Estimates In Order Research Articles

Maximum dry density of sand–kaolin mixtures predicted by using fine content and specific gravity

Numerous studies have covered the comparison of maximum dry density (MDD) toward the combination of soil properties and compaction parameters. These combinations are dominated by Atterberg limits, moisture content, and compaction effort. Nevertheless, minimal efforts have been expended in predicting the MDD using two simple soil properties, i.e., fine content (FC) and specific gravity (Gs). Sand–kaolin mixtures were compacted and then used to examine the correlation between MDD, FC, and Gs through different moisture content, and FC ranged from 20 to 70%. The results showed the importance of considering both Gs and FC in the correlation estimation in order to reduce the uncertainty of using only FC as a single parameter. A linear relationship was obtained with an excellent correlation coefficient (R2 = 0.99) between MDD and the ratio of FC/Gs2. The empirical correlation equation was justified using data from 163 measured MDD obtained from previous studies. All predicted MDD were within the confidence interval of ± 10, which confirmed a low error range of the predicted MDD. The application of the equation was limited to FC and FC/Gs less than 77% and 0.302, respectively. It should, however, be noted that the presence of a high amount of gravel in the soil mixture will not be applicable for this correlation.

TOWARDS DETECTION OF THERMAL ANOMALIES IN LARGE URBAN AREAS USING SIMULATION

Abstract. Anomaly detection in imagery has widely been studied and enhanced towards the requirements of today’s available sensor data, whereas many of them require a background estimation in order to identify an anomaly or target. In this paper, we examine an analysis of simulation as background estimator for anomaly detection in thermal images of urban sceneries. We generate a surface temperature image and a sensor-like infrared image by combined image and elevation data and a thermal model suited for large scenes and fast simulation. With the simulated thermal image, we define anomalies as deviation between measurement and simulation. Pixel-wise image differencing of the measured and simulated temperatures and infrared images respectively are performed and evaluated concerning the full images as well as class-wise, including a material classification of the observed area. Our approach shows complementary results compared to RXD application on the measured infrared images. Metal roofs which appear warm in the thermal image and are not visually distinguishable from the residual image are detected.

Modified Ratio-Cum-Product Estimators of Population Mean Using Two Auxiliary Variables

A percentile is one of the measures of location used by statisticians showing the value below which a given percentage of observations in a group of observations fall. A family of ratio-cum-product estimators for estimating the finite population mean of the study variable when the finite population mean of two auxiliary variables are known in simple random sampling without replacement (SRSWOR) have been proposed. The main purpose of this study is to develop new ratio-cum-product estimators in order to improve the precision of estimation of population mean in sample random sampling without replacement using information of percentiles with two auxiliary variables. The expressions of the bias and mean square error (MSE) of the proposed estimators were derived by Taylor series method up to first degree of approximation. The efficiency conditions under which the proposed ratio-cum-product estimators are better than sample man, ratio estimator, product estimator and other estimators considered in this study have been established. The numerical and empirical results show that the proposed estimators are more efficient than the sample mean, ratio estimator, product estimator and other existing estimators.

Femoral histomorphometric age-at-death studies: The issue of sample size and standard error.

Extant histomorphometric aging methods based on the analysis of the femoral cortex generally report small samples (N<100) and highly variable standard error of the estimate (SEE) values (±1.51‒16.98 years). The present paper reviews the published literature on femoral histomorphometry for age-at-death estimation in order to examine the relationship between sample size and SEE values, and makes recommendations for minimum reporting requirements for age-at-death studies based on statistical data. The SEE from a total of 33 studies are analysed. Sample size and confidence intervals are explored using Hennig and Cooper's simulation modelling. Analysis of effect size through a fixed-effect model is performed on 5/33 studies to examine the relationship between sample size and effect size. The pooled sex formulae from Nor etal., Martrille etal. and Thompson and the two sex-specific formulae of Pfeiffer are examined, as they report mean and standard deviation values for both chronological and estimated ages. The results of these analyses support sampling theory, specifically wide variation in SEE when N<100, narrowing as the sample size increases, and lower effect sizes in the larger of the five studies examined. The findings provide some support for a minimum threshold of 100‒150 individuals for histomorphometric age-at-death estimation. Analysis of effect size is suggested for future investigation in meta-analyses of forensic anthropological age-estimation studies. To ensure increased precision and meaningful comparison, large samples should be used for histomorphometry, and authors should report SEE and discrete statistics (e.g. n, mean, standard deviation) for both chronological age and estimated age.

