Statistics-based Approach Research Articles

A Statistical Method for EEG Channel Selection

A new statistical analysis of medical signals is proposed based on information extracted for diagnosis. Several specialized tests can be run on electroencephalography (EEG) signal due to its nonlinear behavior. Epileptic EEG signals are the main topic of this research. A statistics-based approach is proposed for the automatic selection of the relevant channels that will not only provide more accurate characterization but also require fewer computing resources. Moreover, the outcomes given by specific electrode pairs might indicate epileptic focus. By selecting the most efficient electrode placement, alarms for detecting inappropriate medical behavior could then be triggered.

PyTWMR: transcriptome-wide Mendelian randomization in python.

Mendelian randomization (MR) is a widely used approach to estimate causal effect of variation in gene expression on complex traits. Among several MR-based algorithms, transcriptome-wide summary statistics-based Mendelian Randomization approach (TWMR) enables the uses of multiple SNPs as instruments and multiple gene expression traits as exposures to facilitate causal inference in observational studies. Here we present a Python-based implementation of TWMR and revTWMR. Our implementation offers GPU computational support for faster computations and robust computation mode resilient to highly correlated gene expressions and genetic variants. pyTWMR is available at github.com/soreshkov/pyTWMR.

Using supervised machine learning for regional hydrological hazard estimation in metropolitan France

Study regionThis study is carried out for 1929 gauged catchments in France, ranging from 1 to 10,000 km², where quality hydrometric observations are available for flood frequency analysis. Study focusThe regional estimation of hydrological hazards is studied for flood risk management and prevention in hydrology. For gauged catchments, flow quantiles can be estimated from observations using statistical approaches based on suitable probability distributions or simulation approaches based on rainfall-runoff transformation models. For ungauged catchments, the lack of hydrological observations means that we have to extrapolate our knowledge of hazards from gauged catchments to ungauged catchments, using regionalization methods. It is therefore necessary to combine regionalization methods with the implemented hazard estimation approach. In this paper, two popular machine learning methods, Random Forest and Neural Networks, are tested and compared as regionalization methods. A classical regionalization method using multiple linear regression is also applied as a benchmark to evaluate the performance of all configurations. All these regionalization methods are applied to a simulation-based approach (the SHYREG method) and to a statistical-based approach using generalized extreme value distribution (GEV). New hydrological insights•Regionalization approaches based on multiple linear regression have limitations to explain parameters with environmental descriptors in Regional Flood Frequency Analysis (RFFA) domain.•Regionalizing RFFA parameters using Random Forest allows more explanatory variables to be considered through non-linear relationships, resulting in better parameter estimation.•Machine learning techniques can better handle environmental descriptors for regionalization, this providing a notable performance improvement, especially for the statistical approach.•The tested simulation-based approach is less sensitive to the choice of spatial interpolation method than the studied statistical approach.

Gestational weight gain and pregnancy outcomes in Chinese women with type 2 diabetes mellitus: evidence from a tertiary hospital in Beijing.

To examine the effects of gestational weight gain on pregnancy outcomes and determine the optimal range of weight gain during pregnancy for Chinese women with type 2 diabetes mellitus. This retrospective cohort study included 691 Chinese women with type 2 diabetes mellitus from 2012 to 2020. The study utilized a statistical-based approach to determine the optimal range of gestational weight gain. Additionally, multivariate logistic regression analysis was conducted to assess the impact of gestational weight gain on pregnancy outcomes. (1) In the obese subgroup, gestational weight gain below the recommendations was associated with decreased risks of large for gestational age (adjusted odds ratio [aOR] 0.19; 95% confidence interval [CI] 0.06-0.60) and macrosomia (aOR 0.18; 95% CI 0.05-0.69). In the normal weight subgroup, gestational weight gain below the recommendations of the Institute of Medicine was associated with decreased risks of preeclampsia (aOR 0.18; 95% CI 0.04-0.82) and neonatal hypoglycemia (aOR 0.38; 95% CI 0.15-0.97). (2) In the normal weight subgroup, gestational weight gain above the recommendations of the Institute of Medicine was associated with an increased risk of large for gestational age (aOR 4.56; 95% CI 1.54-13.46). In the obese subgroup, gestational weight gain above the recommendations was associated with an increased risk of preeclampsia (aOR 2.74; 95% CI 1.02, 7.38). (3) The optimal ranges of gestational weight gain, based on our study, were 9-16kg for underweight women, 9.5-14kg for normal weight women, 6.5-12kg for overweight women, and 3-10kg for obese women. (4) Using the optimal range of gestational weight gain identified in our study seemed to provide better prediction of adverse pregnancy outcomes. For Chinese women with type 2 diabetes, inappropriate gestational weight gain is associated with adverse pregnancy outcomes, and the optimal range of gestational weight gain may differ from the Institute of Medicine recommendations.

