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Total least squares (TLS), also named as errors in variables in statistical analysis, is an effective method for solving linear equations with the situations, when noise is not just in observation data but also in mapping operations. Besides, the Tikhonov regularization is widely considered in plenty of ill-posed problems. Moreover, the structure of mapping operator plays a crucial role in solving the TLS problem. Tensor operators have some advantages over the characterization of models, which requires us to build the corresponding theory on the tensor TLS. This paper proposes tensor regularized TLS and structured tensor TLS methods for solving ill-conditioned and structured tensor equations, respectively, adopting a tensor-tensor-product. Properties and algorithms for the solution of these approaches are also presented and proved. Based on this method, some applications in image and video deblurring are explored. Numerical examples illustrate the effectiveness of our methods, compared with some existing methods. [ABSTRACT FROM AUTHOR]

In order to realize the accurate evaluation of queue posture and action, aiming at the problem that it is difficult to accurately measure the three-dimensional human posture characteristics such as kick height in the training scene, this paper proposed a queue three-dimensional posture feature detection method based on binocular information. The method is divided into two stages; the 2D pose estimation stage and binocular stereo matching stage. In order to improve the accuracy of 2D human pose detection, it designed a 2D pose estimation model based on the improved HRNet network. Firstly, it introduced spatial channel attention in the back-bone network to enhance feature extraction capabilities. The feature fusion layer used an adaptive spatial feature fusion module to fuse multi-scale features of the network. Secondly, it used the unbiased data processing method to encode and decode the heat map to reduce the statistical error of the data. Finally, it adopted a coarse-to-fine multi-stage supervision method during model training to improve the detection accuracy of key points. Based on the 2D pose estimation model, it used the standard correlation matching function to achieve binocular stereo matching, and then obtained the 3D human pose through coordinate transformation. The experimental results show that the improved pose estimation network has better accuracy, the accuracy of the COCO dataset reaches 77.1%, and the detection accuracy of the self-made queue training dataset reaches 86.3%, which are respectively 2.2% and 3.1% higher than the original network. In the kick height experiment of the three-dimensional human body posture, the average relative error measured by the proposed method is 2.5%, which fully verifies the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

In many real-world networks, it is often observed that subgraphs or higher-order structures of certain configurations, e.g. triangles and by-fans, are overly abundant compared to standard randomly generated networks (Milo et al. 2002). However, statistical models accounting for this phenomenon are limited, especially when community structure is of interest. This limitation is coupled with a lack of community detection methods that leverage subgraphs or higher-order structures. In this paper, we propose a new community detection method that effectively uses higher-order structures in a network. Furthermore, for the community detection accuracy, under an edge-dependent network model that consists of both community and triangle structures, we develop a finite-sample error bound characterized by the expected triangle degree, which leads to the consistency of the proposed method. To the best of our knowledge, this is the first statistical error bound and consistency result for community detection of a single network considering a network model with dependent edges. We also show, in both simulation studies and a real-world data example, that our method unveils network communities that are otherwise invisible to methods that ignore higher-order structures. [ABSTRACT FROM AUTHOR]

Galaxy–galaxy lensing (GGL) and clustering measurements from the Dark Energy Spectroscopic Instrument Year 1 (DESI Y1) data set promise to yield unprecedented combined-probe tests of cosmology and the galaxy–halo connection. In such analyses, it is essential to identify and characterize all relevant statistical and systematic errors. We forecast the covariances of DESI Y1 GGL + clustering measurements and the systematic bias due to redshift evolution in the lens samples. Focusing on the projected clustering and GGL correlations, we compute a Gaussian analytical covariance, using a suite of N -body and lognormal simulations to characterize the effect of the survey footprint. Using the DESI one percent survey data, we measure the evolution of galaxy bias parameters for the DESI luminous red galaxy (LRG) and bright galaxy survey (BGS) samples. We find mild evolution in the LRGs in |$0.4 < z < 0.8$|⁠ , subdominant to the expected statistical errors. For BGS, we find less evolution for brighter absolute magnitude cuts, at the cost of reduced sample size. We find that for a redshift bin width |$\Delta z = 0.1$|⁠ , evolution effects on DESI Y1 GGL is negligible across all scales, all fiducial selection cuts, all fiducial redshift bins. Galaxy clustering is more sensitive to evolution due to the bias squared scaling. Nevertheless the redshift evolution effect is insignificant for clustering above the 1-halo scale of |$0.1h^{-1}$|  Mpc. For studies that wish to reliably access smaller scales, additional treatment of redshift evolution is likely needed. This study serves as a reference for GGL and clustering studies using the DESI Y1 sample. [ABSTRACT FROM AUTHOR]

Construction in cities and agglomerations is one of the main sources of air pollution in most countries in the world. Fine dust particles, PM0.5–PM10, which form as a result of construction processes, are among the most dangerous pollutants. With the increase in the volume of point-pattern housing development in cities, the task of maintaining clean air and environmental conditions becomes important. This requires research, the monitoring of dust emissions throughout the entire construction period and the development of design solutions based on the results obtained. The study examines the determination of the dispersed composition of dust generated on a construction site. A graphical representation of the dispersed composition is given by constructing integral curves on a logarithmic grid and approximating them using two-link and three-link splines. The gravimetric measurement method was used to analyze the concentration of dust in the air released during construction work near residential areas. Dust analysis at the construction site revealed significant differences in particle size that cannot be explained by statistical errors alone. The reasons for this are both working conditions and climatic factors, including humidity and wind intensity. In this regard, it is preferable to use models that take into account random processes instead of traditional deterministic methods to study the dust that shapes during construction. [ABSTRACT FROM AUTHOR]

