From Packet to Power Switching: Digital Direct Load Scheduling

At present, the power grid has tight control over its dispatchable generation capacity but a very coarse control on the demand. Energy consumers are shielded from making price-aware decisions, which degrades the efficiency of the market. This state of affairs tends to favor fossil fuel generation over renewable sources. Because of the technological difficulties of storing electric energy, the quest for mechanisms that would make the demand for electricity controllable on a day-to-day basis is gaining prominence. The goal of this paper is to provide one such mechanisms, which we call Digital Direct Load Scheduling (DDLS). DDLS is a direct load control mechanism in which we unbundle individual requests for energy and digitize them so that they can be automatically scheduled in a cellular architecture. Specifically, rather than storing energy or interrupting the job of appliances, we choose to hold requests for energy in queues and optimize the service time of individual appliances belonging to a broad class which we refer to as "deferrable loads". The function of each neighborhood scheduler is to optimize the time at which these appliances start to function. This process is intended to shape the aggregate load profile of the neighborhood so as to optimize an objective function which incorporates the spot price of energy, and also allows distributed energy resources to supply part of the generation dynamically.Comment: Accepted by the IEEE journal of Selected Areas in Communications (JSAC): Smart Grid Communications series, to appea

Teardown analysis of a ten cell bipolar nickel-hydrogen battery

Design studies have identified bipolar nickel-hydrogen batteries as an attractive storage option for high power, high voltage applications. A pre-prototype Ni-H2 battery was designed, assembled and tested in the early phases of a concept verification program. The initial stack was built with available hardware and components from past programs. The stack performed well. After 2000 low-earth-orbit cycles the stack was dismantled in order to allow evaluation and analysis of the design and components. The results of the teardown analysis and recommended modifications are discussed

A semi-automated high-throughput approach to the generation of transposon insertion mutants in the nematode Caenorhabditis elegans

The generation of a large collection of defined transposon insertion mutants is of general interest to the Caenorhabditis elegans research community and has been supported by the European Union. We describe here a semi-automated high-throughput method for mutant production and screening, using the heterologous transposon Mos1. The procedure allows routine culture of several thousand independent nematode strains in parallel for multiple generations before stereotyped molecular analyses. Using this method, we have already generated >17 500 individual strains carrying Mos1 insertions. It could be easily adapted to forward and reverse genetic screens and may influence researchers faced with making a choice of model organism

Theory-Driven Longitudinal Study Exploring Indoor Tanning Initiation in Teens Using a Person-Centered Approach

Background Younger indoor tanning initiation leads to greater melanoma risk due to more frequent and persistent behavior. Despite this, there are no published studies exploring the predictors of indoor tanning initiation in teen populations. Purpose This longitudinal study uses latent profile analysis to examine indoor tanning initiation in indoor tanning risk subgroups from a national sample of female adolescents. Methods Latent profile analysis used indoor tanning beliefs and perceptions to identify indoor tanning initiation risk subgroups. The teens in each subgroup were reassessed on indoor tanning initiation after a year. Results Three subgroups were identified: a low risk, anti-tanning subgroup (18.6 %) characterized by low scores on positive indoor tanning belief scales and high scores on beliefs about indoor tanning dangers; a moderate risk aware social tanner subgroup (47.2 %) characterized by high scores on positive indoor tanning belief scales but also high scores on beliefs about indoor tanning dangers; and a high risk risky relaxation tanner subgroup (34.2 %) characterized by high scores on positive indoor tanning belief scales and low scores on beliefs about indoor tanning dangers. Teens in the aware social tanner and risky relaxation tanner subgroups were significantly more likely to initiate indoor tanning in the following year. Conclusions These findings highlight the need to identify teens at risk for indoor tanning initiation and develop tailored interventions that will move them to the lowest risk subgroup. Subgroup correlates suggest parent and peer-based interventions may be successful

An Empirical Approach to Bounding the Axial Reactivity Effects of PWR Spent Nuclear Fuel

One of the significant issues yet to be resolved for using burnup credit (BUC) for spent nuclear fuel (SNF) is establishing a set of depletion parameters that produce an adequately conservative representation of the fuel's isotopic inventory. Depletion parameters (such as local power, fuel temperature, moderator temperature, burnable poison rod history, and soluble boron concentration) affect the isotopic inventory of fuel that is depleted in a pressurized water reactor (PWR). However, obtaining the detailed operating histories needed to model all PWR fuel assemblies to which BUC would be applied is an onerous and costly task. Simplifications therefore have been suggested that could lead to using ''bounding'' depletion parameters that could be broadly applied to different fuel assemblies. This paper presents a method for determining a set of bounding depletion parameters for use in criticality analyses for SNF

Artificial Intelligence Applications in Cardiovascular Magnetic Resonance Imaging: Are We on the Path to Avoiding the Administration of Contrast Media?

In recent years, cardiovascular imaging examinations have experienced exponential growth due to technological innovation, and this trend is consistent with the most recent chest pain guidelines. Contrast media have a crucial role in cardiovascular magnetic resonance (CMR) imaging, allowing for more precise characterization of different cardiovascular diseases. However, contrast media have contraindications and side effects that limit their clinical application in determinant patients. The application of artificial intelligence (AI)-based techniques to CMR imaging has led to the development of non-contrast models. These AI models utilize non-contrast imaging data, either independently or in combination with clinical and demographic data, as input to generate diagnostic or prognostic algorithms. In this review, we provide an overview of the main concepts pertaining to AI, review the existing literature on non-contrast AI models in CMR, and finally, discuss the strengths and limitations of these AI models and their possible future development