LTI/LQE: A Physics-Based Approach for Online Estimation of Dynamic Loads in the Rotating Frame

Accurate and online load monitoring of vital components located in the rotor system is important for not only inferring usage and estimating fatigue in those components but also for developing load alleviation/limiting control schemes. In the literature, on-board models for online (i.e., real-time) estimation of rotor component loads using available in-flight aircraft fixed-frame measurements are developed using neural networks, a statistical-based approach, or a combination of both. These models are aircraft-specific, do not capture the higher harmonic dynamics of the rotor system vibratory loads, and their synthesis requires obtaining a lot of data. To remedy these issues, this paper introduces a purely physics-based approach to online estimation of rotor system loads. The proposed approach can be applied to any rotary-wing vehicle, does not rely on a large amount of data, and captures the higher harmonic dynamics of the rotor system vibratory loads. The developed model entitled LTI/LQE is synthesized using a linear time invariant (LTI) model of helicopter-coupled body-rotor-inflow dynamics and a linear quadratic estimator (LQE), that is designed to correct the LTI model state response using fixed system measurements. The estimation fidelity of the LTI/LQE model is evaluated in simulation using a high-fidelity nonlinear model of a generic helicopter for online prediction of rotor blade pitch link loads arising from vehicle maneuvers. Results obtained using the LTI/LQE model revealed an interesting finding. It was found that the N/rev (where N is the number of blades) fixed system load measurements have information that can be leveraged to make an inference about the N/rev dynamic loads in the rotating system.

Evaluating Cronbach’s Coefficient Alpha and Testing Its Identity to Scale Reliability: A Direct Bayesian Confirmatory Factor Analysis Procedure

ABSTRACT A Bayesian statistics-based approach is discussed that can be used for direct evaluation of the popular Cronbach’s coefficient alpha as an internal consistency index for multiple-component measuring instruments, as well as for testing its identity to scale reliability. The method represents an application of confirmatory factor analysis within the Bayesian inference framework and is widely applicable in empirical measurement research using popular latent variable modeling software. The procedure readily furnishes posterior median point estimates and credible intervals of coefficient alpha. The approach also permits testing a necessary and sufficient condition for population equality of the alpha and scale reliability coefficients, and under its plausibility provides in addition a dependable means for estimation of instrument reliability. The outlined procedure is illustrated using numerical data.

Associations of gestational weight gain with birth weight outcomes in early pregnancy weight loss women: Findings from the Beijing Birth Cohort Study.

