Simple Estimation Research Articles

A roadmap for generating annual bycatch estimates from sparse at-sea observer data

Abstract To support ecosystem-based fisheries management, monitoring data from at-sea observer (ASO) programs should be leveraged to understand the impact of fisheries on discarded species (bycatch). Available techniques to estimate fishery-scale quantities from observations range from simple mean estimators to more complex spatiotemporal models, each making assumptions with differing degrees of support. However, the resulting implementation and analytical trade-offs are rarely discussed when applying these techniques in practice. Using blue shark (Prionace glauca) bycatch in the Canadian pelagic longline fishery as a case study, we evaluated the performance of seven contrasting approaches to estimating total annual discard amounts and assessed their trade-offs in application. Results demonstrated that simple approaches such as mean estimator and nearest neighbors are feasible to implement and can be as efficient for prediction as complex models such as random forest and mixed-effects models. The traditionally used catch-ratio estimator consistently underperformed among all tested models, likely due to misspecified correlative relationships between target and bycatch species. Overall, efforts in model-based approaches were rewarded with very small gains in predictive ability, suggesting that such models relying on environmental, biological, spatial, and/or temporal patterns to improve prediction of bycatch may lack sufficient foundation in data-limited contexts.

A search for mode coupling in magnetic bright points

Magnetic bright points (MBPs) are one of the smallest manifestations of the magnetic field in the solar atmosphere and are observed to extend from the photosphere up to the chromosphere. As such, they represent an excellent feature to use in searches for types of magnetohydrodynamic (MHD) waves and mode coupling in the solar atmosphere. In this work, we aim to study wave propagation in the lower solar atmosphere by comparing intensity oscillations in the photosphere with the chromosphere via a search for possible mode coupling, in order to establish the importance of these types of waves in the solar atmosphere, and their contribution to heating the chromosphere. These observations were conducted in July 2011 with the Rapid Oscillations of the Solar Atmosphere (ROSA) and the Hydrogen-Alpha Rapid Dynamics Camera (HARDCam) instruments at the Dunn Solar Telescope. Observations with good seeing were made in the G-band and Halpha wave bands. Speckle reconstruction and several post facto techniques were applied to return science-ready images. The spatial sampling of the images was 0.069arcsec /pixel (50 km/pixel). We used wavelet analysis to identify traveling MHD waves and derive frequencies in the different bandpasses. We isolated a large sample of MBPs using an automated tracking algorithm throughout our observations. Two dozen of the brightest MBPs were selected from the sample for further study. We find oscillations in the G-band MBPs, with frequencies between 1.5 and 3.6 mHz. Corresponding MBPs in the lower solar chromosphere observed in Halpha show a frequency range of 1.4 to 4.3 mHz. In about 38<!PCT!> of the MBPs, the ratio of Halpha to G-band frequencies was near two. Thus, these oscillations show a form of mode coupling where the transverse waves in the photosphere are converted into longitudinal waves in the chromosphere. The phases of the Halpha and G-band light curves show strong positive and negative correlations only 21<!PCT!> and 12<!PCT!> of the time, respectively. From simple estimates we find an energy flux of approx 45 $ $ W m$^ $ and show that the energy flowing through MBPs is enough to heat the chromosphere, although higher-resolution data are needed to explore this contribution further. Regardless, mode coupling is important in helping us understand the types of MHD waves in the lower solar atmosphere and the overall energy budget.

Efficient ARL estimation for general control charts using censored run lengths

The average run length (ARL), the average number of in-control signals before an out-of-control signal occurs, is a critical measure of the performance of a control chart. Various methods have been explored for estimating the ARL, including Markov chain-based approximation, integration method, and the Monte Carlo method, especially for time-dependent charting statistics. Although computationally expensive, the Monte Carlo method is generic and applicable to all control charts. Typically, it involves choosing the maximum run length in advance and discarding iterations that fail to produce out-of-control signals to accommodate some unusually lengthy runs. However, these discarded runs provide information for estimating the run length. This paper introduces a new Monte Carlo approximation that retains failed iterations as Type I censored observations for ARL estimation. Our approach relies on the memoryless expectation of the run length distribution to have a simple and efficient ARL estimator. This assumption is necessary for mean estimation in Type I censored data and aligns with existing ARL approximations used in popular control charts like Shewhart, exponentially weighted moving average (EWMA), and cumulative sum (CUSUM) charts. Numerical simulations demonstrate our method’s computational and statistical efficiency across the mean, median, EWMA, and CUSUM charts.

