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Two machine-learning-aided thermodynamic integration schemes to compute the chemical potentials of atoms and molecules have been developed and compared. One is the particle insertion method, and the other combines particle insertion with element substitution. In the former method, the species is gradually inserted into the liquid and its chemical potential is computed. In the latter method, after the particle insertion, the inserted species is substituted with another species, and the chemical potential of this new species is computed. In both methods, the thermodynamic integrations are conducted using machine-learned potentials trained on first-principles datasets. The errors of the machine-learned surrogate models are further corrected by performing thermodynamic integrations from the machine-learned potentials to the first-principles potentials, accurately providing the first-principles chemical potentials. These two methods are applied to compute the real potentials of proton, alkali metal cations, and halide anions in water. The applications indicate that these two entirely different thermodynamic pathways yield identical real potentials within statistical error bars, demonstrating that both methods provide reproducible real potentials. The computed real potentials and solvation structures are also in good agreement with past experiments and simulations. These results indicate that machine-learning surrogate models enabling particle insertion and element substitution provide a precise method for determining the chemical potentials of atoms and molecules. [ABSTRACT FROM AUTHOR]

We introduce a novel algorithm that leverages stochastic sampling techniques to compute the perturbative triples correction in the coupled-cluster framework. By combining elements of randomness and determinism, our algorithm achieves a favorable balance between accuracy and computational cost. The main advantage of this algorithm is that it allows for the calculation to be stopped at any time, providing an unbiased estimate, with a statistical error that goes to zero as the exact calculation is approached. We provide evidence that our semi-stochastic algorithm achieves substantial computational savings compared to traditional deterministic methods. Specifically, we demonstrate that a precision of 0.5 millihartree can be attained with only 10% of the computational effort required by the full calculation. This work opens up new avenues for efficient and accurate computations, enabling investigations of complex molecular systems that were previously computationally prohibitive. [ABSTRACT FROM AUTHOR]

Three electric quadrupole transitions in the second overtone band of D2 are newly measured by comb-referenced cavity ring down spectroscopy around 1.18 µm. These extremely weak transitions (line intensities smaller than 10−29 cm/molecule) are the first to be detected in the (3–0) band of D2. The spectra of the O(3), O(2), and Q(2) lines near 8321, 8446, and 8607 cm−1, respectively, are recorded at room temperature for pressure values ranging between 100 and 600 Torr. Accurate transition frequencies and line intensities of the three D2 transitions are determined from a line fitting procedure using beyond-Voigt profiles, including strong Dicke narrowing. Considering statistical fit errors and possible biases due to the interference with water lines (which are six orders of magnitude stronger than the studied D2 lines), total uncertainties on the frequencies extrapolated at zero pressure are estimated below 14 MHz (∼4.7 × 10−4 cm−1). The derived experimental frequencies and intensities are compared to ab initio values. An overall agreement is achieved, confirming the positional accuracy of the most advanced theoretical calculations. [ABSTRACT FROM AUTHOR]

Three electric quadrupole transitions in the second overtone band of D2 are newly measured by comb-referenced cavity ring down spectroscopy around 1.18 µm. These extremely weak transitions (line intensities smaller than 10−29 cm/molecule) are the first to be detected in the (3–0) band of D2. The spectra of the O(3), O(2), and Q(2) lines near 8321, 8446, and 8607 cm−1, respectively, are recorded at room temperature for pressure values ranging between 100 and 600 Torr. Accurate transition frequencies and line intensities of the three D2 transitions are determined from a line fitting procedure using beyond-Voigt profiles, including strong Dicke narrowing. Considering statistical fit errors and possible biases due to the interference with water lines (which are six orders of magnitude stronger than the studied D2 lines), total uncertainties on the frequencies extrapolated at zero pressure are estimated below 14 MHz (∼4.7 × 10−4 cm−1). The derived experimental frequencies and intensities are compared to ab initio values. An overall agreement is achieved, confirming the positional accuracy of the most advanced theoretical calculations. [ABSTRACT FROM AUTHOR]

Colorectal cancer (CRC) is a form of cancer that originates in the colon (large intestine) or rectum, which are components of the digestive system. It generally begins as small, benign growths known as polyps that can form on the inner lining of the colon or rectum. In Malaysia, colorectal cancer ranks among the most prevalent cancers, especially within the Chinese and Malay communities. A study released by the Malaysian National Cancer Registry indicates that colorectal cancer is the second most common cancer type following breast cancer, impacting both genders. The occurrence of colorectal cancer in Malaysia has been consistently increasing, with approximately 15–20% of cancer cases in the nation being colorectal cancer. This type of cancer arises when cells in the body start to multiply excessively, leading to various symptoms. In this research, a novel hybrid fuzzy linear regression with symmetric parameter clustering combined with a support vector machine (FLRWSPCSVM) model is employed to predict the high-risk symptoms associated with the onset of colorectal cancer in Malaysia. The study analyzed secondary data from 180 patients diagnosed with colorectal cancer and treated in a general hospital in Kuala Lumpur, considering twenty-five independent variables with various combinations of variable types. Furthermore, the model included parameters, errors, and explanations, along with two statistical measurement errors. The findings revealed that FLRWSPCSVM identified ovarian symptoms and a history of cancer symptoms as high-risk indicators for the development of colorectal cancer, with the lowest mean square error (MSE) recorded at 100.605 and a root mean square error (RMSE) of 10.030. [ABSTRACT FROM AUTHOR]

This study presents the development and performance evaluation of an ozone differential absorption lidar system. The system could effectively obtain vertical profiles of lower-tropospheric ozone in an altitude range of 0.3 to 4 km with high spatiotemporal resolutions. The system emits three laser beams at wavelengths of 276, 287 and 299 nm by using the stimulated Raman effect of carbon dioxide (CO 2). A 250 mm telescope and a grating spectrometer are used to collect and separate the backscattering signals at the three wavelengths. Considering the influences of aerosol interference and statistical error, a wavelength pair of 276–287 nm is used for the altitude below 600 m and a wavelength pair of 287–299 nm is used for the altitude above 600 m to invert ozone concentration. We also evaluated the errors caused by the uncertainty of the wavelength index. The developed ozone lidar was deployed in a field campaign that was conducted to measure the vertical profiles of ozone using a tethered balloon platform. The lidar observations agree very well with those of the tethered balloon platform. [ABSTRACT FROM AUTHOR]

