rayleigh tests are important tools for analyzing cyclic events. However, current methods are not robust to significant measurement bias, especially incomplete or otherwise non-uniform sampling. One example is studying 24-cyclicity but having data not recorded uniformly over the full 24-hour cycle. Our objective is to present a robust method to estimate circular statistics and their statistical significance in the presence of incomplete or otherwise non-uniform sampling. Our method is to solve the underlying Fredholm Integral Equation for the more general problem, estimating probability distributions in the context of imperfect measurements, with our circular statistics in the presence of incomplete/non-uniform sampling being one special case. The method is based on linear parameterizations of the underlying distributions. We simulated the estimation error of our approach for several toy examples as well as for a real-world example: analyzing the 24-hour cyclicity of an electrographic biomarker of epileptic tissue controlled for states of vigilance. We also evaluated the accuracy of the Rayleigh test statistic versus the direct simulation of statistical significance. Our method shows a very low estimation error. In the real-world example, the corrected moments had a root mean square error of [Formula: see text]. In contrast, the Rayleigh test statistic overestimated the statistical significance and was thus not reliable. The presented methods thus provide a robust solution to computing circular moments even with incomplete or otherwise non-uniform sampling. Since Rayleigh test statistics cannot be used in this circumstance, direct estimation of significance is the preferable option for estimating statistical significance.

Nano-positive up negative down in binary oxide ferroelectrics

Ferroelectric HfO2 and ZrO2-based materials are promising candidates for next-generation ferroelectric devices, but their characterization is challenging due to complex factors such as crystal phases, degradation mechanisms, and local inhomogeneities. In this work, we propose a novel nanosized positive-up-negative-down (PUND) method to assess the ferroelectric response of doped-HfO2. This method is based on actual current probing and is immune to most electrostatic artifacts that plague other electrical atomic force microscopy (AFM) techniques. We demonstrate the PUND method’s ability to induce ferroelectric switching in Si-doped HfO2 and investigate the distinctive PUND response obtained for individual grains. We also extract the polarization charge based on a direct estimate of the tip–sample contact area. The proposed PUND method is a significant innovation as it is a method to combine the high spatial resolution of AFM with the immunity to electrostatic artifacts of current probing. This makes it a powerful tool for studying ferroelectric materials at the nanoscale, where local inhomogeneities and other effects can have a significant impact on their behavior. The PUND method is also notable for its ability to extract polarization charge based on a direct estimate of the tip–sample contact area. This is a significant improvement over previous methods, which often relied on indirect or approximate estimates of the contact area. Overall, the PUND method is a novel and innovative technique that offers significant advantages for the characterization of ferroelectric materials at the nanoscale. It is expected to have a major impact on the research and development of next-generation ferroelectric devices.

Direct estimation of the noise power spectrum from patient data to generate synthesized CT noise for denoising network training.

Developing a deep-learning network for denoising low-dose CT (LDCT) images necessitates paired computed tomography (CT) images acquired at different dose levels. However, it is challenging to obtain these images from the samepatient. In this study, we introduce a novel approach to generate CT images at different doselevels. Our method involves the direct estimation of the quantum noise power spectrum (NPS) from patient CT images without the need for prior information. By modeling the anatomical NPS using a power-law function and estimating the quantum NPS from the measured NPS after removing the anatomical NPS, we create synthesized quantum noise by applying the estimated quantum NPS as a filter to random noise. By adding synthesized noise to CT images, synthesized CT images can be generated as if these are obtained at a lower dose. This leads to the generation of paired images at different dose levels for training denoisingnetworks. The proposed method accurately estimates the reference quantum NPS. The denoising network trained with paired data generated using synthesized quantum noise achieves denoising performance comparable to networks trained using Mayo Clinic data, as justified by the mean-squared-error (MSE), structural similarity index (SSIM)and peak signal-to-noise ratio (PSNR)scores. This approach offers a promising solution for LDCT image denoising network development without the need for multiple scans of the same patient at differentdoses.

From A to E: Uniting vitamins against stroke risk-A systematic review and network meta-analysis.

