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Hypsometric or area-altitude analysis unveils the geomorphic evolution of a river basin and its interactions with tectonics, lithology, and climate. The results of hypsometric analysis i.e., hypsometric curve (HC) along with the hypsometric parameters (HP) namely, hypsometric integral (HI), skewness (SK), kurtosis (KU), density skewness (DSK), and density kurtosis (DKU) can also be further analyzed to evaluate the geological erosional status of a river basin. However, the spatial scale’s significance in such analyses is often underestimated, assuming consistency across various resolutions of Digital Elevation Models (DEMs). To address this oversight, this study compares HP values obtained from DEMs of different sources and resolutions—Shuttle Radar Topographic Mission (30 m), Global Multi-resolution Terrain Elevation Data 2010 (225 m), and Global 30 Arc-Second Elevation Data Set (1000 m). A total of 108 sub-watersheds spanning four large Indian River basins (Mahanadi, Godavari, Krishna, and Cauvery) underwent analysis using both parametric and non-parametric statistical tests, namely Shapiro-Wilk, Kolmogorov-Smirnov, and Anderson-Darling tests, to assess the distribution of hypsometric parameters. Subsequently, the null hypothesis regarding the robustness of HP across varying DEM resolutions was tested using non-parametric statistical tests such as the Mann-Whitney U test, Kruskal-Wallis H test, and Friedman test. The study’s findings challenge the prevalent notion of robustness, revealing that DEM resolution significantly impacts the results of hypsometric analysis. The Friedman test, in particular, highlighted significant differences in the full distribution of hypsometric parameters across different DEM resolutions, suggesting that variations in DEM resolution can alter the shape and variability of these parameters, not just their central tendencies. Higher-resolution DEMs, such as SRTM (30 m), provided more accurate representations of landscape features, especially in capturing subtle variations that influence erosion rates, sediment transport, and geomorphic classification. [ABSTRACT FROM AUTHOR]

Brittleness is one of the most main strength characteristics of rocks that is extensively utilized in drilling operations. This study aimed to estimate the brittleness index (BI) of sandstones using the percentage of quartz, feldspar, fragments, point load value (PLV), water absorption, P-wave velocity (PW), density, and porosity. This study utilized a range of prediction models, namely simple and multivariate linear regression analysis (MVLRA), multilayer backpropagation neural network (MBPN) with diverse learning rates and training algorithms, random forest (RF), Gaussian process regression (GPR), support vector regression (SVR) with various kernel functions, and adaptive neuro-fuzzy inference system (ANFIS). The sandstones under investigation were classified into litharenite and feldspathic litharenite categories. The observation revealed that as the percentage of feldspar and quartz increased, while fragments decreased, there was a corresponding increase in the brittleness index. Quartz and carbonate fragments had the highest and lowest effects on brittleness, respectively. The statistical analysis revealed that PLV and PW have the greatest impact on the BI, while the percentage of minerals had the least influence. Comparing the results of different methods based on error value, performance index, statistical tests, and Taylor diagram, the SVR based on radial basis kernel function demonstrated the highest performance (R2 = 0.99 and RMSE = 0.04) in estimating the brittleness index of the studied sandstones. Tukey's test to check the significance of the mean BI obtained from modeling and measured in the laboratory displayed that there is no significant difference between the BI values ​​obtained from these methods. [ABSTRACT FROM AUTHOR]

The assessment of landslide susceptibility is one of the most important steps in disaster management and the implementation of land use planning and development measures. We propose an approach to modelling landslide susceptibility based on statistical methods, namely frequency ratio (FR), information value (IV), weighted factor (Wf), and weight of evidence (WoE), using GIS software for the Wilaya de Mila (northeastern Algeria). As the Wilaya of Mila is highly affected by landslides, the main objective and expected impact of this study is to provide local authorities and decision-makers in this Wilaya with a decision-making tool. Prior to modelling, a landslide inventory map was prepared based on satellite imagery from various sources, documentary analysis, and field surveys. In addition, several thematic maps (lithologies, slopes, exposures, precipitation, distances to roads, etc.) were created. The recorded landslides (429 bodies) were divided into two samples: 80% for model training and 20% for model validation using ROC–AUC curves. The main results of the susceptibility maps showed a better accuracy of the AUC curve for the WoE model compared to FR, VI, and Wf, with success rate values of 0.774, 0.767, 0.754, and 0.749, respectively, and predictive values of 0.808, 0.785, 0.782, and 0.770, respectively, implying better decision making based on the WoE results. [ABSTRACT FROM AUTHOR]

Artificial intelligence techniques are used in many areas today to find solutions to different problems. Scientists are trying to solve some problems in people's daily lives using these techniques. To solve these problems, researchers often use some Machine Learning (ML) algorithms. It is important for researchers to have preliminary information about some metrics of machine learning algorithms in their scientific studies. In this study, k-Nearest Neighbors (k-NN) and Minimum Distance to Means (MDC) ML algorithm, which classifies according to distance measurement methods, were analyzed using the same distance measurement methods. k-NN, which measures distance according to a specified number of distance, and MDC algorithms, which measure distance according to the averages of the classes, were examined in terms of classification performance. The performance comparison of these two algorithms with 5 different distance measurements (Euclidean, Manhattan, Minkowski, Chebyshev, Hellinger) was made using 2 different datasets (Ecoli and Cardiotocography). For Ecoli dataset, the highest train score 100%, test score 84.85% from the k-NN algorithm, and the highest train score 73.78% and test score 76.12% from the MDC algorithm were obtained. For the cardiotocography dataset, the highest train score 99.94%, test score 84.52% from the k-NN algorithm, and the highest train score 69.50% and test score 61.57% from the MDC algorithm were obtained. According to the results of statistical experimental studies, the k-NN algorithm, which classifies according to a certain number, gave better results in both datasets. [ABSTRACT FROM AUTHOR]

Thin-laminate composites with thicknesses below 200 μm hold significant promise for future, larger, and lighter deployable structures. This paper presents a study of the time-dependent failure behavior of thin carbon-fiber laminates under bending, focusing on establishing a fundamental material-level understanding of this type of failure. A novel test method was developed, enabling in-situ micro-CT imaging during long-term bending. Time-to-rupture experiments revealed the stochastic nature of failure, prompting a statistical approach to account for initial imperfections. The total probability of failure was calculated using separate Weibull functions for instantaneous and delayed time-dependent failures. The resulting function, dependent on curvature and aging time, is a design guideline for the design of future deployable space structures. Time-lapse micro-CT imaging identified kink bands and fiber–matrix debonding as primary failure mechanisms, providing essential insights for the design optimization of composite laminates. [ABSTRACT FROM AUTHOR]

This paper presents a statistical analysis of rainfall data through a practical application of statistical tests, with the aim of highlighting climatic variability in the Kébir Rhumel watershed. This analysis uses rainfall data from 1901 to 2021 collected in the Kébir Rhumel watershed (North–East Algeria). The use of the Mann–Kendall and Pettitt tests has allowed us to confirm the existence of a break in 2013 for all stations except Ain Smara and Gerrah, where it was detected in 2020. The SPI (Standardised Precipitation Index) defines five climatic periods that have alternated on the Kébir Rhumel, the wet condition being the most representative with 44% of the series analysed, and the least present is the severe drought (> 1%). The TIA (Trend Innovative Analysis) shows a positive trend for the El Milia, Tadjenant, Grarem Gouga, Guerrah and Ain Smara stations and a negative trend for the Chebabta and Tiberguent stations. Finally, the graph of the climate index of L. Emberger clearly shows the shift of the stations after the break of the sub-humid stage towards the semiarid stage. [ABSTRACT FROM AUTHOR]