The range of visibility largely depends upon the meteorological parameters. A visibility parameter, termed as visibility potential parameter (VPP) is proposed to estimate minimum potential visibility (MPV) during foggy morning. The parameter is defined as the product of two variables, namely temperature, and the inverse of relative humidity. The whole VPP range (0.5–8.5) is divided into sixteen groups from lowest group (0.5–1.0) to highest group (8.1–8.5). The analysis of VPP for the Indira Gandhi International (IGI) Airport, New Delhi (India) shows that all the very dense (visibility: < 50 m) fog cases and 98.8% of dense fog (visibility: 50–199 m) cases occurred in the lowest two groups of VPP (0.5–1.0) and (1.1–1.5). The entire moderate (visibility: 200–499 m) fog cases and 93.5% of shallow (visibility: 500–999 m) fog cases occurred in the lowest three VPP groups. The probabilities of occurrence of fog for the lowest three VPP groups are 61%, 43.5%, and 25.9% respectively. The empirical relationship between VPP and MPV is found to be linear. Rapid update of initial error in statistical post-processing forecast is also proposed for improvement of visibility forecasts. The improvement of error ranged from 20 to 87.7% for forecast update period from 1200 to 0000 UTC. Results of this study show that the MPV could provide useful guidance to forecasters about the possible lowest visibility, and use of recent trend than mean trend of past visibility with respect to VPP and successive removal of error structure in the forecast trajectory can improve the forecasts. [ABSTRACT FROM AUTHOR]

A simulation can stand its ground against an experiment only if its prediction uncertainty is known. The unknown accuracy of interatomic potentials (IPs) is a major source of prediction uncertainty, severely limiting the use of large-scale classical atomistic simulations in a wide range of scientific and engineering applications. Here we explore covariance between predictions of metal plasticity, from 178 large-scale (~108 atoms) molecular dynamics (MD) simulations, and a variety of indicator properties computed at small-scales (≤102 atoms). All simulations use the same 178 IPs. In a manner similar to statistical studies in public health, we analyze correlations of strength with indicators, identify the best predictor properties, and build a cross-scale "strength-on-predictors" regression model. This model is then used to estimate regression error over the statistical pool of IPs. Small-scale predictors found to be highly covariant with strength are computed using expensive quantum-accurate calculations and used to predict flow strength, within the statistical error bounds established in our study. [ABSTRACT FROM AUTHOR]

Polymers are essential materials for high-pressure hydrogen systems, especially in type IV and V hydrogen storage tanks. Extreme operating conditions, with pressures up to 875 bar and temperatures from −40 °C to 85 °C pose serious challenges for these polymeric materials. In particular, the permeation of hydrogen through such materials, a key property for these applications, is strongly influenced by these environmental conditions. A new permeation test setup for pressures up to 1000 bar and a temperature range of 0–85 °C was developed to characterize the hydrogen permeation properties of polymer materials and to evaluate their suitability for storage applications. The reproducibility of the permeation coefficients obtained with the permeation test setup was verified within this work, by repeated tests with pressures of up to 800 bar on high-density polyethylene. In addition, calculations of statistical deviation and error propagation were performed to further validate the performance of the test setup. [Display omitted] • High pressure permeation test setup for up to 1000 bar developed. • Repeat measurements with HDPE samples for verification successfully performed. • Error propagation calculations conducted. [ABSTRACT FROM AUTHOR]

Small sample sizes contribute to several problems in research and knowledge advancement. This conceptual replication study confirmed and extended the inflation of type II errors and confidence intervals in correlation analyses of small sample sizes common in kinesiology/exercise science. Current population data (N = 18, 230, & 464) on four mechanical variables of three kinds of tennis strings were used to study the effect of small randomly selected sample sizes. All small sample sizes (n = 10, 25, 50, & 100) had one to three type II errors in statistical tests of the six pairwise correlations and partial correlations but no type I errors. Correlation coefficients from these small sample sizes had imprecise mean confidence interval widths (.34 to 1.08) that limit accurate statistical inferences, sizes of associations, and are most vulnerable to not detecting small (ρ < .4) true associations in the population. These data confirm that there are likely numerous type II errors and imprecise correlation coefficients in KSES studies using small samples sizes that may slow the knowledge advancement of the field. [ABSTRACT FROM AUTHOR]

Purpose: To assess efficacy and safety of brolucizumab versus aflibercept in patients with diabetic macular edema (DME). Patients and Methods: We performed a systematic review and meta-analysis with trial sequential analysis (TSA). We searched Embase, Cochrane Central Register of Controlled Trials and PubMed databases from inception to February 16, 2024 for randomized controlled trials (RCTs) comparing brolucizumab with aflibercept in patients with DME and reporting any of the visual, anatomical and safety outcomes of interest. We conducted a TSA of safety outcomes to assess the risk of statistical errors. Results: 1253 patients (1253 eyes) from 3 RCTs were included, of whom 57% received brolucizumab and 43% received aflibercept. Mean follow-up ranged from 52 to 100 weeks. Brolucizumab was non-inferior to aflibercept when comparing the mean change of best-corrected visual acuity from baseline (least squares mean difference [LSMD] 0.29; 95% confidence interval [CI] − 1.37 to 1.95; p = 0.73). Change in central subfield thickness was significantly greater in the brolucizumab group compared with aflibercept (LSMD − 24.5 μm; 95% CI − 48.2 to − 0.7 μm; p < 0.05). Incidence of adverse events of special interest (AESIs) (risk ratio [RR] 1.7; p = 0.08) and incidence of ≥ 1 ocular adverse events (AEs) (RR 0.95; p = 0.45) were not significantly different between groups. Conclusion: Brolucizumab was non-inferior in functional outcomes and was superior to aflibercept in anatomical parameters. Ocular AEs and AESIs were numerically low and not statistically significant. Our findings underscore the importance of new RCTs powered to assess safety outcomes in order to suggest brolucizumab as an alternative to the treatment of DME. [ABSTRACT FROM AUTHOR]

The objective of this study is to propose a computationally inexpensive and effective approach that addresses the challenges faced with computationally expensive and time-consuming trial-and-error and direct optimization methods in well-control optimization. This approach involves combining proxy models such as artificial neural network (ANN) models with optimization algorithms to determine an optimal solution much faster. It was implemented in a heterogeneous oil reservoir undergoing waterflooding. The controllable parameters of the reservoir simulation model were identified as bottom-hole pressure for the producers and water injection rate for the injectors. Minimum and maximum values of each input parameter were defined based on reservoir conditions and used with a Box Behnken design (BBD) method to generate realizations for conducting reservoir simulations to obtain cumulative oil produced (COP) and cumulative water produced (CWP). The input and output data were normalized before being used for model development such that 70:15:15% of data was used for training, validation, testing, and all of the ANN model in which a coefficient of correlation (R) of 0.99756, 0.94354, 0.95813, and 0.98589 were obtained respectively. This indicates the accuracy, validity, and reliability of the model. The coefficient of determination (R2) for training, validation, testing, and all datasets as well as statistical error and trend analysis were used to validate the model. R2 values for each case were not less than 0.80, and the responses were reproduced by the ANN model with average relative error and root mean square error of not more than 0.7%. Weights and biases were extracted from the trained and validated ANN model to aid in outputting a visible ANN model that can be used for optimization studies. A multi-objective genetic algorithm was used to determine an optimal solution that maximized COP and minimized CWP. Average and optimized input data were used to run the developed ANN model. Results revealed that the optimized case outperformed the case for which average input values were used evidenced by the production of 34.198 MSm3 more oil and 14.297 MMSm3 less water. The findings of this study showed that using an ANN-MOGA approach will eliminate the computationally expensive, time-consuming, and inefficient trial-and-error approach for well-control optimization. Oil recovery was improved while water production was reduced resulting in low expenditure on treatment and disposal of produced water. [ABSTRACT FROM AUTHOR]