When a firm selects an assortment of products to offer to customers, it uses a choice model to anticipate their probability of purchasing each product. In practice, the estimation of these models is subject to statistical errors, which may lead to significantly suboptimal assortment decisions. In "Randomized Assortment Optimization," Wang, Peura, and Wiesemann show that the standard approach of deterministically selecting a single assortment to offer is not always optimal in this setting: Instead, the firm can do better by selecting an assortment randomly according to a prudently designed probability distribution. The authors show how an optimal randomization strategy can be determined for common choice models, improving performance in realistic data-driven settings. The results suggest that more general versions of the assortment optimization problem—incorporating business constraints, more flexible choice models and/or more general forms of uncertainty—tend to be more receptive to the benefits of randomization. When a firm selects an assortment of products to offer to customers, it uses a choice model to anticipate their probability of purchasing each product. In practice, the estimation of these models is subject to statistical errors, which may lead to significantly suboptimal assortment decisions. Recent work has addressed this issue using robust optimization, where the true parameter values are assumed unknown and the firm chooses an assortment that maximizes its worst-case expected revenues over an uncertainty set of likely parameter values, thus mitigating estimation errors. In this paper, we introduce the concept of randomization into the robust assortment optimization literature. We show that the standard approach of deterministically selecting a single assortment to offer is not always optimal in the robust assortment optimization problem. Instead, the firm can improve its worst-case expected revenues by selecting an assortment randomly according to a prudently designed probability distribution. We demonstrate this potential benefit of randomization both theoretically in an abstract problem formulation as well as empirically across three popular choice models: the multinomial logit model, the Markov chain model, and the preference ranking model. We show how an optimal randomization strategy can be determined exactly and heuristically. Besides the superior in-sample performance of randomized assortments, we demonstrate improved out-of-sample performance in a data-driven setting that combines estimation with optimization. Our results suggest that more general versions of the assortment optimization problem—incorporating business constraints, more flexible choice models and/or more general uncertainty sets—tend to be more receptive to the benefits of randomization. Funding: Z. Wang acknowledges funding from the Imperial College President's PhD Scholarship programme. W. Wiesemann acknowledges funding from the Engineering and Physical Sciences Research Council [Grants EP/R045518/1, EP/T024712/1, and EP/W003317/1]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/opre.2022.0129. [ABSTRACT FROM AUTHOR]

Clathrate hydrates are crystalline inclusion compounds wherein a water framework encages small guest atoms/molecules within its cavities. Among the others, methane clathrates are the largest fossil fuel resource still available. They can also be used to safely transport gases and can also form spontaneously under suitable conditions plugging pipelines. Understanding the crystallization mechanism is very important, and given the impossibility of experimentally identifying the atomistic path, simulations played an important role in this field. Given the large computational cost of these simulations, in addition to all-atom force fields, scientists considered coarse-grained water models. Here, we have investigated the effect of coarse-graining, as implemented in the water model mW, on the crystallization characteristics of methane clathrate in comparison with the all-atom TIP4P force field. Our analyses revealed that although the characteristics directly depending on the energetics of the water models are well reproduced, dynamical properties are off by the orders of magnitude. Being crystallization a non-equilibrium process, the altered kinetics of the process results in different characteristics of crystalline nuclei. Both TIP4P and mW water models produce methane clathrate nuclei with some amount of the less stable (in the given thermodynamic conditions) structure II phase and an excess of pentagonal dodecahedral cages over the tetrakaidecahedral ones regarding the ideal ratio in structure I. However, the dependence of this excess on the methane concentration in solution is higher with the former water model, whereas with the latter, the methane concentration in solution dependence is reduced and within the statistical error. [ABSTRACT FROM AUTHOR]

The research object is the processes of changes in fuel consumption and harmful emissions of engines and vehicles during their operation. The investigated problem consists in the lack of an approach to the construction of an adapted model for analytical studies of the thermal readiness processes of vehicles with petrol-powered engines. An approach to ensuring vehicle thermal preparation based on fuel consumption and exhaust gas emissions is proposed. The essence of the improved algorithm and model lies in accounting for the specifics of warm-up processes based on the developed thermal preparation cycle for vehicle engines. A feature of the improved approach is the incorporation of experimental research results and features of thermal preparation processes. The field of practical application of the improved approach is the thermal readiness processes of vehicles with engines adapted to work on gasoline and LPG, focusing on fuel consumption and exhaust gas emissions. Improvements have been made to the enlarged algorithm of the mathematical model for ensuring vehicle thermal preparation processes. This includes considering fuel supply and thermal readiness features, as well determining fuel consumption and emissions. The peculiarity of the proposed model is that it allows systematic simulation of thermal preparation processes, taking into account factors and processes that cannot be investigated experimentally. This is validated by the model’s adequacy test, showing that data deviation is within the statistical error range from 4.4 to 5.2 %. The application of the developed approach ensures comprehensive consideration of the specifics of thermal preparation processes and supports decision-making for evaluating results according to the relevant criteria. [ABSTRACT FROM AUTHOR]

The Surface Waves Investigation and Monitoring instrument (SWIM) provides the directional wave spectrum within the wavelength range of 23–500 m, corresponding to a frequency range of 0.056–0.26 Hz in deep water. This frequency range is narrower than the 0.02–0.485 Hz frequency range of buoys used to validate the SWIM nadir Significant Wave Height (SWH). The modulation transfer function used in the current version of the SWIM data product normalizes the energy of the wave spectrum using the nadir SWH. A discrepancy in the cut-off frequency/wavelength ranges between the nadir and off-nadir beams can lead to an overestimation of off-nadir cut-off SWHs and, consequently, the spectral densities of SWIM wave spectra. This study investigates such errors in SWHs due to the wavelength cut-off effect using buoy data. Results show that this wavelength cut-off error of SWH is small in general thanks to the high-frequency extension of the resolved frequency range. The corresponding high-frequency cut-off errors are systematic errors amenable to statistical correction, and the low-frequency cut-off error can be significant under swell-dominated conditions. By leveraging the properties of these errors, we successfully corrected the high-frequency cut-off SWH error using an artificial neural network and mitigated the low-frequency cut-off SWH error with the help of a numerical wave hindcast. These corrections significantly reduced the error in the estimated cut-off SWH, improving the bias, root-mean-square error, and correlation coefficient from 0.086 m, 0.111 m, and 0.9976 to 0 m, 0.039 m, and 0.9994, respectively. [ABSTRACT FROM AUTHOR]