Background: Assessing bias (estimated – measured) is key to evaluating glomerular filtration rate (GFR). Stratification by subgroups can indicate where equations perform differently. However, there is a fallacy in the assessment of two instruments (e.g., eGFR and mGFR) when stratifying on the level of only one of those instruments. Here, we present statistical aspects of the problem and a solution for GFR stratification along with an empirical investigation using data from the CKiD study. Methods: Compared and contrasted biases (eGFR relative to mGFR) with 95% confidence intervals within strata of mGFR only, eGFR only, and the average of mGFR and eGFR using data from the Chronic Kidney Disease in Children (CKiD) study. Results: A total of 304 participants contributed 843 GFR studies with a mean mGFR of 48.46 (SD = 22.72) and mean eGFR of 48.67 (SD = 22.32) and correlation of 0.904. Despite strong agreement, eGFR significantly overestimated mGFR when mGFR < 30 (+ 6.2%; 95%CI + 2.9%, + 9.7%) and significantly underestimated when mGFR > 90 (–12.2%; 95%CI − 17.3%, − 7.0%). Significant biases in opposite direction were present when stratifying by eGFR only. In contrast, when stratifying by the average of eGFR and mGFR, biases were not significant (+ 1.3% and − 1.0%, respectively) congruent with strong agreement. Conclusions: Stratifying by either mGFR or eGFR only to assess eGFR biases is ubiquitous but can lead to inappropriate inference due to intrinsic statistical issues that we characterize and empirically illustrate using data from the CKiD study. Using the average of eGFR and mGFR is recommended for valid inferences in evaluations of eGFR biases. [ABSTRACT FROM AUTHOR]

Principal component analysis (PCA) is employed to extract the principal components (PCs) present in nuclear mass models for the first time. The effects from different nuclear mass models are reintegrated and reorganized in the extracted PCs. These PCs are recombined to build new mass models, which achieve better accuracy than the original theoretical mass models. This comparison indicates that using the PCA approach, the effects contained in different mass models can be collaborated to improve nuclear mass predictions. [ABSTRACT FROM AUTHOR]

In coupled perturbed parameter ensemble (PPE) experiments or for development of a single coupled global climate model (GCM) in general, models can exhibit a slowdown in the Atlantic Meridional Overturning Circulation (AMOC) that can result in unrealistically reduced transport of heat and other tracers. Here we propose a method that researchers running PPE experiments can apply to their own PPE to diagnose what controls the AMOC strength in their model and make predictions thereof. As an example, using data from a 25-member coupled PPE experiment performed with HadGEM3-GC3.05, we found four predictors based on surface heat and freshwater fluxes in four critical regions from the initial decade of the spinup phase that could accurately predict the AMOC transport in the later stage of the experiment. The method, to our knowledge, is novel in that it separates the effects of the drivers of AMOC change from the effects of the changed AMOC. The identified drivers are shown to be physically credible in that the PPE members exhibiting AMOC weakening possess some combination of the following characteristics: warmer ocean in the North Atlantic Subpolar Gyre, fresher Arctic and Tropical North Atlantic Oceans and larger runoff from the Amazon and Orinoco Rivers. These characteristics were further traced to regional responses in atmosphere-only experiments. This study suggests promising potential for early stopping rules for parameter perturbations that could end up with an unrealistically weak AMOC, saving valuable computational resources. Some of the four drivers are likely to be relevant to other climate models so this study is of interest to model developers who do not have a PPE. [ABSTRACT FROM AUTHOR]

Participation in online activities offers older adults enhanced opportunities to cope with age-related limitations and live a socially connected life. For investigating the factors explaining whether older adults use the internet or not, logistic regression has been used, which requires considerable care in the computation and reporting of regression models. The objective of this review is to assess the extent to which the empirical literature examining internet use among older adults (aged 55 +) addresses commonly recommended quality criteria for logistic regression analysis. We carried out a systematic search in Scopus and PubMed for English-language peer-reviewed articles reporting the application of logistic regression, published between 2010 and 2020. Thirty-six articles were eligible for inclusion. We evaluated the articles for six analytic and three documentation criteria. Every article described the selection of independent variables and their coding. Most studies had sufficient events per independent variable (83%), reported point and interval estimates of effect sizes (81%), and stated the fitting procedure used (81%). The reporting rates were lower for the assessment of model fit (53%), consideration of interactions (44%), and testing for conformity with linear gradient (42%) and collinearity (31%). This review provides comprehensive insights into the state of adoption of logistic regression analysis. We suggest improvements for reporting on the studies specific to older adults' internet use, which can promote the accurate interpretation of articles and the integration of evidence from single studies. [ABSTRACT FROM AUTHOR]

The recent seismic activity on Türkiye's west coast, especially in the Aegean Sea region, shows that this region requires further attention. The region has significant seismic hazards because of its location in an active tectonic regime of North–South extension with multiple basin structures on soft soil deposits. Recently, despite being 70 km from the earthquake source, the Samos event (with a moment magnitude of 7.0 on October 30, 2020) caused significant localized damage and collapse in the Izmir city center due to a combination of basin effects and structural susceptibility. Despite this activity, research on site characterization and site response modeling, such as local velocity models and kappa estimates, remains sparse in this region. Kappa values display regional characteristics, necessitating the use of local kappa estimations from previous earthquake data in region–specific applications. Kappa estimates are multivariate and incorporate several characteristics such as magnitude and distance. In this study, we assess and predict the trend in mean kappa values using three–component strong–ground motion data from accelerometer sites with known VS30 values throughout western Türkiye. Multiple linear regression (MLR) and multivariate adaptive regression splines (MARS) were used to build the prediction models. The effects of epicentral distance Repi, magnitude Mw, and site class (VS30) were investigated, and the contributions of each parameter were examined using a large dataset containing recent seismic activity. The models were evaluated using well–known statistical accuracy criteria for kappa assessment. In all performance measures, the MARS model outperforms the MLR model across the selected sites. [ABSTRACT FROM AUTHOR]

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The scientific and technical literature presents a large number of works dedicated to the experimental study of the critical out flow of saturated and subcooled liquid through cylindrical channels. Despite this, the available sources do not provide an assessment of the extent to which certain geometric parameters and operating conditions of experiments affect the critical outflow. This article is aimed at the analysis of experimental data using statistical methods and machine learning on critical outflow obtained at Elektrogorsk Research and Development Center (EREC, Russia). The purpose of the work is to identify statistical relationships between operating and geometric parameters, as well as to quantify the influence of these parameters on the critical mass flow rate and pressure. The analysis of experimental data for channels with a filleted inlet edge showed a strong influence of the inlet edge shape both on the value of the critical mass velocity and on the final pressure in the outlet section of the channel, which is established at the critical outflow mode. A comparison of the experimental data for channels with different shapes of the inlet section with the same operating and other geometric parameters showed that for channels with a rounded inlet edge, the critical mass velocity is approximately 25% higher than for channels with a sharp inlet edge. As the nozzle throat length increases, this difference decreases asymptotically. Among the regime parameters, the main contribution to the dispersion of the critical mass velocity is made by the undersaturation (subcooling) of the medium at the inlet which comprised 51% of the total influence of the regime and geometric parameters. An increase in the undersaturation and a decrease in the length of the channel throat lead to decrease in the back pressure necessary to establish the critical outflow mode. In extreme cases, the critical pressure ratio (outlet/inlet) can be 0.1, which is significantly lower than the generally accepted value of 0.5 in engineering practice. The results obtained can be used in the future for design of experiments aimed at expanding the range of operating parameters or optimization elements whose operation is based on the phenomenon of critical outflow. [ABSTRACT FROM AUTHOR]

