Are the energy states still energy states?

Traditional energy states managed to avoid the early stages of the recent national recession, buoyed by record high crude oil and natural gas prices. Both production and exploration for crude oil and natural gas expanded rapidly in response to the spike in energy prices, propelling strong job and income gains in the energy states. But the strong performance of the energy states through the early stages of the recession subsequently reversed itself under the weight of collapsing energy prices. These states began to underperform non-energy states by the second quarter of 2009. These gyrations in economic activity are reminiscent of the volatility experienced during the 1970s and early 1980s, suggesting that the energy cycle is alive and well in the energy states. ; Snead examines the economic performance of the energy states in the recent energy price spike and recessionary cycle. He finds that the economies of the energy states remain highly sensitive to changes in energy prices and follow a much different economic cycle than non-energy states. The energy states posted far stronger job growth prior to the recession, entered the recession much later and with more momentum, and have posted smaller cumulative job losses than non-energy states. Most of the energy states were nearly as reliant on the energy sector as a source of state earnings in 2008 as they were at the peak of the prior cycle in 1982. He also finds that the historical ranks of the energy states are poised for a shuffling. Unconventional natural gas production will move some states closer to the top as other states enter the ranks of the major oil and gas producers for the first time.

Are U.S. states equally prepared for a carbon-constrained world?

Climate concerns linked to greenhouse gas emissions, particularly carbon dioxide (CO2), have taken center stage in the national energy policy debate. Domestic energy use and carbon emissions continue to rise, and forecasts suggest further increases under the existing regulatory structure. However, heightened international and domestic pressure to reduce U.S. carbon emissions suggests that additional changes to the regulatory framework are probable in coming years. ; Reducing U.S. carbon emissions will likely require a comprehensive national framework that will alter the pattern of energy use and production in all 50 states. At issue for state-level policymakers is that carbon restrictions are unlikely to affect the states equally. Energy use and emission patterns vary widely across states, and there is no accepted framework for allocating shares of a national carbon reduction goal. As a result, states that emit the most carbon or have the most energy- and carbon-intensive economies may shoulder the greatest burden. ; Snead and Jones evaluate the current energy posture of the states and thus how prepared they are to cope with ongoing trends in energy use, especially restrictions on carbon emissions. Their findings suggest that the New England, Mid-Atlantic and West Coast states are generally best prepared. These states have the least energy-intensive economies and use fuel mixes with low average carbon intensity; hence, they already release proportionately less CO2. The states expected to be hardest hit by carbon constraints are the traditional energy-producing and agricultural states. These states have energy-intensive economies, by both domestic and international standards, and will face a considerable challenge in altering their energy use and emissions patterns.

Causality Tests of the Stock Price-inflation Relation

Economic

The use of high frequency oscillations to guide neocortical resections in children with medically-intractable epilepsy: How do we ethically apply surgical innovations to patient care?

AbstractPurposeResective surgical strategies are increasingly applied to treat medically-intractable epilepsy in children as uncontrolled seizures are associated with poor cognitive, developmental and behavioral outcomes. Innovative surgical strategies are, however, needed to improve outcomes and minimize the morbidity of such procedures.MethodThe current article utilizes an axiological approach to explore and highlight ethical issues in the use of high frequency oscillations (HFOs) to guide surgical resections in children with medically-intractable epilepsy. We frame our discussion in the context of the broader challenges in the application of surgical innovation to patient care.ResultsDespite a paucity of knowledge regarding their pathogenesis, limited evidence suggests the use of HFOs as biomarkers of epileptogenicity in resective procedures can improve seizure outcome. Clinicians must therefore weigh deficiencies in knowledge against the limited evidence supporting the utility of HFOs and make ethical decisions for the treatment of individual patients. Important ethical considerations for clinicians include the extent of deviation from established practice, the extent of evidence required to establish clinical validity, and the impact of technique implementation on equitable distribution of healthcare.ConclusionThe use of HFO signatures to guide neocortical resections represents a novel approach for the treatment of epilepsy. It is hoped that the issues discussed herein will contribute to and advance meaningful dialog on the ethical application of this surgical innovation to the treatment of a very vulnerable patient population