Algorithmic differentiation (AD) is a promising tool to accurately and efficiently compute sensitivities of several code outputs w.r.t. the code input parameters for the complex Monte Carlo code EIRENE. We illustrate the procedure by assessing the sensitivity of the total atom content w.r.t. the atomic and molecular cross‐sections and rate coefficients. AD does not suffer from decorrelation between primal and perturbed particle trajectories, which easily happens for finite difference (FD) sensitivity calculations. Consequently, the resulting statistical errors on AD sensitivities are up to five orders of magnitude smaller than the statistical errors on FD sensitivities for a JET low‐recycling background plasma. Although this statistical error reduction is promising for the application of the AD sensitivities in a future optimization or uncertainty quantification framework, we show that the AD sensitivities can blow up for a minority of long‐lived particles, especially occurring in detachment. Finally, we conclude that the Monte Carlo simulation and estimator type possibly need to be adapted to improve the accuracy of the sensitivities. [ABSTRACT FROM AUTHOR]

The kinetics of the F2 reaction with thiirane (C2H4S) was studied for the first time in a flow reactor combined with mass spectrometry at a total helium pressure of 2 Torr and in the temperature range of 220 to 800 K. The rate constant of the title reaction was determined under pseudo-first-order conditions, either monitoring the kinetics of F2 or C2H4S consumption in excess of thiirane or of F2, respectively: k1 = (5.79 ± 0.17) × 10−12 exp(−(16 ± 10)/T) cm3 molecule−1 s−1 (the uncertainties represent precision of the fit at the 2σ level, with the total 2σ relative uncertainty, including statistical and systematic errors on the rate constant being 15% at all temperatures). HF and CH2CHSF were identified as primary products of the title reaction. The yield of HF was measured to be 100% (with an accuracy of 10%) across the entire temperature range of the study. Quantum computations revealed reaction enthalpies ranging from −409.9 to −509.1 kJ mol−1 for all the isomers/conformers of the products, indicating a strong exothermicity. Boltzmann relative populations were then established for different temperatures. [ABSTRACT FROM AUTHOR]

Twist angle errors along the blade radial direction are uncertain and affected by cutting force, tool wear, and other factors. In this paper, the measured twist angle errors of 13 sections of 72 rotor blades were innovatively analyzed to obtain the rational statistical distribution. It is surprisingly found that the under-deflection systematic deviation of twist angle errors shows a gradually increasing W-shaped distribution along the radial direction, while the scatter is nearly linear. Logically, the statistical model is established based on the linear correlation of the scatter by regression analysis to reduce variable dimension from 13 to 1. The influence of the radial non-uniform twist angle errors' uncertainty on the aerodynamic performance of the three-dimensional compressor rotor is efficiently quantified combining the non-intrusive polynomial chaos method. The results show that the mean values of mass flow rate, total pressure ratio, and isentropic efficiency at the typical operating conditions are lower than the nominal values due to the systematic deviation, indicating that the under-deflection twist angle errors lead to the decrease in compressor thrust. The compressor's stable operating range is more sensitive to the scatter of twist angle errors, which is up to an order of magnitude greater than that of the total pressure ratio and isentropic efficiency, indicating the compressor's safe and stable operation risk increases. Additionally, the flow field at the tip region is significantly affected by twist angle errors, especially at the shock wave position of the near-stall condition. [ABSTRACT FROM AUTHOR]

Reinforcement learning policy evaluation problems are often modeled as finite or discounted/averaged infinite-horizon Markov Decision Processes (MDPs). In this paper, we study undiscounted off-policy evaluation for absorbing MDPs. Given the dataset consisting of i.i.d episodes under a given truncation level, we propose an algorithm (referred to as MWLA in the text) to directly estimate the expected return via the importance ratio of the state-action occupancy measure. The Mean Square Error (MSE) bound of the MWLA method is provided and the dependence of statistical errors on the data size and the truncation level are analyzed. The performance of the algorithm is illustrated by means of computational experiments under an episodic taxi environment [ABSTRACT FROM AUTHOR]

We report on a novel, medium-weight (∼ 25 kg) optical spectrometer coupled to an automated sun tracker for direct sun observations from azimuth-controlled balloon platforms weighing approximately 12 kg. It is designed to measure a suite of UV–Vis absorbing gases relevant in the context of stratospheric ozone depletion using the differential optical absorption spectroscopy (DOAS) method, i.e. O3 , NO2 , BrO , OClO , HONO , and IO. Here, we describe the design and major features of the instrument. Further, the instrument's performance during two stratospheric deployments from Esrange near Kiruna (Sweden) on 21 August 2021 and from Timmins (Ontario, Canada) on 23 August 2022 is discussed along with the first results concerning inferred mixing ratios of BrO above balloon float altitude. Using a photochemical correction for the partitioning of stratospheric bromine ([BrO]/[Bry]) obtained by chemical transport simulations, the inferred total stratospheric bromine load [Bry] amounts to (17.5 ± 2.2) ppt , with a purely statistical error amounting to 1.5 ppt in (5.5 ± 1.0)-year old air. The latter is inferred from simultaneous measurements of N2O by the GLORIA (Gimballed Limb Observer for Radiance Imaging of the Atmosphere) mid-IR instrument, resulting in a stratospheric entry of the investigated air mass in early 2017 ± 1 year. [ABSTRACT FROM AUTHOR]

We discuss robust estimations for the variance of normally distributed random variables in the presence of interference. The robust estimators are based on either ranking or the geometric mean. For the interference models used, estimators based on the geometric mean outperform the rank-based ones in both mitigating the effect of interference and reducing the statistical error when there is no interference. One reason for this is that estimators using the geometric mean do not suffer from the "heavy tail" phenomenon like the rank-based estimators do. The ratio of the standard deviation over the mean of the power random variable is sensitive to interference. It can thus be used as a criterion to combine the sample mean with a robust estimator to form a hybrid estimator. We apply the estimators to the Arecibo incoherent scatter radar signals to determine the total power and Doppler velocities in the ionospheric E-region altitudes. Although all the robust estimators selected deal with light contamination well, the hybrid estimator is most effective in all circumstances. It performs well in suppressing heavy contamination and is as efficient as the sample mean in reducing the statistical error. Accurate incoherent scatter radar measurements, especially at nighttime and at E-region altitudes, can improve studies of ionospheric dynamics and composition. [ABSTRACT FROM AUTHOR]