This paper considers the suitability of using multispectral remote sensing data from the Landsat-8 satellite for conducting regional geological and mineralogical mapping of the territory of southeastern Zabaykalsky krai (Russia) under conditions of medium–low mountain relief and continental climate. To increase the spectral information content of Landsat-8 satellite data, statistical processing algorithms are used, including principal component analysis (PCA), minimum noise fraction (MNF), and independent component analysis (ICA). Statistical processing results are compared with the classes of geological materials: minerals of the oxide/hydroxide group containing transitional iron ions (Fe3+ and Fe3+/Fe2+); a group of clay minerals containing Al-OH and Fe, Mg-OH; and minerals containing divalent iron ion (Fe2+) and vegetation cover. Pseudocolor RGB composites are generated and interpreted, reflecting the distribution and multiplication of classes of geological materials. To build a prospectivity scheme for the discovery of the minerals, the integration of informative thematic layers is carried out using a fuzzy logic model. The resulting scheme is compared with geological information, and positive conclusions are made about the suitability of territory for further remote studies on mapping hydrothermally altered zones and hypergenesis products in order to localize areas that are promising for revealing hydrothermal–metasomatic mineralization. [ABSTRACT FROM AUTHOR]

Clinical notes contain valuable patient information. These notes are written by health care providers with various scientific levels and writing styles. It might be helpful for clinicians and researchers to understand what information is essential when dealing with extensive electronic medical records. Entities recognizing them and mapping them to standard terminologies is crucial to reducing ambiguity in processing clinical notes. Although named entity recognition and entity linking are critical steps in clinical natural language processing, they can produce repetitive and low-value concepts. On the other hand, all parts of a clinical text do not share the same importance or content in predicting the patient's condition. As a result, it is necessary to identify the section in which each content item is recorded and critical concepts to extract meaning from clinical texts. In this study, these challenges have been addressed by using clinical natural language processing techniques. In addition, a set of unsupervised essential phrase extraction methods has been verified and evaluated to identify key concepts. Considering that most clinical concepts are in the form of multi-word expressions and their accurate identification requires the user to specify an n-gram range, we have proposed a shortcut method to preserve the structure of the term based on TF-IDF (Term Frequency Inverse Document Frequency). To evaluate, we have designed two types of downstream tasks (multiple and binary classification) using the capabilities of transformer-based models. The results show the proposed method's superiority in combination with the SciBERT model. Also, they offer an insight into the efficacy of general methods for extracting essential phrases from clinical notes. [ABSTRACT FROM AUTHOR]

Underwater soundscape that spans a broad frequency band shows variability consistent with contributing noise sources and ocean environment. However, increased anthropogenic activities result in noise proliferation which can harm natural marine habitat. Continuous monitoring of background sound is useful to assess such spatio-temporal variability of soundscape. Standard noise level metrics, for instance, mean (μ), 90th percentiles (90P), standard deviation (σ), and kurtosis (β), are constructed from noise field measured from three coastal stations in Eastern Arabian Sea. These metrics are found to be suitable to describe the soundscape variability with respect to season, frequency, and depth. Mean and 90P are used to compare the seasonal variations while kurtosis metrics are exercised to check the impulsive nature of composite signal. Histogram representation and probability density function (PDF) were utilized to analyze the spectral variation in soundscape with respect to season. Analysis was carried out at 500-ms temporal window in two spectral bands corresponding to traffic and wind noise fields. Seasonal analysis shows that in summer, mean noise level decreases as hydrophone depth increases, while in winter, deeper depths have higher mean value with the presence of seasonal surface duct. This implication of sound speed profile on noise field has also been confirmed using appropriate noise model. [ABSTRACT FROM AUTHOR]

The time required to identify and confirm risk factors for new diseases and to design an appropriate treatment strategy is one of the most significant obstacles medical professionals face. Traditionally, this approach entails several clinical studies that may last several years, during which time strict preventative measures must be in place to contain the epidemic and limit the number of fatalities. Analytical tools may be used to direct and accelerate this process. This study introduces a six-state compartmental model to explain and assess the impact of age demographics by designing a dynamic, explainable analytics model of the SARS-CoV-2 coronavirus. An age-stratified mathematical model taking the form of a deterministic system of ordinary differential equations divides the population into different age groups to better understand and assess the impact of age on mortality. It also provides a more accurate and effective interpretation of the disease evolution, specifically in terms of the cumulative numbers of infected cases and deaths. The proposed Kermack-Mckendrick model is incorporated into a non-linear least-squares optimization curve-fitting problem whose optimized parameters are numerically obtained using the Levenberg-Marquard algorithm. The curve-fitting model's efficiency is proved by testing the age-stratified model's performance on three U.S. states: Connecticut, North Dakota, and South Dakota. Our results confirm that splitting the population into different age groups leads to better fitting and forecasting results overall as compared to those achieved by the traditional method, i.e., without age groups. By using comprehensive models that account for age, gender, and ethnicity, regional public health authorities may be able to avoid future epidemics from inflicting more fatalities and establish a public health policy that reduces the burden on the elderly population. [ABSTRACT FROM AUTHOR]

Groundwater vulnerability assessment has nowadays evolved into an essential tool towards proper groundwater protection and management, while the DRASTIC method is included among the most widely applied vulnerability assessment methods. However, the high uncertainty of the DRASTIC method mainly associated with the subjectivity in assigning parameters ratings and weights has driven many researchers to apply various methods for improving its efficiency. In this context, in the present study, different techniques were implemented with the aim of modifying the DRASTIC framework and thus enhancing its performance for groundwater vulnerability assessment in the Bouficha aquifer, Tunisia. In a first stage, the land use type (L) was incorporated as an additional parameter in the typical DRASTIC framework, thus taking into consideration the impact of anthropogenic activities on groundwater vulnerability. Subsequently, the rating and weighting systems of the developed DRASTIC-L framework were modified through the application of statistical methods (DRASTIC-L-SA) and genetic algorithms (GA) (DRASTIC-L-GA) in an attempt to investigate and compare both linear and nonlinear modifications. To evaluate the various vulnerability frameworks, correlation between vulnerability values and nitrate concentrations, expressed as Spearman's rank correlation coefficient (ρ) and Correlation Index (CI), was examined. The results revealed that the DRASTIC-L-GA framework developed by applying a fully GA-based optimization procedure provided the highest values in terms of the performance metrics used, making it the most suitable for the study area. In addition, the aquifer under study was found to be less vulnerable to pollution when employing the typical DRASTIC framework instead of the modified ones, leading to the conclusion that the former substantially underestimates pollution potential in the study area. [ABSTRACT FROM AUTHOR]

Digital photos are deemed an important source of information. The global request for lucid images has upsurged in the last decade. Accordingly, increasing the quality of images can be made through different features. Among such, color is a crucial feature as it gives an image a pleasing look and holds key significant information. As known, digital images are obtained with degradations, and deficient colors are an effect that can be observed in different types of images obtained by various modern imaging systems. Improving the colors while preserving the other important image features and details is needed for various real-world uses. Hence, an expeditious tint intensification (TI) algorithm that can boost the colors is introduced in this research, in that it begins by converting the image to the HSV domain and preserving the hue channel while processing the other two channels of saturation and value using different concepts. The image is then converted to the RGB domain and processed again using several methods to produce the desired output. Different experiments on real-world images have been made, comparisons with various algorithms are also attained and the results have been assessed using three dedicated image evaluation metrics. Promising results have been obtained, in that the TI algorithm is proven to deliver visually pleasing results, in that the colors appear vivid, the contrast is adequate, and the brightness is preserved with no visible filtering flaws. This is imperative because a few computations have been used to produce high-quality results in a fast and efficient way. The outcomes of this study are significant as they can be utilized in different important research areas. [ABSTRACT FROM AUTHOR]

