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Between 2008 and 2012, the delivered price of natural gas to the U.S. power sector fell 60 percent. This paper addresses, in theory and in practice, the effects of this negative price shock on electricity consumers and the environment. We demonstrate with a simple model that the larger the effects of gas prices on consumer welfare, the smaller the effects on pollution emissions and the smaller the increase in profits of existing natural gas-fired generators. Using detailed data on electricity prices, fuel consumption, and fuel prices from 2001 to 2012, we confirm this hypothesis. Regions that experience greater reductions in pollution emissions experience smaller reductions in electricity prices and consumer welfare.

In this paper, we build on recent work by Serletis et al. (2010, in press) and report short- and long-run estimates of aggregate interfuel substitution for a number of OECD and non-OECD countries. In doing so, we use recent pooled intercountry data (since 1980), and state-of-the-art advances in microeconometrics, including duality theory and flexible functional forms. Also, motivated by the widespread practice in the empirical energy demand literature of ignoring theoretical regularity, we estimate our model subject to global curvature (but not monotonicity), using methods developed by Diewert and Wales (1987). We provide inference, and also a policy perspective, using parameter estimates that are consistent with the theoretical regularity conditions of neoclassical microeconomic theory.

This paper proposes an approach for analyzing the impacts of large-scale wind power integration on electricity market equilibria. A pool-based oligopolistic electricity market is considered including a day-ahead market and a number of real-time markets. Wind power is considered within the generation portfolio of the strategic producers, and the uncertainty of wind power production is modeled through a set of plausible scenarios. The strategic behavior of each producer is modeled through a stochastic bilevel model. The resulting nonlinear equilibrium problem with equilibrium constraints (EPEC) is linearized and then solved. Numerical results for a test case with increasing levels of the wind power penetration is provided.

Purpose: To examine the effects of the world's most challenging mountain ultramarathon (MUM, 330 km, cumulative elevation gain of +24,000 m) on the energy cost and kinematics of different uphill gaits. Methods: Before (PRE) and immediately after (POST) the competition, 19 male athletes performed three submaximal 5-min treadmill exercise trials in a randomized order: walking at 5 km·h-1, +20%; running at 6 km·h-1, +15%; and running at 8 km·h-1, +10%. During the three trials, energy cost was assessed using an indirect calorimetry system and spatiotemporal gait parameters were acquired with a floor-level high-density photoelectric cells system. Results: The average time of the study participants to complete the MUM was 129 h 43 min 48 s (range: 107 h 29 min 24 s to 144 h 21 min 0 s). Energy costs in walking (-11.5 ± 5.5%, P < 0.001), as well as in the first (-7.2 ± 3.1%, P = 0.01) and second (-7.0 ± 3.9%, P = 0.02) running condition decreased between PRE and POST, with a reduction both in the heart rate (-11.3, -10.0, and -9.3%, respectively) and oxygen uptake only for the walking condition (-6.5%). No consistent and significant changes in the kinematics variables were detected (P-values from 0.10 to 0.96). Conclusion: Though fatigued after completing the MUM, the subjects were still able to maintain their uphill locomotion patterns noted at PRE. The decrease (improvement) in the energy costs was likely due to the prolonged and repetitive walking/running, reflecting a generic improvement in the mechanical efficiency of locomotion after ~130 h of uphill locomotion rather than constraints imposed by the activity on the musculoskeletal structure and function.

SummarySupercapacitors are crucial elements in advanced industrial electronic systems particularly when supplied from renewable energy sources. Here, we derive expressions for the current, power, and stored energy in a supercapacitor excited with a step voltage signal. Although, it is not common practice to charge supercapacitors using a step voltage, these devices are sometimes used in switching‐type applications where they are subject to this type of signal. We validate the derived mathematical expression of the current via experiments on four different commercial devices. By fitting the measured current data to the derived expression, the parameters of these devices, which are modeled as a series connection of an internal resistance and a constant phase element (CPE), are also estimated. Power and energy are then evaluated followed by the estimation of the effective capacitance for each device. Our analysis makes use of fractional order calculus as opposed to classical integer‐order models.

AbstractThe success of transcriptome-wide association studies (TWAS) has led to substantial research toward improving the predictive accuracy of its core component of genetically regulated expression (GReX). GReX links expression information with genotype and phenotype by playing two roles simultaneously: it acts as both the outcome of the genotype-based predictive models (for predicting expressions) and the linear combination of genotypes (as the predicted expressions) for association tests. From the perspective of machine learning (considering SNPs as features), these are actually two separable steps—feature selection and feature aggregation—which can be independently conducted. In this study, we show that the single approach of GReX limits the adaptability of TWAS methodology and practice. By conducting simulations and real data analysis, we demonstrate that disentangled protocols adapting straightforward approaches for feature selection (e.g., simple marker test) and aggregation (e.g., kernel machines) outperform the standard TWAS protocols that rely on GReX. Our development provides more powerful novel tools for conducting TWAS. More importantly, our characterization of the exact nature of TWAS suggests that, instead of questionably binding two distinct steps into the same statistical form (GReX), methodological research focusing on optimal combinations of feature selection and aggregation approaches will bring higher power to TWAS protocols.

The number of pathogens known to infect humans is ever increasing. Whether such increase reflects improved surveillance and detection or actual emergence of novel pathogens is unclear. Nonetheless, infectious diseases are the second leading cause of human mortality and disability-adjusted life years lost worldwide [1], [2]. On average, three to four new pathogen species are detected in the human population every year [3]. Most of these emerging pathogens originate from nonhuman animal species.