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High-resolution peripheral quantitative computed tomography (HR-pQCT) has emerged as a powerful imaging technique for characterizing bone microarchitecture in the human peripheral skeleton. The second-generation HR-pQCT scanner provides improved spatial resolution and a shorter scan time. However, the transition from the first-generation (XCTI) to second-generation HR-pQCT scanners (XCTII) poses challenges for longitudinal studies, multi-center trials, and comparison to historical data. Cross-calibration, an established approach for determining relationships between measurements obtained from different devices, can bridge this gap and enable the utilization and comparison of legacy data. The goal of this study was to establish cross-calibration equations to estimate XCTII measurements from XCTI data, using both the standard and Laplace-Hamming (LH) binarization approaches. Thirty-six volunteers (26-85 yr) were recruited and their radii and tibiae were scanned on both XCTI and XCTII scanners. XCTI images were analyzed using the manufacturer's standard protocol. XCTII images were analyzed twice: using the manufacturer's standard protocol and the LH segmentation approach previously developed and validated by our team. Linear regression analysis was used to establish cross-calibration equations. Results demonstrated strong correlations between XCTI and XCTII density and geometry outcomes. For most microstructural outcomes, although there were considerable differences in absolute values, correlations between measurements obtained from different scanners were strong, allowing for accurate cross-calibration estimations. For some microstructural outcomes with a higher sensitivity to spatial resolution (eg, trabecular thickness, cortical pore diameter), XCTII standard protocol resulted in poor correlations between the scanners, while our LH approach improved these correlations and decreased the difference in absolute values and the proportional bias for other measurements. For these reasons and due to the improved accuracy of our LH approach compared with the standard approach, as established in our previous study, we propose that investigators should use the LH approach for analyzing XCTII scans, particularly when comparing to XCTI data.

ABSTRACT The fixed‐time robust neural learning control for nonlinear strict‐feedback systems with output constraint and unknown dynamics is investigated in this article. The system nonlinearity is identified using neural network (NN) while the prescribed performance design is employed to avoid the output constraint to be violated. Considering the intelligent approximator only working in an active domain, the smooth switching mechanism is introduced to indicate its effectiveness. Based on the designs of neural approximation and switching signal, a robust adaptive controller is constructed where the NN works in the active domain and the fixed‐time robust design works outside of that domain. Especially for the neural update law, the estimation error is constructed based on a state observer and a serial‐parallel identification system, even though the real system nonlinearity is not available. The effective neural approximation is achieved while the error signals are ensured to be practical fixed‐time stable. Simulation tests are ultimately conducted to demonstrate the validity of the design. [ABSTRACT FROM AUTHOR]

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In the realm of speaker identification, pitch frequency serves as a fundamental feature. However, this feature can be compromised when a speaker records his speech in a closed room, resulting in distorted signal features. This distortion not only reduces the effectiveness of speaker identification systems, but also opens the door for potential deception by hackers who exploit the reverberation effects in closed rooms. To address this concern, the correction of estimated pitch frequencies emerges as an essential step for the success of speaker identification systems. This paper presents a Hybrid Approach for Estimating Pitch Frequency (HAEPF) that integrates both the Zero Crossing Rate (ZCR) and Auto-Correlation Function (ACF) methods. Furthermore, the paper delves into the modeling of reverberant speech using comb filtering, shedding light on how multiple reflections impact the accuracy of pitch frequency estimation. Several simulation experiments were conducted to assess pitch frequency estimation for speech signals, both in the presence and absence of reverberation. The estimation errors were calculated for all three scenarios of reverberation (mild, moderate, and severe). The results clearly indicate that as the degree of reverberation, characterized by the comb filter order, increases, the pitch frequency estimation error also increases. The estimation accuracy of the proposed approach is calculated in terms of Pitch Frequency Estimation Error (PFEE), Gross Pitch Error (GPE) and Octave Error (OER) and is compared with those of several established pitch frequency estimation methods. The proposed approach exhibits a notable enhancement even in noisy environments, reducing PFEE by 43%, and achieving GPE and OER of less than 0.3 and 0.12, respectively, at a Signal-to-Noise Ratio (SNR) of 0 dB. [ABSTRACT FROM AUTHOR]

Semi-supervised classification has gained widespread popularity because of their superior ability to handle unlabeled samples in practical problems. This paper has presented a novel estimation error-ranked LSSVM method with double Mahalanobis-kernel which is used for semi-supervised classification. The main point is to construct two Mahalanobis distances in Hilbert space to form double Mahalanobis-kernel by considering the relationship between the characteristics of two sorts of samples, so as to reduce the influence of non-informational dimensions. Furthermore, the implementation of the proposed method is required to solve the label security problem of unlabeled samples. The unlabeled sample with the minimum evaluated error is selected for labeling, which effectively ensures the accuracy of the unlabeled sample labeling. This method not only considers the similarity of sample features, but also focuses on the security of unlabeled samples. And based on the experimental results of four artificial data sets and several UCI data sets, it verifies the effectiveness of the semi-supervised method with double Mahalanobis-kernel. Especially considering the experimental results of five disease diagnosis data sets, it demonstrates the potential of the proposed semi-supervised classification method in medical diagnosis. [ABSTRACT FROM AUTHOR]