Background: Accurate prognostic factors for primary ocular adnexal lymphoma (POAL) are scarce. Survival models and prognostic factors derived without considering competing risk factors suffer from major statistical errors. This study aimed to accurately assess prognostic factors in POAL patients using competing risk models, and compare this to the traditional COX proportional hazards model. Methods: This retrospective study utilised data from the Surveillance, Epidemiology, and End Results (SEER) program 2010–2015 and included patients with B‐cell POAL. The cumulative incidence function and Gray's test for cause‐specific survival were calculated as univariate analysis. The competing risk models were a Fine‐Gray subdistribution hazard model and a cause‐specific model, and a traditional COX model was employed as a multivariate analysis. Results: This study enrolled 846 eligible patients with POAL: 60 patients (7.09%) died from POAL and 123 patients (14.54%) died from other causes. Multivariate competing risk models indicated that age, laterality, histology subtype, the 7th edition of American Joint Committee on Cancer stage T, and radiotherapy were independent predictors for cause‐specific survival of patients with POAL. There was high consistency between the two competing risk models. The COX model made several misestimations on the statistical significance and hazard ratios of prognostic factors. Conclusions: This study established competing risk models as a method to assess POAL prognostic factors, which was more accurate than traditional methods that do not consider competing risk elements. [ABSTRACT FROM AUTHOR]

The wastewater generated by the pharmaceutical industry poses a risk to the environment due to undesirable characteristics such as low biodegradability, high levels of contaminants, and the presence of suspended solids, in addition to the high load of organic matter due to the presence of drugs and other emerging products in the effluent. This study aims to reduce the impact of wastewater pollution by removing amoxicillin (AMO) antibiotics as an organic pollutant. In this concept, two synthesized catalysts, NiAl2O4 and ZnO, are sensitive oxides to light energy. The prepared materials were then characterized using X-ray diffraction, UV–vis solid reflectance diffuse, Raman spectroscopy, scanning electron microscopy, BET, and ATR-FTIR spectroscopy. The effects of principal operating parameters under sunlight, namely, the percentage of the mixture of NiAl2O4 and ZnO, the pH of the medium, and the initial concentration of the antibiotic were studied experimentally to determine the optimal conditions for achieving a high degradation rate. The results showed that photodegradation is higher at a pH of 6, with a weight percentage of the mixture of 50% for both catalysts in 1 g/L of the total catalyst dose. Then, the effect of the initial concentration of AMO on the photodegradation reaction showed an important influence on the photodegradation process; as the degradation rate decreases, the initial AMO concentration increases. A high degradation rate of 92% was obtained for an initial AMO concentration of 10 mg/L and a pH of 6. The kinetic study of degradation established that the first-order model and the Langmuir–Hinshelwood (LH) mechanism fit the experimental data perfectly. The study showed the success of using heterosystem photocatalysts and sustainable energy for effective pharmaceutical removal, which can be extended to treat wastewater with other organic emerging pollutants. On the other hand, modeling was introduced using Gaussian process regression (GPR) to predict the degradation rate of AMO under sunlight in the presence of heterogeneous ZnO and NiAl2O4 systems. The model evaluation criteria of GPR in terms of statistical coefficients and errors show very interesting results and the performance of the model used. Where statistical coefficients were close to one (R = 0.9981), statistical errors were very small (RMSE = 0.1943 and MAE = 0.0518). The results suggest that the model has a strong predictive power and can be used to optimize the process of AMO removal from wastewater. [ABSTRACT FROM AUTHOR]

Uncertainty assessment of Ocean Colour Radiometry (OCR) usually relies on the comparison between satellite marine reflectance and concurrent in situ radiometric measurements. However, when dealing with the standard atmospheric correction (AC), uncertainty of marine reflectance originates fundamentally from two sources: (i) uncertainty of the Level-1 calibration at top of the atmosphere, and (ii) uncertainty of the atmospheric scattering functions computed by the AC (atmospheric path reflectance and transmittance). The aim of this alternative work is thus to assess the uncertainty of these scattering functions, thanks to inherent optical properties of aerosols extracted from the AErosol Robotic NETwork (AERONET) and ingested in a radiative transfer code. The study is conducted for the MEdium Resolution Imaging Spectrometer (MERIS), over three AERONET sites selected in European seas (Aqua Alta Oceanographic Tower, AAOT; Gustav Dalen Tower, GDT; Lampedusa) and Lanai in the Pacific Ocean as a reference. We develop an uncertainty budget based on the statistical analysis of the errors between MERIS and AERONET-derived data. At 443 nm, uncertainty of MERIS atmospheric path reflectance due to random effects is estimated around 2%, while that of total atmospheric transmittance is between 0.6% and 1%. Propagation of these uncertainties to OCR is achieved following metrological principles and varies between match-ups and sites due to the distinctive contribution of the marine signal to the total radiometry: uncertainty of MERIS marine reflectance at 443 nm is 10.8% (±1.1%) at Lanai, 18.3% (±4.6%) at Lampedusa, 29.4% (±12.2%) at AAOT and up to 68.7% (±127.4%) at GDT (i.e. with a significant intra-site variability). The methodology is a first step aimed at deriving a pixel-by-pixel uncertainty budget of OCR, instead of providing overall estimates as generally done in the OC community. Limitations due to uncertainties in the AERONET data are also discussed, with a tentative quantification. [ABSTRACT FROM AUTHOR]

This paper introduces a novel approach to reducing statistical errors in generative models, with a specific focus on generative adversarial networks (GANs). Inspired by the error analysis of GANs, we find that statistical errors mainly arise from random sampling, leading to significant uncertainties in GANs. To address this issue, we propose a selective sampling mechanism called space‐filling sampling. Our method aims to increase the sampling probability in areas with insufficient data, thereby improving the learning performance of the generator. Theoretical analysis confirms the effectiveness of our approach in reducing statistical errors and accelerating convergence in GANs. This research represents a pioneering effort in targeting the reduction of statistical errors in GANs, and it demonstrates the potential for enhancing the training of other generative models. [ABSTRACT FROM AUTHOR]