We develop a set of machine-learning-based cosmological emulators, to obtain fast model predictions for the C (ℓ) angular power spectrum coefficients, characterizing tomographic observations of galaxy clustering and weak gravitational lensing from multiband photometric surveys (and their cross-correlation). A set of neural networks are trained to map cosmological parameters into the coefficients, achieving, with respect to standard Boltzmann solvers, a speed-up of |$\mathcal {O}(10^3)$| in computing the required statistics for a given set of cosmological parameters, with an accuracy better than 0.175  per cent (<0.1  per cent for the weak lensing case). This corresponds to |$\lesssim 2~{{\ \rm per\ cent}}$| of the statistical error bars expected from a typical Stage IV photometric surveys. Such overall improvement in speed and accuracy is obtained through (i) a specific pre-processing optimization, ahead of the training phase, and (ii) an effective neural network architecture. Compared to previous implementations in the literature, we achieve an improvement of a factor of 5 in terms of accuracy, while training a considerably lower amount of neural networks. This results in a cheaper training procedure and a higher computational performance. Finally, we show that our emulators can recover unbiased posteriors when analysing synthetic Stage-IV galaxy survey data sets. [ABSTRACT FROM AUTHOR]

Uncertain quantities are often described through statistical samples. Can samples for numerical weather forecasts be generated dynamically? At a great expense, they can. With statistically constrained perturbations, a cloud of initial states is created and then integrated forward in time. By now, this technique has become ubiquitous in weather and climate research and operations. Ensembles are widely used, with demonstrated value. The atmosphere evolves in a multidimensional phase space. Does a cloud of ensemble solutions encompass the evolution of the real atmosphere? Theoretically, random perturbations in high‐dimensional spaces have negligible projection in any direction, including the error in the best estimate, therefore consistently degrading that. As the bulk of the perturbation variance lies in the null space of error, samples in multidimensional space do not contain reality. An evaluation suggests that initial and short‐range forecast error and ensemble perturbations are random draws from a high‐dimensional domain we call the subspace of possible error. Error in any initial condition is partly a result of stochastic observational and assimilation noise, while perturbations explore other, mostly independent directions from the subspace of possible error that may have resulted from other configurations of stochastic noise. What benefits may arise from the deterministic projection of such noise? Consistent with theoretical expectations, ensemble members consistently degrade the skill of the unperturbed forecast until medium range. The mean and all other products derived from ensembles suffer an 18‐hour loss in forecast Information. Since Information is a sufficient statistic, any rational user can benefit more from the unperturbed, than from an ensemble of weather forecasts. Furthermore, case‐dependent variations in the distribution or spread of ensembles have no impact on commonly used metrics. Can alternative, statistical applications provide comparable, or even higher‐quality probabilistic and other products, at the fraction of the cost of running an ensemble? [ABSTRACT FROM AUTHOR]

AbstractThis article studies noisy low-rank matrix completion in the presence of heavy-tailed and possibly asymmetric noise, where we aim to estimate an underlying low-rank matrix given a set of highly incomplete noisy entries. Though the matrix completion problem has attracted much attention in the past decade, there is still lack of theoretical understanding when the observations are contaminated by heavy-tailed noises. Prior theory falls short of explaining the empirical results and is unable to capture the optimal dependence of the estimation error on the noise level. In this article, we adopt an adaptive Huber loss to accommodate heavy-tailed noise, which is robust against large and possibly asymmetric errors when the parameter in the Huber loss function is carefully designed to balance the Huberization biases and robustness to outliers. Then, we propose an efficient nonconvex algorithm via a balanced low-rank Burer-Monteiro matrix factorization and gradient descent with robust spectral initialization. We prove that under merely a bounded second-moment condition on the error distributions, rather than the sub-Gaussian assumption, the Euclidean errors of the iterates generated by the proposed algorithm decrease geometrically fast until achieving a minimax-optimal statistical estimation error, which has the same order as that in the sub-Gaussian case. The key technique behind this significant advancement is a powerful leave-one-out analysis framework. The theoretical results are corroborated by our numerical studies. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work. [ABSTRACT FROM AUTHOR]

• The moment similarity is explained qualitatively and quantitatively. • The positional accuracy reliability at any position is calculated based on the moment similarity. • An optimal position with the lowest failure probability can be searched in a cubic space. A new positional accuracy reliability analysis method of industrial robots is proposed based on the statistical moment similarity of positional error. The first-two order statistical moments of positional error at some positions are accurately obtained through the differential kinematics method to reduce the computational cost of the proposed method. In practical engineering, the statistical moments of positional error can also be obtained from the limited experimental data. Then, the statistical moment similarity of positional error is quantified by the semivariance function. Besides, the first-two order moments of positional error at any position can be calculated according to the statistical moment similarity. Thus, the positional accuracy reliability analysis of any position can be realized by the first-two order moments according to the saddlepoint approximation method. Besides, an optimal position with the lowest failure probability of positional error can be searched based on the statistical moment similarity. The effectiveness of the proposed method is manifest through calculating the positional accuracy reliability and the optimal position with the lowest failure probability of a 6-degree-of-freedom (6-DoF) industrial robot in a cubic space. [ABSTRACT FROM AUTHOR]

In clinical studies focusing on paired body parts, diseases can manifest on either both sides (bilateral) or just one side (unilateral) of the organs. Consequently, the data in these studies may consist of records from both bilateral and unilateral cases. There are two different methods of analyzing the data. One of the methods is assuming that the pair of measurements from the same subject are independent, while the other considers the correlation between paired organs. In terms of the homogeneity test of proportions, asymptotic methods have been proposed given the moderate size of data. This article extends the existing work by proposing exact methods to deal with the scenarios when the sample size is small and asymptotic methods perform poorly. The impact of the correlation assumption is also explored. Among the proposed methods, calculating p-values by replacing unknown parameters with estimated values while accounting for the correlation is recommended based on its satisfactory type I error controls and statistical powers. The proposed methods are applied to three real examples for illustration. [ABSTRACT FROM AUTHOR]