Bounds on Phase, Frequency, and Timing Synchronization in Fully Digital Receivers With 1-bit Quantization and Oversampling

Digital receivers based on 1-bit quantization and oversampling w.r.t. the transmit signal bandwidth promise lower energy consumption. However, since 1-bit quantization is a highly non-linear operation, standard receiver algorithms cannot be applied. Thus, we derive performance bounds for phase, timing, and frequency estimation in order to gain a deeper insight into the impact of 1-bit quantization and oversampling. We identify uniform phase and sample dithering as crucial to combat the effect of the non-linearity introduced by 1-bit quantization. Since oversampling results in noise correlation, a closed form of the likelihood function is not available. Thus, we study a system model with white noise by adapting the receive filter bandwidth to the sampling rate. Considering the aforementioned dithering, we obtain very tight closed form lower bounds on the Cramér-Rao lower bound (CRLB) in the large sample regime. We show that with uniform phase and sample dithering, all large sample properties of the CRLB of the unquantized receiver are preserved under 1-bit quantization, except for an signal-to-noise ratio (SNR) dependent performance loss that can be decreased by oversampling. Numerical computations show that the properties of the CRLB for white noise still hold for colored noise except that the performance loss due to 1-bit quantization is reduced.

Estimating the Annual Exceedance Probability of Water Levels and Wave Heights from High Resolution Coupled Wave-Circulation Models in Long Island Sound

Accurately estimating the probability of storm surge occurrences is necessary for flood risk assessments. This research models Long Island Sound using a coupled coastal circulation and wave model (FVCOM-SWAVE) to hindcast the 44 highest storms between 1950–2018 and fitted Poisson-GPD distributions to modelled water levels and wave heights. Floodwater elevations and significant wave heights for 10% (1/10), 3% (1/30), 2% (1/50), and 1% (1/100) annual exceedance probabilities are provided for all Connecticut coastal towns. The results show that both water levels and their corresponding return intervals are higher along the western coast of Connecticut than the eastern coast, whereas significant wave heights increase eastward. Comparing our model results with those from the North Atlantic Coast Comprehensive Study (NACCS) shows that the mean NACCS results are higher for water levels and lower for significant wave heights for longer return periods. Likewise, the Federal Emergency Management Agency (FEMA) results in large errors compared to our results in both eastern and western coastal Connecticut regions. In addition to evaluating historical risks, we also added a sea-level height offset of 0.5 m for 2050 estimates in order to examine the effect of rising sea-levels on the analysis. We find that sea-level rise reduces the return period of a 10-year storm to two years. We advise periodically updating this work as improved sea-level rise projections become available.

Data‐driven physics‐based digital twins via a library of component‐based reduced‐order models

SummaryThis work proposes an approach that combines a library of component‐based reduced‐order models with Bayesian state estimation in order to create data‐driven physics‐based digital twins. Reduced‐order modeling produces physics‐based computational models that are reliable enough for predictive digital twins, while still being fast to evaluate. In contrast with traditional monolithic techniques for model reduction, the component‐based approach scales efficiently to large complex systems, and provides a flexible and expressive framework for rapid model adaptation—both critical features in the digital twin context. Data‐driven model adaptation and uncertainty quantification are formulated as a Bayesian state estimation problem, in which sensor data are used to infer which models in the model library are the best candidates for the digital twin. This approach is demonstrated through the development of a digital twin for a 12‐ft wingspan unmanned aerial vehicle. Offline, we construct a library of pristine and damaged aircraft components. Online, we use structural sensor data to rapidly adapt a physics‐based digital twin of the aircraft structure. The data‐driven digital twin enables the aircraft to dynamically replan a safe mission in response to structural damage or degradation.

