Previous Estimation Research Articles

The Complex Exhumation History of Jezero Crater Floor Unit and Its Implication for Mars Sample Return

AbstractDuring the first year of NASA's Mars 2020 mission, Perseverance rover has investigated the dark crater floor unit of Jezero crater and four samples of this unit have been collected. The focus of this paper is to assess the potential of these samples to calibrate the crater‐based Martian chronology. We first review the previous estimation of crater‐based model age of this unit. Then, we investigate the impact crater density distribution across the floor unit. It reveals that the crater density is heterogeneous from areas which have been exposed to the bombardment during the last 3 Ga to areas very recently exposed to bombardment. It suggests a complex history of exposure to impact cratering. We also display evidence of several remnants of deposits on the top of the dark floor unit across Jezero below which the dark floor unit may have been buried. We propose the following scenario of burying/exhumation: the dark floor unit would have been initially buried below a unit that was a few tens of meters thick. This unit then gradually eroded away due to Aeolian processes from the northeast to the west, resulting in uneven exposure to impact bombardment over 3 Ga. A cratering model reproducing this scenario confirms the feasibility of this hypothesis. Due to the complexity of its exposure history, the Jezero dark crater floor unit will require additional detailed analysis to understand how the Mars 2020 mission samples of the crater floor can be used to inform the Martian cratering chronology.

Pharmaceutical and narcotics monitoring in Brno wastewater system and estimation of seasonal effect on the abuse of illicit drugs by a wastewater-based epidemiology approach

The presence of pharmaceuticals (PHA) and narcotics (NAR) in wastewater (WW) has attracting growing interest due to concern for aquatic environment and the possibility to exploit their presence to estimate drug consumption by population. This work aims to (i) quantify PHA and NAR in the WW of the Brno metropolitan area, (ii) determine the effectiveness of the current conventional wastewater treatment plant (WWTP), and (iii) estimate the illicit drug (ID) consumption. 23 PHA and 9 NAR were frequently detected in the untreated WW and studied for their removal. One year monitoring was carried out to highlight a possible seasonal pattern with PHA and NAR load in WW, WWTP effectiveness, and illicit consumption. Results highlighted that the most abundant PHA and NAR were caffeine (73.9 ± 6.9 μg L−1) and methamphetamine (2.3 ± 0.1 μg L−1) while oxazepam (0.13 ± 0.05 μg L−1) and EDDP (0.02 ± 0.01 μg L−1) were the lowest ones, respectively. Only paracetamol, caffeine, atenolol, ciprofloxacin, amphetamine, cocaine, morphine, and benzoylecgonine exhibited a high biodegradability being removed almost completely (> 90 %). A predominant illicit use of methamphetamine was estimated (17 ± 0.6 doses 1000.inh−1 d−1) that might suggest a higher number of consumers than previous official estimation. A lower abuse of cocaine (2.7 ± 0.5 doses 1000.inh−1 d−1), amphetamine (2.2 ± 0.3 doses 1000.inh−1 d−1), methadone (1.1 ± 0.2 doses 1000.inh−1 d−1), heroin (0.9 ± 0.2 doses 1000.inh−1 d−1), and MDMA (0.7 ± 0.1 doses 1000.inh−1 d−1) was found. A seasonality pattern was highlighted for some PHA and NAR influent load and removal, and, about ID consumption, only for heroin and methadone. These results will be useful for water utilities, to enhance the knowledge about the presence and removal of PHA and NAR, and local and national authorities to evaluate and counteract the problem of ID abuse.

Estimation of the size of the COVID-19 pandemic using the epidemiological wavelength model: results from OECD countries

