Kp Index Research Articles

Detrimental impact of solar and geomagnetic activity on plasma B-complex vitamins in the VA normative aging study cohort

It has been hypothesized that ultraviolet (UV) radiation can lead to depletion of plasma folate and B12 vitamin, but few studies have investigated effects of other parameters of solar and geomagnetic activity (SGA). We investigated the association between four SGA parameters—interplanetary magnetic field (IMF), sunspot number (SSN), Kp index, and ground shortwave solar radiation (SWR)—and three plasma B-complex vitamins—folate, B6, and B12—in 910 participants from the Normative Aging Study (NAS) between 1998 and 2017. Mixed-effects regression models were used for 1- to 28-moving day averages of SGA exposure, adjusted for covariates. We compared the impact of SGA in individuals under higher and lower B-complex supplementation (> or < 50th quartile). Our findings show that increases in solar activity variables IMF and SSN were found to be significantly associated with decreases in B12 vitamin. IMF and SSN were associated with decrease in folate levels, especially in individuals under higher levels of B-complex supplementation. No associations were found for SWR and Kp index. To our knowledge, this is the first study that demonstrated the detrimental impact of solar activity on plasma B12 and folate in a large cohort. These findings have clinical implications during periods of high solar activity.

Contraband classification method for X-ray security images considering sample imbalance

X-ray security image contraband classification is widely used to assist in maintaining aviation and transportation security. This paper suggests an end-to-end X-ray security inspection image classification method that takes sample imbalance into account in order to address the issues of different scales of contraband in X-ray images, challenging samples, and unbalanced positive and negative samples inherent in passenger baggage security inspection. The feature fusion module is used to enhance the model's ability to express picture edge and texture features while the multi-scale feature extraction network is used to capture the features of numerous sorts of illegal goods with various scales. Based on the cost-sensitive idea, the loss function is designed to solve the problem of dataset imbalance, and improve the classification accuracy of difficult samples. experimental results of the subset constructed on the public dataset SIXray show that the proposed method improves the mean AP index by compared with the current optimal end-to-end classification model, especially for hard-to-classify samples such as scissors, the AP index has a significant improvement effect.

Estimating the drift velocity of plasma bubbles in airglow images using the scale invariant feature transform and the speeded up robust feature algorithms

Equatorial plasma bubbles (EPBs) are ionospheric irregularities that can significantly impact radio communication, causing signal scintillation that leads to signal loss and error in position calculations. In this study, we introduced a new technique to estimate the eastward and northward drift velocities of the EPB from ASI data by tracking the Scale Invariant Feature Transform (SIFT) and the Speeded Up Robust Features (SURF) points. The technique has been tested by studying multiple EPBs events detected over the Brazilian sector during the autumn equinox, spring equinox and summer solstice. The chosen events occurred during quiet nights (Kp index ≤ 3) and were observable through two All-Sky imager (ASI) stations. Moreover, the study shows the performance of the technique under different circumstances. It achieves optimal performance on clear-sky nights; however, cloud presence within the ASI field of view increases the error within the estimated values. The estimated velocities were found to be within the range reported by previous studies in the Brazilian sector.

Clinical and imaging predictors for the development of diabetes mellitus following a single episode of acute pancreatitis in youth

