A theoretical framework for BL Her stars – I. Effect of metallicity and convection parameters on period–luminosity and period–radius relations

ABSTRACT We present a new grid of convective BL Herculis models using the state-of-the-art 1D non-linear radial stellar pulsation tool mesa-rsp. We investigate the impact of metallicity and four sets of different convection parameters on multiwavelength properties. Non-linear models were computed for periods typical for BL Her stars, i.e. 1 ≤ P(d) ≤ 4 covering a wide range of input parameters – metallicity (−2.0 dex ≤ [Fe/H] ≤ 0.0 dex), stellar mass (0.5–0.8 M⊙), luminosity (50–300 L⊙), and effective temperature (full extent of the instability strip; in steps of 50 K). The total number of BL Her models with full-amplitude stable pulsations used in this study is 10 280 across the four sets of convection parameters. We obtain their multiband (UBVRIJHKLL′M) light curves and derive new theoretical period–luminosity (PL), period–Wesenheit (PW), and period–radius (PR) relations at mean light. We find that the models computed with radiative cooling show statistically similar slopes for PL, PW, and PR relations. Most empirical relations match well with the theoretical PL, PW, and PR relations from the BL Her models computed using the four sets of convection parameters. However, PL slopes of the models with radiative cooling provide a better match to empirical relations for BL Her stars in the Large Magellanic Cloud in the HKS bands. For each set of convection parameters, the effect of metallicity is significant in U and B bands and negligible in infrared bands, which is consistent with empirical results. No significant metallicity effects are seen in the PR relations.

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Deformation of rockfill in bodies of rockfill dams

Stroitel stvo nauka i obrazovanie [Construction Science and Education] ◽

10.22227/2305-5502.2019.3.5 ◽

2019 ◽

pp. 5-5

Author(s):

Mikhail Sainov

Keyword(s):

Reinforced Concrete ◽

Deformation Modulus ◽

Deformation Model ◽

Observation Data ◽

Linear Deformation ◽

Laboratory Test Results ◽

Wide Range ◽

Deformation Properties ◽

Non Linear ◽

The Impact

Introduction. The main factor determining the stress-strain state (SSS) of rockfill dam with reinforced concrete faces is deformability of the dam body material, mostly rockfill. However, the deformation properties of rockfill have not been sufficiently studied yet for the time being due to technical complexity of the matter, Materials and methods. To determine the deformation parameters of rockfill, scientific and technical information on the results of rockfill laboratory tests in stabilometers were collected and analyzed, as well as field data on deformations in the existing rockfill dams. After that, the values of rockfill linear deformation modulus obtained in the laboratory and in the field were compared. The laboratory test results were processed and analyzed to determine the parameters of the non-linear rockfill deformation model. Results. Analyses of the field observation data demonstrates that the deformation of the rockfill in the existing dams varies in a wide range: its linear deformation modulus may vary from 30 to 500 МPа. It was found out that the results of the most rockfill tests conducted in the laboratory, as a rule, approximately correspond to the lower limit of the rockfill deformation modulus variation range in the bodies of the existing dams. This can be explained by the discrepancy in density and particle sizes of model and natural soils. Only recently, results of rockfill experimental tests were obtained which were comparable with the results of the field measurements. They demonstrate that depending on the stress state the rockfill linear deformation modulus may reach 700 МPа. The processing of the results of those experiments made it possible to determine the parameters on the non-linear model describing the deformation of rockfill in the dam body. Conclusions. The obtained data allows for enhancement of the validity of rockfill dams SSS analyses, as well as for studying of the impact of the non-linear character of the rockfill deformation on the SSS of reinforced concrete faces of rockfill dams.

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Climate-dependent propagation of precipitation uncertainty into the water cycle

10.5194/hess-2019-660 ◽

2020 ◽

Author(s):

Ali Fallah ◽

Sungmin O ◽

Rene Orth

Keyword(s):

