Separate Estimates Research Articles

A Bowen ratio-informed method for coordinating the estimates of air-sea turbulent heat fluxes

Abstract Accurate quantification of turbulent heat fluxes [THF, comprising sensible heat flux (SHF) and latent heat flux (LHF)] and Bowen ratio (β, ratio of SHF and LHF) is essential for understanding the air-sea interaction. However, the biased estimates of SHF and LHF by widely applied bulk aerodynamic models, and the separate estimates of SHF and LHF by data-driven models, both may result in unrealistic β estimates. This study for the first time innovatively proposes a Bowen ratio-informed data-driven model (BrTHF) for coordinating the estimations of THF and β using the multivariate random forest technique and a combination of eddy covariance flux observations and meteorological and oceanic observations collected from 53 historical cruises. The result shows that the BrTHF model could not only achieve high-accuracy SHF and LHF estimates, but also avoid the outliers of β estimates that were commonly found in un-synergistic random forest and well-known COARE3.5 models.

Small-Country Mundell–Fleming (IS/LM/BP) Model Predictions Under Both Fixed and Flexible Exchange Rates: Evidence from Australia and S. Korea

The small-country IS/LM/BP (Mundell–Fleming) model predicts that monetary policy is totally ineffective in countries with fixed exchange rates and super-effective in countries with flexible exchange rates. Furthermore, this model predicts that, under fixed exchange rates, fiscal policy is stronger the more mobile capital is in terms of moving in and out of the country, but it predicts the opposite for countries with flexible exchange rates; fiscal policy is stronger the less mobile capital is. This paper tests these predictions by applying reiterative truncated projected least squares (RTPLS) to quarterly data from Australia and the Republic of Korea when they employed fixed and then flexible exchange rates. RTPLS produces a separate total derivative estimate for each observation, where the differences in these estimates are due to omitted variables. By doing so, RTPLS makes it possible to see how the estimated relationship changes over time. I found that the effectiveness of monetary policy was not zero under fixed exchange rates but that its effectiveness did increase when Australia and S. Korea switched to flexible exchange rates. Under flexible exchange rates, I found that the effectiveness of fiscal policy was statistically higher than zero for both countries, which conflicts with the assumption of perfect capital mobility.

Multi-task Learning for Gaussian Graphical Regressions with High Dimensional Covariates

Gaussian graphical regression is a powerful approach for regressing the precision matrix of a Gaussian graphical model on covariates, which permits the response variables and covariates to outnumber the sample size. However, traditional approaches of fitting the model via separate node-wise lasso regressions overlook the network-induced structure among these regressions, leading to high error rates, particularly when the number of nodes is large. To address this issue, we propose a multi-task learning estimator for fitting Gaussian graphical regression models, which incorporates a cross-task group sparsity penalty and a within-task element-wise sparsity penalty to govern the sparsity of active covariates and their effects on the graph, respectively. We also develop an efficient augmented Lagrangian algorithm for computation, which solves subproblems with a semi-smooth Newton method. We further prove that our multi-task learning estimator has considerably lower error rates than the separate node-wise regression estimates, as the cross-task penalty enables borrowing information across tasks. We examine the utility of our method through simulations and an application to a gene co-expression network study with brain cancer patients.

Discrimination of mnemonic similarity is associated with short-term and long-term memory precision.

Remembering specific memories with precision relies on the differentiation of similar memory contents - a process commonly referred to as pattern separation and behaviorally operationalized as lure discrimination in the mnemonic similarity task. Although pattern separation is typically investigated in the context of long-term memory (LTM), recent research extends these findings to short-term memory (STM) within a mixture model framework, emphasizing the distinction between memory quality and quantity. According to this framework, pattern separation is associated with memory precision across STM and LTM, regardless of the overall memory likelihood. However, these associations among memory quality measures may persist without the mixture model assumption. In an alternative model, a unitary memory strength measure quantified as a discrimination score (d') may also capture the association between pattern separation and memory quality, as pattern separation has been previously linked with strength-based memory performance. We tested these possibilities based on individual differences among 132 participants who underwent tasks measuring mnemonic pattern separation and STM/LTM quality. We found that behavioral estimates of pattern separation are significantly correlated with STM and LTM precision, irrespective of the likelihood of STM/LTM recall success. However, these associations are absent when considering the correlation between pattern separation and memory strength under a unitary model framework. By leveraging individual differences to constrain our understanding of cognitive models, our data unravel the intricate relationship between pattern separation and memory quality across timescales. These findings may therefore contribute to identifying sensitive behavioral measures for detecting subtle memory deficits in older adults or clinical populations.

