The spectrum of hot methane in astronomical objects using a comprehensive computed line list

Hot methane spectra are important in environments ranging from flames to the atmospheres of cool stars and exoplanets. A new spectroscopic line list, 10to10, for $^{12}$CH$_4$ containing almost 10 billion transitions is presented. This comprehensive line list covers a broad spectroscopic range and is applicable for temperatures up to 1 500 K. Previous methane data are incomplete leading to underestimated opacities at short wavelengths and elevated temperatures. Use of 10to10 in models of the bright T4.5 brown dwarf 2MASS 0559-14 leads to significantly better agreement with observations and in studies of the hot Jupiter exoplanet HD 189733b leads to up to a twentifold increase in methane abundance. It is demonstrated that proper inclusion of the huge increase in hot transitions which are important at elevated temperatures is crucial for accurate characterizations of atmospheres of brown dwarfs and exoplanets, especially when observed in the near-infrared.Comment: PNAS, Early Edition, June 16, 201

Blind extraction of an exoplanetary spectrum through Independent Component Analysis

Blind-source separation techniques are used to extract the transmission spectrum of the hot-Jupiter HD189733b recorded by the Hubble/NICMOS instrument. Such a 'blind' analysis of the data is based on the concept of independent component analysis. The de-trending of Hubble/NICMOS data using the sole assumption that nongaussian systematic noise is statistically independent from the desired light-curve signals is presented. By not assuming any prior, nor auxiliary information but the data themselves, it is shown that spectroscopic errors only about 10 - 30% larger than parametric methods can be obtained for 11 spectral bins with bin sizes of ~0.09 microns. This represents a reasonable trade-off between a higher degree of objectivity for the non-parametric methods and smaller standard errors for the parametric de-trending. Results are discussed in the light of previous analyses published in the literature. The fact that three very different analysis techniques yield comparable spectra is a strong indication of the stability of these results.Comment: ApJ accepte

Facing Facts: Facial Injuries from Stand-up Electric Scooters

Background Stand-up electric scooters (SES) are a popular public transportation method. Numerous safety concerns have arisen since their recent introduction. Methods A retrospective chart review was performed to identify patients presenting to the emergency departments in Indianapolis, who sustained SES-related injuries. Results A total of 89 patients were included in our study. The average patient age was 29 ± 12.9 years in a predominantly male cohort (65.2%). No patient was documented as wearing a helmet during the event of injury. Alcohol intoxication was noted in 14.6% of accidents. Falling constituted the leading trauma mechanism (46.1%). Injuries were most common on Saturday (24.7%) from 14h00 to 21h59 (55.1%). Injury types included: abrasions/contusions (33.7%), fractures (31.5%), lacerations (27.0%), or joint injuries (18.0%). The head and neck region (H&N) was the most frequently affected site (42.7%). Operative management under general anesthesia was necessary for 13.5% of injuries. Nonoperative management primarily included conservative orthopedic care (34.8%), pain management with nonsteroidal anti-inflammatory drugs (NSAIDs) (34.8%) and/or opioids (4.5%), bedside laceration repairs (27.0%), and wound dressing (10.1%). Individuals sustaining head and neck injuries were more likely to be older (33.8 vs. 25.7 years, p=0.003), intoxicated by alcohol (29.0% vs. 3.9%, p=0.002), and requiring CT imaging (60.5% vs. 9.8%, p <0.001). Conclusion Although SESs provide a convenient transportation modality, unregulated use raises significant safety concerns. More data need to be collected to guide future safety regulations

Artificial intelligence in government: Concepts, standards, and a unified framework