Reflection on exam grades to improve calibration of secondary school students: a longitudinal study

Grades provide students with information about their level of performance. However, grades may also make students more aware of how well they have estimated their performance, their so-called calibration accuracy. This longitudinal quasi-experimental study, set in secondary education, examined how to increase students’ awareness of the accuracy of their grade estimates in order to improve their calibration accuracy. During an entire school year, students from year 1, 2, and 3 provided grade estimates after each of their French exams. Subsequently, when students received their grades, the level of reflection support on their earlier estimates was manipulated. The first group of students just received their grade, the second group had to calculate the difference between their estimate and the actual grade, and the third group also had to reflect on reasons for a possible mismatch. We expected that more reflection support would lead to more improvement in calibration accuracy. Results showed that providing grade estimates already improved calibration accuracy over the school year, regardless of level of reflection support. This finding shows that asking for grade estimates is an easy-to-implement way to improve calibration accuracy of students in secondary education.

Relationship between energy demand, financial development, and carbon emissions in a panel of 101 countries: "go the extra mile" for sustainable development.

The objective of the study is to examine the impact of energy demand on carbon emissions in mediation of financial development and economic growth in a panel of 101 countries by using the time series data from 1995 to 2018. The study employed dynamic GMM estimator in order to reduce possible endogeneity in the given model. Further, the study used Granger causality and innovation accounting matrix (IAM) to find the causal relationships and variance error shocks between the variables. The results show that energy demand and FDI inflows increase carbon emissions, while financial development decreases carbon emissions across countries. Moreover, the results confirmed the inverted U-shaped relationship between income and emissions with a turning point of US$43,500. Among 101 countries, only 13 countries hold environmental Kuznets curve (EKC) hypothesis as their per capita income surpassed the stated turning point, while the remaining countries exhibit "race to the bottom" hypothesis. The feedback relationship is established between (i) income and carbon emissions, (ii) money supply and carbon emissions, and (iii) FDI inflows and energy demand across countries, whereas one-way linkages found in (i) carbon emissions to money supply, (ii) energy demand to money supply, (iii) money supply to FDI inflows and income, and (iv) energy demand to income across countries. The IAM analysis shows that energy demand, FDI inflows, and money supply will likely to increase carbon emissions, while money supply will decrease carbon emissions over a time horizon.

Improving Robot Localization Using Doppler-Based Variable Sensor Covariance Calculation.

This paper describes a localization module for an autonomous wheelchair. This module includes a combination of various sensors such as odometers, laser scanners, IMU and Doppler speed sensors. Every sensor used in the module features variable covariance estimation in order to yield a final accurate localization. The main problem of a localization module composed of different sensors is the accuracy estimation of each sensor. Average static values are normally used, but these can lead to failure in some situations. In this paper, all the sensors have a variable covariance estimation that depends on the data quality. A Doppler speed sensor is used to estimate the covariance of the encoder odometric localization. Lidar is also used as a scan matching localization algorithm, comparing the difference between two consecutive scans to obtain the change in position. Matching quality gives the accuracy of the scan matcher localization. This structure yields a better position than a traditional odometric static covariance method. This is tested in a real prototype and compared to a standard fusion technique.

An innovative ultrasonic time of flight method based on extended Kalman filter for wind speed measurement

Ultrasonic measuring systems have high speed response, good accuracy and they are used in measuring fluid velocity and distance. Techniques based on time difference and signal processing have been used to time of flight estimation in order to reduce uncertainties. This work presents the development of a new methodology for time of flight estimation with ultrasound signals for wind speed measurement. The proposed method, based on the extended Kalman filter, uses a model for indirect time of flight measurement, through signal amplitude measurements of the receiving transducer. Experimental results performed using a wind tunnel, showed a performance of the proposed technique with time of flight values with an accuracy comparable to the phase difference technique in frequency domain, however without restricting the measurement range to one signal cycle (2π).