This paper investigates the moving horizon estimation (MHE) problem of mobile robots with measurement outliers. To deal with measurement outliers, the Euclidean distance of measurement error is introduced to detect and remove abnormal data. Then, we use dimension expansion methods to preprocess the data of heterogeneous sensors, such as UWB and IMU. An MHE-based method is proposed that deals with the localization of mobile robots with measurement outliers in the presence of bounded noise. An MHE-based estimator is obtained by solving a regularized least-squares problem. We analyze the convergence of the estimation error system using the properties of norm inequalities, and an upper bound is derived for the estimation error system by using norm inequality. Finally, simulation and experiment examples are given to verify the effectiveness and applicability of the proposed approach. [ABSTRACT FROM AUTHOR]

While risk tolerance is often elicited using questionnaire-based instruments, in this paper, we evaluate the merits of an inversion-based technique, wherein risk aversion parameters are inferred from an individual's portfolio holdings and a sequence of realized returns. We obtain expressions for the finite sample and asymptotic variance of the estimated risk aversion parameter under the inversion approach with a single risky asset, demonstrating that confidence intervals for parameter estimates are relatively wide. Extending the analysis, we show that inferring risk aversion from multiple risky assets does not typically serve to reduce the estimated parameter variance, but rather propagates estimation error. [ABSTRACT FROM AUTHOR]

Questions: We asked how interobserver error in sampling vegetation (excluding trees) varied over time, space and habitat type; determined whether there were any obvious correlates of observer error; and evaluated evidence of bias among observers. Location: Nine national park units in the Midwestern USA. Methods: We quantified observer error in the context of a long‐term monitoring program employing three observers, evaluating interobserver error across 11 locations in the Midwestern USA over five years. The vegetation (excluding trees) was sampled independently by two teams of observers at prairie and woodland locations (n = 94 plots total). Results: Total pseudoturnover ranged between 20.2% and 22.1% at prairie locations, and between 16.8% and 28.6% at woodland locations. The overlooking component of pseudoturnover accounted for 75% or more of total pseudoturnover, with misidentification and cautious components each contributing 19% or less of the total, depending on location. The percentage of comparisons in which both observers recorded the same cover class ranged from 71.3% to 78.5% at the prairie locations and 56.9% to 85.6% at woodland locations. When observers did not agree on cover class, they were off by more than one class less than 6% of the time. Overlooking error was more likely to occur for species with less cover, while estimation error was more likely to occur for species with greater cover. A bias existed among observers, as the least experienced observer recorded 6.2%–11.8% more species than the other two observers. Interobserver bias also existed for rates of estimation error, as one observer consistently recorded higher cover classes. Conclusions: Observer error is a pervasive aspect of vegetation sampling. Continued training and experience yielded limited increases in precision. Elements of the sampling design probably reduced observer error to a certain degree, although some level of interobserver error in vegetation surveys is unavoidable. [ABSTRACT FROM AUTHOR]

An estimation error is the difference between motor imagery and actual motor time. Previous studies have reported that overestimation (motor imagery time < actual motor time) is related to physical functions in healthy individuals. However, this finding is unclear among individuals diagnosed with a stroke. We investigated whether overestimation is related to physical function in individuals diagnosed with a stroke. This study included 71 individuals diagnosed with a stroke (mean age, 67.2 ± 13.4 years; mean time since stroke, 68.4 ± 44.7 days). Imagined timed up and go test (iTUGT) was performed to assess the estimation error. First, the iTUGT was performed; subsequently, the TUGT was performed. The estimation error was calculated by subtracting the TUGT from the iTUGT, with two standard deviations (2 SDs) being calculated. Furthermore, patients were classified into appropriate estimation (AE, within ±2 SD) and overestimation (OE, over −2 SD) groups. Both groups were tested using the estimation error, iTUGT, TUGT, Berg Balance Scale (BBS), and Brunnstrom Recovery Stage (BRS). Subsequently, a correlation analysis was performed. The OE group had a significantly higher estimation error than the AE group (OE: −7.08 ± 6.87 s, AE: −0.29 ± 1.53 s, P <.001). Moreover, the OE group had significantly lower TUGT and BBS than the AE group. The estimation error was correlated with the TUGT, BBS, and lower-limb BRS (ρ = −0.454, 0.431, 0.291, respectively; P <.05). Overestimation was associated with TUGT and balance function in individuals diagnosed with a stroke. [ABSTRACT FROM AUTHOR]

The paper presents efficiently realized approximations of random functions, which have been developed by the authors and are numerically simulated for study of stochastic processes of particle transfer, including the problems of process criticality fluctuations in random media with multiplication. Efficient correlation-randomized algorithms are constructed for approximating an ensemble of particle trajectories using a correlation function or only a correlation scale of medium. A simple grid model of an isotropic random field is formulated, which reproduces a given average correlation length. This ensures high accuracy in solving stochastic transfer problems for a small correlation scale. The algorithms are tested by solving a test problem of photon transfer and a problem of estimating the overexponential average particle flux in a random medium with multiplication. [ABSTRACT FROM AUTHOR]

The determination of a mobile terminal's position with high accuracy and ubiquitous coverage is still challenging. Global satellite navigation systems (GNSSs) provide sufficient accuracy in areas with a clear view to the sky. For GNSS-denied environments like indoors, complementary positioning technologies are required. A promising approach is to use the Earth's magnetic field for positioning. In open areas, the Earth's magnetic field is almost homogeneous, which makes it possible to determine the orientation of a mobile device using a compass. In more complex environments like indoors, ferromagnetic materials cause distortions of the Earth's magnetic field. A compass usually fails in such areas. However, these magnetic distortions are location dependent and therefore can be used for positioning. In this paper, we investigate the influence of elementary structures, in particular a sphere and a cylinder, on the achievable accuracy of magnetic positioning methods. In a first step, we analytically calculate the magnetic field around a sphere and a cylinder in an outer homogeneous magnetic field. Assuming a noisy magnetic field sensor, we investigate the achievable positioning accuracy when observing these resulting fields. For our analysis, we calculate the Cramér–Rao lower bound, which is a fundamental lower bound on the variance of an unbiased estimator. The results of our investigations show the dependency of the positioning error variance on the magnetic sensor properties, in particular the sensor noise variance and the material properties, i.e., the relative permeability of the sphere with respect to the cylinder and the location of the sensor relative to the sphere with respect to the cylinder. The insights provided in this work make it possible to evaluate experimental results from a theoretical perspective. [ABSTRACT FROM AUTHOR]