New advances in data linkage provide mortality researchers with access to administrative datasets with millions of mortality records and rich demographic covariates. Although these new datasets allow for high-resolution mortality research, administrative mortality records often have technical limitations, such as limited mortality coverage windows and incomplete observation of survivors. We describe a method for fitting truncated distributions that can be used for estimating mortality differentials in administrative data. We apply this method to the CenSoc datasets, which link the United States 1940 Census records to Social Security administrative mortality records. Our approach may be useful in other contexts where administrative data on deaths are available. As a companion to the paper, we release the R package gompertztrunc, which implements the methods introduced in this paper. [ABSTRACT FROM AUTHOR]

To date, there has been relatively little research in the field of credit risk analysis that compares all of the well known statistical, optimization technique (heuristic methods) and machine learning based approaches in a single article. Review on credit risk assessment using sixteen well-known approaches has been conducted in this work. The accuracy of the machine learning approaches in dealing with financial difficulties is superior to that of traditional statistical methods, especially when dealing with nonlinear patterns, according to the findings. Hybrid or Ensemble algorithms, on the other hand have been found to outperform their traditional counterparts – standalone classifiers in the vast majority of situations. Finally, the paper compares the models with nine machine learning classifiers utilizing two benchmark datasets. In this study, we have encountered with 46 datasets, among them 35 datasets have been utilized for once; whereas among the other 11 datasets, Australian, German and Japanese are the three most frequently utilized datasets by the researchers. The study showed that the performance of ensemble classifiers were very much significant. As per the experimental result, for both datasets ensemble classifiers outperformed other standalone classifiers which validate with the prior research also. Although some of these approaches have a high level of accuracy, additional study is required to discover the right parameters and procedures for better outcomes in a transparent manner. Additionally this study is a valuable reference source for analyzing credit risk for both academic and practical domains, since it contains relevant information on the most major machine learning approaches employed so far. [ABSTRACT FROM AUTHOR]

In the natural rubber latex industries, a huge amount of sludge waste is produced in different processes, which creates potential for use as reinforcing filler in plastic composites. The present work aimed to investigate the effects of sludge waste generated from the manufacturing process of fresh latex pond (FLSF) and concentrated latex processing (CLSF) on the thermal, mechanical, and physical properties of polypropylene (PP) composites. Composite samples were produced using a twin-screw extruder and a compression molding machine. From experiments, particle size analysis showed that FLSF had a smaller particle size than CLSF. For both the reinforcing fillers with increasing addition from 30 to 50 wt%, crystallinity degrees increased by about 3.30% and 7.80% for the composites with FLSF and CLSF, respectively. The strength of tension and flexure in the PP composites reduced continuously with an increase of the sludge waste contents in a range of 30–70 wt%, but the modulus from tension and flexure, hardness, and water absorption (WA) increased. The PP composites produced from FLSF displayed superior strength, modulus, hardness, and WA than those produced from CLSF. Further, the morphological, mechanical, and physical properties of the PP-sludge composites were improved with addition of maleic anhydride-grafted polypropylene. The aforementioned results showed that latex sludge waste could be used as an effective filler in plastic composites. [ABSTRACT FROM AUTHOR]

Models considered in the current special issue represent valuable additions to the statistical toolbox of prevention researchers for many types of research questions and designs. Their appropriate use, however, depends on critical evaluation relative to previously existing techniques. This evaluation includes (a.) model choice involving "right-sizing" of the model relative to the amount and quality of data at hand, (b.) examination of the external validity of identified associations relative to observed or latent subgroups, (c.) confirmation of the reasonableness of the functional form assumed by the model, and (d.) identification of influential or outlying observations which unduly affect model fit or parameter estimates. Models in this issue allow for testing of new types of hypotheses in prevention research, and can constitute counterarguments to existing statistical practice. These models may, however, in turn be the object of critical examination of counterarguments a reasonable skeptic may offer. [ABSTRACT FROM AUTHOR]

This study aimed to examine the influence of the random selection of landslide training and testing sets on the predictive performance of the shallow landslide susceptibility modelling at regional scale. The performance of frequency ratio (FR), information value (IV), logistic regression (LR), and maximum entropy (ME) methods were tested and compared for modeling shallow landslide susceptibility in Calabria region, (South Italy). A landslide database of 22,028 shallow landslides, randomly split into training (70%) and testing (30%) sets, was combined with 15 predisposing factors (lithology, soil texture, soil bulk density, soil erodibility, drainage density, land use, elevation, local relief, slope gradient, slope aspect, plan curvature, topographic wetness index, stream power index, topographic ruggedness index, and topographic position index) to calibrate and validate the models. The robustness of the models in response to changes in the landslide dataset was explored through ten training and test sets replicates. The performance of these models was evaluated using several statistical indices and the ROC curve method. The results showed that all the four methods applied achieve promising performance on the prediction of shallow landslide susceptibility at regional scale. The comparison between four methods displayed that the ME is the best performing (AUC = 0.866), followed by the LR (AUC = 0.845), FR (AUC = 0.813), and finally IV (AUC = 0.800). In addition, the findings showed that the accuracy of the four methods for modeling shallow landslide susceptibility was quite robust when the training and testing sets were changed (i.e. a very low sensitivity to varying training/testing sets). [ABSTRACT FROM AUTHOR]

Different statistical methods are used in various fields to qualify processes and products, especially in emerging technologies like Additive Manufacturing (AM) or 3D printing. Since several statistical methods are being employed to ensure quality production of the 3D-printed parts, an overview of these methods used in 3D printing for different purposes is presented in this paper. The advantages and challenges, to understanding the importance it brings for design and testing optimization of 3D-printed parts are also discussed. The application of different metrology methods is also summarized to guide future researchers in producing dimensionally-accurate and good-quality 3D-printed parts. This review paper shows that the Taguchi Methodology is the commonly-used statistical tool in optimizing mechanical properties of the 3D-printed parts, followed by Weibull Analysis and Factorial Design. In addition, key areas such as Artificial Intelligence (AI), Machine Learning (ML), Finite Element Analysis (FEA), and Simulation require more research for improved 3D-printed part qualities for specific purposes. Future perspectives are also discussed, including other methods that can help further improve the overall quality of the 3D printing process from designing to manufacturing. [ABSTRACT FROM AUTHOR]

Rock mass classification systems are used to estimate deformability modulus. This study offers a relationship to estimate deformability modulus using geomechanical rock parameters such as rock quality designation (RQD), Uniaxial Compressive strength test (UCS), joint condition (Jc), joint spacing (Js), dynamic modulus (Edyn), and groundwater condition (Wc). Dilatometer tests (DMTs) were performed on 88 samples taken from Khersan dam-II. Then, the collected data were analyzed using machine learning methods (MLM) and statistical methods. The analyses were performed using different types of MLM, i.e., Multivariate Non-Linear Regression (MNLR), Support Vector Regression (SVR), and Copula-based methods. These analyses revealed that the SVR model predicted the values of rock mass deformability modulus (Em) with the determination coefficient (R2) of 0.868. In comparison, the Copula-based and MNLR power methods predicted Em with an R2 of 0.851 and 0.866, respectively. The cross-validation (CV) technique shows that SVR is the best-designed model for predicting Em. Also, MLMs provided an alternative and effective way to predict Em. Overall, the statistical analysis was an efficient tool for selecting the best parameters and correlations of data for Em. [ABSTRACT FROM AUTHOR]

