Parameter Estimation Research Articles

ASASSN-21js: A multi-year transit of a ringed disc

The early-type star ASASSN-21js started to fade in 2021, as was detected by the All Sky Automated Survey for Supernovae, undergoing a multi-year eclipse that is still underway. We interpret this event as being due to a structured disc of material transiting in front of the star. The disc is in orbit around a substellar object with the mass and luminosity of a brown dwarf or smaller. We want to determine the expected duration and ending date of the eclipse. We modelled a tilted and inclined azimuthally symmetric ring system around an unseen companion and calculated the resulting time-varying light curve as the object transited in front of the star. We made an initial estimate of the ring parameters and used these as inputs to an MCMC algorithm to determine the geometric properties of the rings with associated uncertainties. The model most consistent with the light curve to date is a two-ring system at high inclination with respect to the line of sight that has a semi-major axis of 71.6 stellar radii. With an estimate of the stellar radius, the transverse velocity is around 0.7 which if bound to the star is an orbit with a semi-major axis of around 13000 au, placing it in the Oort cloud of the parent star. The transit is ongoing and will finish around MJD 61526 (May 1 2027). We encourage the community to continue observing this object in order to understand its properties.

Fractional Negative Order Moment Parameter Estimator of Compound-Gaussian Clutter with Lognormal Texture

Fractional Negative Order Moment Parameter Estimator of Compound-Gaussian Clutter with Lognormal Texture

Mapping the Milky Way in 5D with 170 Million Stars

We present Augustus, a catalog of distance, extinction, and stellar parameter estimates for 170 million stars from 14 mag < r < 20 mag and with ∣b∣ > 10° drawing on a combination of optical to near-infrared photometry from Pan-STARRS, 2MASS, UKIDSS, and unWISE along with parallax measurements from Gaia DR2 and 3D dust extinction maps. After applying quality cuts, we find 125 million objects have “high-quality” posteriors with statistical distance uncertainties of ≲10% for objects with well-constrained stellar types. This is a substantial improvement over the distance estimates derived from Gaia parallaxes alone and in line with the recent results from Anders et al. We find the fits are able to reproduce the dereddened Gaia color–magnitude diagram accurately, which serves as a useful consistency check of our results. We show that we are able to detect large, kinematically coherent substructures in our data clearly relative to the input priors, including the Monoceros Ring and the Sagittarius Stream, attesting to the quality of the catalog. Our results are publicly available at doi:10.7910/DVN/WYMSXV. An accompanying interactive visualization can be found at http://allsky.s3-website.us-east-2.amazonaws.com.

Electrification of Agricultural Machinery: One Design Case of a 4 kW Air Compressor

In response to the global pursuit of net-zero carbon emissions, the electrification of agricultural machinery is becoming a significant research and development trend. This study introduces the overall design of a 4 kW air compressor aimed at achieving a green vision for agricultural machinery. The design focuses on providing continuous and stable power and air output using a lithium-ion battery. Durability and cost-effectiveness are prioritized, with a particular emphasis on the Arduino system for integrating battery and motor systems to withstand harsh conditions and ensure ease of maintenance. A permanent magnet brushless motor was selected as the power source, paired with an optimized pulley to supply the proper torque to the air compressor. The system employs an Arduino-based feedback control sensor for air pressure regulation, ensuring energy efficiency. The primary energy source is a 48 V lithium iron phosphate battery, known for its high energy density and safety. The battery design focuses on system integration, addressing specific environmental discharge requirements. The embedded battery management system provides thermal and lifecycle parameter estimation, guaranteeing long-duration power supply and safe operation under various conditions. Unlike traditional fuel-driven systems, lithium iron phosphate batteries do not emit harmful gases, aligning with environmental standards. System integration testing demonstrated that the air pressure feedback control effectively meets the energy-saving requirements by digitally reducing power output as air accumulates in the chamber. Bench testing confirmed that the system performs as designed, achieving the desired results and advancing the goal of sustainable agricultural machinery.