To explore the relationship between gestational weight gain (GWG) and birth weight outcomes and establish suggested GWG patterns in early pregnancy weight loss women. This retrospective study was conducted based on the Beijing Birth Cohort Study from 2014 to 2021 and included 20 688 women. Weight change in early pregnancy was calculated using weight measurements within 16 weeks of gestation. Multivariable logistic regression was used to analyze the relationships of different GWG categories, based on the Chinese standard, and birth weight outcomes. The statistical-based approach was used to determine the optimal GWG ranges and weekly weight gain. Compared to 3313 women who gained appropriate weight in early pregnancy, 2614 women who lost weight in the same period increased the risk of small for gestational age (SGA) (OR = 1.43, 95% CI: 1.14-1.80, P = 0.002). However, the relationship disappeared after adjusting for total GWG. Among the early pregnancy weight loss women, both excessive GWG and inadequate GWG were associated with adverse birth weight outcomes after adjusting for confounders. The suggested GWG range and rate were 11.0 ~ 16.0 kg and 0.46 to 0.67 kg/week from 16 weeks to delivery for women with normal body mass index (BMI) and weight loss in early pregnancy. Weight loss in early pregnancy was not the independent risk factor of birth weight outcomes. GWG may offset the expected effects. To achieve optimal outcomes, women with normal BMI and weight loss in early pregnancy need to have a higher weight gain rate in mid-late pregnancy but similar total GWG ranges with the Chinese standard for general women.

Unveiling flood-generating mechanisms using circular statistics-based machine learning approach without the need for discharge data during inference

Abstract Understanding the drivers of flooding is essential for flood disaster prevention. However, conventional flood prediction methods are hindered by their reliance on local discharge data, which can be constrained by limited spatial resolution. To address this limitation, we present a machine learning model that can categorize floods without requiring discharge data during inference. We first use circular statistics to calculate the relative importance of three candidate flood-generating mechanisms. Global land areas are classified into three primary categories and eight sub-categories based on the proportion of relative importance. A random forest model is then applied to identify the flood types by assuming that the discharge data is unavailable. The findings from circular statistics highlight that globally, soil moisture excess is the most influential driver of floods followed by extreme precipitation and snowmelt, with an average relative importance of 0.535, 0.387, and 0.078, respectively. The RF model performs well in resembling the three primary flood categories with an accuracy of 0.701 and a F1-score of 0.692 in 10-fold cross-validation. The trained gridded-based model provides a swift and efficient approach for analyzing flood mechanisms, even in limited discharge scenarios, allowing for rapid insights.

Model-based assessment of flood generation mechanisms over Poland: The roles of precipitation, snowmelt, and soil moisture excess

Hydrometeorological variability, such as changes in extreme precipitation, snowmelt, or soil moisture excess, in Poland can lead to fluvial flooding. In this study we employed the dataset covering components of the water balance with a daily time step at the sub-basin level over the country for 1952–2020. The data set was derived from the previously calibrated and validated Soil & Water Assessment Tool (SWAT) model for over 4000 sub-basins. We applied the Mann Kendall test and circular statistics-based approach on annual maximum floods and various potential flood drivers to estimate the trend, seasonality, and relative importance of each driver. In addition, two sub-periods (1952–1985 and 1986–2020) were considered to examine changes in flood mechanism in the recent decades. We show that floods in the northeast Poland were decreasing, while in the south the trend showed a positive behavior. Moreover, the snowmelt is a primary driver of flooding across the country, followed by soil moisture excess and precipitation. The latter seemed to be the dominant driver only in a small, mountain-dominated region in the south. Soil moisture excess gained importance mainly in the northern part, suggesting that the spatial pattern of flood generation mechanisms is also governed by other features. We also found a strong signal of climate change in large parts of northern Poland, where snowmelt is losing importance in the second sub-period in favor of soil moisture excess, which can be explained by the temperature warming and diminishing role of snow processes.

Voxel-based design and characterization of functionally graded materials

Functionally graded materials (FGM) have numerous merits that are appealing to various industrial sectors. Great advancements have been made in FGM manufacturing technologies during the past few decades, especially in voxel-based 3D printing in recent years. Nevertheless, the development of FGM design and characterization methods is considerably lagging behind. The existing FGM design methods are mostly geometry based, and the characterization of FGM properties mainly relies on the conventional micromechanical models; both have fundamental limitations that severely hinder the wide application of FGMs in industries. In this study, two types of voxel-based approaches, i.e., micromechanics-based and statistics-based, are developed for FGM design and characterization. Four representative FGMs are designed and characterized using the proposed approaches. The study results show that FGM properties are dependent on the resolution of FGM image for both the approches, particularly if the phase materials have large contrast in their properties. For the micromechanics-based methods, FGM properties are also dependent on the micromechanics formula that is used in the determination of voxel effective properties. The study shows that the statistics-based approach is more reliable and more accurate. With the new voxel-based 3D printers generally available to the industry, the proposed voxel-based design and characterization approaches will have wide applications in various industrial sectors such as aerospace, energy, biomedical and electrical engineering.