Recently, CHIME/FRB project published its first fast radio burst (FRB) catalog (hereafter, Catalog 1), which contains in total 536 unique bursts. With the help of the latest set of FRBs in this large-size catalog, we aim to investigate the dispersion measure (DM) or redshift () distribution of the FRB population, and solution of this problem could be used to clarify the question of FRB origin. In this study, we adopted the M&E 2018 model to fit the observed distribution of FRBs in Catalog 1. In the M&E 2018 model, we are mostly interested in the function, i.e., number of bursts per proper time per comoving volume, which is represented by the star formation rate (SFR) with a power-law index . Our estimated value of is () at the 68 (95) per cent confidence level, implying that the FRB population evolves with redshift consistent with, or faster than, the SFR. Specially, the consistency of the values estimated by this study and the SFR provides a potential support for the hypothesis of FRBs originating from young magnetars. [ABSTRACT FROM AUTHOR]

The study investigates the dynamics of the Russian scientific journals input stream based on the results of article classification, according to the State Rubricator of Scientific and Technical Information in the VINITI RAS Database. The authors aim to trace the changes in the information provision of the codes of the subject field Chemistry, chemical technology, and the chemical industry. The research procedure presented includes the methods of calculating the indicators that allow to automate the assessment of journals thematic profiles—the similarity and scattering coefficients. The proposed methodology facilitates the identification of trends in the thematic profiles of serial publications, i.e., growth or degradation of their headings, thus contributing to the optimization of the information center documents input stream. [ABSTRACT FROM AUTHOR]

We develop ensemble neural networks (ENN) that serve as computationally fast surrogate models of Stockmayer fluid molecular dynamics (MD) simulations for determining the dielectric constants of polar solvents and NaCl solutions. The ENNs are trained using 50-times less data than is used to calculate the dielectric constants from MD simulations. The predictions of ENNs trained on this small amount of data and using batch normalization or bagging are in relatively good agreement with the full MD results. These ENN methods are thus able to extract reliable values from statistically noisy data. [ABSTRACT FROM AUTHOR]

Digital Shoreline Analysis System (DSAS) is the most frequently used coastal engineering system for shoreline change quantification. Factors like human and system errors, wrong perception of the shoreline changes, and non-exact data sources may cause errors in the measured data. Detection and modification of such data can increase the accuracy of results. At present, the DSAS tool lacks this capability, so this research aimed to present a new module for DSAS to detect uncertain data in shoreline change rate measurements. The module's basis for detecting uncertain data is to use statistical methods: adjusted boxplot, Grubbs' test, standard deviation tests, median test, modified Z-score test, and voting method. The module's performance was evaluated based on a data set obtained through Qeshm Island shoreline change quantification in Iran. The details of these methods, the prepared module, the case study, and the shoreline change measurement statistical methods were discussed in this study. The results showed the acceptable output of this module in detecting uncertain data. [ABSTRACT FROM AUTHOR]

Aims: Diabetes patients are at a higher risk of fractures, and glucagon-like peptide-1 receptor agonists (GLP-1RAs) have been suggested to positively impact on bone metabolism. We aim to provide a comprehensive assessment of fracture events associated with GLP-1RAs based on pharmacovigilance data.In this study, fracture-related adverse events (AEs) associated with GLP-1RAs and other commonly used glucose-lowering drugs were identified from Food and Drug Administration Adverse Event Reporting System (FAERS) database (2004–2022). The reporting odds ratio (ROR) and adjusted ROR (adj. ROR) were used to compare the reporting of fracture-related AEs associated with insulin, GLP-1RAs, and Non GLP-1RAs, in patients with diabetes through two scenarios. This involved separately comparing each glucose-lowering drug to all other medications used in diabetic patients and reiterating after excluding insulin cases.A total of 490,107 AE reports for patients with diabetes were identified and 98, 625 of them were for GLP-1RAs. Among all diabetes drugs, GLP-1RAs had the lowest reporting of any fracture-related AEs [adj. ROR = 0.44 (0.40–0.47)], consistent across osteoporotic fracture [adj. ROR = 0.39 (0.34–0.45)] and hip fracture [adj. ROR = 0.34 (0.28–0.41)]. Among GLP-1RA agents, albiglutide was associated with the lowest adj. ROR [0.11 (0.05–0.21)] for any fracture-related AEs. After excluded all insulin reports, GLP-1RAs retained a significantly lower adj. ROR towards any fracture [adj. ROR = 0.45 (0.40–0.50)], osteoporotic fracture [adj. ROR = 0.44 (0.37–0.52)], and hip fracture [adj. ROR = 0.43 (0.33–0.54)].In a real-world pharmacovigilance setting, GLP-1RAs were associated with lower reporting of fracture-related AEs, indicating the protective effect of GLP-1RAs against fractures.Methods: Diabetes patients are at a higher risk of fractures, and glucagon-like peptide-1 receptor agonists (GLP-1RAs) have been suggested to positively impact on bone metabolism. We aim to provide a comprehensive assessment of fracture events associated with GLP-1RAs based on pharmacovigilance data.In this study, fracture-related adverse events (AEs) associated with GLP-1RAs and other commonly used glucose-lowering drugs were identified from Food and Drug Administration Adverse Event Reporting System (FAERS) database (2004–2022). The reporting odds ratio (ROR) and adjusted ROR (adj. ROR) were used to compare the reporting of fracture-related AEs associated with insulin, GLP-1RAs, and Non GLP-1RAs, in patients with diabetes through two scenarios. This involved separately comparing each glucose-lowering drug to all other medications used in diabetic patients and reiterating after excluding insulin cases.A total of 490,107 AE reports for patients with diabetes were identified and 98, 625 of them were for GLP-1RAs. Among all diabetes drugs, GLP-1RAs had the lowest reporting of any fracture-related AEs [adj. ROR = 0.44 (0.40–0.47)], consistent across osteoporotic fracture [adj. ROR = 0.39 (0.34–0.45)] and hip fracture [adj. ROR = 0.34 (0.28–0.41)]. Among GLP-1RA agents, albiglutide was associated with the lowest adj. ROR [0.11 (0.05–0.21)] for any fracture-related AEs. After excluded all insulin reports, GLP-1RAs retained a significantly lower adj. ROR towards any fracture [adj. ROR = 0.45 (0.40–0.50)], osteoporotic fracture [adj. ROR = 0.44 (0.37–0.52)], and hip fracture [adj. ROR = 0.43 (0.33–0.54)].In a real-world pharmacovigilance setting, GLP-1RAs were associated with lower reporting of fracture-related AEs, indicating the protective effect of GLP-1RAs against fractures.Results: Diabetes patients are at a higher risk of fractures, and glucagon-like peptide-1 receptor agonists (GLP-1RAs) have been suggested to positively impact on bone metabolism. We aim to provide a comprehensive assessment of fracture events associated with GLP-1RAs based on pharmacovigilance data.In this study, fracture-related adverse events (AEs) associated with GLP-1RAs and other commonly used glucose-lowering drugs were identified from Food and Drug Administration Adverse Event Reporting System (FAERS) database (2004–2022). The reporting odds ratio (ROR) and adjusted ROR (adj. ROR) were used to compare the reporting of fracture-related AEs associated with insulin, GLP-1RAs, and Non GLP-1RAs, in patients with diabetes through two scenarios. This involved separately comparing each glucose-lowering drug to all other medications used in diabetic patients and reiterating after excluding insulin cases.A total of 490,107 AE reports for patients with diabetes were identified and 98, 625 of them were for GLP-1RAs. Among all diabetes drugs, GLP-1RAs had the lowest reporting of any fracture-related AEs [adj. ROR = 0.44 (0.40–0.47)], consistent across osteoporotic fracture [adj. ROR = 0.39 (0.34–0.45)] and hip fracture [adj. ROR = 0.34 (0.28–0.41)]. Among GLP-1RA agents, albiglutide was associated with the lowest adj. ROR [0.11 (0.05–0.21)] for any fracture-related AEs. After excluded all insulin reports, GLP-1RAs retained a significantly lower adj. ROR towards any fracture [adj. ROR = 0.45 (0.40–0.50)], osteoporotic fracture [adj. ROR = 0.44 (0.37–0.52)], and hip fracture [adj. ROR = 0.43 (0.33–0.54)].In a real-world pharmacovigilance setting, GLP-1RAs were associated with lower reporting of fracture-related AEs, indicating the protective effect of GLP-1RAs against fractures.Conclusion: Diabetes patients are at a higher risk of fractures, and glucagon-like peptide-1 receptor agonists (GLP-1RAs) have been suggested to positively impact on bone metabolism. We aim to provide a comprehensive assessment of fracture events associated with GLP-1RAs based on pharmacovigilance data.In this study, fracture-related adverse events (AEs) associated with GLP-1RAs and other commonly used glucose-lowering drugs were identified from Food and Drug Administration Adverse Event Reporting System (FAERS) database (2004–2022). The reporting odds ratio (ROR) and adjusted ROR (adj. ROR) were used to compare the reporting of fracture-related AEs associated with insulin, GLP-1RAs, and Non GLP-1RAs, in patients with diabetes through two scenarios. This involved separately comparing each glucose-lowering drug to all other medications used in diabetic patients and reiterating after excluding insulin cases.A total of 490,107 AE reports for patients with diabetes were identified and 98, 625 of them were for GLP-1RAs. Among all diabetes drugs, GLP-1RAs had the lowest reporting of any fracture-related AEs [adj. ROR = 0.44 (0.40–0.47)], consistent across osteoporotic fracture [adj. ROR = 0.39 (0.34–0.45)] and hip fracture [adj. ROR = 0.34 (0.28–0.41)]. Among GLP-1RA agents, albiglutide was associated with the lowest adj. ROR [0.11 (0.05–0.21)] for any fracture-related AEs. After excluded all insulin reports, GLP-1RAs retained a significantly lower adj. ROR towards any fracture [adj. ROR = 0.45 (0.40–0.50)], osteoporotic fracture [adj. ROR = 0.44 (0.37–0.52)], and hip fracture [adj. ROR = 0.43 (0.33–0.54)].In a real-world pharmacovigilance setting, GLP-1RAs were associated with lower reporting of fracture-related AEs, indicating the protective effect of GLP-1RAs against fractures. [ABSTRACT FROM AUTHOR]