Evaluating the precision and applicability of high-quality precipitation products in the distinctive terrain and intricate climate of the Yunnan-Kweichow Plateau (YKP) is pivotal for climate research. This study comprehensively assesses four gridded precipitation datasets (AERA5-Asia, AIMERG, ERA5-Land, and IMERG-Final) using the China Meteorological Administration's surface precipitation data. It employs eight statistical indicators and error decomposition methods at various spatiotemporal scales. The main findings are as follows: (1) AERA5-Asia, AIMERG, and IMERG-Final show similar precipitation patterns, with ERA5-Land overestimating. While all display minor seasonal variations, AERA5-Asia underestimates summer rain. ERA5-Land tends to overstate, whereas AIMERG and IMERG-Final are generally accurate but slightly undervalued in southern YKP. (2) Hourly analysis reveals AERA5-Asia leads in performance metrics (CC: 0.23, MAE: 0.49 mm/hour, RMSE: 0.18 mm/hour, CSI: 0.27). In contrast, ERA5-Land lags, marked by the lowest BIAS (35.39%), FAR (0.74), and FBI (2.85). AIMERG and IMERG-Final display comparable results but underperform in CC (0.16, 0.13), POD (0.31, 0.30), and CSI (0.19, 0.18). (3) False bias significantly contributes to the total bias of precipitation products. AERA5-Asia and AIMERG mitigate total bias and enhance false precipitation situations through calibration algorithms, albeit introducing missed bias in the central region of YKP. The study findings offer valuable insights into YKP precipitation, informing the development of grid-based fusion algorithms in the region's complex terrain. [ABSTRACT FROM AUTHOR]

Maintaining accurate real‐time hindcast and forecast specification of the radiation environment is essential for operators to monitor and mitigate the effects of hazardous radiation on satellite components. The Radiation Belt Forecasting Model and Framework (RBFMF) provides real‐time forecasts and hindcasts of the electron radiation belt environment, which are used as inputs for the Satellite Charging Assessment Tool. We evaluated the long‐term statistical error and bias of the RBFMF by comparing the 10‐hr hindcast of electron phase space densities (PSD) to a multi‐mission data set of PSD observations. We found that, between the years 2016–2018, the RBFMF reproduced the radiation belt environment to within a factor of 1.5. While the error and bias of assimilated observations were found to influence the error and bias of the hindcast, data assimilation resulted in more accurate specification of the radiation belt state than real‐time Van Allen Probe observations alone. Furthermore, when real‐time Van Allen Probe observations were no longer available, the hindcast errors increased by an order of magnitude. This highlights two needs; (a) the development of physics‐based modeling incorporated into this framework, and (b) the need for real‐time observations which span the entire outer radiation belt. Plain Language Summary: It is important to accurately predict and monitor the radiation levels in space to safeguard satellites from potential damage. This paper introduces a model called the Radiation Belt Forecasting Model and Framework, which provides real‐time forecasts and historical data (hindcasts) of radiation levels. To test the model's accuracy, we compared its predictions to actual observations from satellites between 2016 and 2018. We found that generally, the model's predictions of the outer radiation belt were within 1.5 times of the actual measurements. Additionally, we discovered that incorporating both real‐time satellite observations and physics‐based simulation improved prediction accuracy compared to relying solely on either method. However, we noticed a significant increase in prediction errors when real‐time observations through the heart of the Van Allen radiation belt were unavailable. This underscores the importance of enhancing the model with physics‐based modeling and ensuring continuous real‐time observations of radiation levels in space. Key Points: Real‐time data‐assimilative hindcasts of the outer electron radiation belt were accurate to within a factor of 1.5Data assimilation substantially improved the error and bias of radiation belt specification but strongly influenced hindcast error and biasImproved physics‐based modeling and continuous real‐time observations through the outer radiation belt are needed for accurate hindcasts [ABSTRACT FROM AUTHOR]

Floods, as major natural disasters, cause massive property destruction and death. Understanding the occurrence time of this event by advance notice helps consider operational flood prevention systems and platforms. Precise flood forecasting provides a suitable time for policymakers and the public to consider helpful responses to this event. This study introduces an innovative methodology to enhance the precision of long-term flood predictions by employing a multistep forecasting approach. Our approach leverages historical time-series data on precipitation and streamflow to train an autoencoder algorithm. The primary objective is to develop advanced forecasting models to predict 12-step-weekly ahead flood occurrences during the critical April to July period from 2019 to 2021 within the DuPage River basin, Illinois, USA. In order to achieve this goal, we explore three deep learning techniques: bidirectional long short-term memory (BI-LSTM), ensemble long short-term memory (E-LSTM), and ensemble long short-term memory-gated recurrent unit (E-LSTM-GRU). Then, we integrate an attention mechanism (AM) that utilizes dynamic fusion techniques to emphasize the salient features of ensemble models. A dedicated fusion model is developed for each forecasting stage, effectively consolidating the predictions from various deep-learning models. Additionally, two traditional machine learning techniques, namely MLP and SVM models, are used to compare and justify the efficiency of applied deep learning models. The performance evaluation of our approach using statistical error metrics, including coefficient of determination (R2), normalized root mean square error, normalized mean absolute error, mean absolute percentage error, Nash–Sutcliffe efficiency, Kling–Gupta efficiency, and percent bias, for the 12th step prediction reveals impressive results, with average values of 0.976, 2.393, 1.892, 20.956, 0.967, 0.923, and 2.307, respectively. These findings underscore the capability of our proposed models to significantly reduce uncertainty in flood forecasting, thus enhancing the reliability and accuracy of future predictions. [ABSTRACT FROM AUTHOR]

In this paper, a new tallying method for a neutron flux distribution using the proper orthogonal decomposition is proposed for dimensionality reduction. The target spatial flux distribution is expanded by orthogonal basis vectors. Expansion coefficients are tallied during the random walk of the Monte Carlo calculation. The orthogonal basis vectors are extracted from the pre-calculated snapshots by the singular value decomposition. The proposed method is verified in the multi-group Monte Carlo calculation with the one-dimensional heterogeneous whole core geometry as a feasibility study. The flux distribution for each of the assemblies and energy groups is expanded by the basis vectors. The fewer basis vectors obtained from snapshots can reconstruct the target distribution well compared with the conventional Legendre polynomials used in the functional expansion tallies. The dimension of the solution in the proposed method is reduced by a factor of twenty compared with the conventional cell tally. In addition, the statistical error is reduced through dimensionality reduction thanks to the methodological feature of the proposed method. The results indicate that the proposed method has the capability of dimensionality reduction to tally the finely discretized flux distribution. [ABSTRACT FROM AUTHOR]

We propose a modified approach for DFT-based spectra and cross-spectra estimation that allows to significantly reduce the problem of spectral leakage at very low frequencies. This results in the ability to select larger resolution bandwidths that, in turn, allow for reduced measurement time for a given maximum level of statistical error in spectral points at very low frequencies. [ABSTRACT FROM AUTHOR]

