Efficient Estimation Research Articles

Efficient Transport Controlled by Biharmonic Frictional Driving.

Dry friction has been proposed as a rectifying mechanism allowing mass transport over a vibrating surface, even when vibrations are horizontal and unbiased. It has been suggested that the drift velocity will always saturate when the energy of the input oscillation increases, leading to a vanishing efficiency that would hinder the applicability of this phenomenon. Contrary to this conjecture, in this Letter we experimentally demonstrate that, by carefully controlling the forcing oscillations, this system can maintain a finite transport efficiency for any input energy. A minimal friction model explains the observed dependencies of the drift velocity on the signal parameters in the case of biharmonic base oscillations, which can be extended to obtain efficiency estimates for any periodic excitation.

Artificial Intelligence Models for Efficiency Estimation of Adsorbents in Rare-Earth Element Recovery Based on Feature Engineering

Artificial Intelligence Models for Efficiency Estimation of Adsorbents in Rare-Earth Element Recovery Based on Feature Engineering

Efficient Estimation of Climate State and Its Uncertainty Using Kalman Filtering with Application to Policy Thresholds and Volcanism

Abstract We present the Energy Balance Model – Kalman Filter (EBM-KF), a hybrid model projecting and assimilating the global mean surface temperature (GMST) and ocean heat content anomaly (OHCA). It combines an annual energy balance model (difference equations) with 17 parameters drawn from the literature and a statistical Extended Kalman Filter assimilating GMST and OHCA, either observed timeseries or simulated by earth system models. Our motivation is to create an efficient and natural estimator of the climate state and its uncertainty, which we believe to be Gaussian at a global scale. We illustrate four applications: 1) EBM-KF generates a similar estimate to the 30-year time-averaged climate state 15 years sooner, or a model-simulated hindcasts’ annual ensemble average, depending on whether volcanic forcing is filtered or not. 2) EBM-KF conveniently assesses annually likelihoods of crossing a policy threshold. For example, based on temperature records up to the end of 2023, p=0.0017 that the climate state was 1.5°C over preindustrial, but there is a 16% likelihood that the GMST in 2023 itself could have been over that threshold. 3) A variant of the EBM-KF also approximates the spread of an entire climate model large ensemble using only one or a few ensemble members. 4) All variants of the EBM-KF are sufficiently fast to allow thorough sampling from non-Gaussian probabilistic futures, e.g., the impact of rare but significant volcanic eruptions. This sampling with the EBM-KF better determines how future volcanism may affect when policy thresholds will be crossed and what an ensemble with thousands of members exploring future intermittent volcanism reveals.

An Approach to Estimate the Temperature of an Induction Motor under Nonlinear Parameter Perturbations Using a Data-Driven Digital Twin Technique

To monitor temperature as a function of varying inductance and resistance, we propose a data-driven digital twin approach for the rapid and efficient real-time estimation of the rotor temperature in an induction motor. By integrating differential equations with online signal processing, the proposed data-driven digital twin approach is structured into three key stages: (1) transforming the nonlinear differential equations into discrete algebraic equations by substituting the differential operator with the difference quotient based on the sampled voltage and current; (2) deriving approximate analytical solutions for rotor resistance and stator inductance, which can be utilized to estimate the rotor temperature; and (3) developing a general procedure for obtaining approximate analytical solutions to nonlinear differential equations. The feasibility and validity of the proposed method were demonstrated by comparing the test results with a 1.5 kW AC motor. The experimental results indicate that our method achieves a minimum estimation error that falls within the standards set by IEC 60034-2-1. This work provides a valuable reference for the overheating protection of induction motors where direct temperature measurement is challenging.

Runoff Estimation in Kajurli Watershed Using SCS-CN and GIS Techniques

The study focuses on estimating surface runoff using the Soil Conservation Service Curve Number (SCS-CN) method, combined with Remote Sensing (RS) and Geographic Information System (GIS) techniques, for the Kajurli Watershed in Ratnagiri District, Maharashtra. Surface runoff is a key factor influencing agricultural productivity, soil erosion, and water management at the watershed level. The SCS-CN method, a widely used tool for runoff estimation, utilizes rainfall, land use, and soil type data. By integrating RS and GIS, the study achieves a more accurate assessment of land use and soil characteristics, which are critical inputs for runoff calculations. The Kajurli Watershed falls under hydrological soil group B, known for moderate runoff potential. The study uses 33 years of historical rainfall data (1990-2022) to compute annual runoff using the SCS-CN method. Key parameters such as Curve Numbers (CN) are derived based on land use, hydrological soil groups, and antecedent moisture conditions (AMC). The average annual rainfall in the watershed is found to be 3392.8 mm, with 39.69% of this rainfall contributing to surface runoff. The integration of RS and GIS simplifies spatial analysis, enabling accurate and efficient runoff estimation. The study highlights the sensitivity of the SCS-CN method to CN values, emphasizing the importance of precise land use and soil data for reliable hydrological modelling. These findings offer crucial insights for water resource management, flood control, and agricultural planning in the region.