The presented study concerns with evaluating the dynamic performance of an isotropic sinusoidal brushless motor drive while utilizing different sensorless schemes. Three estimation algorithms are considered: the first depends on extracting the speed and position via comparing two values of motor's voltage in two co-ordinate systems; the second extracts the speed and position signal via comparing two different values of motor's current defined in two co-ordinates; while the third depends on estimating the motor's flux and use it to get the speed and position. The vector control is adopted to manage the drive dynamics. The detailed mathematical derivations for all system components are presented to facilitate the performance analysis. The theoretical base of each sensorless scheme is also described in detail. The target of the provided comparative analysis is to outline the weakness and strength points of each adopted sensorless schemes while estimating the speed and rotor position for a wide operating speed range. The judgment is measured in terms of the speed and rotor position estimation errors and the dynamic response as well. The performance evaluation process is carried out using MATLAB/Simulink software in which all system parts are simulated using their mathematical models. The findings from the study state that when it comes to dynamic speed behaviour, the voltage-based sensorless technique dominates, while the current-based sensorless approach gives stability in speed estimate priority. Alternatively, the third adopted sensorless scheme offers an acceptable high-speed performance and respectable performance at lower speeds. Statistically, it is found that the voltage-based estimation technique gives respectively lower speed and position estimation errors with percentages of 35% and 10% lower than their values under the currentbased estimation technique, and with percentages of 35% and 30% lower than their values under the third adopted scheme. [ABSTRACT FROM AUTHOR]

This paper presents a fault estimation technique based on the decentralized fuzzy observer for nonlinear large-scale systems, which are considered to be consisted of fuzzy subsystems and uncertain interconnections. Based on a Takagi–Sugeno fuzzy model for the subsystems of the large-scale system and the decentralized fuzzy observer, the $H_{\infty }$ performance of the fault estimation problem is established by using the estimation error model. By using $H_{\infty }$ performance inequalities to address the fault estimation problem, the decentralized fuzzy observer design techniques are proposed to guarantee the fault estimation conditions. Also, sufficient conditions of observer design are converted into the linear matrix inequality formats. Finally, an example is provided to verify the effectiveness of the proposed decentralized fuzzy observer design techniques for fault estimation.

This paper studies an optimization problem on the sum of traces of matrix quadratic forms in m semiorthogonal matrices, which can be considered as a generalization of the synchronization of rotations. While the problem is nonconvex, this paper shows that its semidefinite programming relaxation solves the original nonconvex problems exactly with high probability under an additive noise model with small noise in the order of O(m1/4). In addition, it shows that, with high probability, the sufficient condition for global optimality considered in Won, Zhou, and Lange [SIAM J. Matrix Anal. Appl., 2 (2021), pp. 859-882] is also necessary under a similar small noise condition. These results can be considered as a generalization of existing results on phase synchronization.

Summary: To provide the increasing demand for wireless technologies, high capacity with lower latency is vital. New emerging technologies like 5G, millimeter wave, THz, and so forth are able to characterize such complex fading and shadowed environments. Spatial diversity is one of the techniques to minimize fading in the wireless communication channel. The performance of the wireless communication system can be further optimized by using estimators at the receivers. The influence of imperfect channel estimation on the performance over shadowed Beaulieu‐Xie (SBX) fading channels with L‐maximal ratio combining (MRC) diversity is studied. The probability density function (PDF) of the received output signal‐to‐noise ratio (SNR) with L‐MRC over SBX fading channels is derived. Further PDF of the received output SNR, outage probability, and average bit error rate (ABER) using the derived PDF is evaluated for L‐MRC diversity with channel estimation error over SBX fading channels. ABER expression for Gray‐coded rectangular quadrature amplitude modulation over the SBX channel is also evaluated. The study also includes how the system performs under the effect of different shadowing and fading parameters. [ABSTRACT FROM AUTHOR]

Competing procedures, involving data smoothing, weighting, imputation, outlier removal, etc., may be available to prepare data for parametric model estimation. Often, however, little is known about the best choice of preparatory procedure for the planned estimation and the observed data. A machine learning‐based decision rule, an "oracle," can be constructed in such cases to decide the best procedure from a set C$$ \mathcal{C} $$ of available preparatory procedures. The oracle learns the decision regions associated with C$$ \mathcal{C} $$ based on training data synthesized solely from the given data using model parameters with high posterior probability. An estimator in combination with an oracle to guide data preparation is called an oracle estimator. Oracle estimator performance is studied in two estimation problems: slope estimation in simple linear regression (SLR) and changepoint estimation in continuous two‐linear‐segments regression (CTLSR). In both examples, the regression response is given to be increasing, and the oracle must decide whether to isotonically smooth the response data preparatory to fitting the regression model. A measure of performance called headroom is proposed to assess the oracle's potential for reducing estimation error. Experiments with SLR and CTLSR find for important ranges of problem configurations that the headroom is high, the oracle's empirical performance is near the headroom, and the oracle estimator offers clear benefit. [ABSTRACT FROM AUTHOR]