First principles approaches have revolutionized our ability in using computers to predict, explore, and design materials. A major advantage commonly associated with these approaches is that they are fully parameter-free. However, numerically solving the underlying equations requires to choose a set of convergence parameters. With the advent of high-throughput calculations, it becomes exceedingly important to achieve a truly parameter-free approach. Utilizing uncertainty quantification (UQ) and linear decomposition we derive a numerically highly efficient representation of the statistical and systematic error in the multidimensional space of the convergence parameters for plane wave density functional theory (DFT) calculations. Based on this formalism we implement a fully automated approach that requires as input the target precision rather than convergence parameters. The performance and robustness of the approach are shown by applying it to a large set of elements crystallizing in a cubic fcc lattice. [ABSTRACT FROM AUTHOR]

Domain adaptation is an important subfield of transfer learning, it has been successfully applied in many applications of machine learning. Recently, significant theoretical and algorithmic advances have been achieved in domain adaptation. The theoretical analyses for domain adaptation are based on VC dimension and Rademacher complexity. There are also some covering number-based results, but most of these bounds are based on the results of Rademacher complexity, indirectly given by the relationship between covering number and Rademacher complexity. In this paper, we propose a theoretical analysis framework for domain adaptation, thus the error bound can be derived directly by covering number, which is an effective method for analyzing the generalization error in statistical learning theory. We derive generalization error bound for domain adaptation with a class of loss functions satisfying the assumptions. We also propose a mixup contrastive adversarial network for domain adaptation by introducing a mixup module for enhancing the alignment of the source and target domains during domain transfer, and a contrastive learning module for solving class-level alignment after domain transfer. Experimental results demonstrate the effectiveness of the proposed algorithm and the property of the theoretical results. [ABSTRACT FROM AUTHOR]

Background: Microplastic pollution is a pressing issue with far-reaching environmental and public health consequences. This study delves into the intricacies of predicting microplastic pollution during the COVID-19 pandemic in Tehran, Iran. Methods: The research introduces a rigorous comparative analysis that evaluates the predictive prowess of the Deep Random Forest algorithm and established benchmarks, such as Random Forest, Decision Trees, Gradient Boosting, AdaBoost, and Support Vector Machine. The evaluation process encompasses a meticulous 70–30 training–testing split of the main data set. Performance is assessed by analysis metrics, including ROC and statistical errors. The primary data set encompasses distinct categories, including household wastes, hospital wastes, clinics wastes, and unknown-originated susceptible waste which is categorized in Infected items, PPEs, SUPs, Test kits, Medical packages, Unknown-originated pandemic mircoplastic waste. Deliberately, this data set was partitioned into training and testing subsets, ensuring the robustness and reliability of subsequent analyses. Approximately 70% of the main database was allocated to the training data set, with the remaining 30% constituting the testing data set. Results: The findings underscore the proposed algorithm's supremacy, boasting an impressive AUC = 0.941. This exceptional score reflects the model's precision in categorizing microplastics. These results have profound implications for environmental management and public health during pandemics. Conclusions: The study positions the proposed model as a potent tool for microplastic pollution prediction, encouraging further research to refine predictive models and tap into new data sources for a more comprehensive understanding of microplastic dynamics in urban settings. [ABSTRACT FROM AUTHOR]

Context. Following their birth, globular clusters (GCs) experience a very peculiar dynamical evolution. Gravitational encounters drive these systems toward energy equipartition, mass segregation, and evaporation, which alter structural, spatial, and kinematic features. Aims. We determine the dynamical state of a few GCs by means of a multi-mass King-like dynamical model. Our work focuses on the prediction of the energy equipartition degree and its relationship with model parameters. Methods. We adjusted the dynamical model parameters in order to reproduce the observed velocity dispersion – as derived from Hubble Space Telescope proper motion data – as a function of the stellar mass. By doing so, we estimated Φ0, a measure of the gravitational potential well. We repeated the same fit by means of the Bianchini relation, a function obtained by interpolating on N-body simulation results. We studied the relationship between Φ0 and the Bianchini equipartition mass meq and discuss the structural properties, such as concentration c, the number of core relaxation timescales Ncore, and core radius rc. To obtain an independent estimate of Φ0, we also fitted observed surface brightness profiles using the predicted surface density and a mass-luminosity relation from isochrones. Results. The quality of the fits of the velocity dispersion–mass relationship obtained by means of our dynamical model is comparable to those obtained with the Bianchini function. Nonetheless, when the Bianchini function is used to fit the projected velocity dispersion, the resulting degree of equipartition is underestimated. On the contrary, our approach provides the equipartition degree at any radial or projected distance by means of Φ0. As a result, a cluster in a more advanced dynamical state shows a larger Φ0, as well as larger Ncore and c, while rc decreases. We find the estimates of Φ0 obtained by fitting surface brightness profiles to be compatible at 2σ confidence level with those from internal kinematics, although further investigation of statistical and systematic errors is required. Conclusions. Our work illustrates the predicting power of dynamical models to determine the energy equipartition degree of GCs. These models are a unique tool for determining structural and kinematic properties, and can be used where observational data are poor, as is the case for the most crowded regions of a cluster, where stars are barely resolved. [ABSTRACT FROM AUTHOR]

Surface morphology measurement and reconstruction technology based on point cloud data is one of the key technologies for 3D information processing in the digital manufacturing industry and has been widely applied in fields such as reverse engineering, computer vision, and unmanned driving system navigation. A method for 3D modeling of aircraft-engine blade profiles based on laser measurement point cloud data is proposed to address the difficulties in measuring the 3D morphology of aircraft-engine blades and the low modeling accuracy. This method first preprocesses the measured point cloud and then uses Poisson's algorithm to reconstruct the blade surface in three dimensions based on the calculation of the point cloud normal. Through error statistical analysis, the overall reconstruction effect is good. The experimental results further validated the generality and effectiveness of this method. [ABSTRACT FROM AUTHOR]

We present a computation of triply-heavy baryons on a quantum computer, employing the Cornell quark model in line with the earlier quarkonium work of Gallimore and Liao. These baryons are some of the most interesting Standard Model particles which have not yet been detected, as they bear on the short range (colour) behaviour of the nuclear force. The spectrum here obtained is compatible with predictions from earlier works, with our uncertainty dominated by traditional few-body approximations (size of the variational basis, center of mass recoil, parameter estimation...) and not by the statistical error from the quantum computer (deployed here as a small diagonalizer), which turns out to be negligible with respect to the other sources of uncertainty, at least in the present unsophisticated few-body approximation. We have also substituted one or more heavy quarks for strange quarks. [ABSTRACT FROM AUTHOR]

