Kinetics of phase-separation in the critical spherical model and local scale-invariance

The scaling forms of the space- and time-dependent two-time correlation and response functions are calculated for the kinetic spherical model with a conserved order-parameter and quenched to its critical point from a completely disordered initial state. The stochastic Langevin equation can be split into a noise part and into a deterministic part which has local scale-transformations with a dynamical exponent z=4 as a dynamical symmetry. An exact reduction formula allows to express any physical average in terms of averages calculable from the deterministic part alone. The exact spherical model results are shown to agree with these predictions of local scale-invariance. The results also include kinetic growth with mass conservation as described by the Mullins-Herring equation.Comment: Latex2e with IOP macros, 28 pp, 2 figures, final for

Tearless Logic Model

Even among people who know and have seen the value of logic models, the term can “strike fear into the hearts” of experienced community psychologists and veteran non-profit staff and board members alike. Add the phrase “outcome-based planning” and you are likely to energize those you are working with to run as fast as possible for the door. Such technical terms may confuse and intimidate community members and grassroots partners who are the foundation of the practice of community psychology. At the same time, organizations can benefit from time spent on outcome-based planning, especially in developing a well-conceived logic model

Field Evaluation of Commercially Available Small Unmanned Aircraft Crop Spray Systems

Agricultural research and development on small unmanned aircraft systems (UAS) has been directed toward UAS enabled sensing to detect features of interest. While compelling, there is an immediate need to increase the breadth and depth of UAS-based research, to move beyond sensing, and explore active intervention in agricultural production systems. This paper is focused on the concept of crop protection through ultra-precise, unmanned aerial application systems, and seeks to initiate research discussion in this important area of opportunity. Toward this end, two different, commercially available, small Unmanned Aerial Application Systems (sUAAS - defined as less than 55 lbs. maximum take-off weight) were evaluated for operational techniques and application system efficacy under dynamic field conditions. The performance of the factory supplied spray equipment systems are documented using traditional aerial spray testing methods that have been modified for UAS enabled application systems, referred to as small Unmanned Aerial Application Systems (sUAAS). Results from initial testing protocols indicate that the factory supplied systems are quite different in design and implementation, with spray test results that reflect this difference in design, in both deposition and spray swath. Further, it is apparent that with the advent of unmanned aerial application systems, and the unique characteristics of the integrated aircraft and application systems, there is a very real need for the development of standardized sUAAS testing procedures

Integration of Crop-Livestock Systems: An Opportunity toProtect Grasslands from Conversion to Cropland in the US Great Plains

The Great Plains is a mixture of cropland and grassland mainly used for agricultural purposes, with grasslands under continual threat of conversion to cropland. Agriculturists are advocating for the integration of crop-livestock systems (ICLS) to recouple nutrient cycles, improve biodiversity, and increase resilience of agricultural operations. We address the benefits of ICLS in the Great Plains, contending that focus on improving soil health and financial stability of agricultural operations should reduce the conversion of grasslands to cropland. Using US Department of Agriculture National Agricultural Statistics Service Census of Agriculture survey data from the 1925 to 2017 category “cropland used only for pasture or grazing,” which represents land that had been cropped but converted to annual/perennial pasture and grazed, we showcase that the number of farms and the land area in this category is a reasonable proxy of ICLS. As expected, ICLS dramatically decreased in the entire United States from 1925 to 1945, but from 1945 to 2002 in the Great Plains ICLS remained relatively constant, providing evidence of sustained crop-livestock integration. Consistent high numbers of beef cows during this period and the wide availability of forages and crop residues for ruminants facilitated opportunities for producers to use ICLS on their individual operations (within farm) or among operations where row crop farmers and forage-based producers integrated beef cattle use across the landscape (among farms). This integration, however, was decoupled from 2006 to 2013, a period of high grain prices. As a result, economic value of grasslands was decreased and conversion to cropland was increased. Thus, conservation efforts in the Great Plains for grasslands should focus on keeping grasslands intact for provision of multiple ecosystem goods and services by emphasizing incorporation of ICLS within and among farms to reduce the risk of converting grassland to cropland

Integration of Crop-Livestock Systems: An Opportunity to Protect Grasslands from Conversion to Cropland in the US Great Plains

