188 research outputs found
Reheating and gravitino production in braneworld inflation
We consider the constraints that can be imposed on a wide class of Inflation
models in modified gravity scenarios in which the Friedmann equation is
modified by the inclusion of terms, where is the total energy
density. In particular we obtain the reheating temperature and gravitino
abundance associated with the end of inflation. Whereas models of chaotic
inflation and natural inflation can easily avoid the conventional gravitino
overproduction problem, we show that supersymmetric hybrid inflation models
(driven by both F and D-terms) do not work in the dominated era. We
also study inflation driven by exponetial potentials in this modified
background, and show that the gravitino production is suppressed enough to
avoid there being a problem, although other conditions severely constrain these
models.Comment: 24page
Estimates of Population Highly Annoyed from Transportation Noise in the United States: An Unfair Share of the Burden by Race and Ethnicity
Transportation is one of the most pervasive sources of community noise. In
this study, we used a spatially-resolved model of transportation-related noise
with established transportation noise exposure-response functions to estimate
the population highly annoyed (HA) due to aviation, road, and railway traffic
sources in the United States. Additionally, we employed the use of the Fair
Share Ratio to assess race/ethnicity disparities in traffic noise exposures.
Our results estimate that in 2020, 7.8 million (2.4%) individuals were highly
annoyed by aviation noise, while 5.2 million (1.6%) and 7.9 million (2.4%)
people were highly annoyed by rail and roadway noise, respectively, across the
US. The Fair Share Ratio revealed that Non-Hispanic Asian, Black, NHPI, and
Other, and Hispanic populations were disproportionally highly annoyed by
transportation noise nationwide. Notably, Hispanic populations experienced the
greatest share of high annoyance from aviation noise (1.69 times their
population share). Non-Hispanic Black populations experienced the greatest
share of high annoyance from railway noise (1.48 times their population share).
Non-Hispanic Asian populations experienced the greatest share of high annoyance
from roadway noise (1.51 times their population share). Analyses at the state
and Urban Area levels further highlighted varying disparities in transportation
noise exposure and annoyance across different race ethnicity groups, but still
suggested that Non-Hispanic White populations were less annoyed by all sources
of transportation noise compared to non-White populations. Our findings
indicate widespread presence of transportation noise annoyance across the US
and emphasize the need for targeted source-specific noise mitigation strategies
and policies to minimize the disproportionate impact of transportation noise in
the US.Comment: Added references. Revised the Introduction and Discussion sections. A
detailed description of methodology was added to the supplementary material
Dynamical solutions of warped six dimensional supergravity
We derive a new class of exact time dependent solutions in a warped six
dimensional supergravity model. Under the assumptions we make for the form of
the underlying moduli fields, we show that the only consistent time dependent
solutions lead to all six dimensions evolving in time, implying the eventual
decompactification or collapse of the extra dimensions. We also show how the
dynamics affects the quantization of the deficit angle.Comment: 18 pages, no figure, typos corrected, references added, the final
versio
Assessing the Value of Complex Refractive Index and Particle Density for Calibration of Low-Cost Particle Matter Sensor for Size-Resolved Particle Count and PM2.5 Measurements
Commercially available low-cost particulate matter (PM) sensors provide
output as total or size-specific particle counts and mass concentrations. These
quantities are not measured directly but are estimated by the original
equipment manufacturers' (OEM) proprietary algorithms and have inherent
limitations since particle scattering depends on their composition, size,
shape, and complex index of refraction (CRI). Hence, there is a need to
characterize and calibrate their performance under a controlled environment. We
present calibration algorithms for Plantower PMS A003 sensor as a function of
particle size and concentration. A standardized experimental protocol was used
to control the PM level, environmental conditions and to evaluate
sensor-to-sensor reproducibility. The calibration was based on tests when PMS
A003 were exposed to different polydisperse standardized testing aerosols. The
results suggested particle size distribution from PMS A003 was shifted compared
to reference instrument measures. For calibration of number concentration,
linear model without adjusting aerosol properties corrects the raw PMS A003
measurement for specific size bins with normalized mean absolute error within
4.0% of the reference instrument. Although the Bayesian Information Criterion
suggests that models adjusting for particle optical properties and relative
humidity are technically superior, they should be used with caution as the
particle properties used in fitting were within a narrow range for challenge
aerosols. The calibration models adjusted for particle CRI and density account
for non-linearity in the OEM's mass concentrations estimates and demonstrated
lower error. These results have significant implications for using PMS A003 in
high concentration environments, including indoor air quality and
occupational/industrial exposure assessments, wildfire smoke, or near-source
monitoring scenarios
Disease transmission models for public health decision making: toward an approach for designing intervention strategies for Schistosomiasis japonica.
