We investigate the drivers of 40-150 keV hourly electron flux at geostationary orbit (GOES 13) using autoregressive moving average transfer functions (ARMAX) multiple regression models which remove the confounding effect of diurnal cyclicity and allow assessment of each parameter independently. By taking logs of the variables, we create nonlinear models. While many factors show high correlation with flux in single variable analysis (substorms, ULF waves, solar wind velocity (), pressure (), number density () and electric field ( ), IMF , , and ), ARMAX models show substorms are the dominant influence at 40-75 keV and over 20-12 MLT, with little difference seen between disturbed and quiet periods. The influence is positive post-midnight, negative post-noon. Pressure shows a negative influence, strongest at 150 keV. ULF waves are a more modest influence than suggested by single variable correlation. and show little effect when other variables are included. Using path analysis, we calculate the summed direct and indirect influences through the driving of intermediate parameters. Pressure shows a summed direct and indirect influence nearly half that of the direct substorm effect. , , and , as indirect drivers, are equally influential. While simple correlation or neural networks can be used for flux prediction, neither can effectively identify drivers. Instead, consideration of physical influences, removing cycles that artificially inflate correlations, and controlling the effects of other parameters gives a clearer picture of which are most influential in this system.
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http://dx.doi.org/10.1029/2022JA030538 | DOI Listing |
J Environ Manage
January 2025
Control Engineering Research Group, Environmental and Chemical Engineering Research Unit, University of Oulu, P.O. Box 4300, 90014 Oulu, Finland. Electronic address:
Continuous monitoring of chemical oxygen demand (COD) is essential to ensure efficient and sustainable wastewater treatment and regulatory compliance. However, traditional hardware measurements are laborious, infrequent and costly. In this research, a cost-effective real-time alternative is presented.
View Article and Find Full Text PDFSci Rep
September 2024
Department of Public Finance, National Taipei University, 151, University Rd., San Shia, New Taipei City, 23741, Taiwan.
Osteoarthritis (OA) is one of the most prevalent musculoskeletal diseases in Taiwan, posing a significant public health challenge. In recent years, outdoor air pollution has become an increasingly critical global health issue. Asian Dust Storms (ADS) are known to exacerbate various health conditions due to elevated levels of particulate matter and other pollutants.
View Article and Find Full Text PDFJ Glob Health
January 2024
Graduate School of Nursing Science, St. Luke's International University, Tokyo, Japan.
Background: The objective of this study was to predict when Bangladesh would achieve Sustainable Development Goal Target 3.1, which is to reduce the maternal mortality ratio (MMR) to less than 70 per 100 000 live births.
Methods: We used secondary data from the 1993 to 2017 Bangladesh Demographic and Health Surveys and other sources to project the MMR until 2060 under several scenario assumptions using an autoregressive moving average model with exogenous variables (ARMAX).
J Contam Hydrol
November 2023
Academician Workstation for Big Data in Ecology and Environment, Environment Research Institute, Shandong University, Qingdao 266237, China.
Intelligent prediction of water quality plays a pivotal role in water pollution control, water resource protection, emergency decision-making for sudden water pollution incidents, tracking and evaluation of water quality changes in river basins, and is crucial to ensuring water security. The primary methodology employed in this paper for water quality prediction is as follows: (1) utilizing the comprehensive pollution index method and Mann-Kendall (MK) trend analysis method, an assessment is made of the pollution status and change trend within the basin, while simultaneously extracting the principal water quality parameters based on their respective pollution share rates; (2) employing the spearman method, an analysis is conducted to identify the influential factors impacting each key parameter; (3) subsequently, a water quality parameter prediction model, based on Long Short-Term Memory (LSTM) analysis, is constructed using the aforementioned driving factor analysis outcomes. The developed LSTM model in this study showed good prediction performance.
View Article and Find Full Text PDFWarfarin is a challenging drug to administer due to the narrow therapeutic index of the International Normalized Ratio (INR), the inter- and intra-variability of patients, limited clinical data, genetics, and the effects of other medications. To predict the optimal warfarin dosage in the presence of the aforementioned challenges, we present an adaptive individualized modeling framework based on model (In)validation and semi-blind robust system identification. The model (In)validation technique adapts the identified individualized patient model according to the change in the patient's status to ensure the model's suitability for prediction and controller design.
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