Speckle autocorrelation separation method based on frequency intensity for tracking moving object beyond optical memory effect

Abstract Imaging objects behind a scattering medium beyond the optical memory effect (OME) range is vital in various imaging scenarios. Despite numerous proposed imaging methods aimed at separating objects, effectively tracking moving objects remains challenging. In this paper, we present a non-invasive scattering imaging method to reconstruct target beyond the OME range and track moving objects using a speckle separation and position estimation technique. A speckle autocorrelation separation method based on frequency intensity is proposed to eliminate the noise from the cross-correlation and to separate the objects from the mixed speckles. A position estimation technique is proposed to determine the positional relationships of the objects using speckle deconvolution using the point spread function of imaging regions. A non-invasive imaging system is designed to track the moving object without prior knowledge of the shape and distribution of objects. Experimental validation shows the efficiency of the method for separation and localization, allowing for the reconstruction of the target behind the scattering medium and tracking of the moving object behind the scattering medium. Moreover, the method is capable of separating and tracking a moving object when multiple objects are in the background.

An Effective Exponential Ratio and Product Type Estimator in Post-Stratification

In this paper we present a unique estimator for the population parameter under post-stratification. By segmenting the population into homogeneous subgroups, post-stratification is a widely used strategy in survey sampling that increases the accuracy of estimators. In the post-stratification scenario, this paper addressed the issue of estimating the mean of a finite population. For post- stratification, better separate ratio and product exponential type estimators are proposed. This study's primary goal is to evaluate our suggested estimators' performance against that of current estimators. We perform a thorough simulation research to assess the precision and effectiveness of our estimator. The mean squared errors and biases of the proposed estimators are obtained to the first degree of approximation. Theoretical and practical researches have demonstrated that the proposed estimators are more efficient than other estimators that were taken into consideration.

Fungal secondary metabolite gliotoxin enhances enzymatic activity in soils by reshaping their microbiome

Gliotoxin (GT) is a sulfur-containing epidithiodioxopiperazine produced by various filamentous fungi, including those used in biological plant protection (Trichoderma virens). The pronounced antimicrobial effect of GT on a variety of fungi and bacteria makes it a promising agent for controlling phytopathogens in agricultural systems. In this study, we aim to investigate the microbiological properties of the soil microbiome after the introduction of GT. GT was applied at doses of 10, 25, 50, 100 and 500 μM kg−1 soil. Soil sampling was carried out after 1, 7, 14, 30, 60 and 90 days of incubation. It was found that GT significantly stimulated the respiratory activity of soil microorganisms and maintained this activity throughout the experiment. Carbon of microbial biomass, on the contrary, decreases under the influence of GT and is restored at the end of the experiment only in microcosms with 10 and 25 μM GT. Separate estimates of bacterial and fungal biomass showed that the bacterial community increased in biomass on day 14, while fungal biomass increased on day 30 after the treatment. Under the influence of GT, the activity of soil enzymes involved in the carbon (CB, βG, βX), nitrogen (NAG, LAP) and phosphate (AP) cycles significantly increased. High-throughput amplicon sequencing of the ITS and 16S rDNA markers revealed that the soil fungal community is more susceptible to GT than the bacterial community. This was reflected in changes in alpha-diversity indices and in the pattern of changes in the abundance of some microbial genera. Thus, on the one hand, the data obtained provides insight into the biological effects of GT on the soil microbial community. On the other hand, it sets the direction for further research into the ecological role of antibiotics produced by soil and rhizosphere microorganisms.

