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With the rapid development of intelligent integration in industrial processes, a challenge emerges. Early failures cannot be detected in a timely manner, potentially leading to significant financial losses. While traditional canonical variate analysis (CVA) methods are effective for dynamic process monitoring, they may lack the flexibility required for early fault detection. To address this challenge, a fault detection method based on canonical variate residual analysis (CVRA) is proposed. CVRA introduces a distinctive residual statistic that preserves critical information about the data. It places heightened focus on the primary components of the data, capturing core features of system changes and enhancing sensitivity to early anomalies. Additionally, by incorporating the geometric properties of the Manhattan distance, it mitigates statistical data errors, thereby improving detection accuracy. Simulation results validate the method's effectiveness in the Tennessee Eastman (TE) process. Furthermore, the successful application of the three‐phase flow facility provides a benchmark for evaluation using real process data. [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]

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]

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 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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

Background: Multiple studies have demonstrated a negative correlation between gene expression and positioning of genes at the nuclear envelope (NE) lined by nuclear lamina, but the exact relationship remains unclear, especially in light of the highly stochastic, transient nature of the gene association with the NE. Results: In this paper, we ask whether there is a causal, systematic, genome-wide relationship between the expression levels of the groups of genes in topologically associating domains (TADs) of Drosophila nuclei and the probabilities of TADs to be found at the NE. To investigate the nature of this possible relationship, we combine a coarse-grained dynamic model of the entire Drosophila nucleus with genome-wide gene expression data; we analyze the TAD averaged transcription levels of genes against the probabilities of individual TADs to be in contact with the NE in the control and lamins-depleted nuclei. Our findings demonstrate that, within the statistical error margin, the stochastic positioning of Drosophila melanogaster TADs at the NE does not, by itself, systematically affect the mean level of gene expression in these TADs, while the expected negative correlation is confirmed. The correlation is weak and disappears completely for TADs not containing lamina-associated domains (LADs) or TADs containing LADs, considered separately. Verifiable hypotheses regarding the underlying mechanism for the presence of the correlation without causality are discussed. These include the possibility that the epigenetic marks and affinity to the NE of a TAD are determined by various non-mutually exclusive mechanisms and remain relatively stable during interphase. Conclusions: At the level of TADs, the probability of chromatin being in contact with the nuclear envelope has no systematic, causal effect on the transcription level in Drosophila. The conclusion is reached by combining model-derived time-evolution of TAD locations within the nucleus with their experimental gene expression levels. [ABSTRACT FROM AUTHOR]

Reaction OH + OCS → products (1) has been studied in a discharge–flow reactor combined with modulated molecular beam mass spectrometry. The reaction rate constant has been determined under pseudo-first-order conditions through monitoring OH decays in a high excess of OCS: k1 = (2.35 ± 0.25) × 10−12 exp(−(2144 ± 56)/T) cm3 molecule−1 s−1 at T = 365–960 K (the uncertainties represent precision at the 2σ level, the total 2σ relative uncertainty including statistical and systematic errors on the rate constant being 20% at all temperatures). The rate constant of reaction (1) was found to be similar at a total helium pressure of 1, 2, and 8 torr at around 500 K. The SH radical was identified as the primary product of the reaction, and its yield was determined to be about 100% at T = 500 and 750 K. The kinetic and mechanistic data from the present study are compared to previous experimental and theoretical work. [ABSTRACT FROM AUTHOR]

The aim of this paper is to verify the applicability and accuracy of the Altman model in the case of Slovenian companies. The use of the Altman model is hugely popular and widespread among financiers, analysts and other stakeholders who want to determine the creditworthiness of a company's operations and the likelihood of it running into financial difficulties in the coming years. The study was conducted on a sample of 66 Slovenian companies, which were divided into two equal groups: bankruptcy and non-bankruptcy companies. Based on accounting data for the last five years, the authors of this paper calculated the Z-Score, which is based on the Multiple Discriminant Analysis (MDA). By calculating the statistical error of the estimate (type I and II), the authors verified the extent (in percentage terms) to which the companies had been correctly classified by the model. The Mann-Whitney U test was used to check whether there was a difference in the average Z-Score between the two groups of companies. The authors determined that the reliability of the Altman model was 71.21% when tested at the upper bound (the threshold value of the Z-Score was 2.6) and 80.30% when tested at the lower bound (the threshold value of the Z-Score was 1.1). This is similar to other countries, where the reliability was found to be over 70% in most cases. Despite the lower reliability of the model, the Z-Score proved to be an important factor in differentiating between the two groups of companies, as bankruptcy companies had a lower value of this indicator than non-bankruptcy companies. Based on the results of this study, as well as those of other studies, it can be summarized that the Altman model is a fairly good way for companies to determine the success of their business in a relatively simple and quick way and also to predict the potential risk of their operations in the future. However, since the reliability of the model is not 100%, it is important to be careful when making business predictions and carry out additional in-depth analyses or use other methods. [ABSTRACT FROM AUTHOR]

One of the main theoretical distinctions between reading models is how and when they predict semantic processing occurs. Some models assume semantic activation occurs after word-form is retrieved. Other models assume there is no-word form, and that what people think of as word-form is actually just semantics. These models thus predict semantic effects should occur early in reading. Results showing words with inconsistent spelling-sound correspondences are faster to read aloud if they are imageable/concrete compared to if they are abstract have been used as evidence supporting this prediction, although null-effects have also been reported. To investigate this, I used Monte-Carlo simulation to create a large set of simulated experiments from RTs taken from different databases. The results showed significant main effects of concreteness and spelling-sound consistency, as well as age-of-acquisition, a variable that can potentially confound the results. Alternatively, simulations showing a significant interaction between spelling-sound consistency and concreteness did not occur above chance, even without controlling for age-of-acquisition. These results support models that use lexical form. In addition, they suggest significant interactions from previous experiments may have occurred due to idiosyncratic items affecting the results and random noise causing the occasional statistical error. [ABSTRACT FROM AUTHOR]

Increasing weather variability and corresponding increased threat to the sustainability of the system and to the food security of any nation raises the importance of weather analysis in a range of studies. Meteorological data, hence, is used as a key component while developing a weather-based risk assessment and impact assessment models. However, despite of the availability of global meteorological data in real time and several state-of the art dynamic prediction system, such models demand downscaling of these datasets to the regions of interest. The present scientific fraternity has been able to provide a range of datasets at needed spatial resolution, which are generated through interpolation, weather generation methods, satellite-based remote sensing methods, and others. Each of the datasets has their own advantages and limitations. They are not universal, because of which their robustness and reproducibility varies with location. Therefore, the present study is basically evaluation of the freely available data sources (Grid IMD, NASA POWER and MarkSim) to know which one fits best to the study area. Statistical techniques such as error statistics, correlation analysis, anomaly, and percent deviation have been used for weather dataset at three timescales (daily, weekly, and monthly). Results for maximum and minimum temperature indicated that NASA POWER datasets are more reliable than IMD data for Ranichauri (at all the three timescales) and Roorkee (only at daily and weekly timescale), unlike Udham Singh Nagar for which IMD gives better results for daily data; and MarkSim at weekly and monthly scale. It was also observed that for Udham Singh Nagar and Roorkee, MarkSim results are found to be better for RCP 2.6 as well as RCP 4.5 at higher timescales. Better performance of Tmax under RCP 4.5 indicates that the emission activities have increased in the districts, which can be attributed directly to the increased industrial establishments in the region. [ABSTRACT FROM AUTHOR]