Spectral feature informed variational model and its applications to machinery fault diagnosis

Variational mode extraction (VME), a novel signal decomposition method based on a frequency-domain filter in essence, has recently become a potential tool in fault diagnosis. However, the original VME algorithm is not provided with full self-adaptation, and its performance in the extraction of fault features is subject to predefining the initial parameters, including initial center frequency (ICF) and balance parameter. To address these issues, a spectral feature informed variational model (SFIVM) algorithm is constructed to overcome the defects of parameters setting and efficiently realize the fault diagnosis without prior knowledge. Specifically, a spectral feature detector inspired by the convergence property of ICF is first developed to reveal the spectral features, including the detected center frequencies and boundary frequencies. Then, a balance parameter estimation formula is designed to adaptively determine the target balance parameter by taking advantage of the above spectral features. Finally, a highly efficient decomposition model is proposed to extract the fault-related mode from the vibration signal, where iterative optimization is unnecessary. The effectiveness of the proposed SFIVM method is verified by one simulated and two experimental cases. Moreover, its superiority and high efficiency are demonstrated by comparing it with some advanced and classical fault diagnosis methods.

Bayesian expectation maximization-maximization for robust estimation in proton exchange membrane fuel cells: A comparative study

In this paper, we present a novel approach based on Bayesian Expectation Maximization-Maximization (BEMM) to address this challenge. Unlike traditional optimization methods, which may struggle with high-dimensional and nonlinear optimization problems, BEMM offers a robust framework that combines the benefits of Bayesian inference with the flexibility of expectation maximization and maximization techniques. By iteratively updating parameter estimates based on observed data and maximizing the likelihood of the model, BEMM effectively navigates the solution space to converge on accurate estimates of the unspecified variables in Proton Exchange Membrane Fuel Cells (PEMFCs)models. Through extensive experimentation and comparison with other metaheuristic techniques, including Arithmetic Optimization Algorithm (AOA), Gravitational Search Algorithm (GSA), Flower Pollination Algorithm (FPA), and Biogeography-Based Optimization (BBO), we demonstrate the superior performance of our BEMM approach. Our results show that BEMM outperforms these alternative methods in terms of precision, convergence speed, and stability across various scenarios involving different numbers of unspecified variables. The implications of our findings are significant for both researchers and practitioners in the field of PEMFC modeling and optimization. By providing a more efficient and reliable method for estimating model parameters, our approach can facilitate more accurate predictions of PEMFC performance, leading to better-informed decision-making in the design, operation, and optimization of PEMFC systems. Furthermore, the robustness and versatility of BEMM make it well-suited for a wide range of optimization problems beyond PEMFC modeling, highlighting its potential impact across various domains of engineering and scientific research. In the sensitivity analysis, as the population size increases from 10 to 40, there is a significant improvement in solution quality by approximately 100 %. However, beyond a population size of 40, the marginal gains diminish, with only marginal improvements of less than 1 % observed despite further increases in population size up to 200.

Parameter estimation from the Lyα forest in the Fourier space using an information-maximizing neural network

Our aim is to present a robust parameter estimation with simulated forest spectra from Sherwood-Relics simulations suite by using an information-maximizing neural network (IMNN) to extract maximal information from 1D-transmitted flux in the Fourier space. We performed 1D estimations using IMNN for intergalactic medium (IGM) thermal parameters $T_0$ and gamma at $z=2-4$, and cosmological parameters $ and s $ at $z=3-4$. We compared our results with estimates from the power spectrum using the posterior distribution from a Markov Chain Monte Carlo (MCMC). We then checked the robustness of IMNN estimates against deviation in spectral noise levels, continuum uncertainties, and instrumental smoothing effects. Using mock forest sightlines from the publicly available CAMELS project, we also checked the robustness of the trained IMNN on a different simulation. As a proof of concept, we demonstrated a 2D-parameter estimation for $T_0$ and photoionization rates, $ HI We obtain improved estimates of $T_0$ and gamma using IMNN over the standard MCMC approach. These estimates are also more robust against signal-to-noise deviations at $z=2$ and 3. At $z=4$, the sensitivity to noise deviations is on par with MCMC estimates. The IMNN also provides $T_0$ and gamma estimates that are robust against continuum uncertainties by extracting small-scale continuum-independent information from the Fourier domain. In the cases of $ and s $, the IMNN performs on par with MCMC but still offers a significant speed boost in estimating parameters from a new dataset. The improved estimates with IMNN are seen for high instrumental resolution (FWHM=6 At medium or low resolutions, the IMNN performs similarly to MCMC, suggesting an improved extraction of small-scale information with IMNN. We also find that IMNN estimates are robust against the choice of simulation. By performing a 2D-parameter estimation for $T_0$ and $ HI $, we also demonstrate how to take forward this approach observationally in the future.