What do the value-at-risk measure and the respective legislative framework really offer to financial stability? Critical views and pro-cyclicality

In this paper, we examine how value at risk (VaR) contributes to the financial market's stability. We apply the Guidelines on Risk Measurement and the Calculation of Global Exposure and Counterparty Risk for UCITS of the Committee of European Securities Regulators (CESR 2010) to the main indices of the 12 stock markets of the countries that have used the euro as their official currency since its initial circulation. We show that gaps in the legislative framework give incentives to investment funds to adopt conventional models for the VaR estimation in order to avoid the increased costs that the advanced models involve. For this reason, we apply the commonly used historical simulation VaR (HVaR) model, which is: (i) taught at most finance classes; (ii) widely applied in the financial industry; and (iii) accepted by CESR (2010). The empirical evidence shows the HVaR does not really contribute to financial stability, and the legislative framework does not offer the appropriate guidance. The HVaR model is not representative of the real financial risk, and does not give any signal for trends in the near future. The HVaR is absolutely backward-looking and this increases the stock market's overreaction. The fact that the suggested confidence level in CESR (2010) is set at 99 percent leads to hidden pro-cyclicality. Scholars and researchers should focus on issues such as the abovementioned, otherwise the VaR estimations will become, sooner or later, just a formality, and such conventional statistical measures rarely contribute to financial stability.

Investigations on real time RSSI based outdoor target tracking using kalman filter in wireless sensor networks

Target tracking is essential for localization and many other applications in Wireless Sensor Networks (WSNs). Kalman filter is used to reduce measurement noise in target tracking. In this research TelosB motes are used to measure Received Signal Strength Indication (RSSI). RSSI measurement doesn’t require any external hardware compare to other distance estimation methods such as Time of Arrival (TOA), Time Difference of Arrival (TDoA) and Angle of Arrival (AoA). Distances between beacon and non-anchor nodes are estimated using the measured RSSI values. Position of the non-anchor node is estimated after finding the distance between beacon and non-anchor nodes. A new algorithm is proposed with Kalman filter for location estimation and target tracking in order to improve localization accuracy called as MoteTrack InOut system. This system is implemented in real time for indoor and outdoor tracking. Localization error reduction obtained in an outdoor environment is 75%.

Spatio-temporal modelling of hydro-meteorological derived risk using a Bayesian approach: a case study in Venezuela

Extreme environmental events have considerable impacts on society. Preparation to mitigate or forecast accurately these events is a growing concern for governments. In this regard, policy and decision makers require accurate tools for risk estimation in order to take informed decisions. This work proposes a Bayesian framework for a unified treatment and statistical modeling of the main components of risk: hazard, vulnerability and exposure. Risk is defined as the expected economic loss or population affected as a consequence of a hazard event. The vulnerability is interpreted as the loss experienced by an exposed population due to hazard events. The framework combines data of different spatial and temporal supports. It produces a sequence of temporal risk maps for the domain of interest including a measure of uncertainty for the hazard and vulnerability. In particular, the considered hazard (rainfall) is interpolated from point-based measured rainfall data using a hierarchical spatio-temporal Kriging model, whose parameters are estimated using the Bayesian paradigm. Vulnerability is modeled using zero-inflated distributions with parameters dependent on climatic variables at local and large scales. Exposure is defined as the total population settled in the spatial domain and is interpolated using census data. The proposed methodology was applied to the Vargas state of Venezuela to map the spatio-temporal risk for the period 1970–2006. The framework highlights both high and low risk areas given extreme rainfall events.

Coste de las visitas médicas y urgencias asociadas al estrés laboral en España

Job stress is a serious problem for workers’ health, organizations and society. The aim of this paper is to assess the health care costs caused by the increase of health care services. Using the National Health Surveys (2006/07 and 2011/12), we have applied propensity score matching methods and microeconometric estimation in order to quantify the visits to general practitioner (GP), specialists and emergency visits due to job stress. In 2006/07 the assessment of these visits permits to conclude that the economic costs are 145.26 million € due to GP visits and emergency rooms. In 2011/12 the costs are 55.17 million € due to hospital emergencies.

Peeling of Dirac field on Kerr spacetime

In a recent paper with Nicolas [Ann. Henri Poincaré 20(10), 3419–3470 (2019); arXiv:1801.08996], we studied the peeling for scalar fields on Kerr metrics. The present work extends these results to Dirac fields on the same geometrical background. We follow the approach initiated by Mason and Nicolas [J. Inst. Math. Jussieu 8(1), 179–208 (2009); arXiv:gr-qc/0701049 and L. Mason and J.-P. Nicolas, J. Geom. Phys. 62(4), 867–889 (2012); arXiv:1101.4333] on the Schwarzschild spacetime and extended to Kerr metrics for scalar fields. The method combines the Penrose conformal compactification and geometric energy estimates in order to work out a definition of peeling at all orders in terms of Sobolev regularity near I, instead of Ck regularity at I, and then provides the optimal spaces of initial data such that the associated solution satisfies peeling at a given order. The results confirm that the analogous decay and regularity assumptions on initial data in the Minkowski and Kerr spacetimes produce the same regularity across null infinity. Our results are local near spacelike infinity and are valid for all values of the angular momentum of the spacetime, including for fast Kerr metrics.