Robust and Efficient Estimation of Potential Outcome Means Under Random Assignment

We study efficiency improvements in randomized experiments for estimating a vector of potential outcome means using regression adjustment (RA) when there are more than two treatment levels. We show that linear RA which estimates separate slopes for each assignment level is never worse, asymptotically, than using the subsample averages. We also show that separate RA improves over pooled RA except in the obvious case where slope parameters in the linear projections are identical across the different assignment levels. We further characterize the class of nonlinear RA methods that preserve consistency of the potential outcome means despite arbitrary misspecification of the conditional mean functions. Finally, we apply these regression adjustment techniques to efficiently estimate the lower bound mean willingness to pay for an oil spill prevention program in California.

Research of the efficiency of Kravchenko weighting functions and combinations based on them in the problem of narrow band interference rejection

The influence of weight functions (WFs) of preliminary weighting on the quality of narrowband interference rejection based on direct and inverse discrete Fourier transforms has been studied. Classical and modern WFs are considered: Kravchenko, Kravchenko–Dolph–Chebyshev, Kravchenko–Gauss, Kravchenko–Bernstein–Rogozinsky. Quantitative estimates of efficiency were obtained – the coefficients of interference suppression and signal transmission, as well as their product – the total efficiency coefficient. Graphic examples of the behavior of the total efficiency coefficient depending on the interference frequency are shown. Estimates of the indicated quality indicators are presented when a fixed number of frequency samples are removed from the spectrum. The families of dependences of the probabilities of correctly performing a search for a spread spectrum signal on the “interference/signal” ratio when using modern WFs to weigh the implementations of an additive mixture of signal, interference and noise are presented. The significant advantage of modern WFs formed through combinations with Kravchenko functions in the problem of interference rejection has been demonstrated and confirmed.

The Estimating Parameter and Number of Knots for Nonparametric Regression Methods in Modelling Time Series Data

This research aims to explore and compare several nonparametric regression techniques, including smoothing splines, natural cubic splines, B-splines, and penalized spline methods. The focus is on estimating parameters and determining the optimal number of knots to forecast cyclic and nonlinear patterns, applying these methods to simulated and real-world datasets, such as Thailand’s coal import data. Cross-validation techniques are used to control and specify the number of knots, ensuring the curve fits the data points accurately. The study applies nonparametric regression to forecast time series data with cyclic patterns and nonlinear forms in the dependent variable, treating the independent variable as sequential data. Simulated data featuring cyclical patterns resembling economic cycles and nonlinear data with complex equations to capture variable interactions are used for experimentation. These simulations include variations in standard deviations and sample sizes. The evaluation criterion for the simulated data is the minimum average mean square error (MSE), which indicates the most efficient parameter estimation. For the real data, monthly coal import data from Thailand is used to estimate the parameters of the nonparametric regression model, with the MSE as the evaluation metric. The performance of these techniques is also assessed in forecasting future values, where the mean absolute percentage error (MAPE) is calculated. Among the methods, the natural cubic spline consistently yields the lowest average mean square error across all standard deviations and sample sizes in the simulated data. While the natural cubic spline excels in parameter estimation, B-splines show strong performance in forecasting future values.

Spatiotemporal multilevel joint modeling of longitudinal and survival outcomes in end-stage kidney disease.

Individuals with end-stage kidney disease (ESKD) on dialysis experience high mortality and excessive burden of hospitalizations over time relative to comparable Medicare patient cohorts without kidney failure. A key interest in this population is to understand the time-dynamic effects of multilevel risk factors that contribute to the correlated outcomes of longitudinal hospitalization and mortality. For this we utilize multilevel data from the United States Renal Data System (USRDS), a national database that includes nearly all patients with ESKD, where repeated measurements/hospitalizations over time are nested in patients and patients are nested within (health service) regions across the contiguous U.S. We develop a novel spatiotemporal multilevel joint model (STM-JM) that accounts for the aforementioned hierarchical structure of the data while considering the spatiotemporal variations in both outcomes across regions. The proposed STM-JM includes time-varying effects of multilevel (patient- and region-level) risk factors on hospitalization trajectories and mortality and incorporates spatial correlations across the spatial regions via a multivariate conditional autoregressive correlation structure. Efficient estimation and inference are performed via a Bayesian framework, where multilevel varying coefficient functions are targeted via thin-plate splines. The finite sample performance of the proposed method is assessed through simulation studies. An application of the proposed method to the USRDS data highlights significant time-varying effects of patient- and region-level risk factors on hospitalization and mortality and identifies specific time periods on dialysis and spatial locations across the U.S. with elevated hospitalization and mortality risks.

