Normalized Power Variance: A New Field Orthogonal to Power in EEG Analysis

To date, electroencephalogram (EEG) has been used in the diagnosis of epilepsy, dementia, and disturbance of consciousness via the inspection of EEG waves. In addition, EEG power analysis combined with a source estimation method like exact-low-resolution-brain-electromagnetic-tomography (eLORETA), which calculates the power of cortical electrical activity from EEG data, has been widely used to investigate cortical electrical activity in both healthy individuals and neuropsychiatric patients. However, the recently developed field of mathematics “information geometry” indicates that EEG has another dimension orthogonal to power dimension — that of normalized power variance (NPV). By also introducing the idea of information geometry, a significantly faster convergent estimator of NPV was obtained. In this study, we applied this NPV analysis of eLORETA to idiopathic normal pressure hydrocephalus (iNPH) patients prior to a cerebrospinal fluid (CSF) shunt operation, where traditional power analysis could not detect any difference associated with CSF shunt operation outcome. NPV analysis detected significantly higher NPV values at the high convexity area in the beta frequency band between 17 shunt responders and 19 non-responders. Our findings demonstrated that EEG has another dimension — that of NPV, which contains a great deal of information about cortical electrical activity that can be useful in clinical practice.

Virtual energy storage-gain due to spatiotemporal coordination of wind-, solar- and hydropower over Europe

<p>The functionality of a renewable electricity system in Europe depends on long-term climate variations, uneven spatiotemporal distribution of renewable energy, and constraints of storage and electric transmission. In particular, hydropower offers a large capacity for energy storage and production flexibility, but only stands for a minor part of the total energy potential. Here we explored the spatial and temporal power variance of a combined system consisting of wind-, solar- and hydropower availability for a 35-year period based on historical hydro-meteorological data from large parts of Europe. A spectral analysis of these historical time-series shows that spatiotemporal coordination within the power system can potentially contribute with a “virtual” energy storage capacity that is many times higher than the actual energy storage capacity contained in the existing hydropower reservoirs in Europe. Such virtual energy storage capacity implies reduced water storage demand, hence, indirectly contributes to reduced constraints of the food-water-energy nexus also in a wider system perspective. This study focused on the theoretical maximum potential for virtual energy storage, but the feasibility of this potential is limited by the uncertainty associated with production optimization and the meteorologic forecasts of future energy availability.</p>

Reducing wind farm power variance from wind direction using wind farm layout optimization

This paper investigates reducing power variance caused by different wind directions by using wind farm layout optimization. The problem was formulated as a multi-objective optimization. The [Formula: see text] constraint method was used to solve the bi-objective problem in a two-step optimization framework where two sequential optimizations were performed. The first was maximizing the mean wind farm power alone and the second was minimizing the power variance with a constraint on the mean power. The results show that the variance in power estimates can be greatly reduced, by as much as [Formula: see text], without sacrificing mean plant power for the different farm sizes and wind conditions studied. This reduction is attributed to the multi-modality of the design space which allows for unique solutions of high mean plant power with different power variances due to varying wind direction. Thus, wind farms can be designed to maximize power capture with greater confidence.

EFFECT OF THE UNIFORM FISSION SOURCE METHOD ON LOCAL POWER VARIANCE IN FULL CORE SERPENT CALCULATION

One of challenges of the Monte Carlo full core simulations is to obtain acceptable statistical variance of local parameters throughout the whole reactor core at a reasonable computation cost. The statistical variance tends to be larger in low-power regions. To tackle this problem, the Uniform-Fission-Site method was implemented in Monte Carlo code MC21 and its effectiveness was demonstrated on NEA Monte Carlo performance benchmark. The very similar method is also implemented in Monte Carlo code Serpent under the name Uniform Fission Source (UFS) method. In this work the effect of UFS method implemented in Serpent is studied on the BEAVRS benchmark which is based on a real PWR core with relatively flat radial power distribution and also on 3x3 PWR mini-core simulated with thermo-hydraulic and thermo-mechanic feedbacks. It is shown that the application of the Uniform Fission Source method has no significant effect on radial power variance but equalizes axial distribution of variance of local power.

AN IRSN CONTRIBUTION TO THE UAM PROJECT: THERMAL-HYDRAULIC AND NEUTRONIC UNCERTAINTIES PROPAGATION IN A ROD EJECTION, FIRST RESULTS

The paper presents our first results of the exercise III-I-2c from the OECD-NEA UAM-LWR benchmark intended to an elaboration of the methodology of uncertainty propagation. The considered case studied a full PWR core behavior in fast (~0.1 sec) rod ejection transient. According to the benchmark, the core represented a Hot Zero Power state. Authors used brute-force sampling propagating nuclear data and thermo-fluid uncertainties using 3D computational IRSN chain HEMERA. It couples the reactor physics code CRONOS and thermal-hydraulic core code FLICA4. The nuclear data uncertainties were represented in a form of cross sections standard deviations (in percentage of the mean cross sections values) supplied by the UAM team. In addition to the original benchmark, the study includes a case with an increased power peak by supplementary rod ejection, i.e. with higher reactivity. Both the results are similar to what we obtained in the mini-core rod ejection: the power standard deviation follows, in percentage of the mean power, the mean power curve. We split the variance with a direct calculation: once the cross sections are modified and the thermal-hydraulics inputs are kept constant, another time the contrary. The results show that uncertainties dues to nuclear data dominate over ones due to the thermal-flow area. Furthermore, the major contributors in peak-of-power variance lie in a fast group of cross sections.