Separable Synchronous Gradient‐Based Iterative Algorithms for the Nonlinear ExpARX System

ABSTRACTThis article is aimed to study the parameter identification of the ExpARX system. To overcome the computational complexity associated with a large number of feature parameters, a parameter separation scheme based on the different features of the identification model is introduced. In terms of the phenomenon that the coupling parameters lead to the inability of algorithms, a separable synchronous interactive estimation method is introduced to eliminate the coupling parameters and perform parameter estimation in accordance with the hierarchical principle. For the purpose of achieving high‐accuracy performance and reducing complexity, a separable synchronous gradient iterative algorithm is derived by means of gradient search. In order to improve the identification accuracy, a separable synchronous multi‐innovation gradient iterative algorithm is proposed by introducing the multi‐innovation identification theory. In order to improve the convergence speed, a separable synchronous multi‐innovation conjugate gradient iterative algorithm is proposed by introducing the conjugate gradient theory. Finally, a simulation example and a real‐life example of piezoelectric ceramics are used to verify the effectiveness of the proposed algorithm.

Treatment Effects in Randomized and Nonrandomized Studies of Pharmacological Interventions

Randomized clinical trials (RCTs) are widely regarded as the methodological benchmark for assessing clinical efficacy and safety of health interventions. There is growing interest in using nonrandomized studies to assess efficacy and safety of new drugs. To determine how treatment effects for the same drug compare when evaluated in nonrandomized vs randomized studies. Meta-analyses published between 2009 and 2018 were identified in MEDLINE via PubMed and the Cochrane Database of Systematic Reviews. Data analysis was conducted from October 2019 to July 2024. Meta-analyses of pharmacological interventions were eligible for inclusion if both randomized and nonrandomized studies contributed to a single meta-analytic estimate. For this meta-analysis using a meta-epidemiological framework, separate summary effect size estimates were calculated for nonrandomized and randomized studies within each meta-analysis using a random-effects model and then these estimates were compared. The reporting of this study followed the Guidelines for Reporting Meta-Epidemiological Methodology Research and relevant portions of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting guideline. The primary outcome was discrepancies in treatment effects obtained from nonrandomized and randomized studies, as measured by the proportion of meta-analyses where the 2 study types disagreed about the direction or magnitude of effect, disagreed beyond chance about the effect size estimate, and the summary ratio of odds ratios (ROR) obtained from nonrandomized vs randomized studies combined across all meta-analyses. A total of 346 meta-analyses with 2746 studies were included. Statistical conclusions about drug benefits and harms were different for 130 of 346 meta-analyses (37.6%) when focusing solely on either nonrandomized or randomized studies. Disagreements were beyond chance for 54 meta-analyses (15.6%). Across all meta-analyses, there was no strong evidence of consistent differences in treatment effects obtained from nonrandomized vs randomized studies (summary ROR, 0.95; 95% credible interval [CrI], 0.89-1.02). Compared with experimental nonrandomized studies, randomized studies produced on average a 19% smaller treatment effect (ROR, 0.81; 95% CrI, 0.68-0.97). There was increased heterogeneity in effect size estimates obtained from nonrandomized compared with randomized studies. In this meta-analysis of treatment effects of pharmacological interventions obtained from randomized and nonrandomized studies, there was no overall difference in effect size estimates between study types on average, but nonrandomized studies both overestimated and underestimated treatment effects observed in randomized studies and introduced additional uncertainty. These findings suggest that relying on nonrandomized studies as substitutes for RCTs may introduce additional uncertainty about the therapeutic effects of new drugs.