Entropy of strongly coupled Yukawa fluids.

The entropy of strongly coupled Yukawa fluids is discussed from several perspectives. First, it is demonstrated that a vibrational paradigm of atomic dynamics in dense fluids can be used to obtain a simple and accurate estimate of the entropy without any adjustable parameters. Second, it is explained why a quasiuniversal value of the excess entropy of simple fluids at the freezing point should be expected, and it is demonstrated that a remaining very weak dependence of the freezing point entropy on the screening parameter in the Yukawa fluid can be described by a simple linear function. Third, a scaling of the excess entropy with the freezing temperature is examined, a modified form of the Rosenfeld-Tarazona scaling is put forward, and some consequences are briefly discussed. Fourth, the location of the Frenkel line on the phase diagram of Yukawa systems is discussed in terms of the excess entropy and compared with some predictions made in the literature. Fifth, the excess entropy scaling of the transport coefficients (self-diffusion, viscosity, and thermal conductivity) is reexamined using the contemporary datasets for the transport properties of Yukawa fluids. The results could be of particular interest in the context of complex (dusty) plasmas, colloidal suspensions, electrolytes, and other related systems with soft pairwise interactions.

Armed conflict as a catalyst for increasing flood risk

Abstract Armed conflict has many adverse impacts beyond violence such as increasing risks of natural hazards. Analyses of the interactions between flood risks and armed conflict are essential for developing effective policies and strategies to address both challenges. This study aims to develop conceptual and analytical socio-hydrological frameworks for assessing how armed conflict can impact flood risks. The frameworks postulate a link between armed conflict and flood vulnerability, given that armed conflict creates unique challenges that exacerbate the effects of floods. Our conceptual framework identifies routes through which armed conflict affects vulnerability to floods, such as damage to infrastructure, population displacement and density, weak governance, and less awareness, resulting in lower resilience, higher susceptibility, and increased flood vulnerability and risk. Our analytical framework uses flood modeling to evaluate flood hazards and incorporates spatial data related to armed conflict zones, nighttime light, population classification by age, land price, land cover, and rural/urban areas classification. We take Khartoum, the capital city of Sudan, as a case study in view of its armed conflict that erupted in 2023. By highlighting the linkages between armed conflict and flood risk, this study contributes to conceptualizing the broader interlinkages between conflict and environmental systems. The study emphasizes the importance of integrating conflict analysis with disaster risk management strategies. We encourage collaboration between humanitarian, environmental, and security sectors to improve preparedness, response, and resilience in conflict-affected regions. While our analysis for Khartoum is based on conflict zones in the early stages of the conflict and uses simple estimates for conflict vulnerability contribution, the proposed frameworks provide groundwork for assessing changes in flood risk in Sudan and other conflict regions around the world.

Identifiability of speciation times under the multispecies coalescent

The advent of rapid and inexpensive sequencing technologies has necessitated the development of computationally efficient methods for analyzing sequence data for many genes simultaneously in a phylogenetic framework. The coalescent process is the most commonly used model for linking the underlying genealogies of individual genes with the global species-level phylogeny, but inference under the coalescent model is computationally daunting in the typical inference frameworks (e.g., the likelihood and Bayesian frameworks) due to the dimensionality of the space of both gene trees and species trees. Here we consider estimation of the branch lengths in fixed species trees with three or four taxa, and show that these branch lengths are identifiable. We also show that for three and four taxa simple estimators for the branch lengths can be derived based on observed site pattern frequencies. Properties of these estimators, such as their asymptotic variances and large-sample distributions, are examined, and performance of the estimators is assessed using simulation. Finally, we use these estimators to develop a hypothesis test that can be used to delimit species under the coalescent model for three or four putative taxa.