Groundwater level fluctuation caused by tide and groundwater pumping in coastal multi-layer aquifer system

This paper considered the groundwater head fluctuation induced by tide and pumping in the coastal multi-layered aquifer system. The multi-layered aquifer system comprises an unconfined aquifer, an upper confined aquifer, and a lower confined aquifer. An aquiclude exists between each two aquifers. All the layers terminate at the coastline. The new analytical solutions describing groundwater head variation in the coastal multi-confined aquifer system are derived. Superposition principle and image methods are used for the derivation of the analytical solutions. Analytical solutions of different situations of without considering pumping, of without considering tidal effect, and of N-layered confined aquifers are also derived. The impacts of the parameters of the initial phase shift of tide, pumping rate, position of the pumping well, storage coefficient, and transmissivity on the groundwater head fluctuation are discussed. The analytical solutions are applied with application examples in fitting field observations and parameter estimations. The estimated values of the hydraulic conductivities in the upper and lower confined aquifers are within the range of the values obtained from the field experiments. The fitted results of the analytical solutions capture the main characteristics of groundwater head fluctuation affected by the tide and groundwater pumping. The study of groundwater head fluctuation in the coastal zone is helpful to understand the mechanism of seawater intrusion under the influence of tide and groundwater pumping.

Topology Testing and Demographic Modeling Illuminate a Novel Speciation Pathway in the Greater Caribbean Sea Following the Formation of the Isthmus of Panama.

Recent genomic analyses have highlighted the prevalence of speciation with gene flow in many taxa and have underscored the importance of accounting for these reticulate evolutionary processes when constructing species trees and generating parameter estimates. This is especially important for deepening our understanding of speciation in the sea where fast moving ocean currents, expanses of deep water, and periodic episodes of sea level rise and fall act as soft and temporary allopatric barriers that facilitate both divergence and secondary contact. Under these conditions, gene flow is not expected to cease completely while contemporary distributions are expected to differ from historical ones. Here we conduct range-wide sampling for Pederson's cleaner shrimp (Ancylomenes pedersoni), a species complex from the Greater Caribbean that contains three clearly delimited mitochondrial lineages with both allopatric and sympatric distributions. Using mtDNA barcodes and a genomic ddRADseq approach, we combine classic phylogenetic analyses with extensive topology testing and demographic modeling (10 site frequency replicates x 45 evolutionary models x 50 model simulations/replicate = 22,500 simulations) to test species boundaries and reconstruct the evolutionary history of what was expected to be a simple case study. Instead, our results indicate a history of allopatric divergence, secondary contact, introgression, and endemic hybrid speciation that we hypothesize was driven by the final closure of the Isthmus of Panama and the strengthening of the Gulf Stream Current ~3.5 million years ago. The history of this species complex recovered by model-based methods that allow reticulation differs from that recovered by standard phylogenetic analyses and is unexpected given contemporary distributions. The geologically and biologically meaningful insights gained by our model selection analyses illuminate what is likely a novel pathway of species formation not previously documented that resulted from one of the most biogeographically significant events in Earth's history.