Estimating effects of aging and disease progression in current and former smokers using longitudinal models

ObjectivesTo separate estimates of mean change in a health outcome into components of aging and disease progression for different severity groups of chronic obstructive pulmonary disease (COPD). Study design and methodsA longitudinal model can be used to estimate mean change in a health outcome over time. Methods to separate this change into portions due to aging and disease progression are discussed, including conditions that allow for accurate estimation. Linear mixed models were used to estimate these changes for forced expiratory volume in 1 s (FEV1) for various COPD severity and smoking groups using a large cohort (COPDGene) followed for over 10 years. ResultsBased on an analysis of 4967 subjects, age-related loss in FEV1 was found to be about 1 % per year, consistent with published work. Excess average losses (those beyond natural aging) were significant for all severity groups (except nonsmokers), including those with smoking history but normal lung function. Subjects in higher severity groups tended to have less loss in FEV1, but more relative loss, compared to baseline averages. Losses in FEV1 that included both aging and disease progression ranged from 1 to 3 % over severity groups, with current smokers generally exhibiting greater mean losses in FEV1 than former smokers. DiscussionEffects of disease progression separate from aging can be estimated in observational studies, although care should be taken in order to make sure assumptions involving this separation are reasonable for a given study. This article demonstrates methods to estimate such effects using temporal changes in lung function for subjects in the COPDGene study.

Fission and fusion of heavy nuclei induced by the passage of a radiation-mediated shock in BNS mergers

ABSTRACT We compute the structure of a Newtonian, multi-ion radiation-mediated shock (RMS) for different compositions anticipated in various stellar explosions. We use a multifluid RMS model that incorporates electrostatic coupling between the different plasma constituents as well as Coulomb friction in a self-consistent manner, and approximates the effect of pair creation and the presence of free neutrons in the shock upstream on the shock structure. We find that under certain conditions a significant velocity separation is developed between different ions in the shock downstream and demonstrate that in fast enough shocks ion–ion collisions may trigger fusion and fission events at a relatively high rate. Our analysis ignores anomalous coupling through plasma microturbulence, which might reduce the velocity spread downstream below the activation energy for nuclear reactions. A rough estimate of the scale separation in RMS suggests that for shocks propagating in binary neutron star (BNS) merger ejecta, the anomalous coupling length may exceed the radiation length, allowing a considerable composition change behind the shock via inelastic collisions of $\alpha$ particles with heavy elements at shock velocities $\beta _\mathrm{ u}\gtrsim 0.25$. A sufficient abundance of free neutrons in the shock upstream, as expected during the first second after the merger, is also expected to alter the ejecta composition through neutron capture downstream. The resultant change in the composition profile may affect the properties of the early kilonova emission. The generation of microturbulence due to velocity separation can also give rise to particle acceleration that might alter the breakout signal in supernovae and other systems.

Evaluating food security and nutritional pathways of rural farm families: Empirical evidence from northeast India

The study aimed to ascertain a relationship between agricultural status, socioeconomic factors, and nutrition of farm families. The study was conducted in selected villages in the West Garo Hills district of Meghalaya, using Stratified Random Sampling (St. RS). Using pretested interview schedules, we collected primary data from respondents in 2020 and 2021, focusing on socioeconomic variables, body mass index, and income from agriculture and related sectors. The data was analysed using correlation analyses and separate combined regression estimates for each year and month were obtained. Results from the study indicate that agricultural income significantly influenced nutritional status (p < 0.05) and household income growth was also found significant. The region's agricultural production of cereals, pulses, and vegetables was insufficient, as was the production of meat and meat products, milk, and milk products. Hence, expenditure towards purchasing the above food groups from the market was found to be significant (p < 0.05). Therefore, the markets near the mainland especially in the hilly region play a crucial role in the nutritional pathway of rural farm families.

Separate Channel Estimation and Data Detection for Semi-Passive RIS-Empowered Massive MIMO

Separate Channel Estimation and Data Detection for Semi-Passive RIS-Empowered Massive MIMO

