A space–time Bayesian hierarchical modeling framework for projection of seasonal maximum streamflow

Timely projections of seasonal streamflow extremes can be useful for the early implementation of annual flood risk adaptation strategies. However, predicting seasonal extremes is challenging, particularly under nonstationary conditions and if extremes are correlated in space. The goal of this study is to implement a space–time model for the projection of seasonal streamflow extremes that considers the nonstationarity (interannual variability) and spatiotemporal dependence of high flows. We develop a space–time model to project seasonal streamflow extremes for several lead times up to 2 months, using a Bayesian hierarchical modeling (BHM) framework. This model is based on the assumption that streamflow extremes (3 d maxima) at a set of gauge locations are realizations of a Gaussian elliptical copula and generalized extreme value (GEV) margins with nonstationary parameters. These parameters are modeled as a linear function of suitable covariates describing the previous season selected using the deviance information criterion (DIC). Finally, the copula is used to generate streamflow ensembles, which capture spatiotemporal variability and uncertainty. We apply this modeling framework to predict 3 d maximum streamflow in spring (May–June) at seven gauges in the Upper Colorado River basin (UCRB) with 0- to 2-month lead time. In this basin, almost all extremes that cause severe flooding occur in spring as a result of snowmelt and precipitation. Therefore, we use regional mean snow water equivalent and temperature from the preceding winter season as well as indices of large-scale climate teleconnections – El Niño–Southern Oscillation, Atlantic Multidecadal Oscillation, and Pacific Decadal Oscillation – as potential covariates for 3 d spring maximum streamflow. Our model evaluation, which is based on the comparison of different model versions and the energy skill score, indicates that the model can capture the space–time variability in extreme streamflow well and that model skill increases with decreasing lead time. We also find that the use of climate variables slightly enhances skill relative to using only snow information. Median projections and their uncertainties are consistent with observations, thanks to the representation of spatial dependencies through covariates in the margins and a Gaussian copula. This spatiotemporal modeling framework helps in the planning of seasonal adaptation and preparedness measures as predictions of extreme spring streamflows become available 2 months before actual flood occurrence.

Geographic Disparities in Readmissions for Peripheral Artery Disease in South Carolina

Readmissions constitute a major health care burden among peripheral artery disease (PAD) patients. This study aimed to 1) estimate the zip code tabulation area (ZCTA)-level prevalence of readmission among PAD patients and characterize the effect of covariates on readmissions; and (2) identify hotspots of PAD based on estimated prevalence of readmission. Thirty-day readmissions among PAD patients were identified from the South Carolina Revenue and Fiscal Affairs Office All Payers Database (2010–2018). Bayesian spatial hierarchical modeling was conducted to identify areas of high risk, while controlling for confounders. We mapped the estimated readmission rates and identified hotspots using local Getis Ord (G*) statistics. Of the 232,731 individuals admitted to a hospital or outpatient surgery facility with PAD diagnosis, 30,366 (13.1%) experienced an unplanned readmission to a hospital within 30 days. Fitted readmission rates ranged from 35.3 per 1000 patients to 370.7 per 1000 patients and the risk of having a readmission was significantly associated with the percentage of patients who are 65 and older (0.992, 95%CI: 0.985–0.999), have Medicare insurance (1.013, 1.005–1.020), and have hypertension (1.014, 1.005–1.023). Geographic analysis found significant variation in readmission rates across the state and identified priority areas for targeted interventions to reduce readmissions.

Do Attentional Lapses Account for the Worst Performance Rule?

The worst performance rule (WPR) describes the phenomenon that individuals’ slowest responses in a task are often more predictive of their intelligence than their fastest or average responses. To explain this phenomenon, it was previously suggested that occasional lapses of attention during task completion might be associated with particularly slow reaction times. Because less intelligent individuals should experience lapses of attention more frequently, reaction time distribution should be more heavily skewed for them than for more intelligent people. Consequently, the correlation between intelligence and reaction times should increase from the lowest to the highest quantile of the response time distribution. This attentional lapses account has some intuitive appeal, but has not yet been tested empirically. Using a hierarchical modeling approach, we investigated whether the WPR pattern would disappear when including different behavioral, self-report, and neural measurements of attentional lapses as predictors. In a sample of N = 85, we found that attentional lapses accounted for the WPR, but effect sizes of single covariates were mostly small to very small. We replicated these results in a reanalysis of a much larger previously published data set. Our findings render empirical support to the attentional lapses account of the WPR.

