Frequency-dependent polarizabilities of alkali atoms from ultraviolet through infrared spectral regions

We present results of first-principles calculations of the frequency-dependent polarizabilities of all alkali atoms for light in the wavelength range 300-1600 nm, with particular attention to wavelengths of common infrared lasers. We parameterize our results so that they can be extended accurately to arbitrary wavelengths above 800 nm. This work is motivated by recent experiments involving simultaneous optical trapping of two different alkali species. Our data can be used to predict the oscillation frequencies of optically-trapped atoms, and particularly the ratios of frequencies of different species held in the same trap. We identify wavelengths at which two different alkali atoms have the same oscillation frequency.Comment: 6 pages, 2 figure

Afterschool quality

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/110030/1/yd20111.pd

Genetic algorithm based input selection for a neural network function approximator with applications to SSME health monitoring

A genetic algorithm is used to select the inputs to a neural network function approximator. In the application considered, modeling critical parameters of the space shuttle main engine (SSME), the functional relationship between measured parameters is unknown and complex. Furthermore, the number of possible input parameters is quite large. Many approaches have been used for input selection, but they are either subjective or do not consider the complex multivariate relationships between parameters. Due to the optimization and space searching capabilities of genetic algorithms they were employed to systematize the input selection process. The results suggest that the genetic algorithm can generate parameter lists of high quality without the explicit use of problem domain knowledge. Suggestions for improving the performance of the input selection process are also provided

Preparing Youth to Thrive: Methodology and Findings From the Social and Emotional Learning Challenge

The SEL Challenge was designed in pursuit of two ambitious goals: to identify promising practices for building SEL skills with vulnerable adolescents, and to develop technical supports for use of these SEL practices at scale in thousands of OST settings. The study design included a qualitative methodology, expert practitioners, and performance studies at each of eight exemplary programs. The products of the Challenge - standards for SEL practice and the suite of SEL performance measures - are designed to help OST programs focus deeply on SEL practice, assess their strengths, and improve the quality and effectiveness of their services using a lower stakes continuous improvement approach.By focusing systematically at a granular level of adult and youth behavior, the Challenge content supports use in practice-oriented settings and systems - youth programs, school day classrooms, mentorships, residential treatment, apprenticeships, workplace, families - where the qualities of adult-youth interaction and learning are a primary concern. We hope that local policy makers and funders will use the Challenge as a template for identifying the exemplary SEL services already available in their communities and make sure that they are adequately recognized, resourced, and replicated

Preparing Youth to Thrive: Promising Practices for Social Emotional Learning

In 2014, the Susan Crown Exchange (SCE), a national foundation, and the David P. Weikart Center for Youth Program Quality, a research center, set out to determine how out-of-school programs throughout the country can be more intentional about providing social and emotional skill development. From an extensive pool of applicants, the Weikart Center and SCE selected eight top out-of-school programs that not only make a commitment to SEL, but have a proven track record of working with one of the hardest populations to reach: vulnerable and at-risk adolescents.The programs selected as partners in the Challenge hailed from seven cities and offered a diverse array of opportunities for youth aged 14-19. Weikart and Challenge partners then set out to determine why these programs are so effective and how these practices and approaches to SEL could be shared with others. An extensive research process followed that included interviews, site visits, evaluations, convenings, and surveys.What the Challenge found was that was no matter if teens were building boats in Philadelphia or writing a musical, with the right staff practices, supports, and curriculum, youth participants develop social and emotional skills. Six skill areas - emotion management, empathy, teamwork, responsibility, initiative, and problem solving - also rose to the surface as key skills in social emotional growth. Not only that, but Weikart and Challenge partners determined that these practices and offerings could be replicated at any program.Now, these best practices and examples are available for any program to use through a new guide, "Preparing Youth to Thrive: Promising Practices in Social & Emotional Learning." The field guide sheds new light on how out-of-school programs can equip teens with valuable social and emotional skills. Inside the guide, readers will find key staff practices drilled down and described for each of the eight programs. The guide also shares narratives from staff and youth that tell the stories of how these programs are making a difference in the lives of young people each day

Quality at the Point of Service: Profiles of Practice in After‐School Settings

A unique observational data set was used to explore quality at the point of service in after‐school programs. Staff practices in after‐school settings were represented on a series of unidimensional scales closely indexed to staff behavior. In order to account for heterogeneity of staff performances, pattern‐centered methods were used to construct profiles of common staff practices. Results revealed six pedagogy profiles that were classified in terms of three broad types of performances delivered by after‐school staff: (1) positive youth development, (2) staff‐centered, and (3) low‐quality. Staff membership in these profiles was not related to youth‐staff ratio. However, results revealed significant differences between the profiles on the content of the offering and the age of youth in the setting.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/116957/1/ajcp9315.pd