US State Restrictions and Excess COVID-19 Pandemic Deaths

Despite considerable prior research, it remains unclear whether and by how much state COVID-19-related restrictions affected the number of pandemic deaths in the US. To determine how state restrictions were associated with excess COVID-19 deaths over a 2-year analysis period. This was a cross-sectional study using state-level mortality and population data from the US Centers for Disease Control and Prevention for 2020 to 2022 compared with baseline data for 2017 to 2019. Data included the total US population, with separate estimates for younger than 45 years, 45 to 64 years, 65 to 84 years, and 85 years or older used to construct age-standardized measures. Age-standardized excess mortality rates and ratios for July 2020 to June 2022 were calculated and compared with prepandemic baseline rates. Excess death rates and ratios were then regressed on single or multiple restrictions, while controlling for excess death rates or ratios, from March 2020 to June 2020. Estimated values of the dependent variables were calculated for packages of weak vs strong state restrictions. Behavioral changes were investigated as a potential mechanism for the overall effects. Data analyses were performed from October 1, 2023, to June 13, 2024. Age and cause of death. Excess deaths, age-standardized excess death rates per 100 000, and excess death ratios. Mask requirements and vaccine mandates were negatively associated with excess deaths, prohibitions on vaccine or mask mandates were positively associated with death rates, and activity limitations were mostly not associated with death rates. If all states had imposed restrictions similar to those used in the 10 most restrictive states, excess deaths would have been an estimated 10% to 21% lower than the 1.18 million that actually occurred during the 2-year analysis period; conversely, the estimates suggest counterfactual increases of 13% to 17% if all states had restrictions similar to those in the 10 least-restrictive states. The estimated strong vs weak state restriction difference was 271 000 to 447 000 deaths, with behavior changes associated with 49% to 79% of the overall disparity. This cross-sectional study indicates that stringent COVID-19 restrictions, as a group, were associated with substantial decreases in pandemic mortality, with behavior changes plausibly serving as an important explanatory mechanism. These findings do not support the views that COVID-19 restrictions were ineffective. However, not all restrictions were equally effective; some, such as school closings, likely provided minimal benefit while imposing substantial cost.

Parameter estimation of the hyperbolic frequency-modulated bat calls using hyperbolic scale transform

Echolocating bats are known to vary their waveforms at the phases of searching, approaching, and capturing the prey. It is meaningful to estimate the parameters of the calls for bat species identification and the technological improvements of the synthetic systems, such as radar and sonar. The type of bat calls is species-related, and many calls can be modeled as hyperbolic frequency- modulated (HFM) signals. To obtain the parameters of the HFM-modeled bat calls, a reversible integral transform, i.e., hyperbolic scale transform (HST), is proposed to transform a call into two-dimensional peaks in the “delay-scale” domain, based on which harmonic separation and parameter estimation are realized. Compared with the methods based on time-frequency analysis, the HST-based method does not need to extract the instantaneous frequency of the bat calls, only searching for peaks. The verification results show that the HST is suitable for analyzing the HFM-modeled bat calls containing multiple harmonics with a large energy difference, and the estimated parameters imply that the use of the waveforms from the searching phase to the capturing phase is beneficial to reduce the ranging bias, and the trends in parameters may be useful for bat species identification.

Leveraging browse and grazing forage estimates to optimize index-based livestock insurance

African pastoralists suffer recurrent droughts that cause high livestock mortality and vulnerability to climate change. The index-based livestock insurance (IBLI) program offers protection against drought impacts. However, the current IBLI design relying on the normalized difference vegetation index (NDVI) may pose limitation because it does not consider the mixed composition of rangelands (including herbaceous and woody plants) and the diverse feeding habits of grazers and browsers. To enhance IBLI, we assessed the efficacy of utilizing distinct browse and grazing forage estimates from woody LAI (LAIW) and herbaceous LAI (LAIH), respectively, derived from aggregate leaf area index (LAIA), as an alternative to NDVI for refined IBLI design. Using historical livestock mortality data from northern Kenya as reference ground dataset, our analysis compared two competing models for (1) aggregate forage estimates including sub-models for NDVI, LAI (LAIA); and (2) partitioned biomass model (LAIP) comprising LAIH and LAIW. By integrating forage estimates with ancillary environmental variables, we found that LAIP, with separate forage estimates, outperformed the aggregate models. For total livestock mortality, LAIP yielded the lowest RMSE (5.9 TLUs) and higher R2 (0.83), surpassing NDVI and LAIA models RMSE (9.3 TLUs) and R2 (0.6). A similar pattern was observed for species-specific livestock mortality. The influence of environmental variables across the models varied, depending on level of mortality aggregation or separation. Overall, forage availability was consistently the most influential variable, with species-specific models showing the different forage preferences in various animal types. These results suggest that deriving distinct browse and grazing forage estimates from LAIP has the potential to reduce basis risk by enhancing IBLI index accuracy.