In engineering applications, wireless network control systems are mostly nonlinear systems. Due to long-distance transmission and unreliable communication networks, the measured values of system sensors may be lost in the transmission process, which influences accuracy estimation and system performance. In this study, the problem of unscented Kalman filtering for a class of nonlinear discrete stochastic systems affected by correlated noise and sensor measurement loss is studied. By introducing a random variable that obeys Bernoulli distribution and has known conditional probability to describe the random sensor measurement loss, an algorithm is proposed to compensate the data. The results are verified by standard numerical software. The results show that the filter compensated by the algorithm can estimate the system well, greatly reduce the impact of sensor measurement loss on the filter performance, and increase the accuracy of estimation. [ABSTRACT FROM AUTHOR]

Most PPG-based methods for extracting the respiratory rate (RR) rely on changes in the PPG signal's amplitude, baseline, or frequency. However, several other parameters may provide more valuable information for accurate RR computation. In this study, we explored the capabilities of the respiratory-induced variations in successive systolic differences (RISSDV) of PPG signals to estimate RR. We partitioned fifty-three publicly available recordings into eight 1-min segments and identified peaks and troughs of the PPG signals to quantify respiratory-induced variations in amplitude (RIAV), baseline (RIIV), frequency (RIFV), and peak-to-peak amplitude differences (RISSDV). RR values were extracted by determining the peak frequency of the power spectral density of the four variations and the reference respiratory signal. We assessed each feature's performance by computing the root-mean-squared (RMSE) and mean absolute errors (MAE). RISSDV errors were significantly lower than those of RIAV (RMSE and MAE: p

An unbiased forecast of profit is important in most business environments. Typically, forecasts are generated from data. However, in "Technical Note—Data-Driven Profit Estimation Error in the newsvendor model," Siegel and Wagner identify a strictly positive bias in a natural estimation of expected profit in a data-driven newsvendor model, where managers will expect more profit than will actually be realized, on average. This bias can reach significant proportions (in some cases 50%+) of the true expected profit and could therefore have undesired and damaging effects in the real world. Siegel and Wagner then design a data-driven adjustment that results in an unbiased estimator of expected profit, so that managers may have an accurate forecast of future profit that is free of systematic bias. In this note, we identify a statistically significant error in naively estimating the expected profit in a data-driven newsvendor model, and we show how to correct the error. In particular, we analyze a newsvendor model where the continuous demand distribution is not known, and only a sample of demand data is available. In this context, an empirical demand distribution, that is induced by the sample of data, is used in place of the (unknown) true distribution. The quantity at the critical percentile 1 − c / p of the empirical distribution is known as the sample average approximation order quantity, where p is the unit revenue and c the unit cost. We prove that, if the empirical distribution is used to estimate the expected profit, this estimate exhibits a positive, statistically significant bias. We derive a closed-form expression for this bias that only depends on p and c and the sample of data. The bias expression can then be used to design an adjusted expected profit estimate, which we prove is asymptotically unbiased. Numerical hypothesis testing experiments confirm that the unadjusted estimation error is statistically significant, whereas the adjusted estimation error is not significantly different from zero. The bias is not negligible in our numerical experiments: For lognormally and normally distributed demand, the unadjusted error is 2.4% and 3.0% of the true expected profit, respectively. A more detailed exploration with exact finite-sample results for exponentially distributed demand demonstrates that the estimation error percentage can be much larger. Funding: A. F. Siegel gratefully acknowledges the support of the Grant I. Butterbaugh Professorship. M. R. Wagner gratefully acknowledges the support of a Neal and Jan Dempsey Faculty Fellowship. Both are from the Michael G. Foster School of Business, University of Washington. Supplemental Material: The online appendix is available at https://doi.org/10.1287/opre.2023.0070. [ABSTRACT FROM AUTHOR]

The localization of pollution sources is one of the main tasks in environmental engineering. For this paper, models of spatial distribution of nitrate concentration in groundwater were created, and the point of highest concentration was determined. This point represents the assumed location of the pollution source and differs from the actual location, so there is a certain distance between the measured and assumed location. This paper puts forward a new hypothesis that the distance between the measured and the assumed location is a function of the variance of the estimation error. The scientific contribution of this paper is based on the fact that the interaction of statistical and geostatistical methods can locate the dominant point source of pollution or narrow down the search area. The above hypothesis is confirmed by the example of the Varaždin wellfield, which was closed due to an excessively high groundwater nitrate concentration. Seven different interpolation methods were used to create spatial distribution models. Each method provides a different model, a different variance of the estimation error, and estimates of the location of the pollution source. The smallest value of variance of the estimation error of 1.65 was obtained for the minimum curvature interpolation method and the largest value of variance (24.49) was obtained for the kriging with logarithmic variogram. Our results show a nonlinear and monotonic relationship between the distance and the variance of the estimation error, so logarithmic and rational quadratic models were fitted to the scatter point data. The models were linearized, a t-test was performed, and the results show that the models can be considered reliable, which is confirmed by the values of the coefficients of determination of the linearized models, which are around 0.50. The obtained results can be used in planning additional research work to determine the measured location of the pollution source. The research methodology we used is universal and can be applied to other locations where high concentrations of certain contaminants have been detected in groundwater in alluvial aquifers. [ABSTRACT FROM AUTHOR]