Machine learning (ML) is the branch of computer science that studies computer algorithms that can learn from data. It is mainly divided into supervised learning, where the computer is presented with examples of entries, and the goal is to learn a general rule that maps inputs to outputs, and unsupervised learning, where no label is provided to the learning algorithm, leaving it alone to find structures. Deep learning is a branch of machine learning based on numerous layers of artificial neural networks, which are computing systems inspired by the biological neural networks that constitute animal brains. In asteroid dynamics, machine learning methods have been recently used to identify members of asteroid families, small bodies images in astronomical fields, and to identify resonant arguments images of asteroids in three-body resonances, among other applications. Here, we will conduct a full review of available literature in the field and classify it in terms of metrics recently used by other authors to assess the state of the art of applications of machine learning in other astronomical subfields. For comparison, applications of machine learning to Solar System bodies, a larger area that includes imaging and spectrophotometry of small bodies, have already reached a state classified as progressing. Research communities and methodologies are more established, and the use of ML led to the discovery of new celestial objects or features, or new insights in the area. ML applied to asteroid dynamics, however, is still in the emerging phase, with smaller groups, methodologies still not well-established, and fewer papers producing discoveries or insights. Large observational surveys, like those conducted at the Zwicky Transient Facility or at the Vera C. Rubin Observatory, will produce in the next years very substantial datasets of orbital and physical properties for asteroids. Applications of ML for clustering, image identification, and anomaly detection, among others, are currently being developed and are expected of being of great help in the next few years. [ABSTRACT FROM AUTHOR]

Industrial software often has many parameters that critically impact performance. Frequently, these are left in a sub-optimal configuration for a given application because searching over possible configurations is costly and, except for developer instinct, the relationships between parameters and performance are often unclear and complex. While there have been significant advances in automated parameter tuning approaches recently, they are typically black-box. The high-quality solutions produced are returned to the user without explanation. The nature of optimisation means that, often, these solutions are far outside the well-established settings for the software, making it difficult to accept and use them. To address the above issue, a systematic approach to software parameter optimization is presented. Several well-established techniques are followed in sequence, each underpinning the next, with rigorous analysis of the search space. This allows the results to be explainable to both end users and developers, improving confidence in the optimal solutions, particularly where they are counter-intuitive. The process comprises statistical analysis of the parameters; single-objective optimization for each target objective; functional ANOVA to explain trends and inter-parameter interactions; and a multi-objective optimization seeded with the results from the single-objective stage. A case study demonstrates application to business-critical software developed by the international airline Air France-KLM for measuring flight schedule robustness. A configuration is found with a run-time of 80% that of the tried-and-tested configuration, with no loss in predictive accuracy. The configuration is supplemented with detailed analysis explaining the importance of each parameter, how they interact with each other, how they influence run-time and accuracy, and how the final configuration was reached. In particular, this explains why the configuration included some parameter settings that were outwith the usually recommended range, greatly increasing developer confidence and encouraging adoption of the new configuration. [ABSTRACT FROM AUTHOR]

Due to the increasing detrimental impacts of mass non-renewable plastics over the last decades, cellulose-based materials have been extensively studied as a promising sustainable alternative. Here, we prepare lignocellulosic papers (LBC) from bacterial cellulose (BC) impregnated with lignin, and analyze their mechanical properties, microstructure, and wetting kinetics. We follow a design of experiment analysis to obtain the optimal pressing conditions of the BC and LBC papers, targeted at maximizing the specific ultimate tensile strength and toughness. At optimal conditions, lignin impregnation enhances the absolute modulus, strength, and toughness of BC by 108%, 142%, and 63%, respectively. [ABSTRACT FROM AUTHOR]

This article is a review of the scientific problems that the Honored Professor of the Moscow State University Evgeny Anatolyevich Dmitriev worked on. The main area of his scientific interests was the genesis of soil and land cover data. Numerous works carried out under his supervision convincingly demonstrated the influence of sampling methods on the results of determining certain soil properties. As a consequence, this influence also extends to the final conclusions, which can be highly distorted if the method of obtaining them is not taken into account. In an attempt to solve the question "What classifies soil classification?" he developed the concept of a "single soil," which can be the object of soil classifications, not being directly a soil body, but a kind of standard element of sampling. Theoretical understanding of the influence of heterogeneity of soils and soil cover at all hierarchical levels on the features of its "life" is the subject of the second part of the scientific legacy of E.A. Dmitriev. He proposed the concept of soil bodies of different dimensions, showed the influence of the dimensional characteristics of these bodies on soil regimes and, in particular, on the water regime, and discussed the need to study soil regimes at different hierarchical levels. To fix the heterogeneity of the soil and soil cover, he developed special devices, with which studies were carried out on soddy-podzolic, gray forest, chestnut, and other soils. [ABSTRACT FROM AUTHOR]

This paper presents the analytical and numerical comparison of two methods of estimation of additive × additive × additive (aaa) interaction of QTL effects. The first method takes into account only the plant phenotype, while in the second we also included genotypic information from molecular marker observation. Analysis was made on 150 doubled haploid (DH) lines of barley derived from cross Steptoe × Morex and 145 DH lines from Harrington × TR306 cross. In total, 153 sets of observation was analyzed. In most cases, aaa interactions were found with an exert effect on QTL. Results also show that with molecular marker observations, obtained estimators had smaller absolute values than phenotypic estimators. [ABSTRACT FROM AUTHOR]