Hydrological Model ◽

Water Cycle ◽

State Of The Art ◽

Rain Gauge ◽

Multiple Sources ◽

Current State ◽

Wide Range ◽

The Impact ◽

Conceptual Hydrological Model

Abstract. Precipitation is a crucial variable for hydro-meteorological applications. Unfortunately, rain gauge measurements are sparse and unevenly distributed, which substantially hampers the use of in-situ precipitation data in many regions of the world. The increasing availability of high-resolution gridded precipitation products presents a valuable alternative, especially over gauge-sparse regions. Nevertheless, uncertainties and corresponding differences across products can limit the applicability of these data. This study examines the usefulness of current state-of-the-art precipitation datasets in hydrological modelling. For this purpose, we force a conceptual hydrological model with multiple precipitation datasets in > 200 European catchments. We consider a wide range of precipitation products, which are generated via (1) interpolation of gauge measurements (E-OBS and GPCC V.2018), (2) combination of multiple sources (MSWEP V2) and (3) data assimilation into reanalysis models (ERA-Interim, ERA5, and CFSR). For each catchment, runoff and evapotranspiration simulations are obtained by forcing the model with the various precipitation products. Evaluation is done at the monthly time scale during the period of 1984–2007. We find that simulated runoff values are highly dependent on the accuracy of precipitation inputs, and thus show significant differences between the simulations. By contrast, simulated evapotranspiration is generally much less influenced. The results are further analysed with respect to different hydro-climatic regimes. We find that the impact of precipitation uncertainty on simulated runoff increases towards wetter regions, while the opposite is observed in the case of evapotranspiration. Finally, we perform an indirect performance evaluation of the precipitation datasets by comparing the runoff simulations with streamflow observations. Thereby, E-OBS yields the best agreement, while furthermore ERA5, GPCC V.2018 and MSWEP V2 show good performance. In summary, our findings highlight a climate-dependent propagation of precipitation uncertainty through the water cycle; while runoff is strongly impacted in comparatively wet regions such as Central Europe, there are increasing implications on evapotranspiration towards drier regions.

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NeAT: a Nonlinear Analysis Toolbox for Neuroimaging

Neuroinformatics ◽

10.1007/s12021-020-09456-w ◽

2020 ◽

Vol 18 (4) ◽

pp. 517-530

Author(s):

Adrià Casamitjana ◽

◽

Verónica Vilaplana ◽

Santi Puch ◽

Asier Aduriz ◽

...

Keyword(s):

Linear Models ◽

Brain Aging ◽

Nonlinear Effects ◽

Brain Morphology ◽

Model Estimation ◽

Wide Range ◽

Non Linear ◽

Linear Effects ◽

Interaction With Age ◽

User Friendly

Abstract NeAT is a modular, flexible and user-friendly neuroimaging analysis toolbox for modeling linear and nonlinear effects overcoming the limitations of the standard neuroimaging methods which are solely based on linear models. NeAT provides a wide range of statistical and machine learning non-linear methods for model estimation, several metrics based on curve fitting and complexity for model inference and a graphical user interface (GUI) for visualization of results. We illustrate its usefulness on two study cases where non-linear effects have been previously established. Firstly, we study the nonlinear effects of Alzheimer’s disease on brain morphology (volume and cortical thickness). Secondly, we analyze the effect of the apolipoprotein APOE-ε4 genotype on brain aging and its interaction with age. NeAT is fully documented and publicly distributed at https://imatge-upc.github.io/neat-tool/.

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Cybersecurity of Microgrid: State-of-the-Art Review and Possible Directions of Future Research

Applied Sciences ◽

10.3390/app11219812 ◽

2021 ◽

Vol 11 (21) ◽

pp. 9812

Author(s):

Norziana Jamil ◽

Qais Saif Qassim ◽

Farah Aqilah Bohani ◽

Muhamad Mansor ◽

Vigna Kumaran Ramachandaramurthy

Keyword(s):

Critical Infrastructure ◽

State Of The Art ◽

Advanced Technology ◽

Future Research ◽

Electrical System ◽

Comprehensive Review ◽

Electrical Systems ◽

Wide Range ◽

Future Work ◽

The Impact

The infrastructure of and processes involved in a microgrid electrical system require advanced technology to facilitate connection among its various components in order to provide the intelligence and automation that can benefit users. As a consequence, the microgrid has vulnerabilities that can expose it to a wide range of attacks. If they are not adequately addressed, these vulnerabilities may have a destructive impact on a country’s critical infrastructure and economy. While the impact of exploiting vulnerabilities in them is understood, research on the cybersecurity of microgrids is inadequate. This paper provides a comprehensive review of microgrid cybersecurity. In particular, it (1) reviews the state-of-the-art microgrid electrical systems, communication protocols, standards, and vulnerabilities while highlighting prevalent solutions to cybersecurity-related issues in them; (2) provides recommendations to enhance the security of these systems by segregating layers of the microgrid, and (3) identifies the gap in research in the area, and suggests directions for future work to enhance the cybersecurity of microgrids.