Differential absorption lidar is an advanced tool for investigating tropospheric ozone transport and development. High-quality differential absorption lidar data are the basis for studying the temporal and spatial evolution of ozone pollution. We assessed the quality of the ozone data generated via differential absorption lidar. By correcting the ozone lidar profile in real-time with an atmospheric correction term and comparing the lidar data to ozone data collected using an unmanned aerial vehicle (UAV), we quantified the statistical error of the ozone lidar data in the vertical direction and determined that the data from the two instruments were generally in agreement. To verify the reliability of the ozone lidar system and the atmospheric correction algorithm, we conducted a long-term comparison experiment using data from the Canton Tower. Over the two months, the UAV and lidar data were consistent with one another, which confirmed the viability of the ozone lidar optomechanical structure and the atmospheric correction algorithm, both in real-time and over a given time duration. In addition, we also quantified the relationship between statistical error and signal-to-noise ratio. When the SNR is less than 10, the corresponding statistical error is about 40%. The statistical error was less than 15% when the signal-to-noise ratio was greater than 20, and the statistical error was mostly less than 8% when the signal-to-noise ratio was greater than 40. In general, the statistical error of the differential absorption lidar data was inversely proportional to the signal-to-noise ratio of each echo signal. [ABSTRACT FROM AUTHOR]

Accurate estimation of porosity is a critical factor in reservoir characterization. This study aims to enhance porosity prediction through the implementation and comparison of various stacking ensemble learning strategies. A dataset comprising 273 points, which consists of well logs and core measurements, was collected from two wells for model development. Four base learners, including Support Vector Regression (SVR), Multi-Layer Perceptron (MLP), Random Forest Regression (RFR), and XGBoost, were trained on this dataset. These models were then integrated using multiple stacking ensemble techniques, such as weighted averaging, Bayesian model averaging, and RFR as a metalearner. Meta-learners were trained on predictions from the base learners, generated through crossvalidation on leave-out data. Performance evaluations of both base and meta learners were conducted on a separate testing dataset using statistical and graphical error analysis. Results indicate that all learners demonstrated robust performance, with weighted averaging outperforming other strategies on testing data. The stacking ensemble approach, particularly through weighted averaging, effectively improved base learner performance on testing data by leveraging individual model strengths and mitigating weaknesses. The findings of this study are valuable for geoscientists and reservoir engineers in achieving accurate reservoir characterization and facilitating exploration activities. [ABSTRACT FROM AUTHOR]

Measurement errors may significantly distort the properties of an estimator. In this paper, estimators of the finite population variance using the information on first and second raw moments of the study variable are developed under stratified random sampling that incorporate the variance of a measurement error component. Additionally, combined and separate estimators are also developed for estimating the finite population variance using supplementary information in terms of one or two auxiliary variables. An empirical study is carried out to study the effect of measurement error on the relative efficiencies of the proposed estimators. [ABSTRACT FROM AUTHOR]

In this investigation, a novel application of the RIME optimizer with neighborhood centroid opposite solution is introduced to robustly estimate parameter values for an accurate photovoltaic triple-diode model. The suggested optimizer's performance is rigorously evaluated in comparison to other well-documented methods. The evaluation of the proposed optimizer is conducted using real data from the RTC France solar cell, and the results are assessed through various evaluation metrics, including root mean square error and statistical analyses for multiple independent runs. Specifically, the proposed optimizer demonstrates superior performance by achieving the lowest objective function values compared to other algorithms. Through a comprehensive quantitative and qualitative assessment, it can be inferred that the estimated parameters of the triple-diode model obtained using the proposed optimizer surpass the accuracy of those acquired through other optimization algorithms under consideration. [ABSTRACT FROM AUTHOR]

In this study, a set of four commercially available naturally ventilated radiation shields and a reference mechanically ventilated weather station were tested during three months of a hot Mediterranean summer in southern Europe. The results indicate that among the selected radiation shields, the naturally ventilated shield identified as shelter 2 with its inner temperature sensor has the best overall performance and could be used as a reference radiation shield, as its statistical errors were of the same order of magnitude compared with the reference mechanically ventilated weather station. [ABSTRACT FROM AUTHOR]

Significance: The advancement of multichannel functional near-infrared spectroscopy (fNIRS) has enabled measurements across a wide range of brain regions. This increase in multiplicity necessitates the control of family-wise errors in statistical hypothesis testing. To address this issue, the effective multiplicity (Meff) method designed for channel-wise analysis, which considers the correlation between fNIRS channels, was developed. However, this method loses reliability when the sample size is smaller than the number of channels, leading to a rank deficiency in the eigenvalues of the correlation matrix and hindering the accuracy of Meff calculations. Aim: We aimed to reevaluate the effectiveness of the Meff method for fNIRS data with a small sample size. Approach: In experiment 1, we used resampling simulations to explore the relationship between sample size and Meff values. Based on these results, experiment 2 employed a typical exponential model to investigate whether valid Meff could be predicted from a small sample size. Results: Experiment 1 revealed that the Meff values were underestimated when the sample size was smaller than the number of channels. However, an exponential pattern was observed. Subsequently, in experiment 2, we found that valid Meff values can be derived from sample sizes of 30 to 40 in datasets with 44 and 52 channels using a typical exponential model. Conclusions: The findings from these two experiments indicate the potential for the effective application of Meff correction in fNIRS studies with sample sizes smaller than the number of channels. [ABSTRACT FROM AUTHOR]

Background: Randomized clinical trials (RCTs) shape our clinical practice. Several studies report a mediocre replicability rate of the studied RCTs. Many researchers believe that the relatively low replication rate of RCTs is attributed to the high p value significance threshold. To solve this problem, some researchers proposed using a lower threshold, which is inevitably associated with a decrease in the study power. Methods: The results of 22 500 RCTs retrieved from the Cochrane Database of Systematic Reviews (CDSR) were reinterpreted using 2 fixed p significance threshold (0.05 and 0.005), and a recently proposed flexible threshold that minimizes the weighted sum of errors in statistical inference. Results: With p < 0.05 criterion, 28.5% of RCTs were significant; p < 0.005, 14.2%; and p < flexible threshold, 9.9% (2/3 of significant RCTs based on p < 0.05 criterion, were found not significant). Lowering the p cut-off, although decreases the false-positive rate, is not generally associated with a lower weighted sum of errors; the false-negative rate increases (the study power decreases); important treatments may be left undiscovered. Accurate calculation of the optimal p value thresholds needs knowledge of the variance in each study arm, a posteriori. Conclusions: Lowering the p value threshold, as it is proposed by some researchers, is not reasonable as it might be associated with an increase in false-negative rate. Using a flexible p significance threshold approach, although results in a minimum error in statistical inference, might not be good enough too because only a rough estimation may be calculated a priori; the data necessary for the precise computation of the most appropriate p significance threshold are only available a posteriori. Frequentist statistical framework has an inherent conflict. Alternative methods, say Bayesian methods, although not perfect, would be more appropriate for the data analysis of RCTs. [ABSTRACT FROM AUTHOR]