ObjectivesThis study aimed to simplify the previously developed epidemiological wavelength model and to expand the scope of the model with additional variables to estimate the magnitude of the COVID-19 pandemic. The applicability of the extended wavelength model was tested in Organisation for Economic Cooperation and Development (OECD) member countries. Study designThe epidemiological wavelengths of OECD member countries for the years 2020, 2021 and 2022 were estimated comparatively, considering the cumulative number of COVID-19 cases. MethodsThe size of the COVID-19 pandemic was estimated using the wavelength model. The scope of the wavelength model was expanded to include additional variables. The extended estimation model was improved by adding population density and human development index variables, in addition to the number of COVID-19 cases and number of days since the first case reported from the previous estimation model. ResultsAccording to the findings obtained from the wavelength model, the country with the highest epidemiological wavelength for the years 2020, 2021 and 2022 was the United States (We = 29.96, We = 28.63 and We = 28.86, respectively), and the country with the lowest wavelength was Australia (We = 10.50, We = 13.14 and We = 18.44, respectively). The average wavelength score of OECD member countries was highest in 2022 (We = 24.32) and lowest in 2020 (We = 22.84). The differences in the periodic wavelengths of OECD countries were analysed with the dependent t-test for paired samples in two periods, 2020–2021 and 2021–2022. There was a statistically significant difference between wavelengths in the 2020–2021 and 2021–2022 groups (t(36) = −3.670; P < 0.001). ConclusionsDecision-makers can use the extended wavelength model to easily follow the progress of the epidemic and to make quicker and more reliable decisions.

Point cloud completion in challenging indoor scenarios with human motion.

Combining and completing point cloud data from two or more sensors with arbitrarily relative perspectives in a dynamic, cluttered, and complex environment is challenging, especially when the two sensors have significant perspective differences while the large overlap ratio and feature-rich scene cannot be guaranteed. We create a novel approach targeting this challenging scenario by registering two camera captures in a time series with unknown perspectives and human movements to easily use our system in a real-life scene. In our approach, we first reduce the six unknowns of 3D point cloud completion to three by aligning the ground planes found by our previous perspective-independent 3D ground plane estimation algorithm. Subsequently, we use a histogram-based approach to identify and extract all the humans from each frame generating a three-dimensional (3D) human walking sequence in a time series. To enhance accuracy and performance, we convert 3D human walking sequences to lines by calculating the center of mass (CoM) point of each human body and connecting them. Finally, we match the walking paths in different data trials by minimizing the Fréchet distance between two walking paths and using 2D iterative closest point (ICP) to find the remaining three unknowns in the overall transformation matrix for the final alignment. Using this approach, we can successfully register the corresponding walking path of the human between the two cameras' captures and estimate the transformation matrix between the two sensors.

Unraveling Firms: Demand, Productivity and Markups Heterogeneity

Abstract We develop a novel framework that simultaneously allows recovering heterogeneity in demand, quantity total factor productivity and markups across firms while leaving the correlation between the three dimensions unrestricted. We accomplish this by explicitly introducing demand heterogeneity and systematically exploiting assumptions used in previous productivity estimation approaches. In doing so, we provide an exact decomposition of revenue productivity in terms of the underlying heterogeneities, thus bridging the gap between quantity and revenue productivity estimations. We use Belgian firms’ production data to quantify total factor productivity, demand and markups, and show how they are correlated with each other across time and with measures obtained from other approaches. In doing so, we find quantity total factor productivity and demand to be strongly negatively correlated with each other, so suggesting a trade-off between the quality of a firm’s products and their production cost. We also show how our framework provides deeper and sharper insights on the response of firms to increasing import competition from China. In particular, we find that changes in revenue productivity materialise as the outcome of complex and sometimes offsetting changes in quantity total factor productivity, demand, markups and production scale.

Impact and compensation of carrier synchronization errors in OFDM signals with very large QAM constellations

AbstractLow cost video sensors used for streaming video signals to help firefighters, require high bit rate due to uncompressed images. To increase spectral efficiency given a limited bandwidth, very high order constellations in high signal to noise ratio regimes can be used. However, noise is not the only factor effecting the high order constellations. These constellations are also sensitive to hardware impairments and system non‐linearities. Therefore, in this paper, the effect of carrier frequency offset (CFO) on the performance of an orthogonal frequency division multiplexing (OFDM) system with high order quadrature amplitude modulation (QAM) is studied. A closed form expression is derived for the maximum normalized residual CFO that an OFDM system with M‐QAM constellation can resist to have an error free symbol detection. Finally, the suitability of common previous CFO estimation techniques such as the cyclic prefix based technique and the Moose technique in these systems are investigate. The results show that the maximum residual CFO that an OFDM system with M‐QAM constellation can resist is proportional to the inverse of . The results also show that very large order QAM constellations such as 4096‐QAM are very sensitive to even small residual CFO values and their performance degrades, significantly. However, the bit error rate analysis indicate that the Moose CFO estimation technique can be used in these systems to compensate the CFO effect, accurately.