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Linear and Non-Linear Models for Remotely-Sensed Hyperspectral Image Visualization

Remote Sensing ◽

10.3390/rs12152479 ◽

2020 ◽

Vol 12 (15) ◽

pp. 2479

Author(s):

Radu-Mihai Coliban ◽

Maria Marincaş ◽

Cosmin Hatfaludi ◽

Mihai Ivanovici

Keyword(s):

Linear Model ◽

Linear Models ◽

Hyperspectral Image ◽

Point Of View ◽

Hyperspectral Images ◽

Image Visualization ◽

Color Formation ◽

Amount Of Information ◽

Non Linear ◽

The Impact

The visualization of hyperspectral images still constitutes an open question and may have an important impact on the consequent analysis tasks. The existing techniques fall mainly in the following categories: band selection, PCA-based approaches, linear approaches, approaches based on digital image processing techniques and machine/deep learning methods. In this article, we propose the usage of a linear model for color formation, to emulate the image acquisition process by a digital color camera. We show how the choice of spectral sensitivity curves has an impact on the visualization of hyperspectral images as RGB color images. In addition, we propose a non-linear model based on an artificial neural network. We objectively assess the impact and the intrinsic quality of the hyperspectral image visualization from the point of view of the amount of information and complexity: (i) in order to objectively quantify the amount of information present in the image, we use the color entropy as a metric; (ii) for the evaluation of the complexity of the scene we employ the color fractal dimension, as an indication of detail and texture characteristics of the image. For comparison, we use several state-of-the-art visualization techniques. We present experimental results on visualization using both the linear and non-linear color formation models, in comparison with four other methods and report on the superiority of the proposed non-linear model.

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On a Simple Graphical Approach to Modelling Economic Fluctuations with an Application to United Kingdom Price Inflation, 1265 to 2005

Annals of Actuarial Science ◽

10.1017/s1748499500000075 ◽

2006 ◽

Vol 1 (1) ◽

pp. 103-128

Author(s):

W. S. Chan ◽

M. W. Ng ◽

H. Tong

Keyword(s):

Time Series ◽

United Kingdom ◽

Structural Changes ◽

Linear Models ◽

Stochastic Modelling ◽

Economic Time Series ◽

Price Inflation ◽

Non Linear ◽

Economic Time ◽

The Impact

ABSTRACTStructural instability in economic time series is widely reported in the literature. It is most prevalent in such series as price indices and inflation related data. Many methods have been developed for analysing and modelling structural changes in a univariate time series model. However, most of them assume that the data are generated by one fixed type (linear or non-linear) of the time series processes. This paper proposes a strategy for modelling different segments of an economic time series by different linear or non-linear models. A graphical procedure is suggested for detecting the model change points. The proposed procedure is illustrated by modelling annual United Kingdom price inflation series over the period 1265 to 2005. Stochastic modelling of inflation rates is an important topic to actuaries for dealing with long-term index linked insurance business. The proposed method suggests dividing the U.K. inflation series into four segments for modelling. Inflation projections based on the latest segment of the data are obtained through simulations. To get a better understanding of the impact of structural changes on inflation projections we also perform a forecasting study.

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On Interaction Effects: The Case of Heckit and Two-Part Models

Jahrbücher für Nationalökonomie und Statistik ◽

10.1515/jbnst-2013-0104 ◽

2013 ◽

Vol 233 (1) ◽

Author(s):

Manuel Frondel ◽

Colin Vance

Keyword(s):

Linear Models ◽

Explanatory Variable ◽

Large Fraction ◽

Sample Selection ◽

Interaction Effects ◽

Marginal Effect ◽

Substantive Content ◽

Interaction Terms ◽

Non Linear ◽

The Impact

SummaryInteraction effects capture the impact of one explanatory variable on the marginal effect of another explanatory variable. To explore interaction effects, so-called interaction terms are typically included in estimation specifications. While in linear models the effect of a marginal change in the interaction term is equal to the interaction effect, this equality generally does not hold in non-linear specifications (Ai/Norton 2003). This paper provides for a general derivation of interaction effects in both linear and non-linear models and calculates the formulae of the interaction effects resulting from Heckman’s sample selection model as well as the Two- Part Model, two regression models commonly applied to data with a large fraction of either missing or zero values in the dependent variable. Drawing on a survey of automobile use from Germany, we argue that while it is important to test for the significance of interaction effects, their size conveys limited substantive content. More meaningful, and also more easy to grasp, are the conditional marginal effects pertaining to two variables that are assumed to interact.