A quantitative analysis of acoustic field directionality from a seismic airgun array

Marine seismic reflection surveys generate high amplitude impulsive acoustic events using airgun arrays to study geophysical characteristics of the seabed. These data can be used beyond seismic imaging – modeling short range propagation, considering impacts on marine mammals, extracting seabed properties and their effect on the acoustic field, etc. Knowledge of the source characteristics is necessary to utilize these data and proper modeling of sound propagation from these arrays requires characterization of the array beam pattern. Complex simulations of airgun arrays have been used in the past to model airgun array spectra, but beam patterns have not been thoroughly considered in the literature. Delay-and-sum combinations of these airgun signatures provide a simple beam pattern estimate, but this approach ignores variability in airgun position, timing, amplitude, interactions between airguns, etc. The use of more complex notional airgun signatures can yield more accurate estimates, but these are more challenging to model and still ignores shot-to-shot variability. Experimentally-determined beam patterns are evaluated here and the variability in the results considered, showing both similarities and notable differences from simulated results. The experimental results indicate that the source array depth impacts the ghost-free array beam pattern and that variability between shots is enough to significantly alter beam patterns. Overall, the observations suggest that accurate simulation of array beam patterns may require more complexity than is currently considered and that inclusion of uncertainty due to environmental and airgun shot variability is essential.

Development of a simple estimation method of serum caffeine concentration using a point-of-care test kit for urinary caffeine.

Serum caffeine concentration is an indicator of caffeine intoxication; however, it is difficult to measure it in most emergency departments. We developed a simple estimation method using a point-of-care test kit for urinary caffeine. Caffeine-spiked human serum (100, 50, 25, and 10µg/mL) was diluted 10-, 20-, 50-, and 100-fold with phosphate-buffered saline and applied to the kit. After 5min incubation, the kit was scanned by a flatbed scanner and the membrane image was processed with ImageJ. When the 20-fold diluted serum was applied, serum samples with initial caffeine concentration ≤ 25 and ≥ 50µg/mL were caffeine-negative and -positive, respectively. When the 100-fold diluted serum was applied, none of the caffeine-spiked serum samples gave positive results. Therefore, we proposed the following test procedure: (i) 20-fold diluted serum was initially tested and (ii) 100-fold diluted serum was additionally tested when the initial result was caffeine positive. Using this procedure, caffeine concentration is expected to be classified into three levels: ≤ 25, > 25- ≤ 100, and > 100µg/mL, which almost correspond to no or mild, severe, and potentially fatal intoxication, respectively. The test procedure was validated using postmortem heart blood from two cases of fatal caffeine intoxication (caffeine concentration: 276 and 175µg/mL) and two cases of other intoxication. Our developed method using point-of-care urinary caffeine test kits enabled simple estimation of serum caffeine concentration.

The incidence of lung cancer amongst primary care chest radiograph referrals-an evaluation of national and local datasets within the United Kingdom.

To determine the incidence of lung cancer amongst primary care referrals for investigation with a chest radiograph (CXR). Retrospective evaluation of datasets from the national Clinical Practice Research Datalink (CPRD) and from a single large regional centre. Data were extracted for cohorts of consecutive adults aged over 40 years for whom a CXR had been performed between 2016 and 2018. Using cancer registry data, the incidence of lung cancer within a 2 years of the CXR referral and the variations with age, gender, and smoking status were evaluated. A total of 291294 CXR events were evaluated from the combined datasets. The incidence of lung cancer amongst primary care CXR referrals was 1.4% in CPRD with a consistent correlation with increasing age and smoking status. The incidence of lung cancer within two-years of the CXR varied between 0.03% (95%CI, 0.0-0.1) amongst never smokers aged 40-45 years to 4.8% (95%CI, 4.2-5.5) amongst current-smokers aged 70-75 years. The findings were similar for the single large centre data, although cancer incidence was higher. A simple estimation and stratification of the risk of lung cancer amongst primary care referrals for investigation with a CXR is possible using age and smoking status. This is the first estimate of the incidence of lung cancer amongst primary care CXR referrals and a demonstration of how the demographic information contained within a request could be used to optimize investigations and interpret test results.