Reliable Uplink Synchronization Maintenance for Satellite-Ground Integrated Vehicular Networks: A High-Order Statistics-Based Timing Advance Update Approach

Satellite-ground integrated vehicular network can provision ubiquitous and unlimited network connectivity for massive vehicles, and is expected to play a vital role in 6G-supported intelligent transportation systems (ITS). However, due to its high-dynamic channel environments and limited satellite payload, the uplink synchronization has become a major bottleneck to restrict vehicular communication performance. Focusing on maintaining reliable uplink synchronization, we propose an efficient timing advance (TA) update approach in this paper. Specifically, an enhanced preamble format is first presented based on the periodical pairing sounding reference signals (SRSs), which enables the satellite to continuously track uplink timing variation with a low signaling overhead. By taking full advantage of all the fourth-order autocorrelation produces from the received preamble, we further design a novel timing metric consisted of the correlation and differential normalization functions, which is capable of having a considerably increased correlation length and shaper mainlobe, as compared to the existing ones. Through theoretical performance analysis, it is indicated that the proposed approach not only notably promotes class distance between the correct and wrong timing indexes, but also can achieve the immunity to multi-path effect and large carrier frequency offset (CFO), while having a reduced computational complexity. Simulation results in a typical low-earth-orbit (LEO) scenario reveal the superiority of our approach in terms of the false alarm probability, the missed detection probability, as well as the timing mean square error.

Real-Time Reconstruction of 3D Videos From Single-Photon LiDaR Data in the Presence of Obscurants

Single-photon methods are emerging as a key approach to 3D Imaging. This paper introduces a two step statistical based approach for real-time image reconstruction applicable to a transmission medium with extreme light scattering conditions. The first step is an optional target detection method to select informative pixels which have photons reflected from the target, hence allowing data compression. The second is a reconstruction algorithm that exploits data statistics and multiscale information to deliver clean depth and reflectivity images together with associated uncertainty maps. Both methods involve independent operations that are implemented in parallel on graphics processing units (GPUs), which enables real-time data processing of moving scenes at more than 50 depth frames per second for an image of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$128 \times 128$</tex-math></inline-formula> pixels. Comparisons with state-of-the-art algorithms on simulated and real underwater data demonstrate the benefit of the proposed framework for target detection, and for fast and robust depth estimation at multiple frames per second.

An integrated global model of local urban development and population change

High resolution models are essential to assess the localised impacts of global environmental change. To enable the estimation of the impacts of location-specific change, this paper presents a new modelling approach that disaggregates scenario-based national-level urban population estimates derived from the often-applied Shared Socioeconomic Pathways (SSPs) to high-resolution spatial grid (30 arc sec) representations of urban area and local population. By combining high-resolution spatial data, a sophisticated statistics-based calibration approach and a computationally efficient modelling framework, we can coherently describe local changes in urban area and population across the globe. Both developments are modelled simultaneously using the same scenario assumptions, but divergent patterns are shown to arise when, for example, population decline coincides with decreasing densities that yield an increase in urban area. The model can be applied in integrated global assessment studies focused on trends in local exposure to natural hazards or environmental impacts of urban change. The improved resolution of this new model is crucial to assess the impacts of future population growth in developing regions where most of the planet's biodiversity concentrates near large populations that live in clusters of smaller urban areas previously not captured by coarser resolution models.