Conceptual Design of a Device for Online Calibration of Spirometer Based on Neural Network.

Daily calibration of spirometry devices plays an important role in promoting the accuracy of pulmonary diagnostic results. It is needed to have more precise and adequate instruments for calibrating spirometry during the clinical use. In this work, a device was designed and developed based on a calibrated-volume syringe and an electrical circuit was also built to measure the air flux. Some colored tapes with specific size and order covered the syringe piston. When the piston moved in front of the color sensor, the input air flow was calculated according to the width of the strips and transferred to the computer. A Radial Basis Function (RBF) neural network estimator used new data to modify the previous estimation function for increasing the accuracy and the reliability. The simulation showed that the root mean square of the error improved from 13.7±0.37% to 4.2±0.22%, i.e. the calibration curve has improved about 70%.

정규-비정규 노동자 임금 및 비임금 차별 추이

This study estimates the discrimination against non-regualr workers in wages and non-wages for 13 years from 2004 to 2016. The non-wages means benefit fringes which consist of bonuses, severance pay, overtime pay, and paid leave. Both wages and non-wages differentials could be decomposed by the Oaxaca-Blinder decomposition suggested by Fortin(2008), which would reduce the bias of the explanatory part estimate. The first result is that the wage discrimination had widened recently since the mid-2000s. However, the discrimination estimate was found to be smaller than the previous estimation results. Second, benefit fringes discrimination is very large. The discrimination share is also large around 40-60%. Both are increasing over time. Third, as of 2009, the trend of such discrimination gap and discrimination share has changed discontinuously. In both wages and non-wages, the discrimination against non-regular workers jumped significantly around 2009, and thereafter it does not return to the level before 2009.

Prediction-Oriented Disparity Rectification Model for Geometry-Based Light Field Compression

The geometry-based light field compression approach aims to use the inherent disparity cue provided by the light field to reduce the redundancy of data and improve immersive media broadcasting. Some methods, including our previous one, transmit explicit disparity maps along with sparsely selected views and then exploit disparity-based view prediction to remove correlations in the angular domain. However, these methods neglect two important issues: the 4D correspondence between the disparity array and view array and the practical non-uniform disparity distribution in captured light fields. As a result, the prediction potentiality of disparity maps is heavily restricted, and the limited disparity-based prediction accuracy degrades the final compression performance. In this paper, we focus on these two issues of estimated disparity maps and propose a prediction-oriented disparity rectification (PoDR) model. First, based on our previous disparity estimation, we propose to utilize the 4D structural prior of light fields to refine the estimated disparity map. The 4D correspondence between a disparity array and a view array is enhanced, leading to higher prediction accuracy and lower bit costs. Second, for the refined disparity array, we propose a variable stride-based determination algorithm to obtain the practical non-uniform disparity distribution in captured light fields. Specifically, the angular distance between each pair of disparity maps is efficiently derived by the approximate solution based on the gradient variation of the prediction errors. By combining these two modules, the proposed PoDR model improves the overall compression performance compared with our previous work. Furthermore, we verify that the proposed method can obtain better light field fundamental capability (e.g., refocusing) than state-of-the-art methods.

From start-up to maximum loading: An approach for methane production in upflow anaerobic sludge blanket reactor fed with the liquid fraction of fruit and vegetable waste

This investigation provides a reproducible approach for determining the limits of an upflow anaerobic sludge blanket (UASB) reactor designed for the methanization of the liquid fraction of fruit and vegetable waste (FVWL). Two identical mesophilic UASB reactors were operated for 240 days with a three-day fixed hydraulic retention time and an organic load rate (OLR) increased from 1.8 to 10 gCOD L−1 d−1. Because of the previous estimation of flocculent-inoculum methanogenic activity, it was possible to design a safe OLR for the quick start-up of both UASB reactors. The operational variables obtained from the operation of the UASB reactors did not show statistical differences, ensuring the experiment's reproducibility. As a result, the reactors achieved methane yield close to 0.250 LCH4 gCOD−1 up to the OLR of 7.7 gCOD L−1 d−1. Furthermore, the maximum volumetric methane production rate of 2.0 LCH4 L−1 d−1 was discovered for the OLR ranges between 7.7 and 10 gCOD L−1 d−1. The possible overload at OLR of 10 gCOD L−1 d−1 resulted in a significant reduction of methane production in both UASB reactors. Based on the methanogenic activity of the UASB reactors sludge, a maximum loading capacity of approximately 8 gCOD L−1 d−1 was estimated.