Recent advances in artificial intelligence (AI), especially in generative language modelling, hold the promise of transforming government. Given the advanced capabilities of new AI systems, it is critical that these are embedded using standard operational procedures, clear epistemic criteria, and behave in alignment with the normative expectations of society. Scholars in multiple domains have subsequently begun to conceptualize the different forms that AI applications may take, highlighting both their potential benefits and pitfalls. However, the literature remains fragmented, with researchers in social science disciplines like public administration and political science, and the fast-moving fields of AI, ML, and robotics, all developing concepts in relative isolation. Although there are calls to formalize the emerging study of AI in government, a balanced account that captures the full depth of theoretical perspectives needed to understand the consequences of embedding AI into a public sector context is lacking. Here, we unify efforts across social and technical disciplines by first conducting an integrative literature review to identify and cluster 69 key terms that frequently co-occur in the multidisciplinary study of AI. We then build on the results of this bibliometric analysis to propose three new multifaceted concepts for understanding and analysing AI-based systems for government (AI-GOV) in a more unified way: (1) operational fitness, (2) epistemic alignment, and (3) normative divergence. Finally, we put these concepts to work by using them as dimensions in a conceptual typology of AI-GOV and connecting each with emerging AI technical measurement standards to encourage operationalization, foster cross-disciplinary dialogue, and stimulate debate among those aiming to rethink government with AI.Comment: 35 pages with references and appendix, 3 tables, 2 figure

Managing at the Speed of Light: Improving Mission-Support Performance

The House and Senate Energy and Water Development Appropriations Subcommittees requested this study to help DOE's three major mission-support organizations improve their operations to better meet the current and future needs of the department. The passage of the Recovery Act only increased the importance of having DOE's mission-support offices working in the most effective, efficient, and timely manner as possible. While following rules and regulations is essential, the foremost task of the mission-support offices is to support the department's mission, i.e., the programs that DOE is implementing, whether in Washington D.C. or in the field. As a result, the Panel offered specific recommendations to strengthen the mission-focus and improve the management of each of the following support functions based on five "management mandates":- Strategic Vision- Leadership- Mission and Customer Service Orientation- Tactical Implementation- Agility/AdaptabilityKey FindingsThe Panel made several recommendations in each of the functional areas examined and some overarching recommendations for the corporate management of the mission-support offices that they believed would result in significant improvements to DOE's mission-support operations. The Panel believed that adopting these recommendations will not only make DOE a better functioning organization, but that most of them are essential if DOE is to put its very large allocation of Recovery Act funding to its intended uses as quickly as possible

Examining Criminogenic Risk Levels Among People with Mental Illness Incarcerated in US Jails and Prisons

This study examines criminogenic risk levels of individuals with serious mental illness (SMI) involved in the justice system compared to justice-involved individuals without mental illness. The sample (N = 436) consisted of ninety-three individuals with SMI incarcerated in a county jail in a mid-size Midwest city, 217 individuals with SMI incarcerated in a state prison in the US Northeast, and 126 individuals without mental illness incarcerated in a state prison in the US Southwest. Results indicated that people with SMI incarcerated in jail and prison had higher overall criminal risk levels than prison inmates without mental illness. Results further demonstrated that, on average, higher percentages of persons with SMI had high/very high criminogenic risk scores. Finally, we noted that persons with SMI scored higher on most of the eight criminogenic risk domains measured by the Level of Service Inventory. These findings are possibly the most compelling to date in the growing body of literature demonstrating that justice-involved people with SMI have elevated criminogenic risk comparable to or greater than their non-mentally ill peers involved in the justice system. Consequently, treatment programs and interventions for justice-involved individuals with SMI need to explicitly target criminogenic needs into treatment efforts

Unsupervised feature extraction of aerial images for clustering and understanding hazardous road segments

Aerial image data are becoming more widely available, and analysis techniques based on supervised learning are advancing their use in a wide variety of remote sensing contexts. However, supervised learning requires training datasets which are not always available or easy to construct with aerial imagery. In this respect, unsupervised machine learning techniques present important advantages. This work presents a novel pipeline to demonstrate how available aerial imagery can be used to better the provision of services related to the built environment, using the case study of road traffic collisions (RTCs) across three cities in the UK. In this paper, we show how aerial imagery can be leveraged to extract latent features of the built environment from the purely visual representation of top-down images. With these latent image features in hand to represent the urban structure, this work then demonstrates how hazardous road segments can be clustered to provide a data-augmented aid for road safety experts to enhance their nuanced understanding of how and where different types of RTCs occur