Shoreline Change Analysis along the Tahaddart Coast (NW Morocco): A Remote Sensing and Statistics-Based Approach

El Habti, M.Y.; Zayoun, A.; Zahra, S.F.; Raissouni, A., and El Arrim, A., 2022. Shoreline change analysis along the Tahaddart Coast (NW Morocco): A remote sensing and statistics-based approach. Journal of Coastal Research, 38(6), 1116–1127. Coconut Creek (Florida), ISSN 0749-0208. The coastal areas are increasingly becoming vulnerable because of natural processes, such as climate change and sea-level change, or man-made effects. In Morocco, these areas are exposed to increasing risks and thus undergoing significant changes characterized by erosion or accretion of the shorelines. The following research outlines the changes for a shoreline length of approximately 17 km on the Tahaddart coast (NW of Morocco), based on an analysis of different shoreline rates. The latter was estimated by automatic analytical techniques using datasets of short and long-term aerial photos and satellite images (between 1972 and 2019) over 47 years. Remote sensing tools and GIS, with its extension digital shoreline analysis system, were used jointly for the evaluation of the shoreline changes during the period of the study. The approaches used in this research to assess the Tahaddart coast changes are the net shoreline movement, endpoint rate, linear regression rate, and weighted linear regression rate. These net rate changes of coastline have been calculated, also on three intervals of times (1972–81, 1981–97, 1997–2019). To facilitate the calculations, this study area was divided into four sectors to analyze Haouara Beach, the Tahaddart littoral spit, the Breich Beach, and the Oued Ghrifa estuary. The result shows that over a period of 47 years (1972 and 2019), more than 87% of the coast was eroded, whereas the remaining 13% endured accretion. This study emphasizes the challenges faced in representing coastline changes and reveals the intensity of the shoreline changes along the Tahaddart coast, which implies the setup and implementation of integrated management, effective environmental protection, and sustainable utilization of coastlines.

Siamese Convolutional Neural Networks to Quantify Crack Pattern Similarity in Masonry Facades

ABSTRACT This paper proposes an automated approach to predict crack pattern similarities that correlate well with assessment by structural engineers. We use Siamese convolutional neural networks (SCNN) that take two crack pattern images as inputs and output scalar similarity measures. We focus on 2D masonry facades with and without openings. The image pairs are generated using a statistics-based approach and labelled by 28 structural engineering experts. When the data is randomly split into fit and test data, the SCNNs can achieve good performance on the test data (). When the SCNNs are tested on ”unseen” archetypes, their test values are on average 1% lower than the case where all archetypes are ”seen” during the training. These very good results indicate that SCNNs can generalise to unseen cases without compromising their performance. Although the analyses are restricted to the considered synthetic images, the results are promising and the approach is general.

Multivariate statistical-based approach to the physical-chemical behavior of shallow groundwater in a semiarid dry climate: The case study of the Gadaïne-Ain Yaghout plain NE Algeria

Purpose. Several natural and anthropogenic factors control the hydro-geochemical behavior of groundwater. These factors influence on the quality, even the suitability of this resource for drinking. The main purpose of our study is the application of multivariate statistical methods to compile the mechanisms of mineralization acquisition in confined aquifers. Methods. The adopted method measures the chemical evolution of Ca+2, Mg+2, Na+, K+, HCO-3, Cl-, SO4-2, NO-3, NO-2, NH+4, and PO4-3 using an atomic-absorption spectrometer. The content of nitrogen and dissolved oxygen is measured using a spectrophotometer. Temperature (T°), electrical conductivity (EC), pH and dissolved oxygen are determined using a multi-parameter system. Findings. The main results show that 28 water samples from the Mio-Plio-Quaternary aquifer of the Gadaïne-Ain Yaghout plain have chemical facies of chloride, sulfate-calcium and chloride-magnesium types. Originality. The originality of the study is in the demonstration that water acquires its carbonate mineralization at the supply limits. Whereas it acquires its chloride, sodium and sulfate mineralization in contact with terrigenous saliferous formations, as well as in its interaction with the salt chotts formations. The results show a significant variation in the concentrations of chemical elements, in some cases exceeding drinkability standards. Mineralization is mainly caused by the dissolution of evaporitic minerals such as gypsum, halite and epsomite. Practical implications. The practical implications of this study could be resumed in terms of the convenience of multiva-riate statistical evaluation of complex physical-chemical databases in identifying pollution sources and understanding temporal variations for effective groundwater quality management in semiarid regions.