Factors associated with financial risk due to health spending in Argentina.

This article aims to assess the association between household demographic and socioeconomic characteristics and catastrophic health expenditure (CHE) in Argentina during 2017-2018. CHE was estimated as the proportion of household consumption capacity (using both income and total consumption in separate estimations) allocated for Out-of-Pocket (OOP) health expenditure. For assessing the determinants, we estimated a generalized ordered logit model using different intensities of CHE (10%, 15%, 20% and 25%) as the ordinal dependent variable, and socioeconomic, demographic and geographical variables as explanatory factors. We found that having members older than 65 years and with long-term difficulties increased the likelihood of incurring CHE. Additionally, having an economically inactive household head was identified as a factor that increases this probability. However, the research did not yield consistent results regarding the relationship between public and private health insurance and consumption capacity. Our results, along with the robustness checks, suggest that the magnitude of the coefficients for the household head characteristics could be exaggerated in studies that overlook the attributes of other household members. In addition, these results emphasize the significance of accounting for long-term difficulties and indicate that omitting this factor could overestimate the impact of members aged over 65.

A Hamilton–Jacobi Reachability-Based Minimum Separation Estimation of Integrated Manned and Unmanned Operation in Uncertain Environments

A Hamilton–Jacobi Reachability-Based Minimum Separation Estimation of Integrated Manned and Unmanned Operation in Uncertain Environments

A Study for Comparative Analysis of Dueling DQN and Centralized Critic Approaches in Multi-Agent Reinforcement Learning

In this study, we introduce a deep Q-network agent utilizing a dueling architecture to refine the valuation of actions through separate estimations of the state-value and action-value functions, adapted to facilitate concurrent multi-agent operations within a shared environment. Inspired by the self-organized, decentralized cooperation observed in natural swarms, this study uniquely integrates a centralized mechanism, or a centralized critic. This enhances performance and coherence in decision-making within the multi-agent system. This hybrid approach enables agents to execute informed and optimized decisions by considering the actions of their counterparts while maintaining an element of collective and flexible task-information sharing, thereby presenting a groundbreaking framework for cooperation and information sharing in swarm robot systems. To augment the communication capabilities, we employ low-power wide-area networks, or Long Range (LoRa), which are characterized by their low power consumption and long-range communication abilities, facilitating the sharing of task information and reducing the load on individual robots. The aim is to leverage LoRa as a communication platform to construct a cooperative algorithm that enables efficient task-information sharing among groups. This can provide innovative solutions and promote effective cooperation and communication within multi-agent systems, with significant implications for industrial and exploratory robots. In conclusion, by integrating a centralized system into the proposed model, this approach successfully enhances the performance of multi-agent systems in real-world applications, offering a balanced synergy between decentralized flexibility and centralized control.

Component separation and origin estimation of mining-influenced water based on fluoride ions and water isotopes in underground legacy mine, Central Japan

Study regionA legacy underground tungsten mine in a mountainous area, central Japan. Study focusWe analyzed mining-influenced water (MIW) from mine voids and surface water from rivers to determine the dissolved ion concentrations and water isotopes (δ18O and δ2H). The results were interpreted using principal components and cluster analyses, as well as Eh-pH (Pourbaix) diagram. By integrating the obtained analytical results with mine-related data, we conducted component separation of MIW and estimation of their origins. New hydrological insights for the regionThe MIW and part of surface water were characterized by the SO42− and F− generated via the dissolution of sulfide minerals and fluorite (CaF2) from the ore deposit. MIW components were successfully separated using these indicators and water isotopes. The results of component separation indicated that the MIW consisted of two components, namely infiltrating water that rapidly passes through upper mine voids to reach the mine void at ground level, and groundwater that undergoes some degree of residence time before flowing into the ground level void from shaft I.