The determination of a mobile terminal’s position with high accuracy and ubiquitous coverage is still challenging. Global satellite navigation systems (GNSSs) provide sufficient accuracy in areas with a clear view to the sky. For GNSS-denied environments like indoors, complementary positioning technologies are required. A promising approach is to use the Earth’s magnetic field for positioning. In open areas, the Earth’s magnetic field is almost homogeneous, which makes it possible to determine the orientation of a mobile device using a compass. In more complex environments like indoors, ferromagnetic materials cause distortions of the Earth’s magnetic field. A compass usually fails in such areas. However, these magnetic distortions are location dependent and therefore can be used for positioning. In this paper, we investigate the influence of elementary structures, in particular a sphere and a cylinder, on the achievable accuracy of magnetic positioning methods. In a first step, we analytically calculate the magnetic field around a sphere and a cylinder in an outer homogeneous magnetic field. Assuming a noisy magnetic field sensor, we investigate the achievable positioning accuracy when observing these resulting fields. For our analysis, we calculate the Cramér–Rao lower bound, which is a fundamental lower bound on the variance of an unbiased estimator. The results of our investigations show the dependency of the positioning error variance on the magnetic sensor properties, in particular the sensor noise variance and the material properties, i.e., the relative permeability of the sphere with respect to the cylinder and the location of the sensor relative to the sphere with respect to the cylinder. The insights provided in this work make it possible to evaluate experimental results from a theoretical perspective.

The problem of synchronization over a group G aims to estimate a collection of group elements G 1 ⁎ , ... , G n ⁎ ∈ G based on noisy observations of a subset of all pairwise ratios of the form G i ⁎ G j ⁎ − 1. Such a problem has gained much attention recently and finds many applications across a wide range of scientific and engineering areas. In this paper, we consider the class of synchronization problems in which the group is a closed subgroup of the orthogonal group. This class covers many group synchronization problems that arise in practice. Our contribution is fivefold. First, we propose a unified approach for solving this class of group synchronization problems, which consists of a suitable initialization step and an iterative refinement step based on the generalized power method, and show that it enjoys a strong theoretical guarantee on the estimation error under certain assumptions on the group, measurement graph, noise, and initialization. Second, we formulate two geometric conditions that are required by our approach and show that they hold for various practically relevant subgroups of the orthogonal group. The conditions are closely related to the error-bound geometry of the subgroup — an important notion in optimization. Third, we verify the assumptions on the measurement graph and noise for standard random graph and random matrix models. Fourth, based on the classic notion of metric entropy, we develop and analyze a novel spectral-type estimator. Finally, we show via extensive numerical experiments that our proposed non-convex approach outperforms existing approaches in terms of computational speed, scalability, and/or estimation error. [ABSTRACT FROM AUTHOR]

We investigated the effect of observer error on four commonly used species diversity measures: species richness, Shannon–Weiner diversity, Shannon–Weiner evenness and Simpson's index of diversity. We also evaluated how observer error affects inferences derived from multivariate analyses of species–abundance relationships as determined by non-metric multidimensional scaling (NMS) ordination. Grassland vegetation was sampled by three different botanists at two national park units in Missouri and Kansas, USA. The same plots were sampled by two of the botanists, who compiled lists of species composition and estimated foliar cover. Differences in the data records were then compared. Pseudoturnover (i.e. apparent turnover due to observer error) ranged from 17.1% to 22.1%, and differences in cover class estimation ranged from 21.5% to 30.5%. The percentage difference in species diversity measures between pairs of observers depended on how data were summarized, but were always <20%, and often <10%. Based on these results, species diversity metrics are affected to a relatively smaller extent by observer error than turnover indices. Turnover indices, however, contain more information because they track individual species, whereas species are interchangeable in most species diversity indices. Thus, less of the error is identified because of how species diversity indices are calculated. NMS ordinations revealed that while the characterizations of some plots by different observers were similar, differences between observers' records for other plots resulted in greater separation in ordination space. Points representing one observer's records were often shifted in ordination space in the same direction compared with the other observer. [ABSTRACT FROM AUTHOR]

Previous studies demonstrate that the difference between motor imagery and actual tasks (estimation error) is related to cognitive and physical functions and that a large estimation error (LE) is related to motor imagery ability, including cognitive and physical functions in healthy subjects. The purpose of this study investigated whether estimation error is related to physical and cognitive function in patients with stroke. The study included 60 patients with stroke. The Timed Up and Go Test (TUGT) was employed to assess estimation error. First, the imagined TUGT (iTUGT) was performed; thereafter, the actual TUGT was performed. The estimation error was calculated by subtracting TUGT from iTUGT, with conversion to the absolute value. The patients were classified into the small estimation error (SE) and LE groups, with comparisons of various clinical scores (Mini-Mental State Examination, Berg Balance Scale, 10-m walking speed, Brunnstrom Recovery Stage, and Functional Independence Measure). As a result, the estimation error was significantly larger in the LE group than in the SE group. Cognitive function and balance ability were significantly lower in the LE group than in the SE group. In conclusion, the estimation error was related to physical and cognitive functions in patients with stroke. [ABSTRACT FROM AUTHOR]

In this study, a sampled-data fuzzy fault estimation observer design problem is considered for nonlinear systems. The main idea of this study is to solve the sampled-data output problem by using two approaches of approximate discretization and exact discrete-time design. Especially, in the exact discrete-time design approach, the discretization error problem is solved, and the estimation error is minimized over whole time interval. Based on the above idea, the fuzzy fault estimation observer uses the Takagi–Sugeno fuzzy model to address the fuzzy fault estimation technique, and the fault estimation conditions with an $H_{\infty }$ performance are guaranteed. Next, sufficient conditions of the proposed fault estimation techniques are converted into linear matrix inequality formats. Finally, two examples of circuit and oscillator systems are provided, and the effectiveness of the proposed fault estimation techniques is verified from results of the simulation.