Stroke represents a significant public health challenge, necessitating the exploration of preventive measures. This network meta-analysis aimed to assess the efficacy of different vitamin treatments compared to a placebo in preventing stroke. A systematic electronic search in databases including PubMed, EmBASE, Web of Science, clinicaltrials.gov, and Google Scholar until 31 May 2023 was conducted, to identify published studies investigating the association between vitamin intake and the risk of stroke. Pooled risk ratio (RR) with 95% confidence intervals (CIs) was calculated using a frequentist network meta-analysis. Furthermore, we ranked vitamins based on p-scores, facilitating a comparative assessment of their effectiveness in preventing stroke. A total of 56 studies, including 17 randomized controlled trials (RCTs) and 39 cohort studies were analyzed. Direct estimates obtained from network meta-analysis, we found that vitamin A (RR: .81 [.72-.91]), vitamin B-complex (RR:.85 [.74-.97]), vitamin B6 (RR: 79 [.68-.92]), folate (RR: .86 [.75-.97]), vitamin C (RR: .77 [.70-.85]) and vitamin D (RR: .73 [.64-.83]) were significantly associated with a decreased stroke risk. However, no significant association was observed for vitamin B2, vitamin B12, and vitamin E. Subsequent to network meta-analysis, vitamins were ranked in decreasing order of their efficacy in stroke prevention based on p-score, with vitamin D (p-score = .91), vitamin C (p-score = .79), vitamin B6 (p-score = .70), vitamin A (p-score = .65), vitamin B-complex (p-score = .53), folate (p-score = .49), vitamin B2 (p-score = .39), vitamin E (p-score = .28), vitamin B12 (.13) and placebo (.10). Our study has established noteworthy connections between vitamin A, vitamin B-complex, vitamin B6, folate, vitamin C, and vitamin D in the realm of stroke prevention. These findings add substantial weight to the accumulating evidence supporting the potential advantages of vitamin interventions in mitigating the risk of stroke. However, to solidify and validate these observations, additional research is imperative. Well-designed clinical trials or cohort studies are needed to further explore these associations and formulate clear guidelines for incorporating vitamin supplementation into effective stroke prevention strategies.

Robust data integration from multiple external sources for generalized linear models with binary outcomes.

We aim to estimate parameters in a generalized linear model (GLM) for a binary outcome when, in addition to the raw data from the internal study, more than 1 external study provides summary information in the form of parameter estimates from fitting GLMs with varying subsets of the internal study covariates. We propose an adaptive penalization method that exploits the external summary information and gains efficiency for estimation, and that is both robust and computationally efficient. The robust property comes from exploiting the relationship between parameters of a GLM and parameters of a GLM with omitted covariates and from downweighting external summary information that is less compatible with the internal data through a penalization. The computational burden associated with searching for the optimal tuning parameter for the penalization is reduced by using adaptive weights and by using an information criterion when searching for the optimal tuning parameter. Simulation studies show that the proposed estimator is robust against various types of population distribution heterogeneity and also gains efficiency compared to direct maximum likelihood estimation. The method is applied to improve a logistic regression model that predicts high-grade prostate cancer making use of parameter estimates from 2 external models.

Using proxy pattern-mixture models to explain bias in estimates of COVID-19 vaccine uptake from two large surveys

Abstract Recently, attention was drawn to the failure of two very large internet-based probability surveys to correctly estimate COVID-19 vaccine uptake in the U.S. in early 2021. Both the Delphi-Facebook COVID-19 Trends and Impact Survey (CTIS) and Census Household Pulse Survey (HPS) overestimated uptake substantially, by 17 and 14 percentage points in May 2021, respectively. These surveys had large numbers of respondents but very low response rates (<10%), thus, nonignorable nonresponse could have had substantial impact. Specifically, it is plausible that ‘anti-vaccine’ individuals were less likely to participate given the topic (impact of the pandemic on daily life). In this article, we use proxy pattern-mixture models (PPMMs) to estimate the proportion of adults (18 +) who received at least one dose of a COVID-19 vaccine, using data from the CTIS and HPS, under a nonignorable nonresponse assumption. Data from the American Community Survey provide the necessary population data for the PPMMs. We compare these estimates to the true benchmark uptake numbers and show that the PPMM could have detected the direction of the bias and provide meaningful bias bounds. We also use the PPMM to estimate vaccine hesitancy, a measure for which we do not have a benchmark truth, and compare to the direct survey estimates.