Cyclic Behavior of Interfaces for Seismically Retrofitted RC Buildings

In seismically retrofitted reinforced concrete (RC) structures, new members are connected to existing members using interfaces with roughened surfaces and post-installed dowel bars. During an earthquake, the interfaces are subjected to cyclic shear and normal stresses. Therefore, the structural design of interfaces is essential. In a previous study, normal and shear loads were applied to interface specimens. Moreover, a shear strength estimation method was proposed; however, the cyclic behavior was not discussed. This paper presents a cyclic model of the roughened surface in retrofitted RC structures. First, an envelope model was constructed using the previous shear strength estimation method and Saenz model, a constitutive law of concrete. Subsequently, the cyclic rules were modeled. Moreover, a previous dowel model was incorporated into the proposed cyclic model to estimate the test results. Finally, the proposed model provided reasonably estimated test results; the average ratio of the test results to the model results was 1.04. In addition, as δ increased, the effect of the dowel bar intensified in terms of shear.

Efficient parameter estimation for parabolic SPDEs based on a log-linear model for realized volatilities

We construct estimators for the parameters of a parabolic SPDE with one spatial dimension based on discrete observations of a solution in time and space on a bounded domain. We establish central limit theorems for a high-frequency asymptotic regime. The asymptotic variances are shown to be substantially smaller compared to existing estimation methods. Moreover, asymptotic confidence intervals are directly feasible. Our approach builds upon realized volatilities and their asymptotic illustration as a response of a log-linear model with spatial explanatory variable. This yields efficient estimators based on realized volatilities with optimal rates of convergence and minimal variances. We demonstrate efficiency gains compared to previous estimation methods numerically and in Monte Carlo simulations.

The ocean fine spray

An important fraction of the atmospheric aerosols comes from the ocean spray originated by the bursting of surface bubbles. A theoretical framework that incorporates the latest knowledge on film and jet droplets from bubble bursting is here proposed, suggesting that the ejected droplet size in the fine and ultrafine (nanometric) spectrum constitutes the ultimate origin of primary and secondary sea aerosols through a diversity of physicochemical routes. In contrast to the latest proposals on the mechanistic origin of that droplet size range, when bubbles of about 10–100 μm burst, they produce an extreme energy focusing and the ejection of a fast liquid spout whose size reaches the free molecular regime of the air. Simulations show that this spout yields a jet of sub-micrometer and nanometric scale droplets whose number and speed can be far beyond any previous estimation, overcoming by orders of magnitude other mechanisms recently proposed. The model proposed can be ultimately reduced to a single controlling parameter to predict the global probability density function of the ocean spray. The model fits remarkably well most published experimental measurements along five orders of magnitude of spray size, from about 5 nm to about 0.5 mm. According to this proposal, the majority of ocean aerosols would have their extremely elusive birth in the collapsing uterus-like shape of small bursting bubbles on the ocean surface.

Click-through rate prediction model integrating user interest and multi-head attention mechanism

The purpose of click-through rate (CTR) prediction is to anticipate how likely a person is to click on an advertisement or item. It's required for a lot of internet applications, such online advertising and recommendation systems. The previous click-through rate estimation approach suffered from the following two flaws. On the one hand, input characteristics (such as user id, user age, user age, item id, item category) are usually sparse and multidimensional, making them effective. High-level combination characteristics are used for prediction. Obtaining it manually by domain experts takes a long time and is difficult to finish; also, customer interests are not all the same. The accuracy of the model findings will significantly increase if this immediately recognized component is incorporated in the prediction model. As a consequence, this study creates an IARM (interactive attention rate estimation model) that incorporates user interest as well as a multi-head self-attention mechanism. The deep learning network is used in the model to determine the user's interest expression based on user attributes. The multi-head self-attention mechanism with residual network is then employed to get feature interaction, which enhances the degree of effect of significant characteristics on the estimation result as well as its accuracy. The IARM model outperforms other recent prediction models in the assessment metrics AUC and LOSS, and it has superior accuracy, according to the results from the public experimental data set.