Independent, Tough Identical Results: The Class of Tweedie on Power Variance Functions and the Class of Bar-Lev and Enis on Reproducible Natural Exponential Families

The Rao-Blackwell theorem has had a fundamental role in statistical theory. However, as opposed to what seems natural, Rao and Blackwell did not investigate and write the theorem jointly. In fact, they both published the same result independently, two years apart. Indeed, as C.R. Rao writes in Wikipedia: ”the result on one parameter case was published by Rao (1945) in the Bulletin of the Calcutta Mathematical Society and by Blackwell (1947) in The Annals of Mathematical Statistics. Only Lehmann and Sche ´e (1950) called the result as Rao-Blackwell theorem”. Forty years later, a situation very similar to the previous one seems to have happened. Tweedie (1984) in a paper published in a proceedings to a conference held in Calcutta and Bar-Lev and Enis (1986) in a paper published in The Annals of Statistics both presented for the first time, albeit two years apart, independently and in di erent contexts, the class of natural exponential families having power variance functions (NEF-PVFs). Tweedie’s results were then mentioned by Jorgensen (1987) in his fundamental paper on exponential dispersion models published in the Journal of the Royal Statistical Society, Series B. Jorgensen, however, mentioned also other researchers, including Bar-Lev and Enis, as dealt with the same problem. Nonetheless, Jorgensen (1987) stated in his paper that ”The most complete study” of NEF-PVFs was given by Tweedie (1984), a statement which has led to naming the class of NEF-PVFs as the Tweedie class. This statement of Jorgensen is entirely and utterly incorrect. Accordingly, one of the goals of this note is to 'prove' such incorrectness. Based on this 'proof' it will be evident, so I trust, that both Bar-Lev and Enis should have received the appropriate credit by re-naming the class of NEF-PVFs via the exploitation of the names of Tweedie, Bar-Lev and Enis. This would resemble the dignified and elegant manner Lehmann and Sche ´e acted on the Rao-Blackwell Theorem. Notwithstanding, the main aim of the note is to encourage young researchers to present their results with self-confidence and to get the credit they deserve.

A bivariate power generalized Weibull distribution: A flexible parametric model for survival analysis

We are concerned with the flexible parametric analysis of bivariate survival data. Elsewhere, we argued in favour of an adapted form of the ‘power generalized Weibull’ distribution as an attractive vehicle for univariate parametric survival analysis. Here, we additionally observe a frailty relationship between a power generalized Weibull distribution with one value of the parameter which controls distributional choice within the family and a power generalized Weibull distribution with a smaller value of that parameter. We exploit this relationship to propose a bivariate shared frailty model with power generalized Weibull marginal distributions linked by the BB9 or ‘power variance function’ copula, then change it to have adapted power generalized Weibull marginals in the obvious way. The particular choice of copula is, therefore, natural in the current context, and the corresponding bivariate adapted power generalized Weibull model a novel combination of pre-existing components. We provide a number of theoretical properties of the models. We also show the potential of the bivariate adapted power generalized Weibull model for practical work via an illustrative example involving a well-known retinopathy dataset, for which the analysis proves to be straightforward to implement and informative in its outcomes.

Long-term frailty modeling using a non-proportional hazards model: Application with a melanoma dataset

The semiparametric Cox regression model is often fitted in the modeling of survival data. One of its main advantages is the ease of interpretation, as long as the hazards rates for two individuals do not vary over time. In practice the proportionality assumption of the hazards may not be true in some situations. In addition, in several survival data is common a proportion of units not susceptible to the event of interest, even if, accompanied by a sufficiently large time, which is so-called immune, “cured,” or not susceptible to the event of interest. In this context, several cure rate models are available to deal with in the long term. Here, we consider the generalized time-dependent logistic (GTDL) model with a power variance function (PVF) frailty term introduced in the hazard function to control for unobservable heterogeneity in patient populations. It allows for non-proportional hazards, as well as survival data with long-term survivors. Parameter estimation was performed using the maximum likelihood method, and Monte Carlo simulation was conducted to evaluate the performance of the models. Its practice relevance is illustrated in a real medical dataset from a population-based study of incident cases of melanoma diagnosed in the state of São Paulo, Brazil.

Spatiotemporal Correlations in the Power Output of Wind Farms: On the Impact of Atmospheric Stability

The dependence of temporal correlations in the power output of wind-turbine pairs on atmospheric stability is explored using theoretical arguments and wind-farm large-eddy simulations. For this purpose, a range of five distinct stability regimes, ranging from weakly stable to moderately convective, were investigated with the same aligned wind-farm layout used among simulations. The coherence spectrum between turbine pairs in each simulation was compared to theoretical predictions. We found with high statistical significance (p < 0.01) that higher levels of atmospheric instability lead to higher coherence between turbines, with wake motions reducing correlations up to 40%. This is attributed to higher dominance of atmospheric motions over wakes in strongly unstable flows. Good agreement resulted with the use of an empirical model for wake-added turbulence to predict the variation of turbine power coherence with ambient turbulence intensity (R 2 = 0.82), though other empirical relations may be applicable. It was shown that improperly accounting for turbine–turbine correlations can substantially impact power variance estimates on the order of a factor of 4.