Effort-reward and overcommitment at work and psychiatric symptoms in healthcare professionals: The mediation role of allostatic load

BackgroundDespite decades of advancement to support interventions for managing work-related stress, mental health issues have significantly escalated among healthcare professionals. Effort-reward imbalance (ERI) and overcommitment in the workplace are linked to several psychiatric disorders. However, the underlying biological mechanisms remain unclear. This study investigated whether ERI and overcommitment among healthcare professionals were linked to Allostatic Load (AL) and whether AL mediates the relationship between ERI, overcommitment and mental health issues. MethodsOne hundred forty-two nursing workers (n = 142; 90.1 % female, mean age: 39.5 ± 9.6) were randomly recruited from a university hospital in Sao Paulo, Brazil, and applied the ERI scale that assesses work effort, reward, and overcommitment. The Perceived Stress Scale (PSS), The Beck Depression Inventory (BDI), and the Self-Report Questionnaire for psychiatric symptoms (SRQ-20) evaluated the mental health outcomes. Ten neuroendocrine, metabolic, immunologic and cardiovascular biomarkers were analyzed, and values were transformed into an AL index using clinical reference cutoffs. ResultsLinear regression adjusted for covariates showed that higher scores for overcommitment were associated with higher AL indexes, which in turn were associated with higher SRQ-20, but not with PSS and DBI scores. As expected, higher scores for effort, lower for reward, and higher ERI were associated with higher scores for PSS, SRQ-20, and DBI, but not with AL index. Direct effect estimates showed that overcommitment was directly associated with higher SRQ-20 scores, and indirectly via AL. ConclusionOur study reveals that overcommitment, rather than ERI, was linked to increased AL in healthcare workers. Additionally, AL mediates the relationship between overcommitment and higher psychiatric symptoms, highlighting a key mechanism by which work stress can lead to mental health problems. Individual's responses to high work demands need to be considered when designing predictive models and interventions for mental health issues.

Identifying indicators of polar bear population status

Monitoring trends in large mammal populations is a fundamental component of wildlife management and conservation. However, direct estimates of population size and vital rates of large mammals can be logistically challenging and expensive. Indicators that reflect trends in abundance, therefore, can be valuable tools for supporting population monitoring. Polar bears have a relatively simple life history such that a few key variables may be effective indicators for tracking changes in body condition and recruitment that affect abundance. Direct estimates of polar bear abundance are difficult to obtain due to their large home ranges in remote Arctic habitats. Changes in abundance associated with environmental conditions appear to affect polar bears largely via effects on female body condition which influence reproduction and cub survival (i.e., recruitment). Loss of sea ice habitat is further limiting researcher access for population monitoring creating a need for alternative approaches. Here we used relationships established from eight years (2008–2017) of data collected on 439 polar bears in the Chukchi Sea, to transform previously published individual-based relationships with annually available sea ice, atmospheric circulation, and prey body condition variables to predict annual mean body condition and recruitment during 2018–2022. Although annual sample sizes were limited for verifying predicted body condition and recruitment via techniques such as cross-validation, in most cases predicted annual means were closely correlated with observed means for 2008–2017. Summer sea ice and prey body condition remained within or increased relative to levels observed during 2008–2017 and predicted polar bear body condition and recruitment during 2018–2022 were largely within or above observed annual means during 2008–2017. A lack of trend in environmental and ecological variables or polar bear body condition and recruitment metrics during 2008–2022 is suggestive that the Chukchi Sea polar bear population was likely stable during this time. Our results provide support for developing models that predict important population parameters of large mammals based on environmental and ecological indicators. Given that trend information is lacking for 10 of the 19 recognized polar bear populations and is outdated for others, the use of environmental and ecological indicators may be particularly useful for augmenting direct estimates of polar bear vital rates in between periods of data collection. Although demographic assessments for polar bears have primarily focused on correlations with sea ice availability, our study and others highlight that prey health is also an important indicator of polar bear population status.