New computational technique based on Bernstein polynomials and higher order finite difference schemes for temporal and spatial reactor calculations

In this research, two main objectives about solving the space time dependent diffusion equations are addressed. The first is to present higher order finite difference schemes at the core interior meshes and also at the points adjacent to the core boundaries, for spatial discretization. The second is to provide an accurate iteratively numerical method for temporal calculations based on convergent Bernstein polynomials. Bernstein series coefficients are estimated by using an easily computable recurrence relation. Estimation of the Bernstein recurrence relation requires nothing except the previous estimation of the series coefficients. Stability and convergence of the proposed method is discussed. It has proved that the method is conditionally stable and it has an exponential rate of convergence. Recently, we provided fourth and sixth order schemes to approximate the neutron leakage terms. These schemes provided highly accurate results but a large amount of these gained accuracies are lost due to using the classical second order finite difference formula at the core boundaries which uses three point stencils with order of accuracy OΔ2. Here, Lagrange’ polynomials are used to construct another second order scheme using five point stencils instead of three points at the core edges. It is proved that using central fourth order scheme at the interior meshes associated with the new scheme at the boundaries significantly improves the accuracy. Also, it is proved that using such finite difference schemes do not affect the system complexity and the system size remains unchanged. Homogenous and heterogeneous problems are considered to test the efficiency and the validity of the proposed method. In addition to the simplicity of the method algorithm, the results are so accurate that they can be compared with the recently methods that divided the same cores into thousands of fuel assemblies using mesh generator tools like GAMBIT or ICEM-CFD.

BLOCK MODEL OPTIMIZATION AND RESOURCE ESTIMATION OF THE ANGOURAN MINE BY TRANSFERRING THE EXPLORATORY DATA FROM THE LOCAL COORDINATE SYSTEM TO THE UTM

Resource estimation is one of the most important steps in the mining process. Precise resource estimation has a significant influence on the optimization of subsequent mining steps, i.e. mine planning and scheduling. The previous resource estimation in the Angouran Mine was conducted based on the provided information in the local coordinate system which causes considerable errors in estimations. Therefore, an attempt is made in this research to optimize the block model of the Angouran Mine and resource estimation based on the information in the UTM global coordinate system. For this purpose, exploratory data is firstly transferred from the local coordinate system to the UTM environment. Then, block model optimization is conducted using indicator kriging (IK) in which the waste blocks are removed and the block model was optimized. Finally, resource estimation is performed using the inverse distance weighting (IDW) and simple kriging (SK) methods. After variogram analyses in different directions, it was found that the mine deposit is anisotropic. Also, validation results showed that the acquired correlation coefficient in the carbonate and sulfide sections for IDW, SK and IK is 0.86, 0.87 and 0.92, and 0.88, 0.87 and 0.92, respectively. Finally, the obtained grades and tonnages are compared with the actual data of the exploratory boreholes, mined blocks and previous resource estimation in the mine. Comparative results showed that the obtained grades and tonnages from both previous and new models are over-estimated and higher than the actual values. The minimum errors of grade estimation equal 46% and 23.1% for previous and new resource estimations (before and after the waste removal), respectively. Also, the mining errors of tonnage estimation are 50.29% and 28.37% for previous and new models, respectively. This field comparison proved that transferring the exploratory data to the UTM system, utilization of the IK to remove the waste blocks and applying the SK for resource estimation lead to the optimization of the block model and a reduction in the estimation error compared to the previous estimations for the mine.