BackgroundAcute pancreatitis (AP) increases the risk of diabetes mellitus (DM). Our aim was to identify clinical, laboratory and imaging predictors of preDM/DM in youth post index AP. MethodsThis was a prospective cohort study of patients ≤21 years-old with an index admission for AP and follow up at 3 and/or 12 months. Clinical laboratory values, imaging findings, admission course, and plasma chemokine and cytokine measures collected at index admission were tested for association with preDM/DM development. A multivariable regression model was used to predict preDM/DM. ResultsAmong 187 enrolled participants, 137 (73 %) and 144 (77 %) underwent DM screening at 3 and 12 months respectively, and 137 (73 %) had imaging available. PreDM/DM occurred in 22/137 (16 %; preDM n = 21, DM n = 1) at 3 months and 23/144 (16 %; preDM n = 18, DM n = 5) participants at 12 months. Univariate associations with preDM/DM at 12 months included: severe AP (SAP) (52 % preDM/DM vs. 17 % no DM; p = 0.0008), median [IQR] IL-6 (910 pg/ml [618–3438] vs. 196 pg/ml [71–480], p < 0.05) and CRP (4.16 mg/L [1.67–10.7] vs. 1.55 mg/L [0.4–3.68], p = 0.1) at time of AP attack. The optimal multivariable model to predict preDM/DM included with clinical variables was severe acute pancreatitis (SAP), c reactive protein (CRP), interleukin-6 (IL-6), and age [AUC = 0.80; (0.70, 0.88)]. Including imaging markers, the ideal model included SAP, CRP, IL-6, subcutaneous fat area, age and presence of autoimmune disease with an AUC [0.82 (0.71, 0.90)]. ConclusionsDevelopment of preDM/DM following an index AP episode can be predicted by baseline AP severity, baseline CRP, IL-6 levels, and subcutaneous fat area.

Graph-enabled spatio-temporal transformer for ionospheric prediction

The ionosphere, crucial for satellite navigation and radio communication, is highly sensitive to solar and geomagnetic activities. Traditional ionospheric prediction models often struggle to capture its complexities. This paper introduces a Graph-Enabled Spatio-temporal transformer (GEST) model, designed to predict the Total electron content (TEC) in the ionosphere with higher accuracy. The GEST model innovatively integrates six dynamic space weather parameters, including the Kp index, and refines grid distances and weights in the Global ionospheric maps (GIM). Leveraging the efficiency of the Transformer architecture in processing spatio-temporal data, the GEST model adapts to varying ionospheric conditions and provides superior precision. Our evaluations, based on data from the Center for orbit determination in Europe (CODE), demonstrate that the GEST model significantly outperforms traditional methods and other intelligent algorithms such as conv-LSTM. It shows marked improvements in Mean absolute error (MAE), Root mean square error (RMSE), Pearson correlation coefficient (CC), and Mean absolute percentage error (MAPE), particularly during periods of intense solar and geomagnetic activity. These advancements highlight the GEST model’s substantial contributions to the field of ionospheric prediction technology, offering a robust tool for enhancing the reliability of satellite-based systems.

An investigation into the influence of solar flares and geomagnetic storms on the F2 layer of the ionosphere in Western Europe during March 2024

On March 23 and 24, 2024, the M-class and X-class solar storms significantly intensified. Subsequently, from March 24 to 26, the monitoring station’s Kp index notably increased, at times exceeding 8, indicating that a severe geomagnetic storm was occurring. During this period, the IGS Global Ionosphere Maps (GIMs) displayed a substantial decrease in Total Electron Content (TEC) in Western Europe due to the impact of the intense geomagnetic storms. To investigate this occurrence, data from the Lowell GIRO Data Center were utilized. The analysis revealed that a concentrated solar flare eruption on March 23, 2024, triggered geomagnetic storm events on Earth, causing significant ionospheric anomalies in Western European countries. Specifically, compared to typical levels, parameters such as foF2, m3000F2, and TEC values experienced significant reductions in Western European countries. Moreover, the hmF2 data exhibited higher values at night and lower values during the day, deviating from the expected diurnal pattern. Analysis of the Rate of TEC Index (ROTI) indicated varying effects of geomagnetic storms across different latitudes in Western Europe. Through detailed graphical analysis, this study elucidates the alterations in the ionosphere under geomagnetic storm conditions in Western Europe, shedding light on the dynamic behavior of the ionosphere during a specific timeframe.