Summary: We develop forecast superiority tests that are robust to the choice of loss function by following Jin, Corradi and Swanson (JCS: 2017), and relying on a mapping between generic loss forecast evaluation and stochastic dominance principles. However, unlike JCS tests, which are not uniformly valid and are correctly sized only under the least favorable case, our tests are uniformly asymptotically valid and non‐conservative. To show this, we establish uniform convergence of HAC variance estimators. Monte Carlo experiments indicate good finite sample performance of our tests, and an empirical illustration suggests that prior forecast accuracy matters in the Survey of Professional Forecasters. [ABSTRACT FROM AUTHOR]

Finite element analysis of complex bodies is frequently used in design to determine the size of deformations. Successive iterations, with progressive refinement of mesh densities, are most often required to obtain a sufficiently accurate convergent numerical solution. This process is costly, time consuming, and requires superior hardware and software. The paper presents a quick and effortless way to determine a sufficiently accurate value of the numerical solution. The mentioned solution is obtained by amending the numerical solution resulting for a certain value of the mesh density of the studied body with an adequate proportionality coefficient determined following the deformation study of simple bodies differently subject to external forces. It is assumed that the elastic displacement of the various bodies has a similar evolution as the mesh density increases and that the values of the proportionality coefficients considered are approximately equal for identical mesh densities. Examples presented are related to the reference body of the mechanical press PAI 25. [ABSTRACT FROM AUTHOR]

Despite the extensive use of sampling to estimate the average number of grape bunches per vine, there is no clearly established sampling protocol that can be used as a reference when performing these estimations. Each practitioner therefore has their own sampling protocol. This study characterised the effect of differences between sampling protocols in terms of estimation errors. The goal was to identify the most efficient practices that will improve the early estimation of an important yield component: average bunch number. First, the appropriateness of including non-productive vines (i.e., dead and missing vines) in the sampling protocol was tested; the objective was to determine whether it is relevant to estimate two yield components simultaneously. Second, sampling protocols with sampling sites of varying size were compared to determine how the spatial distribution of observations and potential spatial autocorrelation affect estimation error. Third, a new confidence interval for estimation error was determined to express expected error as a percentage. It aimed at designing a new tool for finding the best sample size in an operational context. Tests were performed on two vineyards in the South of France, in which the number of bunches per vine had been exhaustively determined on all the plants before flowering. The results show that the simultaneous estimation of number of bunches and proportion of dead and missing vines increased the estimation errors by a factor of 2. Despite the low spatial autocorrelation of bunch number, the results show that the observation must be spread across at least 2 or 3 sampling sites to reduce estimation errors. Finally, the confidence intervals expressed as a percentage were validated and used to define an adequate sample size based on a compromise between the expected precision and the variability observed in the first measurements.

Research Question: This paper investigates the performances of six portfolios constructed using robust optimization methods in the Serbian stock market. Motivation: Motivated by the lack of research that analyses the allocation strategies based on robust optimization in the other non-US markets, this paper analyses the ability of these strategies to produce positive performance in the Serbian financial market. Idea: This paper aims to check whether robust strategies can provide positive risk-adjusted performance compared to simple strategies. Data: The analysis was performed on daily data from 2017 to 2020. Tools: We used monthly portfolio rebalancing with an estimation period of 24 months, applying budget and no-short selling constraints in portfolio construction. As a benchmark, we used two simple strategies, the strategy of market index replication and the equal weighting strategy (1/N). Consequently, the performance of the portfolios is evaluated once a month and calculated for the entire investment period. Findings: Empirical results suggest that robust optimization methods improve portfolio performance on a risk-adjusted basis. The increase in performance is affected by an increase in turnover, so the stability of weights in the portfolio depends on the compliance of the model characteristics with the conditions prevailing in the market. Contribution: To the best of our knowledge, this is the first article that analyses the performance of robust optimization portfolios for the Serbian stock markets. Analysing the performance of robust optimization strategies and comparing them to two simple strategies, this paper contributes to the existing literature by checking their possibility of obtaining a positive performance in less developed markets. Additionally, all information presented in this paper could help investors optimize their risk allocation and profitability.

To extend the lifetime, improve the power density and reduce the system cost, the film capacitor is applied to replace the large-volume electrolytic capacitor in motor drives. As the dc-link voltage fluctuates periodically, the rotor position estimation error and operation stability are necessary to be concerned in the position sensorless control methods. A cross-decoupling complex filtering-based position estimation method is investigated for the reduced capacitance interior permanent magnet synchronous motor drive system. Harmonic components of the extended back electromotive force (EEMF) caused by the dc-link voltage fluctuation are analyzed to evaluate the additional rotor position error. A cross-decoupling complex filter realized by the multiple first-order complex filters with different center frequencies is designed to improve the position estimation precision. Harmonics of the EEMF are suppressed and the position error can be eliminated. The estimation precision and operation ability are improved. Experimental results are performed to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Integrated sensing and communications (ISAC) is emerging as a key technology of 6G. Owing to the low peak-to-average power ratio (PAPR) property, discrete Fourier transform spread orthogonal frequency-division multiplexing (DFT-s-OFDM) is helpful to improve the sensing range and suitable for high-frequency transmission. However, compared to orthogonal frequency-division multiplexing (OFDM), the sensing accuracy of DFT-s-OFDM is relatively poor. In this paper, frequency-domain spectral shaping (FDSS) is adopted to enhance the performances of DFT-s-OFDM including sensing accuracy and PAPR by adjusting the correlation of signals. Specifically, we first establish a signal model for the ISAC system, followed by the description of performance indicators. Then, we analyze the influence of amplitude fluctuation of frequency domain signals on sensing performance, which shows the design idea of FDSS-enhanced DFT-s-OFDM. Further, a FDSS-enhanced DFT-s-OFDM framework is introduced for ISAC, where two types of FDSS filters including a pre-equalization filter and an isotropic orthogonal transform algorithm (IOTA) filter are designed. The simulation results show that the proposed scheme can obtain about 4 dB performance gain in terms of sensing accuracy over DFT-s-OFDM. In addition, FDSS-enhanced DFT-s-OFDM can significantly reduce PAPR and improve the power amplifier efficiency. [ABSTRACT FROM AUTHOR]