To improve the luminescent efficiency of and to design the color spectrum of phosphors, the comprehensive understanding of the correlation between physical parameters and luminescent properties is imperative, necessitating systematic experimental studies. However, unintentional variations across individually prepared samples impede the thorough investigation of the correlation. In this study, we investigate the possible sources of unintentional variation in the photoluminescence properties of phosphors during sample preparation using a solid-state reaction, explicitly focusing on the ball milling process. Based on the quantitative features of the photoluminescent properties and their associated statistical errors, we explore the impact of unintentional variation alongside intended systematic variation, highlighting its potential to obscure meaningful trends. [ABSTRACT FROM AUTHOR]

Background A clear classification of the subtype and grade of soft tissue sarcoma is important for predicting prognosis and establishing treatment strategies. However, the rarity and heterogeneity of these tumors often make diagnosis difficult. In addition, it remains challenging to predict the response to chemotherapy and prognosis. Thus, we need a new method to help diagnose soft tissue sarcomas and determine treatment strategies in conjunction with traditional methods. Genetic alterations can be found in some subtypes of soft tissue sarcoma, but many other types show dysregulated gene expression attributed to epigenetic changes, such as DNA methylation status. However, research on DNA methylation profiles in soft tissue sarcoma is still insufficient to provide information to assist in diagnosis and therapeutic decisions. Questions/purposes (1) Do DNA methylation profiles differ between normal tissue and soft tissue sarcoma? (2) Do DNA methylation profiles vary between different histologic subtypes of soft tissue sarcoma? (3) Do DNA methylation profiles differ based on tumor grade? Methods Between January 2019 and December 2022, we treated 85 patients for soft tissue sarcomas. We considered patients whose specimens were approved for pilot research by the Human Biobank of St. Vincent’s Hospital, The Catholic University of Korea, as potentially eligible. Based on this, 41% (35 patients) were eligible; 1% (one patient) was excluded because of gender mismatch between clinical and genetic data after controlling for data quality. Finally, 39 specimens (34 soft tissue sarcomas and five normal samples) were included from 34 patients who had clinical data. All tissue samples were collected intraoperatively. The five normal tissue samples were from muscle tissues. There were 20 female patients and 14 male patients, with a median age of 58 years (range 19 to 82 years). Genomic DNA was extracted from frozen tissue, and DNA methylation profiles were obtained. Genomic annotation of DNA methylation sites and hierarchical cluster analysis were performed to interpret results from DNA methylation profiling. A t-test was used to analyze different methylation probes. Benjamini-Hochberg-adjusted p value calculations were used to account for bias resulting from evaluating thousands of methylation sites. Results The most common histologic subtypes were liposarcoma (n = 10) and leiomyosarcoma (n = 9). The tumor grade was Fédération Nationale des Centres de Lutte Contre Le Cancer Grades 1, 2, and 3 in 3, 15, and 16 patients, respectively. DNA methylation profiling demonstrated differences between soft tissue sarcoma and normal tissue as 21,188 cytosine-phosphate-guanine sites. Despite the small number of samples, 72 of these sites showed an adjusted p value of < 0.000001, suggesting a low probability of statistical errors. Among the 72 sites, 70 exhibited a hypermethylation pattern in soft tissue sarcoma, with only two sites showing a hypomethylation pattern. Thirty of 34 soft tissue sarcomas were distinguished from normal samples using hierarchical cluster analysis. There was a different methylation pattern between leiomyosarcoma and liposarcoma at 7445 sites. Using the data, hierarchical clustering analysis showed that liposarcoma was distinguished from leiomyosarcoma. When we used the same approach and included other subtypes with three or more samples, only leiomyosarcoma and myxofibrosarcoma were separated from the other subtypes, while liposarcoma and alveolar soft-part sarcoma were mixed with the others. When comparing DNA methylation profiles between low-grade (Grade 1) and high-grade (Grades 2 and 3) soft tissue sarcomas, a difference in methylation pattern was observed at 144 cytosine-phosphate-guanine sites. Among these, 132 cytosine-phosphate-guanine sites exhibited hypermethylation in the high-grade group compared with the low-grade group. Hierarchical clustering analysis showed a division into two groups, with most high-grade sarcomas (28 of 31) separated from the low-grade group and few (3 out of 31) clustered together with the low-grade group. However, three high-grade soft tissue sarcomas were grouped with the Grade 1 cluster, and all of these sarcomas were Grade 2. When comparing Grades 1 and 2 to Grade 3, Grade 3 tumors were separated from Grades 1 and 2. Conclusion We observed a different DNA methylation pattern between soft tissue sarcomas and normal tissues. Liposarcoma was distinguished from leiomyosarcoma using methylation profiling. High-grade soft tissue sarcoma samples showed a hypermethylation pattern compared with low-grade ones. Our findings indicate the need for research using methylation profiling to better understand the diverse biological characteristics of soft tissue sarcoma. Such research should include studies with sufficient samples and a variety of subtypes, as well as analyses of the expression and function of related genes. Additionally, efforts to link this research with clinical data related to treatment and prognosis are necessary. [ABSTRACT FROM AUTHOR]