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Interpretable machine learning prediction of all-cause mortality

10.1101/2021.01.20.21250135 ◽

2021 ◽

Author(s):

Wei Qiu ◽

Hugh Chen ◽

Ayse Berceste Dincer ◽

Su-In Lee

Keyword(s):

Machine Learning ◽

Risk Factors ◽

Prediction Accuracy ◽

Linear Models ◽

Age Groups ◽

Mortality Prediction ◽

Linear Relationships ◽

All Cause Mortality ◽

Non Linear ◽

The Impact

AbstractExplainable artificial intelligence provides an opportunity to improve prediction accuracy over standard linear models using “black box” machine learning (ML) models while still revealing insights into a complex outcome such as all-cause mortality. We propose the IMPACT (Interpretable Machine learning Prediction of All-Cause morTality) framework that implements and explains complex, non-linear ML models in epidemiological research, by combining a tree ensemble mortality prediction model and an explainability method. We use 133 variables from NHANES 1999–2014 datasets (number of samples: n = 47, 261) to predict all-cause mortality. To explain our model, we extract local (i.e., per-sample) explanations to verify well-studied mortality risk factors, and make new discoveries. We present major factors for predicting x-year mortality (x = 1, 3, 5) across different age groups and their individualized impact on mortality prediction. Moreover, we highlight interactions between risk factors associated with mortality prediction, which leads to findings that linear models do not reveal. We demonstrate that compared with traditional linear models, tree-based models have unique strengths such as: (1) improving prediction power, (2) making no distribution assumptions, (3) capturing non-linear relationships and important thresholds, (4) identifying feature interactions, and (5) detecting different non-linear relationships between models. Given the popularity of complex ML models in prognostic research, combining these models with explainability methods has implications for further applications of ML in medical fields. To our knowledge, this is the first study that combines complex ML models and state-of-the-art feature attributions to explain mortality prediction, which enables us to achieve higher prediction accuracy and gain new insights into the effect of risk factors on mortality.

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Machine learning for air quality prediction using meteorological and traffic related features

Journal of Ambient Intelligence and Smart Environments ◽

10.3233/ais-200572 ◽

2020 ◽

Vol 12 (5) ◽

pp. 379-391

Author(s):

Ihsane Gryech ◽

Mounir Ghogho ◽

Hajar Elhammouti ◽

Nada Sbihi ◽

Abdellatif Kobbane

Keyword(s):

Machine Learning ◽

Air Quality ◽

Linear Models ◽

Prediction Models ◽

Road Traffic ◽

Support Vector ◽

Air Quality Prediction ◽

Monitoring Process ◽

Non Linear ◽

The Impact

The presence of pollutants in the air has a direct impact on our health and causes detrimental changes to our environment. Air quality monitoring is therefore of paramount importance. The high cost of the acquisition and maintenance of accurate air quality stations implies that only a small number of these stations can be deployed in a country. To improve the spatial resolution of the air monitoring process, an interesting idea is to develop data-driven models to predict air quality based on readily available data. In this paper, we investigate the correlations between air pollutants concentrations and meteorological and road traffic data. Using machine learning, regression models are developed to predict pollutants concentration. Both linear and non-linear models are investigated in this paper. It is shown that non-linear models, namely Random Forest (RF) and Support Vector Regression (SVR), better describe the impact of traffic flows and meteorology on the concentrations of pollutants in the atmosphere. It is also shown that more accurate prediction models can be obtained when including some pollutants’ concentration as predictors. This may be used to infer the concentrations of some pollutants using those of other pollutants, thereby reducing the number of air pollution sensors.

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CHAOS THEORY: IMPACT ON AND APPLICATIONS IN MEDICINE

Journal of Health and Allied Sciences NU ◽

10.1055/s-0040-1703623 ◽

2012 ◽

Vol 02 (04) ◽

pp. 93-99

Author(s):

Arunachalam Kumar

Keyword(s):

Chaos Theory ◽

Chaotic Systems ◽

Linear Models ◽

Clinical Diagnostics ◽

Health Science ◽

New Science ◽

Physiological Processes ◽

Non Linear ◽

Laboratory Investigations ◽

The Impact

AbstractThe advent of a revolutionary and exciting theory that proposes non-linear models as effective adjuncts to linear mathematics in interpreting long-held scientific tenets has provided novel and innovative designs and methodologies that help the medical world to better understand inferences from laboratory investigations, physiological processes, pharmarmaco-therapeutics and clinical diagnostics.This overview outlines a few salient areas in medicine that have successfully applied the principles of the chaos theory. Chaotic systems have been shown to operate in quite a few physiological processes. The impact and implications of the new science on the future course of medical diagnosticsand health science systems as a whole cannot be overstressed.

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