Sliding Shear Failure of Basement-Clamped Reinforced Concrete Shear Walls.

After an earthquake struck Chile in 1985, sliding shear failure was observed in the clamping zone of stabilising walls. Even houses with only four storeys can fail due to sliding shear below basement ceilings. This type of failure has received little attention in the past; however, it leads to premature failure of the clamping effect in the basement box. Bending deformations cannot fully develop, which significantly reduces the seismic safety; it is thus important to learn more about the sliding shear failure of basement-clamped shear walls. The overall behaviour is analysed based on three large-scale shear wall tests. The deformation states around the sliding shear zone are evaluated and a simple estimate is given of when sliding shear failure can occur.

The Relationship Between Fibrinogen and Glycated Hemoglobin (HbA1c) in Diabetic Foot Ulcers.

Diabetic foot ulcers (DFUs) are a common complication of diabetes that affects patients' quality and prognosis of life. The study aims to assess the correlation between fibrinogen and glycated hemoglobin (HbA1c) in DFUs at the firstand sixth months and to compare fibrinogen levels with Wagner classification in DFU patients. This observational study was conducted at SRM Medical College Hospital and Research Centre from January 2021 to July 2022. Fifty diabetes patients with DFUs were selected, and informed consent was obtained before the study started. Blood samples were collected from all the participants for HbA1C, serum fibrinogen, hemoglobin, and white blood cells. In this study, data were entered into MS Excel (Microsoft Corporation, Redmond, WA) and analyzed using SPSS version 24 (IBM Corp., Armonk, NY). ANOVA and Pearson's correlation were used to examine the relationships between serum fibrinogen levels and clinical parameters. Among 50 patients, the females were 16 (32%), and the males were 34 (68%). Most patients (34%) were in the 56-60 age group. Twenty patients haddiabetes for 10 years, and 24 were diabetic for 11-15 years. The ankle-brachial index (ABI score) was mild in 14 patients (28%), moderate in 28 patients (56%), and normal in eightpatients (16%). There is a significant difference in comparison between the Wagner classification and ABI. A significant difference was observed in fibrinogen at the first and sixth months between HbA1c first, third, and sixth months. Significant differences were also observed in fibrinogen and ABI in the first and sixth months. Key findings include significant differences between fibrinogen and HbA1c levels (p < 0.0001) and a strong association between fibrinogen levels and ABI scores (p < 0.0001), underscoring fibrinogen's potential as an early marker for glycemic control and peripheral arterial disease in DFU patients. We concluded that simple fibrinogen estimation helps predict glycemic control in diabetic patients with DFUs.

Research on the Measurement and Estimation Method of Wheel Resistance on a Soil Runway

In view of the fact that there is no suitable measurement method for tire driving resistance during the operation of a transporter on soil pavement, this paper proposes a simple measurement and estimation method for driving resistance on a soil runway based on the Bekker settlement model. In this paper, using the driving resistance equation based on the Bekker model, a simplified equation of driving resistance related to mass is proposed. Using the principle of kinematics, a simple test device for driving resistance was developed, and the test data of the test device were used to determine the parameters of the driving resistance estimation equation of the road surface. The driving resistance of the wheel running state was tested by the simulated aircraft load loading vehicle through the mechanical sensor, and the consistency between the theoretical value and the actual value of the driving resistance value under 8T, 10T, and 12T loads was verified. This research shows that the test method and estimation method have low cost and good accuracy and are suitable for wide promotion. The results of this paper provide a new idea and method for estimating the driving resistance of a wheel when a transporter runs on a soil road and also provide a reference for designing the length of a soil runway.

Simple closed-form estimation of a binary latent variable model

Abstract This paper develops a closed-form nonparametric estimator of the conditional distribution function for a binary outcome variable given an unobserved latent variable. This type of function is commonly used in models that involve measurement error and dynamic models with agent-specific unobserved heterogeneity. This paper presents a consistent extremum sieve estimator with the following advantages: i) it has a closed-form expression for all the sieve coefficients; ii) it is computationally straightforward, equivalent to computing eigenvalues and eigenvectors of matrices without the use of iterative optimization algorithms. While as flexible as the sieve maximum likelihood estimator (MLE) previously proposed for this model, our estimator proves computationally simpler. The finite sample properties of the estimator are investigated through a Monte Carlo study, and the developed estimator is applied to a probit model to assess the targeting performance of a social welfare program.