The error in modulus backcalculation is a crucial component in validating the rationality and reliability of results for engineering applications. The objective of this study is to identify the theoretical limitations associated with backcalculated modulus errors under typical parameter uncertainties and to determine the primary factors contributing to these errors. Firstly, using the actual measurements or data from the Long-Term Pavement Performance (LTPP) project, the statistical distributions of errors for typical parameters in the modulus backcalculation model were determined. Subsequently, a factor level table for orthogonal experimental design was developed, leading to the construction of 81 orthogonal design experimental schemes and their corresponding theoretical pavement structure models based on the actual error distributions. The deflection responses of 81 theoretical pavement structure models were then computed using an ABAQUS finite element batch analysis method devised in Python. Furthermore, a multi-parameter error model for modulus was established using multiple linear regression and variance analysis. Finally, the theoretical limitations of modulus errors under actual errors were analyzed. The results show that the errors of thickness, load amplitude and load frequency follow a normal distribution, while the distribution of backcalculated modulus errors follows an approximate mixed Gaussian distribution. When the errors of multiple parameters are combined randomly, the modulus errors range from −100% to 595%, and the probability of the modulus errors being less than 15% is highest in the asphalt surface layer, followed by the subgrade, and then the base and subbase layers. Within the same error range, the modulus error is random. However, with different error ranges, the overall level of modulus error increases in proportion to the size of those ranges. Compared to factors such as thickness, load amplitude, and load frequency, the errors in deflections have a highly contribution rate on the modulus errors exceeding 99%. [ABSTRACT FROM AUTHOR]

Observations in various applications are frequently represented as a time series of multidimensional arrays, called tensor time series, preserving the inherent multidimensional structure. In this paper, we present a factor model approach, in a form similar to tensor CANDECOMP/PARAFAC (CP) decomposition, to the analysis of high-dimensional dynamic tensor time series. As the loading vectors are uniquely defined but not necessarily orthogonal, it is significantly different from the existing tensor factor models based on Tucker-type tensor decomposition. The model structure allows for a set of uncorrelated one-dimensional latent dynamic factor processes, making it much more convenient to study the underlying dynamics of the time series. A new high-order projection estimator is proposed for such a factor model, utilizing the special structure and the idea of the higher order orthogonal iteration procedures commonly used in Tucker-type tensor factor model and general tensor CP decomposition procedures. Theoretical investigation provides statistical error bounds for the proposed methods, which shows the significant advantage of utilizing the special model structure. Simulation study is conducted to further demonstrate the finite sample properties of the estimators. Real data application is used to illustrate the model and its interpretations. [ABSTRACT FROM AUTHOR]

Weak gravitational lensing simulations serve as indispensable tools for obtaining precise cosmological constraints. In particular, it is crucial to address the systematic uncertainties in theoretical predictions, given the rapid increase in galaxy numbers and the reduction in observational noise. Both on-the-fly and post-processing methods for constructing lensing light-cones encounter limitations due to the finite simulated volume, necessitating the replication of the simulation box to encompass the volume to high redshifts. To address this issue, our primary focus lies on investigating and quantifying the impact of box replication on the convergence power spectrum and higher order moments of lensing fields. Subsequently, a univariate model is utilized to estimate the amplitude parameter A by fitting four statistics measured from partial sky light-cones along specific angles, to the averaged result from random directions. The investigation demonstrates that the systematic bias stemming from the box replication phenomenon falls within the bounds of statistical errors for the majority of cases. However, caution should be exercised when considering high-order statistics on a small sky coverage (⁠|${\lesssim} 25~\mathrm{deg^2}$|⁠). For this case, we have developed a code that facilitates the identification of optimal viewing angles for the light-cone construction. This code has been made publicly accessible at https://github.com/czymh/losf. [ABSTRACT FROM AUTHOR]

Previous studies highlighted the significance of tailoring alkaline activators (AA) to specific fly ash (FA) sources for optimal properties of geopolymer concrete (GPC). This study examines the influence of various AA's properties on mechanical properties and microstructures of local low-calcium FA-based GPC under varying curing conditions. A comprehensive investigation consists of several factors such as NaOH molarities (10 M, 12 M, 14 M, 16 M), Na 2 SiO 3 / N a O H ratios (1.5, 2.0, 2.5) and A A / F A ratios (0.5, 0.6). The results reveal a complex relationship, demonstrating that NaOH molarity positively influences compressive strength up to a threshold of 14 M, beyond which an adverse effect was observed while, the flexural strength was increased up to 16 M. Moreover, the study highlights the complex relationship between Na 2 SiO 3 / N a O H ratios and mechanical strengths. Notably, these properties exhibited an increase as the ratio rose up to 2.0, but a subsequent decrease was observed when the ratio reached 2.5. Moreover, proposed regression equations predict the compressive and flexural strengths of both ambient-cured GPC and heat-cured GPC with marginal statistical errors. The optimal GPC mix exhibited 49% lower embodied CO 2 emissions than the corresponding OPC concrete. GPC has higher cost, but it exhibited lower cost-to-strength ratio compared to OPC concrete. [ABSTRACT FROM AUTHOR]

Progress in the semiconductor industry relies on the development of increasingly compact devices consisting of complex geometries made from diverse materials. Precise, label‐free, and real‐time metrology is needed for the characterization and quality control of such structures in both scientific research and industry. However, optical metrology of 2D sub‐wavelength structures with nanometer resolution remains a major challenge. Here, a single‐shot and label‐free optical metrology approach that determines 2D features of nanostructures, is introduced. Accurate experimental measurements with a random statistical error of 18 nm (λ/27) are demonstrated, while simulations suggest that 6 nm (λ/81) may be possible. This is far beyond the diffraction limit that affects conventional metrology. This metrology employs neural network processing of images of the 2D nano‐objects interacting with a phase singularity of the incident topologically structured superoscillatory light. A comparison between conventional and topologically structured illuminations shows that the presence of a singularity with a giant phase gradient substantially improves the retrieval of object information in such an optical metrology. This non‐invasive nano‐metrology opens a range of application opportunities for smart manufacturing processes, quality control, and advanced materials characterization. [ABSTRACT FROM AUTHOR]

This study explores innovative methodologies for estimating the energy baseline (EnBL) of a university classroom building, emphasizing the critical roles of data quality and model selection in achieving accurate energy efficiency assessments. We compare time series models that are suitable for buildings with limited consumption data with univariate and multivariate regression models that incorporate additional variables, such as weather and occupancy. Furthermore, we investigate the advantages of dynamic simulation using the EnergyPlus engine (V5, USDOE United States) and Design Builder software v7, enabling scenario analysis for various operational conditions. Through a comprehensive case study at the UAO University Campus, we validate our models using daily monitoring data and statistical analysis in RStudio. Our findings reveal that model choice significantly influences energy consumption forecasts, leading to potential overestimations or underestimations of savings. By rigorously assessing statistical validation and error analysis results, we highlight the implications for decarbonization strategies in building design and operation. This research provides a valuable framework for selecting appropriate methodologies for energy baseline estimation, enhancing transparency and reliability in energy performance assessments. These contributions are particularly relevant for optimizing energy use and aligning with regulatory requirements in the pursuit of sustainable building practices. [ABSTRACT FROM AUTHOR]