Methanothermobacter thermautotrophicus and Alternative Methanogens: Archaea-Based Production.

Methanogenic archaea convert bacterial fermentation intermediates from the decomposition of organic material into methane. This process has relevance in the global carbon cycle and finds application in anthropogenic processes, such as wastewater treatment and anaerobic digestion. Furthermore, methanogenic archaea that utilize hydrogen and carbon dioxide as substrates are being employed as biocatalysts for the biomethanation step of power-to-gas technology. This technology converts hydrogen from water electrolysis and carbon dioxide into renewable natural gas (i.e., methane). The application of methanogenic archaea in bioproduction beyond methane has been demonstrated in only a few instances and is limited to mesophilic species for which genetic engineering tools are available. In this chapter, we discuss recent developments for those existing genetically tractable systems and the inclusion of novel genetic tools for thermophilic methanogenic species. We then give an overview of recombinant bioproduction with mesophilic methanogenic archaea and thermophilic non-methanogenic microbes. This is the basis for discussing putative products with thermophilic methanogenic archaea, specifically the species Methanothermobacter thermautotrophicus. We give estimates of potential conversion efficiencies for those putative products based on a genome-scale metabolic model for M. thermautotrophicus.

Parameter estimation for second-order SPDEs in multiple space dimensions

AbstractWe analyse a second-order SPDE model in multiple space dimensions and develop estimators for the parameters of this model based on discrete observations of a solution in time and space on a bounded domain. While parameter estimation for one and two spatial dimensions was established in recent literature, this is the first work which generalizes the theory to a general, multi-dimensional framework. Our approach builds upon realized volatilities, enabling the construction of an oracle estimator for volatility within the underlying model. Furthermore, we show that the realized volatilities have an asymptotic illustration as response of a log-linear model with spatial explanatory variable. This yields novel and efficient estimators based on realized volatilities with optimal rates of convergence and minimal variances. For proving central limit theorems, we use a high-frequency observation scheme. To showcase our results, we conduct a Monte Carlo simulation.

Robust Regression with Covariate Filtering: Heavy Tails and Adversarial Contamination

We study the problem of linear regression where both covariates and responses are potentially (i) heavy-tailed and (ii) adversarially contaminated. Several computationally efficient estimators have been proposed for the simpler setting where the covariates are sub-Gaussian and uncontaminated; however, these estimators may fail when the covariates are either heavy-tailed or contain outliers. In this work, we show how to modify the Huber regression, least trimmed squares, and least absolute deviation estimators to obtain estimators which are simultaneously computationally and statistically efficient in the stronger contamination model. Our approach is quite simple, and consists of applying a filtering algorithm to the covariates, and then applying the classical robust regression estimators to the remaining data. We show that the Huber regression estimator achieves near-optimal error rates in this setting, whereas the least trimmed squares and least absolute deviation estimators can be made to achieve near-optimal error after applying a postprocessing step. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

Leveraging information from secondary endpoints to enhance dynamic borrowing across subpopulations.

Randomized trials seek efficient treatment effect estimation within target populations, yet scientific interest often also centers on subpopulations. Although there are typically too few subjects within each subpopulation to efficiently estimate these subpopulation treatment effects, one can gain precision by borrowing strength across subpopulations, as is the case in a basket trial. While dynamic borrowing has been proposed as an efficient approach to estimating subpopulation treatment effects on primary endpoints, additional efficiency could be gained by leveraging the information found in secondary endpoints. We propose a multisource exchangeability model (MEM) that incorporates secondary endpoints to more efficiently assess subpopulation exchangeability. Across simulation studies, our proposed model almost uniformly reduces the mean squared error when compared to the standard MEM that only considers data from the primary endpoint by gaining efficiency when subpopulations respond similarly to the treatment and reducing the magnitude of bias when the subpopulations are heterogeneous. We illustrate our model's feasibility using data from a recently completed trial of very low nicotine content cigarettes to estimate the effect on abstinence from smoking within three priority subpopulations. Our proposed model led to increases in the effective sample size two to four times greater than under the standard MEM.