Integrating renewable energy and diverse active consumers into distribution networks causes electric power quality indicators (EPQI) to deviate from their standard values. In the industrial setting, production lines and distributed generation facilities adopted are susceptible to voltage dips and interruptions to non-sinusoidal voltage and current, which means that EPQIs at the ownership demarcation point must remain at any network operating mode to be maintained. For this purpose, industrial facilities implement systems for continuous or selective monitoring of EPQIs. This paper demonstrates the effectiveness of using statistical methods in EPQI monitoring systems for selective control of EPQIs and a quantitative characteristic with exponentially distributed deviations in specific indicators. It further presents the algorithm of the developed quantitative characteristic-based EPQI analyzer, which obtains data from the EPQI monitoring system equipped with EPQI control instrumentation. Calculations showed that Wald's sequential analysis is effective for selective EPQI monitoring and can triple the performance of decision-making algorithms that compute deviations in certain parameters compared to a fixed sample. A generated database of simulation modeling and tracking of the current operating modes of the network ensures adaptation of the monitoring process and selective control of EPQI to the needs of the specific facility's internal power supply network. [ABSTRACT FROM AUTHOR]

Representativeness and quality of collected meteorological data impact accuracy and precision of climate, hydrological, and biogeochemical analyses and predictions. We developed a comprehensive Quality Assurance (QA) and Quality Control (QC) statistical framework, consisting of three major phases: Phase I—Preliminary data exploration, i.e., processing of raw datasets, with the challenging problems of time formatting and combining datasets of different lengths and different time intervals; Phase II—QA of the datasets, including detecting and flagging of duplicates, outliers, and extreme data; and Phase III—the development of time series of a desired frequency, imputation of missing values, visualization and a final statistical summary. The paper includes two use cases based on the time series data collected at the Billy Barr meteorological station (East River Watershed, Colorado), and the Barro Colorado Island (BCI, Panama) meteorological station. The developed statistical framework is suitable for both real-time and post-data-collection QA/QC analysis of meteorological datasets. [ABSTRACT FROM AUTHOR]

We propose a local earthquake tomography method that applies a structured regularization technique to determine sharp changes in Earth's seismic velocity structure using arrival time data of direct waves. Our approach focuses on the ability to better image two common features that are observed in Earth's seismic velocity structure: sharp changes in velocities that correspond to material boundaries, such as the Conrad and Moho discontinuities; and gradual changes in velocity that are associated with pressure and temperature distributions in the crust and mantle. We employ different penalty terms in the vertical and horizontal directions to refine the earthquake tomography. We utilize a vertical-direction (depth) penalty that takes the form of the l 1 -sum of the l 2 -norms of the second-order differences of the horizontal units in the vertical direction. This penalty is intended to represent sharp velocity changes caused by discontinuities by creating a piecewise linear depth profile of seismic velocity. We set a horizontal-direction penalty term on the basis of the l 2 -norm to express gradual velocity tendencies in the horizontal direction, which has been often used in conventional tomography methods. We use a synthetic data set to demonstrate that our method provides significant improvements over velocity structures estimated using conventional methods by obtaining stable estimates of both steep and gradual changes in velocity. We also demonstrate that our proposed method is robust to variations in the amplitude of the velocity jump, the initial velocity model, and the number of observed arrival times, compared with conventional approaches, and verify the adaptability of the proposed method to dipping discontinuities. Furthermore, we apply our proposed method to real seismic data in central Japan and present the potential of our method for detecting velocity discontinuities using the observed arrival times from a small number of local earthquakes. [ABSTRACT FROM AUTHOR]

Certain characteristics make machine learning (ML) a powerful tool for processing large amounts of data, and also particularly unsuitable for explanatory purposes. There are worries that its increasing use in science may sideline the explanatory goals of research. We analyze the key characteristics of ML that might have implications for the future directions in scientific research: epistemic opacity and the 'theory-agnostic' modeling. These characteristics are further analyzed in a comparison of ML with the traditional statistical methods, in order to demonstrate what it is specifically that makes ML methodology substantially unsuitable for reaching explanations. The analysis is given broader philosophical context by connecting it with the views on the role of prediction and explanation in science, their relationship, and the value of explanation. We proceed to show, first, that ML disrupts the relationship between prediction and explanation commonly understood as a functional relationship. Then we show that the value of explanation is not exhausted in purely predictive functions, but rather has a ubiquitously recognized value for both science and everyday life. We then invoke two hypothetical scenarios with different degrees of automatization of science, which help test our intuitions on the role of explanation in science. The main question we address is whether ML will reorient or otherwise impact our standard explanatory practice. We conclude with a prognosis that ML would diversify science into purely predictively oriented research based on ML-like techniques and, on the other hand, remaining faithful to anthropocentric research focused on the search for explanation. [ABSTRACT FROM AUTHOR]

The M4 forecasting competition challenged the participants to forecast 100,000 time series with different frequencies: hourly, daily, weekly, monthly, quarterly, and yearly. These series come mainly from the economic, finance, demographics, and industrial areas. This paper describes the model used in the competition, which is a combination of statistical methods, namely auto-regressive integrated moving-average, exponential smoothing (ETS), bagged ETS, temporal hierarchical forecasting method, Box-Cox transformation, ARMA errors, Trend and Seasonal components (BATS), and Trigonometric seasonality BATS (TBATS). Forty-nine submissions were evaluated by the organizers and compared with 12 benchmarks and standards for comparison forecasting methods. Based on the results, the proposed model is listed among the 17 submissions that outperform the 12 benchmarks and standards for comparison forecasting methods, ranked 15th on average and 4th with the weekly time series. In addition, a further comparison was conducted between the proposed model and other forecasting methods on forecasting EUR/USD exchange rate and Bitcoin closing price time series. It is apparent from the results that the proposed model can produce accurate results compared to many forecasting methods. [ABSTRACT FROM AUTHOR]

Several tsunami record catalogues have been developed for extensive tsunami research. However, completeness analysis of tsunami catalogues, an important step in using these catalogues for tsunami research, has not received sufficient attention. This could result in many biases and incorrect results. In this study, two methods proposed are employed and applied to quantitatively, credibly, and accurately estimate the completeness of a global and regional tsunami catalogue. The results show that: (1) The tsunami runup catalogue for runup-height intervals 0.1 ~ 0.5, 0.5 ~ 1, 1 ~ 5, and 5 ~ 10 m, are complete and homogeneous since the 1950s, which coincides with the extensive development of tide-gauge technology. The catalogue for 10 ~ 20 is complete and homogeneous since the 1930s, which may be related to the end of the global economic crisis that occurred from 1928 to 1933. And the catalogue for 20 m and more is complete and homogenous since the 1890s, the beginning of the age of electricity. For runup-height heights from 0.1 to 5 m, the completeness starting time exhibits minimal variation among six regions (Europe region, Indian region, South America, North America, South Pacific, and East Asia), which is related to the rapid economic development period of the world and the establishment of tsunami warning systems. For runup-height intervals, 5 ~ 10, 10 ~ 20, and 20 ~ , the completeness starting time varies considerably among regions. The completeness starting time in the EU was earlier than that in other regions, which is related to the level of economic and political development after the Enlightenment of early Europe. [ABSTRACT FROM AUTHOR]