Metodologia badań służących szacowaniu skali nielegalnego rynku napojów spirytusowych

Illicit spirit drink market is a very complex social phenomenon. The existing estimates of the scale of the unofficial alcohol consumption differ a lot. The European Commission, the World Health Organization and health institution researches indicated Poland as the leader of unregistered alcohol consumption in the European Union. On the other hand, Polish tax authorities and the Supreme Chamber of Control estimate unregistered spirits consumption on a much lower level. The aim of the paper is to present various research methods that can help estimate the scale of this phenomenon. Standardized methodologies are necessary conditions to reduce the disproportions in the previous estimation of unregistered alcohol consumption in Poland. The need of new quality approach is justified due to the fact that illicit spirits market causes individual and social health problems, public finance losses and contributes to corruption and other illegal activities. Comprehensive and reliable tools and scientific approach which at least allow to verify the dynamics of changes in the illegal spirit drinks market, will allow to perform periodic regulatory impact assessments. Thanks to this, it will be possible to recommend changes to the provisions that may discourage violations of the law.

Detail-Aware Deep Homography Estimation for Infrared and Visible Image

Homography estimation of infrared and visible images is a highly challenging task in computer vision. Recently, the deep learning homography estimation methods have focused on the plane, while ignoring the details in the image, resulting in the degradation of the homography estimation performance in infrared and visible image scenes. In this work, we propose a detail-aware deep homography estimation network to preserve more detailed information in images. First, we design a shallow feature extraction network to obtain meaningful features for homography estimation from multi-level multi-dimensional features. Second, we propose a Detail Feature Loss (DFL), which utilizes refined features for computation and retains more detailed information while reducing the influence of unimportant features, enabling effective unsupervised learning. Finally, considering that the evaluation indicators of the previous homography estimation tasks are difficult to reflect severe distortion or the workload of manually labelling feature points is too large, we propose an Adaptive Feature Registration Rate (AFRR) to adaptive extraction of image pair feature points to calculate the registration rate. Extensive experiments demonstrate that our method outperforms existing state-of-the-art methods on synthetic benchmark dataset and real dataset.

Inverse material parameter estimation of patient-specific finite element models at the carotid bifurcation: The impact of excluding the zero-pressure configuration and residual stress.

The carotid bifurcation experiences a complex loading environment due to its anatomical structure. Previous in-vivo material parameter estimation methods often use simplified model geometries, isotropic hyperelastic constitutive equations or neglect key aspects of the vessel, such as the zero-pressure configuration or residual stress, all of which have independently been shown to alter the stress environment of the vessel wall. Characterizing the location of high stress in the vessel wall has often been proposed as a potential indicator of structural weakness. However, excluding the afore-mentioned zero-pressure configuration, residual stress and patient-specific material parameters can lead to an incorrect estimation of the true stress values observed, meaning that stress alone as a risk indicator of rupture is insufficient. In this study, we investigate how the estimated material parameters and overall stress distributions in geometries of carotid bifurcations, extracted from in-vivo MR images, alter with the inclusion of the zero-pressure configuration and residual stress. This approach consists of the following steps: (1) geometry segmentation and hexahedral meshing from in-vivo magnetic resonance images (MRI) at two known phases; (2) computation of the zero-pressure configuration and the associated residual stresses; (3) minimization of an objective function built on the difference between the stress states of an "almost true" stress field at two known phases and a "deformed" stress field by altering the input material parameters to determine patient-specific material properties; and (4) comparison of the stress distributions throughout these carotid bifurcations for all cases with estimated material parameters. This numerical approach provides insights into the need for estimation of both the zero-pressure configuration and residual stress for accurate material property estimation and stress analysis for the carotid bifurcation, establishing the reliability of stress as a rupture risk metric.

Multi illumination color constancy based on multi-scale supervision and single-scale estimation cascade convolution neural network.

Color constancy methods are generally based on a simplifying assumption that the spectral distribution of a light source is uniform across scenes. However, in reality, this assumption is often violated because of the presence of multiple light sources, that is, more than two illuminations. In this paper, we propose a unique cascade network of deep multi-scale supervision and single-scale estimation (CN-DMS4) to estimate multi-illumination. The network parameters are supervised and learned from coarse to fine in the training process and estimate only the final thinnest level illumination map in the illumination estimation process. Furthermore, to reduce the influence of the color channel on the Euclidean distance or the pixel-level angle error, a new loss function with a channel penalty term is designed to optimize the network parameters. Extensive experiments are conducted on single and multi-illumination benchmark datasets. In comparison with previous multi-illumination estimation methods, our proposed method displays a partial improvement in terms of quantitative data and visual effect, which provides the future research direction in end-to-end multi-illumination estimation.