A STUDY OF THE ACTIVITY OF SOCIAL ENTERPRISES IN BULGARIA

Many companies in Bulgaria are defined themselves as Social enterprises (SE). The process of arising of new SE and their stop of working is a significant challenge. The situation with SE is very dynamic and their real status should be studied to estimate their impact on the Bulgarian economy. Here we describe the real picture of SE in Bulgaria, based on officially published data. In result of data analysis we make conclusions and estimations in order to stabilize these processes. The strong dominance of Sofia (Capital) was detected in both Non-financial (NFE) and Non-for-profit (NPE) sectors of SE. It could be further ascertain if the operations are really in Sofia city or only their registration. Lightly positive trend was found in the number of both NFE and NPE. Highly negative was the trend in the number of employees engaged in NFE, while the NPE sector remained relatively stable. This deviation in employment rate could be explained by a type of funding: NPE were funded predominantly by state or EU projects, while NFE tried to keep the enterprises and balanced by lowering the personnel, probably due to insufficient financing and economic crisis. In conclusion, more attention in needed by the government and the EU regarding the funding of NFE.

A Unified Algorithm for the Non-Convex Penalized Estimation: The ncpen Package

Various R packages have been developed for the non-convex penalized estimation but they can only be applied to the smoothly clipped absolute deviation (SCAD) or minimax concave penalty (MCP). We develop an R package, entitled ncpen, for the non-convex penalized estimation in order to make data analysts to experience other non-convex penalties. The package ncpen implements a unified algorithm based on the convex concave procedure and modified local quadratic approximation algorithm, which can be applied to a broader range of non-convex penalties, including the SCAD and MCP as special examples. Many user-friendly functionalities such as generalized information criteria, cross-validation and L2-stabilization are provided also.

Mudfold: An R Package for Nonparametric IRT Modelling of Unfolding Processes

Abstract Item response theory (IRT) models for unfolding processes use the responses of individuals to attitudinal tests or questionnaires in order to infer item and person parameters located on a latent continuum. Parametric models in this class use parametric functions to model the response process, which in practice can be restrictive. MUDFOLD (Multiple UniDimensional unFOLDing) can be used to obtain estimates of person and item ranks without imposing strict parametric assumptions on the item response functions (IRFs). This paper describes the implementation of the MUDFOLD method for binary preferential-choice data in the R package mudfold. The latter incorporates estimation, visualization, and simulation methods in order to provide R users with utilities for nonparametric analysis of attitudinal questionnaire data. After a brief introduction in IRT, we provide the method ological framework implemented in the package. A description of the available functions is followed by practical examples and suggestions on how this method can be used even outside the field of psychometrics.

Estimation of ARMA Model Order via Artificial Neural Network for Modeling Physiological Systems

Model order estimation is the most important but challenging step for system identification using an autoregressive moving average (ARMA) model. In this paper, we propose an artificial neural network (ANN) structure to estimate the best model order for ARMA modeling of linear, time-invariant systems using the system’s input and output data. The proposed algorithm creates an equivalent ANN structure corresponding to an ARMA model and chooses the best model order using the neural network’s mean squared error (MSE) loss function. The proposed method is validated on simulated ARMA model data and the performance is compared with the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). We considered three hypothetical linear systems and performed 100 Monte Carlo simulations for each model, with different data lengths, and with additive noise. For each of the three simulation models, the proposed method significantly outperformed the AIC and BIC in terms of the correct model order selection. Finally, the proposed ANN-based model order estimator was successfully applied to determine the dynamic relationship between heart rate (HR) and instantaneous lung volume (ILV) using an ARMA model. The results indicate that physiological and biological systems can be modeled with appropriate ARMA models obtained by the proposed algorithm to better understand the system dynamics.