Heating Mechanisms for Intermittent Loops in Active Region Cores from AIA/SDO EUV Observations

We investigate intensity variations and energy deposition in five coronal loops in active region cores. These were selected for their strong variability in the AIA/SDO 94 Å intensity channel. We isolate the hot Fe XVIII and Fe XXI components of the 94 Å and 131 Å by modeling and subtracting the "warm" contributions to the emission. HMI/SDO data allow us to focus on "inter-moss" regions in the loops. The detailed evolution of the inter-moss intensity time series reveals loops that are impulsively heated in a mode compatible with a nanoflare storm, with a spike in the hot 131 Å signals leading and the other five EUV emission channels following in progressive cooling order. A sharp increase in electron temperature tends to follow closely after the hot 131 Å signal confirming the impulsive nature of the process. A cooler process of growing emission measure follows more slowly. The Fourier power spectra of the hot 131 Å signals, when averaged over the five loops, present three scaling regimes with break frequencies near 0.1 min–1 and 0.7 min–1. The low frequency regime corresponds to 1/f noise; the intermediate indicates a persistent scaling process and the high frequencies show white noise. Very similar results are found for the energy dissipation in a 2D "hybrid" shell model of loop magneto-turbulence, based on reduced magnetohydrodynamics, that is compatible with nanoflare statistics. We suggest that such turbulent dissipation is the energy source for our loops

Intermittent and Scale-Invariant Intensity Fluctuations in Hot Coronal Loops

ABSTRACT BODY: To resolve outstanding questions on heating of coronal loops, we study intensity fluctuations in inter-moss portions of active region core loops as observed with AIA/SDO. The 94Å fluctuations (Figure 1) have structure on timescales shorter than radiative and conductive cooling times. Each of several strong 94Å brightenings is followed after ~8 min by a broader peak in the cooler 335Å emission. This indicates that we see emission from the hot component of the 94Å contribution function. No hotter contributions appear, and we conclude that the 94Å intensity can be used as a proxy for energy injection into the loop plasma. The probability density function of the observed 94Å intensity has 'heavy tails' that approach zero more slowly than the tails of a normal distribution. Hence, large fluctuations dominate the behavior of the system. The resulting 'intermittence' is associated with power-law or exponential scaling of the related variables, and these in turn are associated with turbulent phenomena. The intensity plots in Figure 1 resemble multifractal time series, which are common to various forms of turbulent energy dissipation. In these systems a single fractal dimension is insufficient to describe the dynamics and instead there is a spectrum of fractal dimensions that quantify the self-similar properties. Figure 2 shows the multifractal spectrum from our data to be invariant over timescales from 24 s to 6.4 min. We compare these results to outputs from theoretical energy dissipation models based on MHD turbulence, and in some cases we find substantial agreement, in terms of intermittence, multifractality and scale invariance. Figure 1. Time traces of 94A intensity in the inter-moss portions of four AR core loops. Figure 2. Multifractal spectra showing timescale invariance. The four cases of Figure 1 are included.

Evolution of Hot Loops in an Active Region Core Observed with the SDO Atmospheric Image Assembly

Evidence has accumulated of high temperature (> 4 MK) coronal emission in active region cores that corresponds to structures in equilibrium. Other studies have found evidence of evolving loops. We investigate the EUV intensity and temperature variations of short coronal loops observed in the core of NOAA Active Region 11250 on 13 July 2011. The loops, which run directly between the AR opposite polarities, are first detectable in the 94Å band of Fe XVIII, implying an effective temperature ~ 7 MK. The low temperature component of the 94 Å signal is modeled in terms of a linear superposition of the 193 Å and 171 Å signals in order to separate the hot component. After identifying the loops we have used contemporaneous HMI observations to identify the corresponding inter-moss regions, and we have investigated their time evolution in six AIA EUV channels. The results can be separated into two classes. Group 1 (94Å, 335Å, 211Å) is characterized by hotter temperatures (~2-7 MK), and Group 2 (193Å, 171Å, 131Å) by cooler temperatures (0.4 - 1.6 MK). For Group 1 the intensity peaks in the 94Å channel are followed by maxima in the 335 Å channel with a time lag of ~8 min, suggestive of a cooling pattern with an exponential decay. While the 211Å maxima follow those in the 335 Å channel, there is no systematic relation which would indicate a progressive cooling process through the lower temperatures, as has been observed in other investigations. In Group 2 the signals in the 171 and 131Å channels track each other closely, and lag behind the 193Å. In the inter-moss region of the loop the peak temperature and peak emission measure have opposite trends. The hot 94Å brightenings occur in the central part of the loops with maximum temperatures ~7 MK. Subsequently the loops appear to fill with plasma with an emission measure compatible with the 193 Å signal and temperature in the range ~ 1.5-2 MK. Although the exact details of the time evolution are still under investigation, these non static loops show high levels of intermittency in the 94Å signal (please see poster "Intermittent and Scale-Invariant Intensity Fluctuations in Hot Coronal Loops," by Lawrence et al. in this session).