Estimates of Direct Medical and Indirect Costs Associated With COVID-19 Among U.S. Active Duty Army Soldiers.

This study estimated the direct medical and indirect costs associated with coronavirus disease 2019 (COVID-19) diagnoses among U.S. active duty (AD) Army service members (SMs). These cost estimates provide the U.S. Military with a better understanding of the financial burden of COVID-19 and provide a foundation for cost-effectiveness estimates. The study was approved as Public Health Practice (#17-605) by the U.S. Army Public Health Center, Public Health Review Board. U.S. AD Army SMs with COVID-19 were identified using an Army COVID-19 testing and surveillance database. Encounters for these SMs were captured from medical record where International Classification of Disease Tenth Revision, Clinical Modification code U07.1 was in the first or second diagnostic position. Analyses were conducted on SMs with COVID-19 who either had no healthcare encounters in the Military Health System (MHS); at least one MHS COVID-19 inpatient hospitalization; or at least one MHS outpatient COVID-19 encounter. Coronavirus disease 2019 (COVID-19) costs captured from the encounters were used to develop direct medical cost estimates. Literature on COVID-19 recovery post-hospitalization, along with the number of COVID-19 hospitalizations and outpatient visits from encounters were used to describe the intensity of COVID-19 care. Estimates of the indirect cost of lost duty were based on SMs salary information, along with recovery time, bed days, or outpatient visit time. The indirect cost of limited duty was estimated using the time associated with the Department of Defense (DoD) COVID-19 pandemic mitigation strategies in place when these SMs were identified as positive for COVID-19. Coronavirus disease 2019 (COVID-19) cost estimates were developed for the Army using data from 19,086 SMs identified as positive for COVID-19 between June 1, 2020, and December 31, 2020. Direct medical costs, or the amount paid by the DoD to facilities for COVID-19 care, averaged $606 per SM with an encounter. Indirect costs for lost duty or the cost for recovery and the time taken to seek care for COVID-19 averaged $319 per SM, while indirect costs for limited duty or isolation associated with COVID-19 averaged $4,111 per SM or $411 per day. Service members (SMs) with an inpatient hospitalization averaged 4.8 bed days (range 1-43) and 266 recovery hours while SMs who sought outpatient care for COVID-19 averaged two outpatient visits (range 1-60 visits). The direct medical costs of a COVID-19 encounter in the MHS ($606) are a small portion of the costs for a SM with COVID-19. Indirect costs of lost and limited duty associated with COVID-19 averaged seven times higher ($4,331) and accounted for the vast majority of costs. Recognition of these costs is important especially given that soldiers in the hospital or in quarters being quarantined are complete losses of manpower to the Army. While the COVID-19 pandemic is ongoing and prevention, treatment, and mitigation efforts continue to evolve, having reliable estimates of direct medical and indirect costs from this study allows the U.S. Army and MHS to better account for the cost of this pandemic for its population.

Local Approximation Properties for the New Type q-Stancu-Durrmeyer Operators

In this article, we propose a new kind of q-Stancu-Durrmeyer operators (or shortly q-SD operators). We calculate first and second order moments, for and we give an estimation for the second order central moment, . Furthermore, through the use of the second modulus of continuity, we invent approximation properties of the q-SD operators in the local sense and by using order Lipschitz kind maximal function, we give a direct estimation for the q-SD operators again in the local sense.