Synthetic gas networks for the statistical assessment of low-carbon distribution systems

Most of the simulation studies on energy networks, including gas grids, derive their results from a limited number of network models. The findings of these works are therefore affected by a substantial case-specificity, which partially limits their validity and prevents their generalisation. To overcome this limitation, the present work proposes a novel statistical-based approach for studying distribution gas networks, enabled by a generator of random gas grids with accurate technical designs and structural features. Ten thousand random and unique networks are produced in three different tests, where increasingly tight constraints are applied to the synthetisation process for a higher control over the generated grids. The experiments verify the accuracy of the tool and highlight that substantial variations can be found in the hydraulic behaviour (pressures and gas velocities) and structural properties (pipe diameters and network volumes) of real-world gas networks. The observed 10,000 gas grids evidence the information gain offered by statistical-based approaches with respect to traditional case-specific studies. The tool opens a broad range of applications which include, but are not limited to, statistical analyses on the distributed injection of alternative gases, like hydrogen, in integrated, low-carbon, energy systems.

Analysis of Chinese Text Automatic Proofreading Technology

The development of computer technology promotes the research on automatic text proofreading technology. The research of Chinese text automatic proofreading started late but developed rapidly. This paper analyzes the difficulties of Chinese text proofreading and reviews the research methods of Chinese text automatic proofreading technology, including statistical-based approach, rule-based approach, deep learning-based approach and hybrid approach. Although some new progress has been made in Chinese text automatic proofreading in recent years, there are many problems remain to be settled due to the complexity of Chinese and the scale of the universal data set of Chinese proofreading corpus.

Incorporating soft information from financial news media for management decisions in dynamic business environments

Because the nature of numerical information is intuitive and comprehensible, it has been widely used to form a basis for decision making, yet numerical information based on historical principle does not reflect messages about future corporate performance. To confront this issue, one may consider textual information that can transmit future corporate potential without any hysteresis. The key point is how to digest an extensive amount of textual information and identify those topics most likely to precede changes in operation status. Topic modeling can categorize these textual disclosures based on their underlying content and help examine which topics have a strong relevance to corporate operations. To extract decisive words from textual information, we set up a statistical-based approach with objectivity as opposed to frequently used heuristics (i.e., dictionary-based approaches with human involvement). Joint utilization of topic modelling and a statistical-based approach can compress an excessive amount of textual information into a manageable size in a timely manner and further realize a discrepancy among various topics in terms of relevance and influence on corporate operations. Our results benefit managers and current and future investors in how to structure regulatory filings and how word choices are decisive to them in their decision judgments.

Assessment of Neurological Impairment and Recovery Using Statistical Models of Neurologically Healthy Behavior

While many areas of medicine have benefited from the development of objective assessment tools and biomarkers, there have been comparatively few improvements in techniques used to assess brain function and dysfunction. Brain functions such as perception, cognition, and motor control are commonly measured using criteria-based, ordinal scales which can be coarse, have floor/ceiling effects, and often lack the precision to detect change. There is growing recognition that kinematic and kinetic-based measures are needed to quantify impairments following neurological injury such as stroke, in particular for clinical research and clinical trials. This paper will first consider the challenges with using criteria-based ordinal scales to quantify impairment and recovery. We then describe how kinematic-based measures can overcome many of these challenges and highlight a statistical approach to quantify kinematic measures of behavior based on performance of neurologically healthy individuals. We illustrate this approach with a visually-guided reaching task to highlight measures of impairment for individuals following stroke. Finally, there has been considerable controversy about the calculation of motor recovery following stroke. Here, we highlight how our statistical-based approach can provide an effective estimate of impairment and recovery.