The Great Plains is a mixture of cropland and grassland mainly used for agricultural purposes, with grasslands under continual threat of conversion to cropland. Agriculturists are advocating for the integration of crop-livestock systems (ICLS) to recouple nutrient cycles, improve biodiversity, and increase resilience of agricultural operations. We address the benefits of ICLS in the Great Plains, contending that focus on improving soil health and financial stability of agricultural operations should reduce the conversion of grasslands to cropland. Using US Department of Agriculture National Agricultural Statistics Service Census of Agriculture survey data from the 1925−2017 category “cropland used only for pasture or grazing,” which represents land that had been cropped but converted to annual/perennial pasture and grazed, we showcase that the number of farms and the land area in this category is a reasonable proxy of ICLS. As expected, ICLS dramatically decreased in the entire United States from 1925 to 1945, but from 1945 to 2002 in the Great Plains ICLS remained relatively constant, providing evidence of sustained crop-livestock integration. Consistent high numbers of beef cows during this period and the wide availability of forages and crop residues for ruminants facilitated opportunities for producers to use ICLS on their individual operations (within farm) or among operations where row crop farmers and forage-based producers integrated beef cattle use across the landscape (among farms). This integration, however, was decoupled from 2006 to 2013, a period of high grain prices. As a result, economic value of grasslands was decreased and conversion to cropland was increased. Thus, conservation efforts in the Great Plains for grasslands should focus on keeping grasslands intact for provision of multiple ecosystem goods and services by emphasizing incorporation of ICLS within and among farms to reduce the risk of converting grassland to cropland

The stellar contribution to the extra-galactic background light and absorption of high-energy gamma-rays

TeV gamma rays from distant astrophysical sources are attenuated due to electron-positron pair creation by interacting with ultraviolet/optical to infrared photons which fill the universe and are collectively known as the extra-galactic background light (EBL). We model the ~0.1-10 eV starlight component of the EBL derived from expressions for the stellar initial mass function, star formation history of the universe, and wavelength-dependent absorption of a large sample of galaxies in the local universe. These models are simultaneously fitted to the EBL data as well as to the data on the stellar luminosity density in our local universe. We find that the models with modified Salpeter A initial mass function together with Cole et al. (2001) or Hopkins and Beacom (2006) star formation history best represent available data. Since no dust emission is included, our calculated EBL models can be interpreted as the lower limits in the ~0.1-1 eV range. We present simple analytic fits to the best-fit EBL model evolving with redshift. We then proceed to calculate gamma-ray opacities, and absorption of ~10-300 GeV gamma-rays coming from different redshifts. We discuss implications of our results for the Fermi Gamma Ray Space Telescope and ground-based Air Cherenkov Telescopes.Comment: 30 pages, 11 figures and 1 table. Substantially revised but the formalism remains unchanged. Accepted in Ap

The effect of distance on reaction time in aiming movements

Target distance affects movement duration in aiming tasks but its effect on reaction time (RT) is poorly documented. RT is a function of both preparation and initiation. Experiment 1 pre-cued movement (allowing advanced preparation) and found no influence of distance on RT. Thus, target distance does not affect initiation time. Experiment 2 removed pre-cue information and found that preparing a movement of increased distance lengthens RT. Experiment 3 explored movements to targets of cued size at non-cued distances and found size altered peak speed and movement duration but RT was influenced by distance alone. Thus, amplitude influences preparation time (for reasons other than altered duration) but not initiation time. We hypothesise that the RT distance effect might be due to the increased number of possible trajectories associated with further targets: a hypothesis that can be tested in future experiments