Considering a class of nonlinear systems subject to actuator saturation and uncertain control input, this article explored the control capability of an extended-state observer-based active disturbance rejection controller in the presence of disturbances and uncertainties. First, the actuator saturation is handled with the convex hull in the extended-state observer-based control scheme, and an ellipsoid invariant set inside the domain of attraction for control systems is obtained using the Lyapunov method. Second, the enlargement problem of the invariant set is also studied, which illustrates that the convex hull of the ellipsoid invariant sets is also an enlargement invariant set. Finally, comprehensive comparisons with existing methods on a brushless dc motor demonstrate the stronger capability of the designed controller on disturbance rejection and actuator saturation. The results show that the disturbance offset and performance index (integrated time and absolute error and integral absolute error) are about half of the classical proportional–integral–derivative(PID) and double-integral sliding-mode control, or the disturbance rejection capability is about twice in this case. [ABSTRACT FROM AUTHOR]

Impaired baroreflex sensitivity (BRS) is partially responsible for erratic blood pressure fluctuations in End-Stage Renal Disease (ESRD) patients on chronic hemodialysis (HD), which is related to autonomic nervous dysfunction. The sequence method with delayed signals allows for the measurement of BRS in a non-invasive fashion and the investigation of alterations in this physiological feedback system that maintains BP within healthy limits. Our objective was to evaluate the modified delayed signals in the sequence method for BRS assessment in ESRD patients without pharmacological antihypertensive treatment and compare them with those of healthy subjects. We recruited 22 healthy volunteers and 18 patients with ESRD. We recorded continuous BP to obtain a 15-min time series of systolic blood pressure and interbeat intervals during the supine position (SP) and active standing (AS) position. The time series with delays from 0 to 5 heartbeats were used to calculate the BRS, number of data points, number of sequences, and estimation error. The BRS from the ESRD patients was smaller than in healthy subjects (p < 0.05). The BRS estimation with the delayed sequences also increased the number of data points and sequences and decreased the estimation error compared to the original time series. The modified sequence method with delayed signals may be useful for the measurement of baroreflex sensitivity in ESRD patients with a shorter recording time and maintaining an estimation error below 0.01 in both the supine and active standing positions. With this framework, it was corroborated that baroreflex sensitivity in ESRD is decreased when compared with healthy subjects. [ABSTRACT FROM AUTHOR]

The aim of this article is an attempt to provide a more credible control mode or a fault-tolerant control strategy for permanent magnet synchronous motor (PMSM) drives when an unexpected failure occurs to the current or position sensor. Considering that, a position sensorless control method that uses only one current sensor is proposed. Different from a traditional position sensorless control scheme that utilizes additional current sensors to obtain information of multiple phase currents, only a single current sensor (SCS) arranged on one of the current branches is included among the whole control circuit. Three-phase currents are reconstructed by sequentially sampling the SCS under specific voltage vectors, and directly employed for motor position estimation. A nonlinear position observer established according to the gradient descent is adopted. Focusing on the special current-acquisition pattern in this article, a step-size optimization method based on golden section search is proposed to minimize position estimation error. The experimental results reveal that the method in this article is comparable to the traditional full-current-sensor method in terms of position estimation, which confirms the fault-tolerant capability as one certain sensor of the control structure malfunctions. [ABSTRACT FROM AUTHOR]

This article proposes a novel sensorless control strategy for permanent magnet synchronous motor drives using reduced order quasi resonant-based extended state observer (ROQR-ESO) and Newton–Raphson method-based phase-locked loop (NRM-PLL). Due to the low-pass filtering characteristics, the conventional linear extended state observer (LESO) is not adequately able to track the fast-varying back electromotive forces (BEMFs). On this basis, the ROQR-ESO, which plants a ROQR controller into the internal model of conventional LESO, is presented to obtain BEMFs without phase lag. Moreover, the NRM-PLL, regarded as an improved finite position set-PLL, is proposed. After the rotor position is divided into four parts by utilizing a position quartering method, the extremely accurate rotor position can be obtained only by three iterations. In this method, no parameters need to be adjusted and great dynamic response can be achieved. The capacity of the proposed scheme is verified on a 750 W permanent magnet synchronous motor drive platform. [ABSTRACT FROM AUTHOR]