Observations of how boundary conditions affect estimates of Poisson's ratio from simulated mode shapes

This research extends a method for direct estimation of Poisson's ratio from mode shapes of plates with hinged-hinged boundary conditions to include combinations of clamped and free boundary conditions. Using finite element simulations, we observe the effects of boundary conditions on the estimate of Poisson's ratio using the combination of Time-Average Scanning Digital Holography and Cornu's method. First, we introduce the abstraction of the span-to-width ratio to an effective antinode region. This abstraction enables us to extend the direct estimation technique to plates with various boundary conditions. Second, for long and thin enough plates, the effects of boundary conditions become negligible and Cornu's method is sufficient for direct estimation of the true value of Poisson's ratio from an out-of-plane bending mode shape. We note that for many situations, the necessary plate dimensions could be a limitation. Therefore, as a third concept, we discuss the behavior of the direct estimate of Poisson's ratio with respect to each type of boundary condition. Ultimately, this research initiates a path forward for a common processing technique for making a direct estimate of Poisson's ratio across different types of opposing boundary conditions for thin plates.

Efficacy of different routes of vitamin B12 supplementation for the treatment of patients with vitamin B12 deficiency: A systematic review and network meta-analysis.

This systematic review and network meta-analysis aimed to evaluate the three different administration routes of vitamin B12: oral, intramuscular (IM), and sublingual (SL) routes. We searched four electronic databases (PubMed, Scopus, Web of Science, and Cochrane CENTRAL Register of Controlled Trials). We included only comparative studies. We performed a frequentist network meta-analysis to measure network estimates for the relative outcomes. Moreover, we conducted a pairwise meta-analysis using a random effect model to obtain direct estimates for outcomes. All outcomes were continuous, and the relative treatment effects were pooled as mean difference (MD) with 95% confidence intervals. Thirteen studies were included in the meta-analysis, with a total of 4275 patients. Regarding increasing vitamin B12 levels, the IM route ranked first, followed by the SL route (MD = 94.09 and 43.31pg/mL, respectively) compared to the oral route. However, these differences did not reach statistical significance owing to the limited number of studies. Regarding the hemoglobin level, the pooled effect sizes showed no difference between all routes of administration that could reach statistical significance. However, the top two ranked administration routes were the oral route (78.3) and the IM route (49.6). All IM, oral, and SL routes of administration of vitamin B12 can effectively increase the level of vitamin B12 without significant differences between them, as thought previously. However, the IM route was the top-ranked statistically but without clinical significance. We found no significant difference among studied administrated routes in all other CBC parameters and homocysteine levels.

Long-term ice mass changes in Greenland and Antarctica derived from satellite laser ranging

Time-variable gravity field models obtained from satellite gravimetric techniques allow for the assessment of ice sheet mass changes in remote polar regions, such as Greenland and Antarctica. So far, GRACE has been the primary mission for obtaining the global time-variable gravity field models. However, GRACE was launched in 2002, thus very little is known about the global mass changes before this data, as well as between GRACE and its successor – GRACE Follow-On. We derive a method of gravity field recovery based on Satellite Laser Ranging (SLR) data to geodetic satellites that allows for obtaining direct ice mass change estimates for a period longer by 10 years than that provided by the GRACE missions. The developed method is based on splitting normal equation systems and re-stacking the solutions which allow for stable inversion, reduces the correlations between obtained parameters, stabilizes the ice mass estimates in polar regions, and reduces the noise over oceans by a factor of four. The secular trends obtained from SLR are equal to −113.5 and −82.8 Gt/year, whereas these are −119.1 and −83.3 Gt/year from GRACE and GRACE-FO to degree and order 10 for Greenland and West Antarctica, respectively, for the common period of 2002–2021 and after removing the post-glacial rebound effect. Despite the conformity of the trend and patterns, an underestimation is observed in the solutions expanded to degree and order 10. Therefore, scaling factors between GRACE/GRACE-FO expanded up to a degree and order 10 × 10 and 60 × 60 were derived and applied to SLR solutions to account for the differences in mass estimates due to the truncation of the models. SLR data revealed that in Greenland the smallest ice mass trends are for 1995–2000, 2000–2005, and 2015–2020 which are equal to +54.3, −15.5, and −75.9 Gt/year. The largest ice mass depletion periods took place in 2005–2010, 2010–2015, and recently in 2019–2021 with trends of −213.9, −287.2, and −276.1 Gt/year, respectively. For Greenland and West Antarctica, the period 2010–2015 is characterized by the most enormous ice depletion events, whereas the later 5-year period of 2015–2020 provided a near mass-equilibrium for Antarctica reducing the negative trend and returning to the situation from the ‘90s when no significant ice mass changes were observed.