Floods are the most frequent of extreme events and they can easily impact lives and property. Flood mapping and mitigation are prohibitively expensive and time-consuming. The need for accurate maps of flood probability is vital and meeting that need is a challenge. This study presents an effective flood-probability mapping framework that compares one standalone machine-learning model, the reduced-error pruning tree (REPT), to four hybrid models using meta-classifiers: Bagging (BA-REPT), Dagging (DA-REPT), AdaBoost (AB-REPT), and LogitBoost (LB-REPT). Nine continuous and categorical variables that reflect the conditions that influence flood probabilities—slope, elevation, aspect, topographic wetness index (TWI), distance to river, land use, lithology, rainfall, and valley depth—were used to prepare flood-probability maps of a catchment in western Isfahan Province, Iran. To train the five algorithms, 108 flood events were mapped in the catchment, and these locations were randomly separated into two subsets at a ratio of 70:30. The data (comprised of the nine flood-conditioning variables) for the larger portion (70% of cases) were used to build the models and the remaining 30% of cases were used to test them. The performances of the models were evaluated with several statistical and error tests (TP, TN, FP, FN, accuracy, sensitivity, and specificity), receiver operating characteristic (ROC) curve, and the area under the ROC curve (AUC). The results show that the hybrid (ensemble) methods boosted accuracy performance above the standalone REPT by between 2.56 and 15.38%. AUC statistics indicated that the predictive powers of models were REPT = 0.78, LB-REPT = 0.80, DA-REPT = 0.83, BA-REPT = 0.85, and AB-REPT = 0.90. [ABSTRACT FROM AUTHOR]

Motivation The Peter Clark (PC) algorithm is a popular causal discovery method to learn causal graphs in a data-driven way. Until recently, existing PC algorithm implementations in R had important limitations regarding missing values, temporal structure or mixed measurement scales (categorical/continuous), which are all common features of cohort data. The new R packages presented here, micd and tpc , fill these gaps. Implementation micd and tpc packages are R packages. General features The micd package provides add-on functionality for dealing with missing values to the existing pcalg R package, including methods for multiple imputations relying on the Missing At Random assumption. Also, micd allows for mixed measurement scales assuming conditional Gaussianity. The tpc package efficiently exploits temporal information in a way that results in a more informative output that is less prone to statistical errors. Availability The tpc and micd packages are freely available on the Comprehensive R Archive Network (CRAN). Their source code is also available on GitHub (https://github.com/bips-hb/micd ; https://github.com/bips-hb/tpc). [ABSTRACT FROM AUTHOR]

The SWOT satellite, carrying the KaRIN first wide-swath onboard altimeter, was launched in December 2022, and has now delivered more than a year of surface water elevation data over the ocean and inland lakes/rivers. These data are affected by systematic errors which constitute the dominant part of the error budget at scales larger than a few thousands of kilometers. Some strategies for their estimation and calibration were explored during the pre-launch studies with performance estimations. Now, based on the real data, we propose in this study to assess the systematic error budget with statistical methods relying on spectral and co-spectral analysis. From this assessment, suggesting very low error levels (below requirements), we propose the implementation of the calibration algorithms at Level-2 and Level-3 with a few minor adjustments justified by the error spectra. The calibrated products are then validated with usual CalVal metrics. [ABSTRACT FROM AUTHOR]

Intelligent voltage stability monitoring remains an essential feature of modern research into secure operations of power system networks. This research developed an adaptive neuro-fuzzy expert system (ANFIS)-based predictive model to validate the viability of two contemporary voltage stability indices (VSIs) for intelligent voltage stability monitoring, especially at intricate loading and operation points close to voltage collapse. The Novel Line Stability Index (NLSI) and Critical Boundary Index are VSIs deployed extensively for steady-state voltage stability analysis, and thus, they are selected for the predictive model implementation. Six essential power system operational parameters with data values calculated at varying real and reactive loading levels are input features for ANFIS model implementation. The model's performance is evaluated using reliable statistical error performance analysis in percentages ( M A P E and R R M S E p ) and regression analysis based on Pearson's correlation coefficient (R). The IEEE 14-bus and IEEE 118-bus test systems were used to evaluate the prediction model over various network sizes and complexities and at varying clustering radii. The percentage error analysis reveals that the ANFIS predictive model performed well with both VSIs, with CBI performing comparatively better based on the comparative values of M A P E , R R M S E p , and R at multiple simulation runs and clustering radii. Remarkably, CBI showed credible potential as a reliable voltage stability indicator that can be adopted for real-time monitoring, particularly at loading levels near the point of voltage instability. [ABSTRACT FROM AUTHOR]

This paper proposes an improved high-precision surface solar radiation estimation model using the integration of the local meteorological data and air quality index based linear regression analysis. The proposed model was evaluated and compared to 8 conventional models and one generated by the commonly used PVsyst simulation software. The actual solar radiation, meteorological data and air quality index collected over 10 years (during 2011-2021) from standard measuring stations located at the northern zone of Thailand were used for developing the models while the collected data year 2022 were used for validating the developed models compared to the conventional models. The statistical error estimations in terms of mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE) were used for the precision evaluation. The study found that the proposed models achieved better prediction results and the highest precision for monthly estimating of solar radiation than the other models by having the highest estimation precision of 94.70-97.19% compared to 87.53-96.74% of the conventional models and 90.38-95.96% of the PVsyst program. [ABSTRACT FROM AUTHOR]

This paper examines the effectiveness of implementing artificial intelligence (AI) in the Google Ads advertising service. The study analyzes the advantages and disadvantages of AI integration, focusing on attribution models and end-to-end analytics. The findings show that traditional metrics, such as CTR, CPC, and ROI, used to evaluate advertising campaign performance, exhibit significant statistical errors when AI tools are applied, with errors reaching up to 35%, exceeding typical business margins. A comparative analysis in the construction industry highlights discrepancies of 10% to 35% between traditional and AI-driven models. The study concludes that universal AI algorithms often fail to account for industry-specific dynamics, leading to inaccurate evaluations. The practical significance of this research lies in proposing an alternative approach that combines traditional evaluation methods with AI-based tools, offering a more reliable framework for assessing campaign effectiveness. [ABSTRACT FROM AUTHOR]

Pulmonary embolism (PE) is correlated with serious morbidity and mortality. Efforts have been made to establish and validate mortality predictive scores based mainly on clinical parameters. Patients with PE and traditional indices of echocardiographic right ventricular (RV) dysfunction or pressure overload have a higher probability of a worse outcome. During the last two decades, studies regarding the use of two-dimensional speckle-tracking echocardiography (2DSTE) and its derived indices in the setting of acute PE have been conducted. In this comprehensive review of the literature, we aimed to summarize these studies. Safe conclusions and comparisons among the reviewed studies are prone to statistical errors, mainly because the studies published were heterogenous in design, different 2DSTE-derived parameters were tested, and different clinical outcomes were used as endpoints. Nonetheless, RV strain indices and, more commonly, regional longitudinal strain of the RV free wall have shown a promising correlation with mortality, assisting in the differential diagnosis between PE and other acute or chronic disorders. [ABSTRACT FROM AUTHOR]