Irradiance separation model parameter estimation from historical cloud cover statistical properties

Irradiance separation models allow the decomposition of Global Horizontal Irradiance into Diffuse Horizontal and Direct Normal Irradiances. These models need fitting to the irradiance characteristics of the location of interest using locally measured ground data. For locations that only measure Global Horizontal Irradiance, current state of the art establishes the use of parameters obtained for another location that measures the three components, with similar climate characteristics. Nevertheless, this results in a lack of localized character for estimates and requires fitting model parameters for all possible climates, which can be infeasible given data availability. This work presents a novel approach based on the hypothesis that the separation model's parameters are a function of the statistical properties of satellite-derived cloud cover estimates. The proposed methodology was evaluated in 23 sites covering all main Köppen-Geiger climatic types and different cloud coverage properties using the Boland-Ridley-Lauret diffuse fraction model. The model performs similarly as locally adjusting the model, with Root Mean Square Errors of 0.087–0.15 diffuse fraction units versus 0.077–0.127 for locally optimized parameters, and offers adequate performance across climates and cloud characteristics. These results encourage future research by generalizing parameter estimation for other diffuse fraction models. The main applications for this research are the estimation of irradiance components where no local data is available for model fitting and the enhancement or complementarity of satellite estimates of surface irradiance. Furthermore, it allows the estimation of missing irradiance components due to equipment failure in locations with insufficient data for a representative, locally adapted model.

CML based parameter estimation of FH signals in alpha stable noise environment

CML based parameter estimation of FH signals in alpha stable noise environment

Integrating a dimensional perturbation module into exponential distribution optimizer for solving optimization problems

Exponential distribution optimizer (EDO) is a recently proposed optimization technique that is based on the exponential probability distribution model. As most swarm intelligence algorithms (SIAs), EDO is very good at handling common optimization problems. However, when addressing multimodal and centre-bias problems, it exhibits limitations like imbalanced global and local search capabilities and poor solution accuracy. Building upon this, we introduce an improved version of exponential distribution optimizer that incorporates a dimensional perturbation module (DPM) called EDO_DPM. By testing the impact of dimensional perturbation, the K-dimensional evolution strategy is adopted in our approach. Concurrently, to modulate the usage frequency of DPM, an adjustment parameter, named evolutionary state factor (ESF), is designed contingent on the population’s evolutionary state. Moreover, to alleviate the deficiency of non full-dimensional perturbation, a bounce out operation is embedded in the algorithm. The efficacy of EDO_DPM has been substantiated through testing on 20 different type of functions and CEC2017 benchmark suite. Comparative analyses with the state-of-the-art algorithms have demonstrated a marked enhancement in EDO_DPM’s capability to manage multimodal problems and its proficiency in resolving centre-bias problems. Meanwhile, it can achieve excellent results on CEC2017 by comparing excellent improved variants. Furthermore, EDO_DPM is applied to the problem of parameter estimation for photovoltaic (PV) generation systems. Comparison results show that EDO_DPM has good application capability.

ASReml-based estimation of early genetic parameters in hybrid mutton sheep populations

The aim of this study was to analyze the effects of non-genetic factors on the estimation of genetic parameters of early growth traits in hybrid mutton sheep using ASReml software, in order to provide theoretical basis for screening the optimal hybriding combinations and accelerating the breeding process of new breeds of specialized housed-feeding mutton sheep. We selected the wellgrown hybrid Southhu (Southdown × Hu sheep) and Dorhu (Dorset × Hu sheep) sheep as the research objects, constructed weight correction formulae for SH and DH sheep at 60 and 180 days; and used ASReml software to investigate the effects of non-genetic factors on the estimation of genetic parameters of early growth traits in hybrid sheep. The results showed that the birth month and birth type were found significant for all traits (p < 0.001); the heritability of maternal effects ranged from 0.0709 to 0.1859. It was found that both SH and DH sheep emerged the potential for rapid early growth and development, early growth traits are significantly affected by maternal genetic effects, thereby the maternal effect should be taken into consideration for the purpose of improving accuracy in parameter estimations and therefore increasing the success of breeding programs.

