A Note on Allowed and Realized Rates of Return in the Electric Utility Industry

Most empirical investigations of electric utility behavior use the realized rate of return as a proxy for the allowed rate of return, We examine the validity of this assumption and investigate the relationship of the allowed and realized rates to the cost of capital between 1973 and 1982, We use two measures of the cost of capital: one based on returns to book equity, the other derived from a market price of equity. While realized and allowed rates were generally higher than the book measure throughout the period, both of the rates of return were less than the market price of capital after 1979. We also find firms did not earn their allowed rate of return after 1974. Therefore, the use of the realized rate as a proxy for the allowed rate in empirical models will lead to biased parameter estimates. To help correct this bias, we give data for allowed rates

A Note on Allowed and Realized Rates of Return in the Electric Utility Industry

Most empirical investigations of electric utility behavior use the realized rate of return as a proxy for the allowed rate of return, We examine the validity of this assumption and investigate the relationship of the allowed and realized rates to the cost of capital between 1973 and 1982, We use two measures of the cost of capital: one based on returns to book equity, the other derived from a market price of equity. While realized and allowed rates were generally higher than the book measure throughout the period, both of the rates of return were less than the market price of capital after 1979. We also find firms did not earn their allowed rate of return after 1974. Therefore, the use of the realized rate as a proxy for the allowed rate in empirical models will lead to biased parameter estimates. To help correct this bias, we give data for allowed rates

RADIAL VELOCITY MONITORING OFKEPLERHEARTBEAT STARS

Heartbeat stars (HB stars) are a class of eccentric binary stars with close periastron passages. The characteristic photometric HB signal evident in their light curves is produced by a combination of tidal distortion, heating, and Doppler boosting near orbital periastron. Many HB stars continue to oscillate after periastron and along the entire orbit, indicative of the tidal excitation of oscillation modes within one or both stars. These systems are among the most eccentric binaries known, and they constitute astrophysical laboratories for the study of tidal effects. We have undertaken a radial velocity (RV) monitoring campaign of Kepler HB stars in order to measure their orbits. We present our first results here, including a sample of 22 Kepler HB systems, where for 19 of them we obtained the Keplerian orbit and for 3 other systems we did not detect a statistically significant RV variability. Results presented here are based on 218 spectra obtained with the Keck/HIRES spectrograph during the 2015 Kepler observing season, and they have allowed us to obtain the largest sample of HB stars with orbits measured using a single instrument, which roughly doubles the number of HB stars with an RV measured orbit. The 19 systems measured here have orbital periods from 7 to 90 days and eccentricities from 0.2 to 0.9. We show that HB stars draw the upper envelope of the eccentricity–period distribution. Therefore, HB stars likely represent a population of stars currently undergoing high eccentricity migration via tidal orbital circularization, and they will allow for new tests of high eccentricity migration theories

Nucleotide excision repair gene expression after cisplatin treatment in melanoma

Two of the hallmark features of melanoma are its development as a result of chronic UV radiation exposure and the limited efficacy of cisplatin in the disease treatment. Both of these DNA-damaging agents result in large helix-distorting DNA damage that is recognized and repaired by nucleotide excision repair (NER). The aim of this study was to examine the expression of NER gene transcripts, p53, and p21 in melanoma cell lines treated with cisplatin compared with melanocytes. Basal expression of all genes was greater in the melanoma cell lines compared with melanocytes. Global genome repair (GGR) transcripts showed significantly decreased relative expression (RE) in melanoma cell lines 24 hours after cisplatin treatment. The basal RE of p53 was significantly higher in the melanoma cell lines compared with the melanocytes. However, induction of p53 was only significant in the melanocytes at 6 and 24 hours after cisplatin treatment. Inhibition of p53 expression significantly decreased the expression of all the GGR transcripts in melanocytes at 6 and 24 hours after cisplatin treatment. Although the RE levels were lower with p53 inhibition, the induction of the GGR genes was very similar to that in the control melanocytes and increased significantly across the time points. The findings from this study revealed reduced GGR transcript levels in melanoma cells 24 hours after cisplatin treatment. Our findings suggest a possible mechanistic explanation for the limited efficacy of cisplatin treatment and the possible role of UV light in melanoma

Accounting for training data error in machine learning applied to earth observations

Remote sensing, or Earth Observation (EO), is increasingly used to understand Earth system dynamics and create continuous and categorical maps of biophysical properties and land cover, especially based on recent advances in machine learning (ML). ML models typically require large, spatially explicit training datasets to make accurate predictions. Training data (TD) are typically generated by digitizing polygons on high spatial-resolution imagery, by collecting in situ data, or by using pre-existing datasets. TD are often assumed to accurately represent the truth, but in practice almost always have error, stemming from (1) sample design, and (2) sample collection errors. The latter is particularly relevant for image-interpreted TD, an increasingly commonly used method due to its practicality and the increasing training sample size requirements of modern ML algorithms. TD errors can cause substantial errors in the maps created using ML algorithms, which may impact map use and interpretation. Despite these potential errors and their real-world consequences for map-based decisions, TD error is often not accounted for or reported in EO research. Here we review the current practices for collecting and handling TD. We identify the sources of TD error, and illustrate their impacts using several case studies representing different EO applications (infrastructure mapping, global surface flux estimates, and agricultural monitoring), and provide guidelines for minimizing and accounting for TD errors. To harmonize terminology, we distinguish TD from three other classes of data that should be used to create and assess ML models: training reference data, used to assess the quality of TD during data generation; validation data, used to iteratively improve models; and map reference data, used only for final accuracy assessment. We focus primarily on TD, but our advice is generally applicable to all four classes, and we ground our review in established best practices for map accuracy assessment literature. EO researchers should start by determining the tolerable levels of map error and appropriate error metrics. Next, TD error should be minimized during sample design by choosing a representative spatio-temporal collection strategy, by using spatially and temporally relevant imagery and ancillary data sources during TD creation, and by selecting a set of legend definitions supported by the data. Furthermore, TD error can be minimized during the collection of individual samples by using consensus-based collection strategies, by directly comparing interpreted training observations against expert-generated training reference data to derive TD error metrics, and by providing image interpreters with thorough application-specific training. We strongly advise that TD error is incorporated in model outputs, either directly in bias and variance estimates or, at a minimum, by documenting the sources and implications of error. TD should be fully documented and made available via an open TD repository, allowing others to replicate and assess its use. To guide researchers in this process, we propose three tiers of TD error accounting standards. Finally, we advise researchers to clearly communicate the magnitude and impacts of TD error on map outputs, with specific consideration given to the likely map audience