Magnetic methods are simple, quick, and cost-effective for detecting the degree of pollution. The prime objective of this study was to determine the magnetic susceptibility in the soil samples of Tiruvannamalai district in Tamil Nadu to identify the pollution and sources of pollution from anthropogenic sources or natural sources using magnetic susceptibility studies and rigorous statistical analysis. Using standard protocol and procedure, the physicochemical properties such as percentage of sand, silt, clay, pH and electrical conductivity in the soil samples were determined. The findings of the study reveal that sand was the dominant particle size in samples, followed by silt and then clay. In the laboratory, magnetic susceptibility at low and high frequencies (χlf and χhf) as well as frequency-dependent magnetic susceptibility (χfd) were determined. The mean value of low- and high-frequency magnetic susceptibility is found to be 273.38 × 10−8 m3 kg−1 and 270.50 × 10−8 m3 kg−1, respectively. In some locations, the magnetic enhancement value suggests a high concentration of ferrimagnetic minerals in the soil. Multivariate statistical analysis, such as factor analysis, Pearson correlation, and cluster analysis was used to determine the role of physicochemical parameters on magnetic susceptibilities and the identification of pollution sources. This analysis revealed that magnetic susceptibility can be used as a proxy for determining pollution in the soil. These methods emphasize that magnetic susceptibility studies broaden the spectrum of research in the field of magnetic studies on urban and industrial pollution. [ABSTRACT FROM AUTHOR]

Application of statistical analysis and quantitative genetics varies among sugarcane breeding programs around the world. Some methods and concepts are difficult to understand for those without an educational background in statistics. This paper is mainly targeted at readers with little prior experience in statistical analysis and quantitative genetics and aims to encourage further study and application of these potentially useful topics in sugarcane breeding and research. A basic introduction to the application of statistical analysis and quantitative genetics in sugarcane breeding programs is provided. These areas of science are potentially valuable to sugarcane breeders and researchers, for assessing data quality, interpreting and reporting results, making better selections, and designing efficient breeding and selection methods. [ABSTRACT FROM AUTHOR]

Morphometric analyses are based on multiparametric data sets that describe quantitatively the shapes of objects. The stochastic nature of fracture formation processes that break up magma during explosive eruptions yields mixtures of particles that have highly varied shapes. In volcanology, morphometric analysis is applied to these mixtures of particles with diverse shapes for two purposes: (1) to fingerprint tephra from individual eruptions and use the fingerprints to distinguish among tephra layers and determine their extents and (2) to reconstruct eruption processes, by linking particles formed by known fragmentation processes in experiments with particles from natural pyroclastic deposits. Here, we review the most commonly adopted statistical techniques for morphometric analysis of pyroclasts. We provide sets of objects with different shapes, along with their morphometric data, in order to demonstrate and illustrate the methods. They can be used not only for addressing the processes of fragmentation during explosive eruptions, but also for the characterization of other types of solid particles with complex morphologies. [ABSTRACT FROM AUTHOR]

Landslide susceptibility mapping is a necessary tool in order to manage the landslides hazard and improve the risk mitigation. In this research, we validate and compare the landslide susceptibility maps (LSMs) produced by applying four geographic information system (GIS)-based statistical approaches including frequency ratio (FR), statistical index (SI), weights of evidence (WoE), and logistic regression (LR) for the urban area of Azazga. For this purpose, firstly, a landslide inventory map was prepared from aerial photographs and high-resolution satellite imagery interpretation, and detailed fieldwork. Seventy percent of the mapped landslides were selected for landslide susceptibility modeling, and the remaining (30%) were used for model validation. Secondly, ten landslide factors including the slope, aspect, altitude, land use, lithology, precipitation, distance to drainage, distance to faults, distance to lineaments, and distance to roads have been derived from high-resolution Alsat 2A satellite images, aerial photographs, geological map, DEM, and rainfall database. Thirdly, we established LSMs by evaluating the relationships between the detected landslide locations and the ten landslides factors using FR, SI, LR, and WoE models in GIS. Finally, the obtained LSMs of the four models have been validated using the receiver operating characteristics curves (ROCs). The validation process indicated that the FR method provided more accurate prediction (78.4%) in generating LSMs than the SI (78.1%),WoE (73.5%), and LR (72.1%) models. The results revealed also that all the used statistical models provided good accuracy in landslide susceptibility mapping. [ABSTRACT FROM AUTHOR]

Data mining is the process of analyzing a massive amount of data to identify meaningful patterns and detect relations, which can lead to future trend prediction and appropriate decision making. Data mining applications are significant in marketing, banking, medicine, etc. In this paper, we present an overview of data mining applications in medicine to provide a clear view of the challenges and previous works in this area for researchers. Data mining techniques such as Decision Tree, Random Forest, K-means Clustering, Support Vector Machine, Logistic Regression, Neural Network, Naive Bayes, and association rule mining are used for diagnosing, prognosis, classifying, constructing predictive models, and analyzing risk factors of various diseases. The main objective of the paper is to analyze and compare different data mining techniques used in the medical applications. We present a summary of the results and provide comparison analysis of the data mining methods employed by the reviewed articles. [ABSTRACT FROM AUTHOR]

Chaotic motion affecting small bodies in the Solar system can be caused by close encounters or collisions or by resonance overlapping. Chaotic motion can be detected using approaches that measure the separation rate of trajectories that starts infinitesimally close or changes in the frequency power spectrum of time series, among others. In this work, we introduce an approach based on the autocorrelation function of time series, the ACF index (ACFI ). Autocorrelation coefficients measure the correlation of a time series with a lagged copy of itself. By measuring the fraction of autocorrelation coefficients obtained after a given time lag that are higher than the 5% null hypothesis threshold, we can determine how the time series autocorrelates with itself. This allows identifying unpredictable time series, characterized by low values of ACFI . Applications of ACFI to orbital regions affected by both types of chaos show that this method can correctly identify chaotic motion caused by resonance overlapping, but it is mostly blind to close encounters induced chaos. ACFI could be used in these regions to select the effects of resonance overlapping. [ABSTRACT FROM AUTHOR]

Dust is a phenomenon occurring in most parts of the world causing financial loss and adverse environmental effects and impacts. Many parameters are involved in dust phenomenon occurrence, some of which are natural and some of them are due to human processes and their influence on the environment. Monitoring the environmental changes can be worthwhile and efficient procrastination the occurrence of this phenomenon. In this research, we scrutinized the Persian and English database in a structured way using dust keywords, heavy metals, remote sensing, geographical information systems and statistical methods. Articles taking into consideration these three fields were retrieved, analyzed descriptively and reported. In accordance with this methodology, the study was conducted in a period from 1992 to 2016. Findings: Among the methods used, the method of interpolation, zoning of dust and their distribution with GIS is more applicable, and among satellite images, MODIS images are the most applicable ones. The results obtained from this study indicated that statistical methods, remote sensing and geographical information system are among effective tools in studying germane researches done on dust monitoring and detection. [ABSTRACT FROM AUTHOR]

There is a long history of fitting biometrical structural-equation models (SEMs) in the pregenomic behavioral-genetics literature of twin, family, and adoption studies. Recently, a method has emerged for estimating biometrical variance–covariance components based not upon the expected degree of genetic resemblance among relatives, but upon the observed degree of genetic resemblance among unrelated individuals for whom genome-wide genotypes are available—genomic-relatedness-matrix restricted maximum-likelihood (GREML). However, most existing GREML software is concerned with quickly and efficiently estimating heritability coefficients, genetic correlations, and so on, rather than with allowing the user to fit SEMs to multitrait samples of genotyped participants. We therefore introduce a feature in the OpenMx package, "mxGREML", designed to fit the biometrical SEMs from the pregenomic era in present-day genomic study designs. We explain the additional functionality this new feature has brought to OpenMx, and how the new functionality works. We provide an illustrative example of its use. We discuss the feature's current limitations, and our plans for its further development. [ABSTRACT FROM AUTHOR]