Density matters: How population dynamics of house mice (Mus musculus) inform the epidemiology of Leptospira

Abstract Rodents are maintenance hosts of numerous pathogens, and both their density and the pathogen prevalence determine the risk they pose to other animals or humans. However, density is often overlooked. We investigated a capture‐mark‐recapture‐sampling strategy to study introduced mice (Mus musculus) and Leptospira as a model and demonstrate the advantages of a combined approach. We estimated population density and Leptospira prevalence in mice in a replicated longitudinal survey conducted between 2016 and 2018. Capture‐mark‐recapture sessions were undertaken at two sites in Spring and Autumn and blood and kidney samples were collected at the end of each session. Mouse density and areas of activity were estimated using spatially explicit capture–recapture (SECR) models and both were compared between Leptospira positive and negative mice. Leptospira exposure and shedding status were estimated using Microscopic Agglutination Test, and a combination of culture and lipL32 PCR on kidneys. Leptospira prevalence was higher in spring (83%–86%) than in autumn (31%–37%) and mouse densities simultaneously varied from 3.6 to 55.9/ha. However, despite these variations in prevalence and density, the density of infected animals remained relatively constant over time (3–8/ha). Shedding or being seropositive was also associated with the activity of mice. Shedding or seropositive mice had a larger activity area, and seropositive mice were trapped on average 1 day earlier than seronegative mice. Synthesis and applications: Our results show how understanding the population dynamics of pathogen‐carrying rodents is critical in epidemiology. The wider movement patterns and easier encounters of positive mice highlight the possibility of biases in classical prevalence surveys and have implications for disease transmission within and between species. Importantly, and quite counter‐intuitively, Leptospira prevalence was negatively associated with mouse density, resulting in a constant density of shedders that contradicts the conventional view of higher exposure risk at high rodent density. More broadly, such sampling designs can improve animal and disease control policies and better inform modelling studies by providing more parameter estimates than classical prevalence surveys.

Tidal heating as a discriminator for horizons in equatorial eccentric extreme mass ratio inspirals

Tidal heating in a binary black hole system is driven by the absorption of energy and angular momentum by the black hole’s horizon. Previous works have shown that this phenomenon becomes particularly significant during the late stages of an extreme mass ratio inspiral (EMRI) into a rapidly spinning massive black hole, a key focus for future low-frequency gravitational-wave observations by (for instance) the Laser Interferometer Space Antenna mission. Past analyses have largely focused on quasicircular inspiral geometry, with some of the most detailed studies looking at equatorial cases. Though useful for illustrating the physical principles, this limit is not very realistic astrophysically, since the population of EMRI events is expected to arise from compact objects scattered onto relativistic orbits in galactic centers through many-body events. In this work, we extend those results by studying the importance of tidal heating in equatorial EMRIs with generic eccentricities. Our results suggest that accurate modeling of tidal heating is crucial to prevent significant dephasing and systematic errors in EMRI parameter estimation. We examine a phenomenological model for EMRIs around exotic compact objects by parametrizing deviations from the black hole (BH) picture in terms of the fraction of radiation absorbed compared to the BH case. Based on a mismatch calculation, we find that reflectivities as small as |R|2∼O(10−5) are distinguishable from the BH case, irrespective of the value of the eccentricity. We stress, however, that this finding should be corroborated by future parameter estimation studies. Published by the American Physical Society 2024

Facility Location Problem: Modeling Joint Disruptions Using Subordination

We study the facility location problem with disruptions where the objective is to choose a set of locations that minimizes the sum of expected servicing and setup costs. Disruptions can affect multiple locations simultaneously and are caused by multiple factors like geography, supply chain characteristics, politics, and ownership. Accounting for the various factors when modeling disruptions is challenging due to a large number of required parameters, the lack of calibration methodologies, the sparsity of disruption data, and the number of scenarios to be considered in the optimization. Because of these reasons, existing models neglect dependence or prespecify the dependence structures. Using partially subordinated Markov chains, we present a comprehensive approach that starts from disruption data, models dependencies, calibrates the disruption model, and optimizes location choices. We construct a metric and a calibration algorithm that learns from the data the strength of dependence, the number of necessary factors (subordinators), and the locations each subordinator affects. We prove that our calibration approach yields consistent estimates of the model parameters. Then, we introduce a variant of the standard approach to the underlying optimization problem, which leverages partially subordinated Markov chains to solve it quickly and precisely. Finally, we demonstrate the efficacy of our approach using twelve different disruption data sets. Our calibrated parameters are robust, and our optimization algorithm performs better than the simulation-based algorithm. The solutions from our model for disruptions have lower costs than those from other disruption models. Our approach allows for better modeling of disruptions from historical data and can be adapted to other problems in logistics, like the hub location, capacitated facility location, and so on., with joint disruptions. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.0103 .