Tropical Cyclones (TCs) have devastating effects on several coastal regions worldwide. Accurate estimation of TC characteristics such as wind direction, wind speed, eye, rainfall bands, and radius of maximum wind helps to understand the evolution of TCs throughout their life cycle and plays an essential role in mitigating TC impact. Existing literature has focused on TC wind direction, intensity, cloud shape, and eye, but there has been limited research on the estimation of the Radius of Maximum Wind (RMW). An accurate estimate of RMW helps in identifying the regions where TC's strongest wind and most intense cooling of the ocean occur. In this study, our objective is to formulate a method to determine the value of the RMW over the North Indian Ocean (NIO), this region has been chosen as it has the Bay of Bengal and the Arabian Sea which are one of the six globally prominent areas prone to TCs. Our study is on the relationship between the center of the TC, the estimated pressure drop at the center, and the RMW, using historical observations and mathematical correlations. We validate the accuracy of our developed method using three statistical measures: error percentage, t-test, and root mean square error. Our findings indicate that our method exhibits a mean absolute error percentage ranging from approximately 6 % to 32 % , whereas the formulation of RMW from other studies has a mean absolute error range of 13 % to 128 % concerning IMD best track data. [ABSTRACT FROM AUTHOR]

Bridge early warning based on structural health monitoring (SHM) system is of significant importance for ensuring bridge safe operation. The temperature-induced deflection (TID) is a sensitive indicator for performance degradation of continuous rigid frame bridges, but the time-lag effect makes it challenging to predict the TID accurately. A bridge early warning method based on nonlinear modeling for the TID is proposed in this article. Firstly, the SHM data of temperature and deflection of a continuous rigid frame bridge are analyzed to examine the temperature gradient variation patterns. Kernel principal component analysis (KPCA) is used to extract principal temperature components. Then, the TID is extracted through wavelet transform, and a nonlinear modeling method for the TID considering the temperature gradient is proposed using the support vector machine (SVM). Finally, the prediction errors of the KPCA-SVM algorithm are analyzed, and the early warning thresholds are determined based on the statistical patterns of the errors. The results show that the KPCA-SVM algorithm achieves high-precision nonlinear modeling for the TID while significantly reducing the computational load. The prediction results have coefficients of determination above 0.98 and fluctuate within a small range with clear statistical patterns. Setting the early warning thresholds based on the statistical patterns of errors enables dynamic and multi-level warnings for bridge structures. [ABSTRACT FROM AUTHOR]

In this article, the Leading Point Multi-Regression model was applied to identify days with anomalous energy consumption profiles. The data for the analysis come from the German energy system and they represent the hourly energy demand observed between 2006 and 2015. Days with abnormal daily profiles were identified based on the statistical analysis of the errors observed for the model. The accuracy of the model is very high and comparable with other models, e.g., LSTM, K-means, Recurrent NN, and tree-based ML methods. However, these methods rely on external factors (e.g., humidity, temperature, and sunshine) impacting energy consumption while our model uses only the energy consumption at specific fixed hours, regardless of external factors, thus being universal. Days with anomalous energy consumption profiles were identified as days related to celebration of New Year's Eve and the New Year. Also, anomalies were identified for some other days, which were not that obvious, including Good Friday, National Day of Mourning, and, interestingly, the day of the Germany–Turkey match during the European Championship in 2008. [ABSTRACT FROM AUTHOR]

We present gausSN , a Bayesian semiparametric Gaussian Process (GP) model for time-delay estimation with resolved systems of gravitationally lensed supernovae (glSNe). gausSN models the underlying light curve non-parametrically using a GP. Without assuming a template light curve for each SN type, gausSN fits for the time delays of all images using data in any number of wavelength filters simultaneously. We also introduce a novel time-varying magnification model to capture the effects of microlensing alongside time-delay estimation. In this analysis, we model the time-varying relative magnification as a sigmoid function, as well as a constant for comparison to existing time-delay estimation approaches. We demonstrate that gausSN provides robust time-delay estimates for simulations of glSNe from the Nancy Grace Roman Space Telescope and the Vera C. Rubin Observatory's Legacy Survey of Space and Time (Rubin-LSST). We find that up to 43.6 per cent of time-delay estimates from Roman and 52.9 per cent from Rubin-LSST have fractional errors of less than 5  per cent. We then apply gausSN to SN Refsdal and find the time delay for the fifth image is consistent with the original analysis, regardless of microlensing treatment. Therefore, gausSN maintains the level of precision and accuracy achieved by existing time-delay extraction methods with fewer assumptions about the underlying shape of the light curve than template-based approaches, while incorporating microlensing into the statistical error budget. gausSN is scalable for time-delay cosmography analyses given current projections of glSNe discovery rates from Rubin-LSST and Roman. [ABSTRACT FROM AUTHOR]

This paper proposes a novel hybrid methodology for short-term forecasting of hourly. The F-Multi Criteria Decision Making-Neural Network (F-MCDM-NN) hybrid model is designed to forecast Global Horizontal Irradiance (GHI) 1 day ahead based on Meteorological data for the target photovoltaic generation. The proposed hybrid modeling is based on meteorology data i.e. wind speed, wind direction, pressure, rainfall, temperature, humidity, gust, and visibility, data especially for optimizing the operation of power generating electricity from photovoltaic (PV) generation system. This modeling is a hybrid of Fuzzy-Multi Criteria Decision Making (F-MCDM) algorithm modeling and Neural Network (NN) model. The novelty of this combination model is taking into account the meteorology data. A set of data on GHI was from the PV station in Indonesia, which is used as research test data. The first hybrid model implements F-MCDM as an input data preprocessing technique prior to the NN method. The error statistical indicators of the F-MCDMNN hybrid model using the Mean Absolute Percentage Error (MAPE) is 7.5% and the Root-Mean- Square Error (RMSE) is 73.7 W/m². The hybrid model prediction is then compared to measured data and simulation results indicate that the F-MCDM-NN-based hybrid model presented in this research can calculate hourly GHI with satisfactory accuracy with better result compared to the Neural Network method. [ABSTRACT FROM AUTHOR]

The article discusses the current state of orthopaedic research, highlighting concerns about the quality of studies being produced. Issues such as incomplete methodology descriptions, reliance on review papers over original research, and statistical errors are identified as contributing to the decline in research quality. The authors emphasize the need to shift from quantity to quality in research, advocating for improved education in research fundamentals and statistics, transparent peer-review processes, and the potential use of artificial intelligence to enhance research quality. Ultimately, the article calls for technological advancements and better education to elevate the quality of orthopaedic research and address the lack of real progress in recent decades. [Extracted from the article]