Unusual Forbush Decreases and Geomagnetic Storms on 24 March, 2024 and 11 May, 2024

As the current solar cycle 25 progresses and moves towards solar maxima, solar activity is increasing and extreme space weather events are taking place. Two severe geomagnetic storms accompanied by two large Forbush decreases in galactic cosmic ray intensity were recorded in March and May, 2024. More precisely, on 24 March 2024, a G4 (according to the NOAA Space Weather Scale for Geomagnetic Storms) geomagnetic storm was registered, with the corresponding geomagnetic indices Kp and Dst equal to 8 and −130 nT, respectively. On the same day, the majority of ground-based neutron monitor stations recorded an unusual Forbush decrease. This event stands out from a typical Forbush decrease because of its high amplitude decrease phase and rapid recovery phase, i.e., 15% decrease and an extremely rapid recovery of 10% within 1.5 h, as recorded at the Oulu neutron monitor station. Furthermore, on 10–13 May 2024, an unusual G5 geomagnetic storm (geomagnetic indices Kp = 9 and Dst = −412 nT) was registered (the last G5 storm had been observed in 2003). In addition, the polar neutron monitor stations recorded a Ground Level Enhancement (GLE74) during the recovery phase of a large Forbush decrease of 15%, which started on 10 May 2024. In this study, a detailed analysis of these two severe events in regard to the accompanying solar activity, interplanetary conditions and solar energetic particle events is provided. Moreover, the results of the NKUA “GLE Alert++ system”, the NKUA/IZMIRAN “FD Precursory Signals” method and the NKUA “ap Prediction tool” concerning these events are presented.

Global Thermospheric Infrared Response to the Mother's Day Weekend Extreme Storm of 2024

AbstractEarth experienced the strongest geomagnetic storm in 20 years over 10–13 May 2024. The Ap and Dst geomagnetic indices were 273 and −291.94 nT on 11 May. The Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) instrument on the Thermosphere‐Ionosphere‐Mesosphere Energetics and Dynamics satellite observed significant enhancement in thermospheric infrared emission at 15, 5.3, 4.3, and 1.27 μm. On 11 May the daily global power radiated by nitric oxide (NO) at 5.3 μm was 1.41 TW and by carbon dioxide (CO2) at 15 μm was 1.35 TW. These are the largest single day power values observed by SABER in 22 years and the first time the daily power radiated by NO exceeded that of CO2. The total infrared power (above background) radiated during the storm was 2.64 TW (2.28 × 1017 J). Significant enhancement in limb radiance observed at 4.3 μm (to 250 km tangent height) is likely indicative of NO + formation during the storm.

Analysis of solar plasma parameters during intense geomagnetic storm (SYM/H

We utilize numerous solar plasma and field parameters with geomagnetic indices for the geomagnetic storm in 25 solar cycles. Using Planetary index (Kp) having 3 h data, Dst index hourly data, Symmetric horizontal field (SYM/H) minute resolution data and solar plasma measurements from multiple satellites, examine the chain of significant events that occurred in the year 2023. Statical analysis of geomagnetic provisional activity Sym/H has better results than Kp index during various main phases of geomagnetic activity. The statistical correlation technique is used to examine the relationship between Interplanetary parameters and geomagnetic indices during major geomagnetic storms. This research improves our comprehension of solar–terrestrial interactions and provides valuable insights for enhancing space weather forecasting models.

Analysis of TEC variations and prediction of TEC by RNN during Indonesian earthquakes occurred from 2004 to 2024 and comparison with IRI-2020 model