LHS6343C: A Transiting Field Brown Dwarf Discovered by the Kepler Mission

We report the discovery of a brown dwarf that transits one member of the M+M binary system LHS6343AB every 12.71 days. The transits were discovered using photometric data from the Kelper public data release. The LHS6343 stellar system was previously identified as a single high-proper-motion M dwarf. We use high-contrast imaging to resolve the system into two low-mass stars with masses 0.45 Msun and 0.36 Msun, respectively, and a projected separation of 55 arcsec. High-resolution spectroscopy shows that the more massive component undergoes Doppler variations consistent with Keplerian motion, with a period equal to the transit period and an amplitude consistent with a companion mass of M_C = 62.8 +/- 2.3 Mjup. Based on an analysis of the Kepler light curve we estimate the radius of the companion to be R_C = 0.832 +/- 0.021 Rjup, which is consistent with theoretical predictions of the radius of a > 1 Gyr brown dwarf.Comment: Our previous analysis neglected the dependence of the scaled semimajor axis, a/R, on the transit depth. By not correcting a/R for the third-light contamination, we overestimated the mass of Star A, which led to an overestimate the mass and radius of the LHS6343

Challenge of Reducing Perinatal Mortality in Rural Congo: Findings of a Prospective, Population-based Study

Each year, an estimated six million perinatal deaths occur worldwide, and 98% of these deaths occur in lowand middle-income countries. These estimates are based on surveys in both urban and rural areas, and they may underrepresent the problem in rural areas. This study was conducted to quantify perinatal mortality, to identify the associated risk factors, and to determine the most common causes of early neonatal death in a rural area of the Democratic Republic of the Congo (DRC). Data were collected on 1,892 births. Risk factors associated with perinatal deaths were identified using multivariate analysis with logistic regression models. Causes of early neonatal deaths were determined by physician-review of information describing death. The perinatal mortality rate was 61 per 1,000 births; the stillbirth rate was 30 per 1,000 births; and the early neonatal death rate was 32 per 1,000 livebirths. Clinically-relevant factors independently associated with perinatal death included: low birthweight [odds ratio (OR)=13.51, 95% confidence interval (CI) 7.82-23.35], breech presentation (OR)=12.41; 95% CI 4.62-33.33), lack of prenatal care (OR=2.70, 95% CI 1.81-4.02), and parity greater than 4 (OR=1.93 95% CI 1.11-3.37). Over one-half of early neonatal deaths (n=37) occurred during the first two postnatal days, and the most common causes were low birthweight/prematurity (47%), asphyxia (34%), and infection (8%). The high perinatal mortality rate in rural communities in the DRC, approximately one-half of which is attributable to early neonatal death, may be modifiable. Specifically, deaths due to breech presentation, the second most common risk factor, may be reduced by making available emergency obstetric care. Most neonatal deaths occur soon after birth, and nearly three-quarters are caused by low birthweight/prematurity or asphyxia. Neonatal mortality might be reduced by targeting interventions to improve neonatal resuscitation and care of larger preterm infants

Automatic Hip Fracture Identification and Functional Subclassification with Deep Learning

Purpose: Hip fractures are a common cause of morbidity and mortality. Automatic identification and classification of hip fractures using deep learning may improve outcomes by reducing diagnostic errors and decreasing time to operation. Methods: Hip and pelvic radiographs from 1118 studies were reviewed and 3034 hips were labeled via bounding boxes and classified as normal, displaced femoral neck fracture, nondisplaced femoral neck fracture, intertrochanteric fracture, previous ORIF, or previous arthroplasty. A deep learning-based object detection model was trained to automate the placement of the bounding boxes. A Densely Connected Convolutional Neural Network (DenseNet) was trained on a subset of the bounding box images, and its performance evaluated on a held out test set and by comparison on a 100-image subset to two groups of human observers: fellowship-trained radiologists and orthopaedists, and senior residents in emergency medicine, radiology, and orthopaedics. Results: The binary accuracy for fracture of our model was 93.8% (95% CI, 91.3-95.8%), with sensitivity of 92.7% (95% CI, 88.7-95.6%), and specificity 95.0% (95% CI, 91.5-97.3%). Multiclass classification accuracy was 90.4% (95% CI, 87.4-92.9%). When compared to human observers, our model achieved at least expert-level classification under all conditions. Additionally, when the model was used as an aid, human performance improved, with aided resident performance approximating unaided fellowship-trained expert performance. Conclusions: Our deep learning model identified and classified hip fractures with at least expert-level accuracy, and when used as an aid improved human performance, with aided resident performance approximating that of unaided fellowship-trained attendings.Comment: Presented at Orthopaedic Research Society, Austin, TX, Feb 2, 2019, currently in submission for publicatio