Age- and Sex-Specific Causal Effects of Adiposity on Cardiovascular Risk Factors

Observational studies have reported different effects of adiposity on cardiovascular risk factors across age and sex. Since cardiovascular risk factors are enriched in obese individuals, it has not been easy to dissect the effects of adiposity from those of other risk factors. We used a Mendelian randomization approach, applying a set of 32 genetic markers to estimate the causal effect of adiposity on blood pressure, glycemic indices, circulating lipid levels, and markers of inflammation and liver disease in up to 67,553 individuals. All analyses were stratified by age (cutoff 55 years of age) and sex. The genetic score was associated with BMI in both nonstratified analysis (P = 2.8 × 10(-107)) and stratified analyses (all P < 3.3 × 10(-30)). We found evidence of a causal effect of adiposity on blood pressure, fasting levels of insulin, C-reactive protein, interleukin-6, HDL cholesterol, and triglycerides in a nonstratified analysis and in the <55-year stratum. Further, we found evidence of a smaller causal effect on total cholesterol (P for difference = 0.015) in the ≥55-year stratum than in the <55-year stratum, a finding that could be explained by biology, survival bias, or differential medication. In conclusion, this study extends previous knowledge of the effects of adiposity by providing sex- and age-specific causal estimates on cardiovascular risk factors.

Children's use of interventions to learn causal structure

Children aged between 5 and 8 years freely intervened on a three-variable causal system, with their task being to discover whether it was a common-cause structure or one of two causal chains. From 6-7 years, children were able to use information from their interventions to correctly disambiguate the structure of a causal chain. We used a Bayesian model to examine children’s interventions on the system; this showed that with development children became more efficient in producing the interventions needed to disambiguate the causal structure and that the quality of interventions, as measured by their informativeness, improved developmentally. The latter measure was a significant predictor of children’s correct inferences about the causal structure. A second experiment showed that levels of performance were not reduced in a task in which children did not select and carry out interventions themselves, indicating no advantage for self-directed learning. However, children’s performance was not related to intervention quality in these circumstances, suggesting that children learn in a different way when they carry out interventions themselves.

Moral asymmetries in judgments of agency withstand ludicrous causal deviance

Americans have been shown to attribute greater intentionality to immoral than to amoral actions in cases of causal deviance, that is, cases where a goal is satisfied in a way that deviates from initially planned means (e.g., a gunman wants to hit a target and his hand slips, but the bullet ricochets off a rock into the target). However, past research has yet to assess whether this asymmetry persists in cases of extreme causal deviance. Here, we manipulated the level of mild to extreme causal deviance of an immoral versus amoral act. The asymmetry in attributions of intentionality was observed at all but the
most extreme level of causal deviance, and, as we hypothesized, was mediated by attributions of Blame/credit and judgments of action performance. These findings are discussed as they support a multiple-concepts interpretation of the asymmetry, wherein blame renders a naïve concept of intentional action (the outcome matches the intention) more salient than a composite concept (the outcome matches the intention and was brought about by planned means), and in terms of their implications for cross-cultural research on judgments of agency.

Automated Equation Formulation for Causal Loop Diagrams

The annotation of Business Dynamics models with parameters and equations, to simulate the system under study and further evaluate its simulation output, typically involves a lot of manual work. In this paper we present an approach for automated equation formulation of a given Causal Loop Diagram (CLD) and a set of associated time series with the help of neural network evolution (NEvo). NEvo enables the automated retrieval of surrogate equations for each quantity in the given CLD, hence it produces a fully annotated CLD that can be used for later simulations to predict future KPI development. In the end of the paper, we provide a detailed evaluation of NEvo on a business use-case to demonstrate its single step prediction capabilities.