The Ionosphere, a crucial region of Earth’s atmosphere extending from approximately 50 to 1000 km above the Earth’s surface, plays a pivotal role in global communication, navigation, and satellite-based technologies. Among its various parameters, Total Electron Content (TEC) stands out as a key metric, representing the integrated electron density along a specific path through the ionosphere. This research focuses on analyzing TEC variations during Indonesian earthquakes between 2004 and 2024 using a Recurrent Neural Network (RNN) model. The study investigates the ionospheric response to seismic activity, particularly in Indonesia, a region prone to earthquakes due to its location within the Pacific Ring of Fire and complex tectonic setting involving multiple lithospheric plates. The RNN model, designed to predict TEC variations during earthquakes, utilizes data on solar and geomagnetic activity, including the Kp index, solar wind, and geomagnetic storm activity, obtained from the OMNIWEB data centre, along with TEC data from the IONOLAB data servers for the BAKO station in Cibinong, Indonesia. The earthquakes considered for observation include significant events such as the 2004 Indian Ocean earthquake and tsunami, the 2012 Wharton Basin earthquake, and subsequent seismic events up to 2024. Statistical metrics such as Mean Bias Deviation (MBD), Relative Error (REL_E), Absolute Error (ABS_E), and Root Mean Square Error (RMSE) are used to evaluate the predictive capability of the RNN model and compare it with the International Reference Ionosphere (IRI) 2020 model, a widely accepted empirical model for describing ionospheric parameters. The comparison reveals the performance of the RNN model in capturing TEC variations during seismic events, providing valuable insights for earthquake monitoring and early warning systems.

Performance Analyzes of Thermodynamic Indices and Atmospheric Parameters in Thunderstorm and Non-thunderstorm Days in Istanbul, Turkey

This study aims to analyze the thunderstorm (TS) events in the megacity Istanbul by using thermodynamic indices and atmospheric stability parameters for the period of 2001–2022. It was determined that TS events did not show any trend on an annual basis, mostly (%69) occurred in the warm season (May–September), and mostly (%93) lasted for a few hours (0–3 h). The thermodynamic indices and atmospheric stability parameters used in the study are Showalter Index (SI), Lifted Index (LI), Severe Weather Threat Index (SWEAT), K-Index (KI), Totals Totals Index (TTI), Convective Available Potential Energy (CAPE), Convective Inhibition (CIN), and Bulk Richardson Number (BRN). Annual and seasonal analyzes of all indices and parameters were performed for TS and non-TS events. Significant differences were found in both average, maximum, and minimum values. The Probability of Detection (POD), False Alarm Ratio (FAR), Miss Rate (MR), Critical Success Index (CIS), Hiedke Skill Score (HSS), and True Skill Score (TSS) were used to analyze the success of the threshold values presented in the literature in detecting TS events. Then, the seasonal successes of these threshold values were tested. It was observed that the performance of the selected indices varied across seasons. The highest predictive skill was generally observed during the summer season, with the POD value ranging between 0.58 and 0.97 and the TSS value varying between 0.32 and 0.57. Conversely, the lowest predictive skill was typically observed during the winter season, where the POD value ranged from 0.00 to 0.75 and the TSS value varied between 0.00 and 0.40. The ideal threshold values were determined for indices and parameters by increasing or decreasing the existing threshold values at certain rates. Success increases of up to 15% in skill scores for the proposed threshold values.

Airborne particle radioactivity during desert dust days in Cyprus

Mediterranean countries are often affected by desert dust storms, which have significant effects on the environment and public health. We compared airborne particle radioactivity levels during desert dust and non-dust days in Cyprus.Gross α- and β-radioactivity from Total Suspended Particle (TSP) samples, collected at two urban routine monitoring stations in Limassol and Nicosia, were available for the period 2017–2020 and 2008–2020, respectively. Radionuclides 137Cs and 40K, from TSP samples, were also available from a semi-industrial monitoring station in Nicosia during 2008–2020. Information on desert dust presence, dust origin, particulate matter (PM) levels, and solar activity (KP index and solar sunspot numbers - SSN) were also obtained. We used linear regression models adjusting for seasonality and long-term trends, and solar activity to assess the effect of dust storms on TSP gross α- and β-, and 137Cs and 40K radioactivity levels.Gross α- and β-radioactivity, and 137Cs and 40K radioactivity levels were significantly higher on days with desert dust compared to days characterized with no influence of desert dust. Levels of gross α- and β-radioactivity during dust days were higher when dust originated from the Middle East deserts than from the Sahara Desert. The same trend was observed for the ratios 137Cs to 40K and 137Cs to PM10. Conversely, ratios of TSP gross α- and β-radioactivity to PM10 were significantly lower during desert dust days in comparison to days without dust influence.This study suggests that desert dust increase both TSP gross α- and β-radioactivity, as well as 137Cs and 40K radioactivity levels. Further studies should clarify the contribution of anthropogenic and other natural sources to the emission or transportation of particles radioactivity, to better mitigate future exposures.