A Hierarchical Modeling Framework for Cyber-Physical Micro Energy Grid System

This paper proposes a hierarchical modeling framework for micro energy grid (MEG) from the perspective of cyber-physical integration, including three layers: object layer, integration layer and decision layer. This modeling framework can fully reveal the interplay of the information flow and energy flow in MEG. The energy hub approach is used to uniformly describe the conversion and distribution of different energy sources in the object layer. The state machine is used to describe the characteristics of energy flow and information flow and their dynamic relationships in the integration layer, where the energy flow describes the dynamic balance of energy between the supply side and demand side and the energy changes of each unit in MEG, and the information flow describes the transferring process of each unit’s operating state and the conditions triggering the state transformation. The optimization objective of the decision layer is established based on the actual requirements with optimal operation. The combination of the three-layer model forms the overall model of the MEG. Finally, a typical MEG system is taken as an example to verify the proposed modeling approach, and the results show that the proposed modeling method effectively improves the observability and optimal operation of the MEG.

Extended Hierarchical Fuzzy Interpreted Petri Net

Petri nets (PNs) have many advantages such as graphical representation, formal description, and the possibility of sequential and concurrent control. An important aspect of using PNs is hierarchical modeling, which may be provided in different ways. In this paper, a new concept and definition of the hierarchical structure for Fuzzy Interpreted Petri Net (FIPN) are proposed. The concept of macroplace with several input, output, and input-output places is introduced to the net. The functionality of the macroplace instances and the hierarchy graph are also proposed. They are implemented in a computer simulator called HFIPN-SML. In this study, FIPN is employed since it allows the use of analogue sensors directly for process control. Better visualization and more precise control are among advantages of the introduced approach.

Looking from the top: enhanced top-down sensorimotor processing in somatic anxiety

BackgroundFunctional neuroimaging research on anxiety has traditionally focused on brain networks associated with the complex psychological aspects of anxiety. In this study, instead, we target the somatic aspects of anxiety. Motivated by the growing recognition that top-down cortical processing plays crucial roles in perception and action, we investigate effective connectivity among hierarchically organized sensorimotor regions and its association with (trait) anxiety.MethodsWe selected 164 participants from the Human Connectome Project based on psychometric measures. We used their resting-state functional MRI data and Dynamic Causal Modeling (DCM) to assess effective connectivity within and between key regions in the exteroceptive, interoceptive, and motor hierarchy. Using hierarchical modeling of between-subject effects in DCM with Parametric Empirical Bayes we first established the architecture of effective connectivity in sensorimotor networks and investigated its association with fear somatic arousal (FSA) and fear affect (FA) scores. To probe the robustness of our results, we implemented a leave-one-out cross validation analysis.ResultsAt the group level, the top-down connections in exteroceptive cortices were inhibitory in nature whereas in interoceptive and motor cortices they were excitatory. With increasing FSA scores, the pattern of top-down effective connectivity was enhanced in all three networks: an observation that corroborates well with anxiety phenomenology. Anxiety associated changes in effective connectivity were of effect size sufficiently large to predict whether somebody has mild or severe somatic anxiety. Interestingly, the enhancement in top-down processing in sensorimotor cortices were associated with FSA but not FA scores, thus establishing the (relative) dissociation between somatic and cognitive dimensions of anxiety.ConclusionsOverall, enhanced top-down effective connectivity in sensorimotor cortices emerges as a promising and quantifiable candidate marker of trait somatic anxiety. These results pave the way for a novel approach into investigating the neural underpinnings of anxiety based on the recognition of anxiety as an embodied phenomenon and the emerging interest in top-down cortical processing.