Supporting Remote Survey Data Analysis by Co-researchers with Learning Disabilities through Inclusive and Creative Practices and Data Science Approaches

Through a process of robust co-design, we created a bespoke accessible survey platform to explore the role of co-researchers with learning disabilities (LDs) in research design and analysis. A team of co-researchers used this system to create an online survey to challenge public understanding of LDs [3]. Here, we describe and evaluate the process of remotely co-analyzing the survey data across 30 meetings in a research team consisting of academics and nonacademics with diverse abilities amid new COVID-19 lockdown challenges. Based on survey data with >1,500 responses, we first coanalyzed demographics using graphs and art & design approaches. Next, co-researchers co-analyzed the output of machine learningbased structural topic modelling (STM) applied to open-ended text responses. We derived an efficient five-steps STM co-analysis process for creative, inclusive, and critical engagement of data by coresearchers. Co-researchers observed that by trying to understand and impact public opinion, their own perspectives also changed

The Mechanistic Classification of Addictive Drugs

The authors review recent research on the molecular mechanisms of addiction and propose a new classification for addictive drugs

Recent advances in modeling and simulation of the exposure and response of tungsten to fusion energy conditions

Under the anticipated operating conditions for demonstration magnetic fusion reactors beyond ITER, structural and plasma-facing materials will be exposed to unprecedented conditions of irradiation, heat flux, and temperature. While such extreme environments remain inaccessible experimentally, computational modeling and simulation can provide qualitative and quantitative insights into materials response and complement the available experimental measurements with carefully validated predictions. For plasma-facing components such as the first wall and the divertor, tungsten (W) has been selected as the leading candidate material due to its superior high-temperature and irradiation properties, as well as for its low retention of implanted tritium. In this paper we provide a review of recent efforts in computational modeling of W both as a plasma-facing material exposed to He deposition as well as a bulk material subjected to fast neutron irradiation. We use a multiscale modeling approach-commonly used as the materials modeling paradigm-to define the outline of the paper and highlight recent advances using several classes of techniques and their interconnection. We highlight several of the most salient findings obtained via computational modeling and point out a number of remaining challenges and future research directions.Peer reviewe

Screening of normal endoscopic large bowel biopsies with interpretable graph learning: a retrospective study

Objective To develop an interpretable artificial intelligence algorithm to rule out normal large bowel endoscopic biopsies, saving pathologist resources and helping with early diagnosis. Design A graph neural network was developed incorporating pathologist domain knowledge to classify 6591 whole-slides images (WSIs) of endoscopic large bowel biopsies from 3291 patients (approximately 54% female, 46% male) as normal or abnormal (non-neoplastic and neoplastic) using clinically driven interpretable features. One UK National Health Service (NHS) site was used for model training and internal validation. External validation was conducted on data from two other NHS sites and one Portuguese site. Results Model training and internal validation were performed on 5054 WSIs of 2080 patients resulting in an area under the curve-receiver operating characteristic (AUC-ROC) of 0.98 (SD=0.004) and AUC-precision-recall (PR) of 0.98 (SD=0.003). The performance of the model, named Interpretable Gland-Graphs using a Neural Aggregator (IGUANA), was consistent in testing over 1537 WSIs of 1211 patients from three independent external datasets with mean AUC-ROC=0.97 (SD=0.007) and AUC-PR=0.97 (SD=0.005). At a high sensitivity threshold of 99%, the proposed model can reduce the number of normal slides to be reviewed by a pathologist by approximately 55%. IGUANA also provides an explainable output highlighting potential abnormalities in a WSI in the form of a heatmap as well as numerical values associating the model prediction with various histological features. Conclusion The model achieved consistently high accuracy showing its potential in optimising increasingly scarce pathologist resources. Explainable predictions can guide pathologists in their diagnostic decision-making and help boost their confidence in the algorithm, paving the way for its future clinical adoption