A multifractal approach to understanding Forbush Decrease events: Correlations with geomagnetic storms and space weather phenomena

The Forbush decrease phenomenon has significant impacts on several environmental conditions, including interference in radio communications, satellite navigation systems, and the health of astronauts in space, among others. It is characterized by a temporary and noticeable reduction in the observed flux of galactic cosmic rays recorded at the Earth’s surface. This decrease occurs due to the modulation of cosmic rays through their interaction with shock waves generated by coronal mass ejections. As these shock waves traverse the interplanetary medium, which includes the solar wind and galactic cosmic rays, they exert compression forces on the cosmic ray flux, leading to a reduction in observed flux levels at Earth. This study investigates Forbush Decrease events across different solar cycles and explores their correlation with geomagnetic storm conditions using multifractal detrended fluctuation analysis. The findings indicate variations in the multifractal spectra for series under different geomagnetic storm conditions compared to the full Forbush decrease series. Moreover, it is observed that the amplitude of the multifractal spectrum is greater in the series that include events with a maximum Kp index exceeding 6, suggesting a significant influence of geomagnetic storm conditions on the fractality and variability of Forbush Decrease magnitudes.

Space Weather Effects on Heart Rate Variations: Sex Dependence

The effects of solar activity and the accompanying space weather events on human pathological conditions, physiological parameters and other psycho-physiological disturbances have been analyzed in numerous recent investigations. Moreover, many of these studies have particularly focused on the different physical reactions humans have, according to their sex, during variations in the physical environment. In the framework of the above, this work analyses heart rate data obtained from volunteers (687 men and 534 women) from three different regions (Athens, Piraeus and Heraklion) of Greece in relation to the geophysical activity and variations of environmental factors. Dst index and Ap index data, along with cosmic ray intensity data derived from the Athens Neutron Monitor Station (A.Ne.Mo.S.), were used. The study expands from April 2011 to January 2018, covering solar cycle 24. The ANalysis Of Variance (ANOVA) and the superimposed epochs methods were used in order to examine heart rate variations depending on sex. Results revealed that women tend to be more sensitive to physical environmental changes. Statistically significant results are related to the geomagnetic activity but were not obtained for cosmic ray variations.

A New Model of Electron Pitch Angle Distributions and Loss Timescales in the Earth's Radiation Belts

AbstractAs the number of satellites on orbit grows it is increasingly important to understand their operating environment. Physics‐based models can simulate the behavior of the Earth's radiation belts by solving a Fokker‐Planck equation. Three‐dimensional models use diffusion coefficients to represent the interactions between electromagnetic waves and the electrons. One‐dimensional radial diffusion models neglect the effects of energy diffusion and represent the losses due to the waves with a loss timescale. Both approaches may use pitch angle distributions (PADs) to create boundary conditions, to map observations from low to high equatorial pitch angles and to calculate phase‐space density from observations. We present a comprehensive set of consistent PADs and loss timescales for 2 ≤ L* ≤ 7, 100 keV ≤ E ≤ 5 MeV and all levels of geomagnetic activity determined by the Kp index. These are calculated from drift‐averaged diffusion coefficients that represent all the VLF waves that typically interact with radiation belt electrons and show good agreement with data. The contribution of individual waves is demonstrated; magnetosonic waves have little effect on loss timescales when lightning‐generated whistlers are present, and chorus waves contribute to loss even in low levels of geomagnetic activity. The PADs vary in shape depending on the dominant waves. When chorus is dominant the distributions have little activity dependence, unlike the corresponding loss timescales. Distributions peaked near 90° are formed by plasmaspheric hiss for L* ≤ 3 and E &lt; 1 MeV, and by EMIC waves for L* &gt; 3 and E &gt; 1 MeV. When hiss dominates, increasing activity broadens the distribution but when EMIC waves dominate increasing activity narrows the distribution.