Non-negative least-squares variance and covariance component estimation using the positive-valued function for errors-in-variables models

Abstract Although (co)variance component estimation has been widely applied in the errors-in-variables (EIV) model, the occurrence of negative variance components is still a major issue in the estimated variance components. This problem may be due to the following unfavorable factors: 1) unreasonable selection of initial variance values; 2) low redundancy in the EIV functional model; 3) improper design in the EIV stochastic model, and 4) other data quality problems. Many attempts have been made to prevent the appearance of negative variance components. In this contribution, a novel and efficient non-negative least-squares variance component estimation using the PVF (PVF-NLS-VCE) is introduced, which can simultaneously estimate the unknown (co)variance components in the EIV stochastic model and the parameters in the EIV functional model. Its principle is to implicitly impose a non-negative constraint by replacing the variance component with the positive-valued function (PVF) whose range is the set of positive real numbers. Two numerical examples using real and simulated data are presented. The numerical results of linear regression are identical to those obtained based on least-squares variance component estimation (LS-VCE) with positive initial values of variance components. The numerical results of two-dimensional affine transformation are shown to prevent negative variance components and precede those obtained by LS-VCE with a negative initial value of variance component. Both numerical examples verify the effectiveness of the PVF-NLS-VCE method whether the initial values of variance components are positive or negative. The proposed PVF-NLS-VCE is a simple, convenient and flexible method to achieve the non-negative estimates of variance components, which can reduce sensitivity to initial value selection and automatically guarantee a non-negative definite covariance matrix.

Reconciling water-use efficiency estimates from carbon isotope discrimination of leaf biomass and tree rings: nonphotosynthetic fractionation matters.

Carbon isotope discrimination (∆) in leaf biomass (∆BL) and tree rings (∆TR) provides important proxies for plant responses to climate change, specifically in terms of intrinsic water-use efficiency (iWUE). However, the nonphotosynthetic 12C/13C fractionation in plant tissues has rarely been quantified and its influence on iWUE estimation remains uncertain. We derived a comprehensive, ∆ based iWUE model (iWUEcom) which includes nonphotosynthetic fractionations (d) and characterized tissue-specific d-values based on global compilations of data of ∆BL, ∆TR and real-time ∆ in leaf photosynthesis (∆online). iWUEcom was further validated with independent datasets. ∆BL was larger than ∆online by 2.53‰, while ∆BL and ∆TR showed a mean offset of 2.76‰, indicating that ∆TR is quantitatively very similar to ∆online. Applying the tissue-specific d-values (dBL = 2.5‰, dTR = 0‰), iWUE estimated from ∆BL aligned well with those estimated from ∆TR or gas exchange. ∆BL and ∆TR showed a consistent iWUE trend with an average CO2 sensitivity of 0.15 ppm ppm-1 during 1975-2015. Accounting for nonphotosynthetic fractionations improves the estimation of iWUE based on isotope records in leaf biomass and tree rings, which is ultimate for inferring changes in carbon and water cycles under historical and future climate.

Quantitative analysis of failure performance uncertainty based on multiscale mechanical analysis of PEMFCs’ C/C composite bipolar plates

In order to quantify the failure performance of PEMFCs’ C/C composite bipolar plates (BPs) under considering the uncertainty in multiscale sizes and loads, the failure probability function (FPF) of BPs at cold start is estimated. A three-stage multiscale simulation method is proposed for mechanical behavior analysis of multi-component heterogeneous composites. And the efficient estimation of FPF for C/C composite BPs is achieved via the proposed multiscale simulation method and decoupling sparse integration method, in which the unified density weight is constructed to decouple the double-loop framework of analyzing FPF, thereby reducing the computational complexity. Results indicate that the dangerous positions of BPs are around the gas flow channels and bolt holes. And the dangerous components are the carbon matrix and the interface between the matrix and fiber. The radius of elliptical fibers affects the distribution of failure factors on microscopic components, thereby affecting the variation of failure probability with fiber sizes significantly. The distribution parameter of composite layer thickness in BPs has a significant effect on structural safety, and the FPF result can be used to effectively guide the design of structure. Therefore, the proposed method is of great significance for guiding the rapid parameter design of composite BPs.