Volterra Black-Box Models Identification Methods: Direct Collocation vs. Least Squares

The Volterra integral-functional series is the classic approach for nonlinear black box dynamical system modeling. It is widely employed in many domains including radiophysics, aerodynamics, electronic and electrical engineering and many others. Identifying the time-varying functional parameters, also known as Volterra kernels, poses a difficulty due to the curse of dimensionality. This refers to the exponential growth in the number of model parameters as the complexity of the input-output response increases. The least squares method (LSM) is widely acknowledged as the standard approach for tackling the issue of identifying parameters. Unfortunately, the LSM suffers with many drawbacks such as the sensitivity to outliers causing biased estimation, multicollinearity, overfitting and inefficiency with large datasets. This paper presents an alternative approach based on direct estimation of the Volterra kernels using the collocation method. Two model examples are studied. It is found that the collocation method presents a promising alternative for optimization, surpassing the traditional least squares method when it comes to the Volterra kernels identification including the case when input and output signals suffer from considerable measurement errors.

Unveiling Prasinovirus diversity and host specificity through targeted enrichment in the South China Sea.

Unicellular green picophytoplankton from the Mamiellales order are pervasive in marine ecosystems and susceptible to infections by prasinoviruses, large double-stranded DNA viruses within the Nucleocytoviricota phylum. We developed a double-stranded DNA virus enrichment and shotgun sequencing method, and successfully assembled 80 prasinovirus genomes from 43 samples in the South China Sea. Our research delivered the first direct estimation of 94% accuracy in correlating genome similarity to host range. Stirkingly, our analyses uncovered unexpected host-switching across diverse algal lineages, challenging the existing paradigms of host-virus co-speciation and revealing the dynamic nature of viral evolution. We also detected six instances of horizontal gene transfer between prasinoviruses and their hosts, including a novel alternative oxidase. Additionally, diversifying selection on a major capsid protein suggests an ongoing co-evolutionary arms race. These insights not only expand our understanding of prasinovirus genomic diversity but also highlight the intricate evolutionary mechanisms driving their ecological success and shaping broader virus-host interactions in marine environments.

Time-lapse seismic inversion for CO2 saturation with SeisCO2Net: An application to Frio-II site

Seismic monitoring of geological CO2 storage (GCS) involves highly nonlinear seismic inversion and petrophysical inversion, making it challenging to estimate CO2 volume efficiently and detect possible early CO2 leakages. Deep learning (DL) using convolutional neural networks (CNNs) has shown promise in solving highly nonlinear seismic inversion problems. However, direct estimation of CO2 plume extent/saturation from time-lapse seismic gathers using DL is still underexplored, with no reported field applications to date. The investigation of field data is primarily hindered by scarcity of field data for neural network training. Other obstacles include highly nonlinear seismic-petrophysics inverse relationship, and presence of noise in field seismic data. We introduce SeisCO2Net, a deep CNN that predicts CO2 saturation maps directly from time-lapse full waveform shot gathers. For training, we use site-specific geological information, fluid flow physics, rock physics, and seismic modeling to generate synthetic datasets that closely resemble the CO2 storage site. Synthetic tests show promising results, inspiring us to apply SeisCO2Net's trained weights on field data collected at Frio-II GCS site by leveraging transfer learning principles. As reference, we compare SeisCO2Net's predicted CO2 saturation maps with results obtained from physics-based inversion. Our analyses show both methods display similar CO2 plume shapes, reasonable CO2 plume characteristics, and comparable saturation values. Our results suggest pre-training CNNs on physics-informed synthetic datasets and then applying the learned weights to field data is a viable approach to estimating field CO2 saturation. This method effectively addresses the scarcity of field training data, thus encouraging the feasibility of long-term GCS monitoring.