Juvenile morphology of the large Antarctic canopy-forming brown alga, Desmarestia menziesii J. Agardh

Open Access via Springer Compact Agreement. We are grateful to the UK Natural Environment Research Council for funding to FCK (grants NE/D521522/1 and NE/J023094/1), in particular through the Collaborative Antarctic Science Scheme (Grant CASS-134, 2017) to FCK and LSP. Funding for cruise-based observations in 2019 was from US National Science Foundation award OPP-1744550 to CDA. We thank Kate Stanton, Teresa Murphy and Ben Robinson (British Antarctic Survey) for support with diving operations around Rothera in January–February 2018, and also Richard L. Moe (UC Berkeley) for locating specimens corresponding to the morphology described here in the UC collection. Special thanks are due to Charlie Bibby (Financial Times) for taking professional photographs of the unknown Desmarestia sp. in the aquarium of the Bonner Lab at Rothera (Fig. 2a). We would also like to thank Richard L. Moe (UC Berkeley) and Christian Wiencke (AWI Bremerhaven) for their very helpful reviews of this paper. Also, the MASTS pooling initiative (Marine Alliance for Science and Technology for Scotland, funded by the Scottish Funding Council and contributing institutions; grant reference HR09011) is gratefully acknowledged for supporting FCK. This research contributes to the SCAR Ant-ERA research programme.Peer reviewedPublisher PD

Model development of the Aquistore CO2 storage project

AbstractThe Plains CO2 Reduction (PCOR) Partnership, through the Energy & Environmental Research Center, is collaborating with Petroleum Technology Research Centre in site characterization; risk assessment; public outreach; and monitoring, verification, and accounting activities at the Aquistore project. The PCOR Partnership constructed a static geological model to assess the potential volumetric storage capacity of the Aquistore site and provide the foundation for dynamic simulation for the dynamic CO2 storage capacity. Results of the predictive simulations will be used in the risk assessment process to define an overall monitoring plan and assure stakeholders that the injected CO2 will remain safely stored

Marketing Missouri farm timber crops

Cover title

Prediction of autism spectrum disorder diagnosis using nonlinear measures of language-related EEG at 6 and 12 months

BACKGROUND: Early identification of autism spectrum disorder (ASD) provides an opportunity for early intervention and improved developmental outcomes. The use of electroencephalography (EEG) in infancy has shown promise in predicting later ASD diagnoses and in identifying neural mechanisms underlying the disorder. Given the high co-morbidity with language impairment, we and others have speculated that infants who are later diagnosed with ASD have altered language learning, including phoneme discrimination. Phoneme learning occurs rapidly in infancy, so altered neural substrates during the first year of life may serve as early, accurate indicators of later autism diagnosis. METHODS: Using EEG data collected at two different ages during a passive phoneme task in infants with high familial risk for ASD, we compared the predictive accuracy of a combination of feature selection and machine learning models at 6 months (during native phoneme learning) and 12 months (after native phoneme learning), and we identified a single model with strong predictive accuracy (100%) for both ages. Samples at both ages were matched in size and diagnoses (n = 14 with later ASD; n = 40 without ASD). Features included a combination of power and nonlinear measures across the 10‑20 montage electrodes and 6 frequency bands. Predictive features at each age were compared both by feature characteristics and EEG scalp location. Additional prediction analyses were performed on all EEGs collected at 12 months; this larger sample included 67 HR infants (27 HR-ASD, 40 HR-noASD). RESULTS: Using a combination of Pearson correlation feature selection and support vector machine classifier, 100% predictive diagnostic accuracy was observed at both 6 and 12 months. Predictive features differed between the models trained on 6- versus 12-month data. At 6 months, predictive features were biased to measures from central electrodes, power measures, and frequencies in the alpha range. At 12 months, predictive features were more distributed between power and nonlinear measures, and biased toward frequencies in the beta range. However, diagnosis prediction accuracy substantially decreased in the larger, more behaviorally heterogeneous 12-month sample. CONCLUSIONS: These results demonstrate that speech processing EEG measures can facilitate earlier identification of ASD but emphasize the need for age-specific predictive models with large sample sizes to develop clinically relevant classification algorithms.K23 DC017983 - NIDCD NIH HHS; P50 HD105351 - NICHD NIH HHS; R01 DC010290 - NIDCD NIH HHS; R21 DC008637 - NIDCD NIH HHSPublished versio