Validation of Simulated Statistical Characteristics of Magnetosphere‐Ionosphere Coupling in Global Geospace Simulations Over an Entire Carrington Rotation

AbstractWe study the statistical features of magnetosphere‐ionosphere (M‐I) coupling using a two‐way M‐I model, the GT configuration of the Multiscale Atmosphere Geospace Environment (MAGE) model. The M‐I coupling characteristics, such as field‐aligned current, polar cap potential, ionospheric Joule heating, and downward Alfvénic Poynting flux, are binned according to the interplanetary magnetic field clock angles over an entire Carrington Rotation event between 20 March and 16 April 2008. The MAGE model simulates similar distributions of field‐aligned currents compared to empirical Weimer/AMPS models and Iridium observations and reproduces the Region 0 current system. The simulated convection potential agrees well with the Weimer empirical model and displays consistent two‐cell patterns with SuperDARN observations, which benefit from more extensive data sets. The Joule heating structure in MAGE is generally consistent with both empirical Cosgrove and Weimer models. Moreover, our model reproduces Joule heating enhancements in the cusp region, as presented in the Cosgrove model and observations. The distribution of the simulated Alfvénic Poynting flux is consistent with that observed by the FAST satellite in the dispersive Alfvén wave regime. These M‐I coupling characteristics are also binned by the Kp indices, indicating that the Kp dependence of these patterns in the M‐I model is more effective than the empirical models within the Carrington Rotation. Furthermore, the MAGE simulation exhibits an improved M‐I current‐voltage relation that closely resembles the Weimer model, suggesting that the updated global model is significantly improved in terms of M‐I coupling.

Statistical study of BDS-2/BDS-3 satellite signal quality and positioning accuracy during weak and moderate geomagnetic storms

We statistically study 10 weak to moderate geomagnetic storms that occurred since the 25th solar activity cycle to evaluate the quality of satellite signal and positioning accuracy of Beidou Navigation Satellite System (BDS). The results show that the PAS (Percentage of Affected Satellites) index, which integrates three scenarios: satellite signal loss, satellite signal loss of lock, and carrier phase signal anomaly, appears significant anomaly changes during weak and moderate geomagnetic storms, and its changing trend is closely related to the geomagnetic storm Kp index and Dst index, the correlation coefficient is greater than 0.75. Based on the results of PAS signal quality evaluation, the effect of PAS on the positioning error of Precision Point Positioning (PPP) of BDS-2/BDS-3 systems is analyzed and verified in conjunction with PAS. The results show that there is a strong agreement between the time of PAS anomaly and the time of PPP positioning accuracy anomaly. The positioning error of PPP increases by 9 to 44 times compared to the quiet period. The statistical results indicate that the PAS index is useful for the signal quality evaluation of the Beidou Navigation Satellite System under different space weather conditions.

Comparing Influences of Solar Wind, ULF Waves, and Substorms on 20 eV–2 MeV Electron Flux (RBSP) Using ARMAX Models

AbstractElectron fluxes (20 eV–2 MeV, RBSP‐A satellite) show reasonable simple correlation with a variety of parameters (solar wind, IMF, substorms, ultralow frequency (ULF) waves, geomagnetic indices) over L‐shells 2–6. Removing correlation‐inflating common cycles and trends (using autoregressive and moving average terms in an ARMAX analysis) results in a 10 times reduction in apparent association between drivers and electron flux, although many are still statistically significant (p &lt; 0.05). Corrected influences are highest in the 20 eV–1 keV and 1–2 MeV electrons, more modest in the midrange (2–40 keV). Solar wind velocity and pressure (but not number density), IMF magnitude (with lower influence of Bz), SME (a substorm measure), a ULF wave index, and geomagnetic indices Kp and SymH all show statistically significant associations with electron flux in the corrected individual ARMAX analyses. We postulate that only pressure, ULF waves, and substorms are direct drivers of electron flux and compare their influences in a combined analysis. SME is the strongest influence of these three, mainly in the eV and MeV electrons. ULF is most influential on the MeV electrons. Pressure shows a smaller positive influence and some indication of either magnetopause shadowing or simply compression on the eV electrons. While strictly predictive models may improve forecasting ability by including indirect driver and proxy parameters, and while these models may be made more parsimonious by choosing not to explicitly model time series behavior, our present analyses include time series variables in order to draw valid conclusions about the physical influences of exogenous parameters.