Evolution of Light Absorption Enhancement of Black Carbon Aerosols From Biomass Burning in Atmospheric Photochemical Aging

AbstractThe light absorption enhancement (Eabs) of black carbon (BC) coated with non‐BC materials is crucial in the assessment of radiative forcing, yet its evolution during photochemical aging of plumes from biomass burning, the globe's largest source of BC, remains poorly understood. In this study, plumes from open burning of corn straw were introduced into a smog chamber to explore the evolution of Eabs during photochemical aging. The light absorption of BC was measured with and without coating materials by using a thermodenuder, while the size distributions of aerosols and composition of BC coating materials were also monitored. Eabs was found to increase initially, and then decrease with an overall downward trend. The lensing effect dominated in Eabs at 520 nm, with an estimated contribution percentages of 47.5%–94.5%, which is far greater than light absorption of coated brown carbon (BrC). The effects of thickening and chemical composition changes of the coating materials on Eabs were evaluated through comparing measured Eabs with that calculated by the Mie theory. After OH exposure of 1 × 1010 molecules cm−3 s, the thickening of coating materials led to an Eabs increase by 3.2% ± 1.6%, while the chemical composition changes or photobleaching induced an Eabs decrease by 4.7% ± 0.6%. Simple forcing estimates indicate that coated BC aerosols exhibit warming effects that were reduced after aging. The oxidation of light‐absorbing CxHy compounds, such as polycyclic aromatic hydrocarbons (PAHs), to CxHyO and CxHyO&gt;1 compounds in coating materials may be responsible for the photobleaching of coated BrC.

Pore‐Morphology‐Based Estimation of the Freezing Characteristic Curve of Water‐Saturated Porous Media

AbstractAssessment of freezing effects on soil requires estimating the soil freezing characteristic curve (SFCC)—the variation of unfrozen water content with temperature. The existing methods for obtaining SFCCs often involve either costly experiments or heuristic inference from water retention data. Here, we propose a pore‐morphology‐based method for simple and efficient estimation of the freezing characteristic curve of water‐saturated porous media, whereby the pore‐scale configurations of water and ice phases are simulated in a digital image of porous microstructure. Idealizing the pore space as a system of overlapping spherical pores, the method simulates the freezing process with the Gibbs‐Thomson equation that can consider freezing‐point depression—a decrease in the freezing point due to spatial confinement—based on thermodynamics. For validation, we apply the proposed method to estimate the SFCC of a field soil for which the experimental freezing characteristics data are available. Results show that even with a digital pore image extracted from a surrogate discrete‐element packing of the soil, the proposed method provides an SFCC very close to the experimental data.

A single-bubble source for gravitational waves in a cosmological phase transition

Abstract We show that quantum fluctuations of an expanding phase transition bubble give rise to gravitational wave (GW) emission, even when considering a single bubble, without bubble collisions or plasma effects. The ratio of GW energy to the total bubble energy reservoir increases with time as ∝ t. If the bubble expands for long enough before percolation destroys it, back-reaction due to the GW emission becomes important after t br ~ (16π5)m pl 2 R 0 3, where R 0 is the bubble nucleation radius and m pl is the reduced Planck mass. As seen by experiments today, the GW energy spectrum would appear blue. However, simple estimates suggest that the signal falls short of detection by even ambitious future experiments.

Evaluation of direct point dose estimation based on the distribution of the size-specific dose estimate.