Research Question: This paper investigates the performances of six portfolios constructed using robust optimization methods in the Serbian stock market. Motivation: Motivated by the lack of research that analyses the allocation strategies based on robust optimization in the other non-US markets, this paper analyses the ability of these strategies to produce positive performance in the Serbian financial market. Idea: This paper aims to check whether robust strategies can provide positive risk-adjusted performance compared to simple strategies. Data: The analysis was performed on daily data from 2017 to 2020. Tools: We used monthly portfolio rebalancing with an estimation period of 24 months, applying budget and no-short selling constraints in portfolio construction. As a benchmark, we used two simple strategies, the strategy of market index replication and the equal weighting strategy (1/N). Consequently, the performance of the portfolios is evaluated once a month and calculated for the entire investment period. Findings: Empirical results suggest that robust optimization methods improve portfolio performance on a risk-adjusted basis. The increase in performance is affected by an increase in turnover, so the stability of weights in the portfolio depends on the compliance of the model characteristics with the conditions prevailing in the market. Contribution: To the best of our knowledge, this is the first article that analyses the performance of robust optimization portfolios for the Serbian stock markets. Analysing the performance of robust optimization strategies and comparing them to two simple strategies, this paper contributes to the existing literature by checking their possibility of obtaining a positive performance in less developed markets. Additionally, all information presented in this paper could help investors optimize their risk allocation and profitability. [ABSTRACT FROM AUTHOR]

Ecosystems face various emergent uncertainties owing to factors such as climate change and accelerating anthropogenic impacts. Uncertainty is a major challenge and a barrier that ecosystem management faces, because it is difficult to precisely predict a priori risks that can have significant impacts on ecosystems. Hence, management with adaptive capacity is recommended to deal with such uncertainties, and feedback structures are central mechanisms for such flexible management. This study used mathematical models to clarify the specific impacts of feedback structures on ecosystem management, such as resource and wildlife management. In particular, the impact of errors in estimating ecosystem status when providing feedback and the impact of the time lag before feedback effects were implemented into management were examined. Overestimation of ecosystem status or a large time lag led to undesirable temporal oscillations in ecosystem status. However, these scenarios can be avoided when combined with management practices that limit the impact of management on the ecosystem, such as input control. Ecosystem management tends to have a large spatiotemporal scale, and implementing highly accurate monitoring and sophisticated feedback structures is difficult. However, the results suggest that effective ecosystem management with a simple feedback structure can be achieved through such complementary institutional design. [ABSTRACT FROM AUTHOR]

In this paper, the high-dimensional linear regression problem is explored via the Elastic Net under the transfer learning framework. Within this framework, potentially related source datasets are leveraged to enhance estimation or prediction beyond what can be achieved solely with the target data. When transferable sources are known, an oracle transfer learning algorithm is proposed based on the Elastic Net. Additionally, the ℓ 1 / ℓ 2 estimation error bounds for the corresponding estimator are established. When the transferable sources are unknown, a novel procedure for detecting transferable sources via the Elastic Net is also proposed, with its selection consistency demonstrated under regular conditions. This method transforms the source detection problem into a variable selection problem in high-dimensional space and always gets results that are consistent with the true outcomes. The performance of these methods is further demonstrated through a variety of numerical examples. Finally, our approach is applied to analyze several real datasets for illustrative purposes. [ABSTRACT FROM AUTHOR]

Context: The success or failure of any software development project significantly depends on the accuracy of its effort estimates. Software development effort estimation is the foundation for project bidding, budgeting, planning, and cost control. Problem: The literature shows that a lot of work has been done on software effort estimation. But still, there is a need for improvement in effort estimation by introducing new methodologies. The structured group-based and analogy-based effort estimations are the widely used estimation methods. Nevertheless, there are several shortcomings of using these methods such as lack of experts, lack of historical data, and biasness in expert opinion, which negatively affect the estimation results. Motivation: With the advancement of technologies, such limitations could be overcome. Such as leveraging the applicability of blockchain in several domains such as improvement in the software development process and network security. Method: In this article, we have proposed a Blockchain-Based Software Effort Estimation (BBSEE) methodology to improve the software effort estimation. We employ the proposed method using Web and blockchain technologies. Moreover, we also proposed evaluation criteria to assess the efficacy of the proposed method in terms of Mean Magnitude of Relative Error (MMRE), Mean Absolute Error (MAE) and percentage of successful predictions falling (PRED (25)). Result: We performed several case studies and analyses of expert opinions of 52 organizations to present the efficacy of the proposed method. Conclusion: We observe that the BBSEE method outperforms expert judgment and analogy-based effort estimation methodologies in terms of software effort estimation.

Tomato commercialization in Mexican and Latin-American markets is economically affected by three main physical aspects of the fruit: ripening time, size, and mass. Digital image processing combined with mathematical models and machine learning approaches allows the development of prediction models to minimize fruit waste, among other applications. Particularly crossed validation, linear and non-linear adjustment by quadratic mean least error approximation, and digital image processing are used to obtain a post-harvest mass loss estimation model based upon the fruit’s area. A database for fruit characterization of 97,200 images and mass (kg) and area (cm2) measurement entries over a continuous post-harvest timeline of 54 days was considered in the methodology. Results from the linear (polynomial) adjustment model presented an efficiency of 94.65%, while the non-linear (exponential and potential) adjustment models gave in their turn efficiencies of 99.21 and 99.82%, respectively. It was concluded that the best mass loss estimation model was the potential adjustment one, with an approximation error of just 0.18% between actual and estimated data.

Strong nonlinearities, uncertainties, and disturbances present great challenges to the control of the dissolved oxygen (DO) in a wastewater treatment process (WWTP). To deal with those undesired issues, in this article, a scalable-bandwidth extended state observer (SESO) is proposed, and the SESO-based adaptive sliding-mode control (ASMC) is designed. By the SESO, the time-varying total disturbance can be estimated more accurately and compensated more effectively. For the disturbances that are not addressed completely, an ASMC is employed to suppress them. Due to the advantages of both SESO and ASMC, the DO can be regulated more desirably. The benchmark simulation model Number 1 is taken to verify the proposed SESO-based ASMC. Comparative simulation results highlight the advantages of the proposed approach. [ABSTRACT FROM AUTHOR]