The logarithmic exponential regression model is proposed for the pure MgB2 superconductor in this study. The predicted values of normalized pinning force as a function of the reduced field are evaluated and compared to the measured values at a wide range of temperatures. The proposed model well described the best-suited model for the estimated predicted values. The predicted values of the reduced field dependence of normalized pinning force are in good agreement with the measured values for the pure MgB2 superconductor. Some statistical error estimations namely mean bias error, mean absolute bias error and root mean square error is calculated at different temperatures from 10-30K. The large value of the coefficient of regression, R² confirms the effectiveness and significance of the proposed model. The proposed model is applicable in this study to find the accurate and precise values of the computational data. [ABSTRACT FROM AUTHOR]

Model gesture performance is one of the basic means of expression of fashionable clothing. The requirements of all walks of life have gradually increased to a high level of unity of spirit, form and emotion, which poses a higher level of challenge to the professional level and creative level of brand planners, advertising creative personnel, models and other related practitioners. This article takes the physical performance of sports models as the starting point. Considering the complexity of the background in crowd scenes, this paper proposes to combine the background subtraction algorithm with the pedestrian detection technology. The background subtraction algorithm is used to provide a target area for pedestrian detection, thereby effectively narrowing the search range and improving the detection rate. The removal of the background can effectively reduce the occurrence of false alarms and false detections in the detection process, thereby improving the detection accuracy. The relevant parameters required for advertisement evaluation can be counted, such as traffic flow, number of advertisement viewers, viewing time, viewing mode, etc. Therefore, the research on the influence of sports model body movements and clothing print network advertising effects needs to be focused on. According to experimental results, by placing sports clothing print online advertisements on the big screen, pedestrians were divided into two viewing modes, and the pedestrian viewing ratio was 88.82%. Among them, the proportion of watching while standing was 96.68%, and the proportion of watching while walking was 89.06%. In the statistics of the proportion of viewing time, the average statistical error was 3.9%. It shows that the system can effectively obtain the relevant experimental parameters in the actual detection, and make a scientific and reasonable evaluation of the advertising effect. [ABSTRACT FROM AUTHOR]

We investigate the dimensionality of bipartite quantum systems by construction of a device-independent null witness test. This test assesses whether a given bipartite state conforms with the expected quantum dimension, Schmidt number, and distinguishes between real and complex spaces. By employing local measurements on each party, the proposed method aims to determine the minimal rank. By performing an experimental demonstration on IBM Quantum devices, we prove the exceptional accuracy of the test and its usefulness in diagnostics beyond routine calibrations. One of the tests shows agreement with theoretical expectations within statistical errors. However, the second test failed by more than 6 standard deviations, indicating unspecified parasitic entanglements, with no known simple origin. [ABSTRACT FROM AUTHOR]

Joint multimodal functional data acquisition, where functional data from multiple modes are measured simultaneously from the same subject, has emerged as an exciting modern approach enabled by recent engineering breakthroughs in the neurological and biological sciences. One prominent motivation to acquire such data is to enable new discoveries of the underlying connectivity by combining multimodal signals. Despite the scientific interest, there remains a gap in principled statistical methods for estimating the graph underlying multimodal functional data. To this end, we propose a new integrative framework that models the data generation process and identifies operators mapping from the observation space to the latent space. We then develop an estimator that simultaneously estimates the transformation operators and the latent graph. This estimator is based on the partial correlation operator, which we rigorously extend from the multivariate to the functional setting. Our procedure is provably efficient, with the estimator converging to a stationary point with quantifiable statistical error. Furthermore, we show recovery of the latent graph under mild conditions. Our work is applied to analyze simultaneously acquired multimodal brain imaging data where the graph indicates functional connectivity of the brain. We present simulation and empirical results that support the benefits of joint estimation. for this article are available online including a standardized description of the materials available for reproducing the work. [ABSTRACT FROM AUTHOR]

In this paper, based on the extreme learning machine idea and randomized neural networks, a new continuity-preserved method is proposed to efficiently and accurately solve linear and nonlinear elliptic interface problems. For linear interface problems, an error estimate including an approximation error and a statistical error is established for shallow randomized neural networks with the activation function tanh(x). Especially, the approximation error under the L∞ norm is given for the first time to the best of the authors' knowledge. For nonlinear cases, a novel multilevel method is further proposed to significantly improve efficiency. Various numerical tests on different examples are carried out to verify the proposed method, not only showing it can outperform classical numerical methods in terms of accuracy, but also illustrating its effectiveness, especially the improvements of the multilevel method in terms of computational costs. [ABSTRACT FROM AUTHOR]

Preregistration has gained traction as one of the most promising solutions to improve the replicability of scientific effects. In this project, we compared 193 psychology studies that earned a Preregistration Challenge prize or preregistration badge to 193 related studies that were not preregistered. In contrast to our theoretical expectations and prior research, we did not find that preregistered studies had a lower proportion of positive results (Hypothesis 1), smaller effect sizes (Hypothesis 2), or fewer statistical errors (Hypothesis 3) than non-preregistered studies. Supporting our Hypotheses 4 and 5, we found that preregistered studies more often contained power analyses and typically had larger sample sizes than non-preregistered studies. Finally, concerns about the publishability and impact of preregistered studies seem unwarranted, as preregistered studies did not take longer to publish and scored better on several impact measures. Overall, our data indicate that preregistration has beneficial effects in the realm of statistical power and impact, but we did not find robust evidence that preregistration prevents p-hacking and HARKing (Hypothesizing After the Results are Known). [ABSTRACT FROM AUTHOR]

Regarding round-off errors as random is often a necessary simplification to describe their behavior. Assuming, in addition, the symmetry of their distributions, we show that one can, in unstable (ill-conditioned) computer calculations, suppress their effect by statistical averaging. For this, one slightly perturbs the argument of f x 0 many times and averages the resulting function values. In this text, we forward arguments to support the assumed properties of round-off errors and critically evaluate the validity of the averaging approach in several numerical experiments. [ABSTRACT FROM AUTHOR]

The train vibration levels at a receiver are primarily governed by the trackform and its offset from the tracks. For a given trackform and offset, however, there can still be a wide variation in vibration generated by trains depending on the wheel and rail surface conditions, composition of the fleet and train speeds. In Australia, policies for the assessment of ground-borne noise and vibration are generally focused on the 95th percentile of train pass-bys. The use of this statistical descriptor is equivalent to a 5% exceedance level, i.e. vibration from one in twenty trains can be expected to be greater. This paper analyses four vibration datasets measured in Australasia. Three sets were measured on busy metropolitan train networks with direct fixation tracks in tunnels, and one dataset was measured on a ballasted surface track. The study focuses on the calculation of 95th percentiles and the effect of dataset size on the resulting 95th percentile vibration levels. Statistical error bands are calculated as a function of the number of consecutive pass-bys used in the dataset which allows for estimating the potential risks associated with working with small datasets. The effect of different approaches for calculating the percentiles is also discussed. [ABSTRACT FROM AUTHOR]

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]