The aim of this study was to evaluate the point doses using a distribution of the size-specific dose estimate (SSDE) from axial CT images of in-house phantoms having diameters from 8 to 40cm. In-house phantoms made of polyester-resin (PESR) mixed with methyl ethyl ketone peroxide (MEKP) were used. The phantoms were built with different diameter sizes of 8, 16, 24, 32, and 40cm. The phantoms were scanned by Siemens a SOMATOM Perspective-128 slice CT scanner with constant input parameters. The point doses were interpolated from the central SSDE (SSDEc) and the peripheral SSDE (SSDEp). The SSDEc and SSDEp were calculated from the SSDE with h- and k-factors. The point doses were compared to the direct measurements using the nanoDot™ optically-stimulated luminescence dosimeter (OSLD) in dedicated holes on the phantoms. It was found that the point dose decreases as the phantom diameter increased. The doses obtained using two approaches differed by 11% on average. The highest difference was 40% and the lowest difference was < 1%. It was found that dose based on the SSDE concept tended to be higher compared to the measured dose by OSLD. Point dose estimation using the concept of SSDE distribution can be considered an alternative for accurate and simple estimation. This approach still requires improvements to increase its accuracy and its application to estimate the organ dose needs further investigation.

Upper Limit for the Superconducting Transition Temperature in Eliashberg–McMillan Theory

We present simple qualitative estimates for the maximal superconducting transition temperature, which may be achieved due to electron–phonon coupling in Eliashberg–McMillan theory. It is shown that in the limit of very strong coupling the upper limit for transition temperature is determined in fact by a combination of atomic constants and density of conduction electrons.

Construction of a clinical prediction model for complicated appendicitis based on machine learning techniques

Acute appendicitis is a typical surgical emergency worldwide and one of the common causes of surgical acute abdomen in the elderly. Accurately diagnosing and differentiating acute appendicitis can assist clinicians in formulating a scientific and reasonable treatment plan and providing high-quality medical services for the elderly. In this study, we validated and analyzed the different performances of various machine learning models based on the analysis of clinical data, so as to construct a simple, fast, and accurate estimation method for the diagnosis of early acute appendicitis. The dataset of this paper was obtained from the medical data of elderly patients with acute appendicitis attending the First Affiliated Hospital of Anhui University of Chinese Medicine from January 2012 to January 2022, including 196 males (60.87%) and 126 females (39.13%), including 103 (31.99%) patients with complicated appendicitis and 219 (68.01%) patients with uncomplicated appendicitis. By comparing and analyzing the prediction results of the models implemented by nine different machine learning techniques (LR, CART, RF, SVM, Bayes, KNN, NN, FDA, and GBM), we found that the GBM algorithm gave the optimal results and that sensitivity, specificity, PPV, NPV, precision, recall, F1 and brier are 0.9167, 0.9739, 0.9429, 0.9613, 0.9429, 0.9167, 0.9296, and 0.05649, respectively. The GBM model prediction results are interpreted using the SHAP technology framework. Calibration and Decision curve analysis also show that the machine learning model proposed in this paper has some clinical and economic benefits. Finally, we developed the Shiny application for complicated appendicitis diagnosis to assist clinicians in quickly and effectively recognizing patients with complicated appendicitis (CA) and uncomplicated appendicitis (UA), and to formulate a more reasonable and scientific clinical plan for acute appendicitis patient population promptly.

Estimate of Temperature Rise in the 21st Century for Various Scenarios

The International Panel on Climate Change (IPCC) published a lengthy report on climate change in early 2023. This report hypothesizes five potential scenarios of greenhouse gas emissions from 2015 to the end of the century (2100) and estimates the global average temperature gain in the year 2100 from the mid-1800s for each scenario. The method of calculation in the IPCC report is obscure. The results are merely stated. The present paper provides a clear method for estimating the temperature gain each year from 2015 until 2100, along with yearly estimates of ppm of CO2. To facilitate the calculations, a set of scenarios of future emissions was chosen that is analogous to the scenarios used by the IPCC but is more amenable to computation. The basic assumption in this paper is that most of the temperature gain from the mid-1800s to 2015 (1.15 C – as reported by the IPCC) was due to rising CO2 concentration in the atmosphere and a relationship is thereby derived between warming and gigatons of CO2 emitted for the period: 1800s to 2015. If it is assumed that the amount of warming per gigaton CO2 from the past persists into the 21st century, then future warming in the 21st century can be estimated for any assumed future scenario of CO2 emissions. This paper provides a simple and clear estimate of yearly CO2 ppm and temperature rise from 2015 to 2100 since the 1800s for a set of scenarios that cover the likely range of future emissions.