A second-order many-body perturbation correction to the relativistic Dirac–Hartree–Fock energy is evaluated stochastically by integrating 13-dimensional products of four-component spinors and Coulomb potentials. The integration in the real space of electron coordinates is carried out by the Monte Carlo (MC) method with the Metropolis sampling, whereas the MC integration in the imaginary-time domain is performed by the inverse-cumulative distribution function method. The computational cost to reach a given relative statistical error for spatially compact but heavy molecules is observed to be no worse than cubic and possibly quadratic with the number of electrons or basis functions. This is a vast improvement over the quintic scaling of the conventional, deterministic second-order many-body perturbation method. The algorithm is also easily and efficiently parallelized with 92% strong scalability going from 64 to 4096 processors. [ABSTRACT FROM AUTHOR]

Background: Non‐inferiority trials comparing different active drugs are often subject to treatment non‐adherence. Intention‐to‐treat (ITT) and per‐protocol (PP) analyses have been advocated in such studies but are not guaranteed to be unbiased in the presence of differential non‐adherence. Methods: The REMoxTB trial evaluated two 4‐month experimental regimens compared with a 6‐month control regimen for newly diagnosed drug‐susceptible TB. The primary endpoint was a composite unfavorable outcome of treatment failure or recurrence within 18 months post‐randomization. We conducted a simulation study based on REMoxTB to assess the performance of statistical methods for handling non‐adherence in non‐inferiority trials, including: ITT and PP analyses, adjustment for observed adherence, multiple imputation (MI) of outcomes, inverse‐probability‐of‐treatment weighting (IPTW), and a doubly‐robust (DR) estimator. Results: When non‐adherence differed between trial arms, ITT, and PP analyses often resulted in non‐trivial bias in the estimated treatment effect, which consequently under‐ or over‐inflated the type I error rate. Adjustment for observed adherence led to similar issues, whereas the MI, IPTW and DR approaches were able to correct bias under most non‐adherence scenarios; they could not always eliminate bias entirely in the presence of unobserved confounding. The IPTW and DR methods were generally unbiased and maintained desired type I error rates and statistical power. Conclusions: When non‐adherence differs between trial arms, ITT and PP analyses can produce biased estimates of efficacy, potentially leading to the acceptance of inferior treatments or efficacious regimens being missed. IPTW and the DR estimator are relatively straightforward methods to supplement ITT and PP approaches. [ABSTRACT FROM AUTHOR]

Evapotranspiration (ETo) is an important component of the hydrological cycle and reliable estimates of ETo are essential for assessing crop water requirements and irrigation management. Direct measurement of evapotranspiration is both costly and involves complex and intricate procedures. Hence, empirical models are commonly utilized to estimate ETo using accessible meteorological data. Given that empirical methods operate on various assumptions, it is essential to assess their performance to pinpoint the most suitable methods for ETo calculation based on the availability of input data and the specific climatic conditions of a region. This study aims to evaluate different empirical methods of ETo in the tropical highland Udhagamandalam region of Tamil Nadu, India, utilizing sixty years of meteorological data from 1960–2020. In this study, 8 temperature-based and 10 radiation-based empirical models are evaluated against ETo estimates derived from pan evaporation observation and the FAO Penman–Monteith method (FAO-PM), respectively. Statistical error metrics indicate that both temperature and radiation-based models perform better for the Udhagamandalam region. However, radiation-based models performed better than the temperature based models. This is possibly due to the high humidity of the study region throughout the year. The results suggest that simple temperature and radiation-based models using minimum meteorological information are adequate to estimate ETo and thus find potential application in agricultural water practices, hydrological processes, and irrigation management. [ABSTRACT FROM AUTHOR]

The object of this study is the operational model of a reconnaissance-fire system. The problem that was solved is the lack of an approach to building a model of the functioning of a combat system, in particular a reconnaissance-fire system, which would take into account the influence of all subsystems and include the necessary number of system states. An improved procedure for building an adapted operational model of the reconnaissance-fire system has been proposed. The essence of the improved methodology is the formalization of processes through the definition of system states and intensities of transitions from state to state. The improved procedure is based on the Kolmogorov-Chapman equations and the goal tree construction method. A feature of the improved methodology is the breakdown of the states of the reconnaissance-fire system by hierarchy levels, which allows taking into account more necessary states of the system. The field of practical use of the improved methodology is planning and management processes during the development of action algorithms during combat operations. An adapted operational model of the reconnaissance-fire system has been built. The essence of the model is to determine the probability of the reconnaissance-fire system being in a certain state based on the Chapman-Kolmogorov equations, taking into account the necessary level of detail in the process of its operation. Special feature of the proposed model is that it makes it possible to model by taking into account 39 states of the system with the necessary accuracy both for the system as a whole and separately for subsystems. This is explained by the fact that the test of the adequacy of the model showed that the discrepancy of the results is within the statistical error from 2 to 9 %. The field of application of the adapted operational model of the reconnaissance-fire system is the processes of making a decision on the application of the operation of the intelligence-fire system during hostilities and their management during combat operations. [ABSTRACT FROM AUTHOR]

Spectral characterization of noise environments that lead to the decoherence of qubits is critical to developing robust quantum technologies. While dynamical decoupling offers one of the most successful approaches to characterize noise spectra, it necessitates applying large sequences of π pulses that increase the complexity and cost of the method. Here, we introduce a noise spectroscopy method that utilizes only the Fourier transform of free induction decay or spin echo measurements, thus removing the need for the application many π pulses. We show that our method faithfully recovers the correct noise spectra for a variety of different environments (including 1/f-type noise) and outperforms previous dynamical decoupling schemes while significantly reducing their experimental overhead. We also discuss the experimental feasibility of our proposal and demonstrate its robustness in the presence of statistical measurement error. Our method is applicable to a wide range of quantum platforms and provides a simpler path toward a more accurate spectral characterization of quantum devices, thus offering possibilities for tailored decoherence mitigation. [ABSTRACT FROM AUTHOR]