Machine learning techniques for estimation of Pc5 geomagnetic pulsations observed at geostationary orbits during solar cycle 23

Pc5 geomagnetic pulsations can accelerate electrons in the radiation belts, which can pose adverse threats to both astronauts and satellites in space. The estimation of Pc5 waves in space is crucial to radiation belt dynamics studies and will help mitigate these challenges. Here, we explore the advantages of the Feed-forward Neural Network (FFNN) and Random Forest (RF) algorithm for effective estimation of Pc5 geomagnetic pulsations observed in space at geostationary orbit during solar cycle 23. The dataset used in this study is the vector magnetic field measurements retrieved from the Geostationary Operational Environmental Satellite-10 (GOES-10) and the solar wind parameters: Bz and Vx component of the solar wind in the Geocentric Solar Ecliptic (GSE) coordinate system, proton density, flow pressure, and plasma beta obtained from the OMNI Web database during part of solar cycle 23. Pc5 geomagnetic pulsations were extracted from the toroidal component of the magnetic field time series using a bandpass Butterworth filter. The continuous wavelet transform (CWT) was utilized to study the characteristics of the extracted wave in the time-frequency domain for its validation. The validated Pc5 events were used as the target in the model's development, with the solar wind parameters as the inputs. In addition to the solar wind parameters, we included an attribute of the magnetic field time series as an input variable in the model. The dataset is carefully divided to ensure effective training and testing of the models. Finally, we trained both models using the same inputs and targets and explored their estimation abilities. The model was tested during the maximum, descending, and minimum phases of solar cycle 23. Both the FFNN and RF models have a similar estimation, with average cross-correlation score (R) values of 0.74 and 0.73 and corresponding average root mean squared error (RMSEs) of 0.16 nT and 0.67 nT, respectively. The model was deployed to investigate the response of Pc5 waves during three storm days in each testing year. The machine learning (ML) model outputs showed good coherence with the observed Pc5 waves. To validate the models, we studied the correlation between the estimated Pc5 events with the Kp index, and a good correlation was seen to exist between both events. This validates the good performance of the developed models. This work will aid in the study of radiation belt dynamics and the construction of electron depletion regions in the radiation belt.

Associations between solar and geomagnetic activity and cognitive function in the Normative Aging study

BackgroundStudies show that changes in solar and geomagnetic activity (SGA) influence melatonin secretion and the autonomic nervous system. We evaluated associations between solar and geomagnetic activity and cognitive function in the Normative Aging Study from 1992 to 2013. MethodsWe used logistic and linear generalized estimating equations and regressions to evaluate the associations between moving averages of sunspot number (SSN) and Kp index (a measure of geomagnetic activity) and a binary measure for Mini-Mental State Examination (MMSE) scores (≤25 or > 25) and six other cognitive tests as continuous measures, combined into one global composite score and considered separately. ResultsA one-IQR increase in same-day SSN and Kp index were associated with 17% (95% CI: 3%, 34%) and 19% (95% CI: 4%, 36%) increases in the odds of low MMSE score. We observed small increases in the global cognitive score with increasing SSN, although we observed decreases specifically in relation to the backwards digit span test. ConclusionsPeriods of high SGA were associated with cognitive function. SGA may not equally impact all aspects of cognitive function, as evidenced by differences in associations observed for the MMSE, global cognitive score, and individual cognitive tests. Given that much of the pathology of cognitive decline in the elderly remains unexplained, studies specifically targeting decline and with longer follow-up periods are warranted.