Turkey’s Hydropower Potential in the Near Future and the Possible Impacts of Climate Change—A Case Study of the Euphrates–Tigris Basin

Hydropower is becoming an important renewable energy source in Turkey, but the ever-changing atmospheric and climatic conditions of Turkey make it very difficult to be projected efficiently. Thus, an efficient estimation technique is crucial for it to be adopted as a reliable energy source in the future. This study evaluates Turkey’s hydropower potential in the Euphrates–Tigris Basin under changing climatic conditions. We adapted an empirical equation to model reservoir outflows, considering the site-specific characteristics of 14 major dams. Initial results from employing a model with a constant empirical coefficient, α, yielded moderate predictive accuracy, with R2 values ranging from 0.289 to 0.612. A polynomial regression identified optimal α values tailored to each dam’s surface area, significantly improving model performance. The adjusted α reduced predictive bias and increased R2 values, enhancing forecast reliability. Seasonal analysis revealed distinct hydropower trends: Ataturk Dam showed a notable decrease of 5.5% in hydropower generation up to 2050, while Birecik and Keban Dams exhibited increases of 2.5% and 2.2%, respectively. By putting these discoveries into practice, water resource management may become more robust and sustainable, which is essential for meeting Turkey’s rising energy needs and preparing for future climatic challenges. This study contributes valuable insights for optimizing reservoir operations, ensuring long-term hydropower sustainability, and enhancing the resilience of water resource management systems globally.

A novel embedded spherical semiconductor neutron detector efficiency estimation using GEANT4 simulation methodology

The present research work explores on the estimation of thermal neutron detection efficiency for the novel embedded spherical configuration design using GEANT4 toolkit. Initially, single and multiple layer of embedded spherical configurations were designed in geometry category class of GEANT4 toolkit using Boron (10B) converter material followed by, estimation of simulated thermal neutron detection efficiency at different radii which varies between 0.05 μm to 5 μm for both single and multiple layer embedded spherical detector configuration. The maximum thermal neutron detection efficiency obtained for the single layer embedded spherical detector configuration is ∼ 5.1% at an optimum (critical) radius of ∼ 4.55 μm. It was noticed that with the rise in the number of layers of embedded spherical detector configuration the thermal neutron detection efficiency was also increasing. Furthermore, investigation of the effect of varying level of Low Level Discriminator and different enrichment level of 10B on the efficiency was also studied. Finally, the histoplot was investigated for a typical 10 layers of embedded spherical detector configuration at typical lower (200 nm) and higher (3 μm) radius.

Using query semantic and feature transfer fusion to enhance cardinality estimating of property graph queries

With the increasing complexity and diversity of query tasks, cardinality estimation has become one of the most challenging problems in query optimization. In this study, we propose an efficient and accurate cardinality estimation method to address the cardinality estimation problem in property graph queries, particularly in response to the current research gap regarding the neglect of contextual semantic features. We first propose formal representations of the property graph query and define its cardinality estimation problem. Then, through the query featurization, we transform the query into a vector representation that can be learned by the estimation model, and enrich the feature vector representation by the context semantic information of the query. We finally propose an estimation model for property graph queries, specifically introducing a feature information transfer module to dynamically control the information flow meanwhile achieving the model’s feature fusion and inference. Experimental results on three datasets show that the estimation model can accurately and efficiently estimate the cardinality of property graph queries, the mean Q_error and RMSE are reduced by about 30% and 25% than the state-of-art estimation models. The context semantics features of queries can improve the model’s estimation accuracy, the mean Q_error result is reduced by about 20% and the RMSE result is about 5%.

Contributions of tactile information to the sense of agency and its metacognitive representations.

Despite the ubiquitous presence of tactile information in our daily activities, studies of how we experience agency of our actions have rarely relied on manipulated visuo-tactile feedback. Instead, what is often manipulated are the distal (and arbitrarily associated) consequences of our actions. The few studies that did investigate whether tactile information contributes to the experience of agency have been limited to the binary assessment of tactile feedback about the outcome of an action being present or absent. Here, we went beyond the coarse comparison of agency with versus without tactile feedback and introduced instead an experimental manipulation where we could control the amount of mismatch between predictions and observations. Participants (N = 40) reached with their right hand toward a ridged plate with a specific orientation and saw online feedback that could match or differ from their action in one of three ways: the physical plate's orientation, the action's timing, or the hand's position in space. Absolute subjective ratings revealed that an increased mismatch in tactile information led to a diminished sense of agency, similar to what has been reported for spatial and temporal mismatches. Further, estimations of metacognitive efficiency revealed similar Mratios in identifying visuo-tactile violation predictions as compared to visuo-temporal violations (but lower than visuospatial). These findings emphasize the importance of tactile information in shaping our experience of acting voluntarily and show how this important component can be experimentally probed. (PsycInfo Database Record (c) 2024 APA, all rights reserved).