Techno-economic assessment of supercritical, cold liquid, and dissolved CO2 injection into sub-seafloor basalt

Injecting CO2 into subsea basalt can provide permanent storage via multiple trapping mechanisms, including mineralization reactions which convert the CO2 into solid carbonates over time. Injecting CO2 together with water can accelerate the process of mineralization, but presents additional challenges, such as high energy and water requirements. A techno-economic model of CO2 transport and injection into ocean basalt was developed to compare injection strategies using pure supercritical CO2, pure liquid CO2, and CO2 dissolved in seawater. The model was applied to a representative injection site off the coast of British Columbia, Canada. Injection of CO2 dissolved into seawater was found to be more energy and cost intensive than injection of supercritical or liquid CO2; this is primarily due to the reduced quantities of CO2 that can be injected into each well, and additional pumping energy required for the accompanying seawater. For the base assumptions, transport and storage costs for supercritical, liquid, and dissolved injection were estimated as $43/t, $38/t, and $250/t respectively. Their energy requirements were estimated as 93 kWh/t, 90 kWh/t, and 213 kWh/t respectively. The current best estimates of geological parameters for ocean basalt suggest good injectivity and very large storage capacities per well. This may help to compensate for the additional project expenses incurred by deep water, allowing cost-effective liquid and supercritical injection. However, this result is sensitive to high uncertainties in both geological parameters and component cost data.

On non parametric kernel estimation of the mode of the regression function in the strong mixing random design model with censored data

. This study delves into the conditional mode estimation of a randomly censored scalar response variable operating within the framework of strong mixing conditions. We introduce a kernel-based estimator for the conditional mode function. The principal contribution of this investigation lies in the derivation of the asymptotic distribution and the strong rate of convergence of the newly proposed estimators. These findings are established under a set of fairly comprehensive structural assumptions governing the underlying models. Additionally, we conduct a series of simulation studies to showcase the finite sample performance characteristics of the proposed estimator.

Using instruments for selection to adjust for selection bias in Mendelian randomization.

Selection bias is a common concern in epidemiologic studies. In the literature, selection bias is often viewed as a missing data problem. Popular approaches to adjust for bias due to missing data, such as inverse probability weighting, rely on the assumption that data are missing at random and can yield biased results if this assumption is violated. In observational studies with outcome data missing not at random, Heckman's sample selection model can be used to adjust for bias due to missing data. In this paper, we review Heckman's method and a similar approach proposed by Tchetgen Tchetgen and Wirth (2017). We then discuss how to apply these methods to Mendelian randomization analyses using individual-level data, with missing data for either the exposure or outcome or both. We explore whether genetic variants associated with participation can be used as instruments for selection. We then describe how to obtain missingness-adjusted Wald ratio, two-stage least squares and inverse variance weighted estimates. The two methods are evaluated and compared in simulations, with results suggesting that they can both mitigate selection bias but may yield parameter estimates with large standard errors in some settings. In an illustrative real-data application, we investigate the effects of body mass index on smoking using data from the Avon Longitudinal Study of Parents and Children.

Speeding up squeezing with a periodically driven Dicke model

We present a simple and effective method to create highly entangled spin states on a faster timescale than that of the commonly employed one-axis twisting (OAT) model. We demonstrate that by periodically driving the Dicke Hamiltonian at a resonance frequency, the system effectively becomes a two-axis countertwisting Hamiltonian, which is known to quickly create Heisenberg limit scaled entangled states. For these states we show that simple quadrature measurements can saturate the ultimate precision limit for parameter estimation determined by the quantum Cramér-Rao bound. An example experimental realization of the periodically driven scheme is discussed with the potential to quickly generate momentum entanglement in a recently described experimental vertical cavity system. We analyze effects of collective dissipation in this vertical cavity system and find that our squeezing protocol can be more robust than the previous realization of OAT. Published by the American Physical Society 2024