Mathematical models of disease transmission processes can serve as platforms for integration of diverse data, including site-specific information, for the purpose of designing strategies for minimizing transmission. A model describing the transmission of schistosomiasis is adapted to incorporate field data typically developed in disease control efforts in the mountainous regions of Sichuan Province in China, with the object of exploring the feasibility of model-based control strategies. The model is studied using computer simulation methods. Mechanistically based models of this sort typically have a large number of parameters that pose challenges in reducing parametric uncertainty to levels that will produce predictions sufficiently precise to discriminate among competing control options. We describe here an approach to parameter estimation that uses a recently developed statistical procedure called Bayesian melding to sequentially reduce parametric uncertainty as field data are accumulated over several seasons. Preliminary results of applying the approach to a historical data set in southwestern Sichuan are promising. Moreover, technologic advances using the global positioning system, remote sensing, and geographic information systems promise cost-effective improvements in the nature and quality of field data. This, in turn, suggests that the utility of the modeling approach will increase over time
Developing a Model Curriculum for a University Course in Health Impact Assessment in the United States
As Health Impact Assessment (HIA) become increasingly common in the U.S. there is growing demand for instruction beyond sho1i courses and online training. As of October 2013, there are graduate level courses that include instruction on HIA in at least 17 universities in the U.S., including 4 courses that focus explicitly on HIA. Instructors of these four courses collaborated to develop a model curriculum for teaching HIA that draws on a framework for experiential learning and on a theoretical model of curriculum formulation. This article includes an in-depth analysis of these courses and presents a model curriculum for HIA instruction during an academic quaiier or semester course in a University. This model curriculum may help faculty develop a graduate level HIA course at their institution, as well as inform public health and community design professionals interested in building capacity to conduct HIAs, and students considering taking an HIA course. International instructors could also learn from the U.S. experience, and apply the model curriculum to their setting and educational structure.This work was supported in part by the Johns Hopkins Bloomberg School of Public Health
Faculty Innovation Fund
Remote Imaging Applied to Schistosomiasis Control: The Anning River Project
The use of satellite imaging to remotely detect areas of high risk for transmission of infectious disease is an appealing prospect for large-scale monitoring of these diseases. The detection of large-scale environmental determinants of disease risk, often called landscape epidemiology, has been motivated by several authors (Pavlovsky 1966; Meade et al. 1988). The basic notion is that large-scale factors such as population density, air temperature, hydrological conditions, soil type, and vegetation can determine in a coarse fashion the local conditions contributing to disease vector abundance and human contact with disease agents. These large-scale factors can often be remotely detected by sensors or cameras mounted on satellite or aircraft platforms and can thus be used in a predictive model to mark high risk areas of transmission and to target control or monitoring efforts. A review of satellite technologies for this purpose was recently presented by Washino and Wood (1994) and Hay (1997) and Hay et al. (1997)
Connectivity sustains disease transmission in environments with low potential for endemicity: modelling schistosomiasis with hydrologic and social connectivities
Social interaction and physical interconnections between populations can influence the spread of parasites. The role that these pathways play in sustaining the transmission of parasitic diseases is unclear, although increasingly realistic metapopulation models are being used to study how diseases persist in connected environments. We use a mathematical model of schistosomiasis transmission for a distributed set of heterogeneous villages to show that the transport of parasites via social (host movement) and environmental (parasite larvae movement) pathways has consequences for parasite control, spread and persistence. We find that transmission can be sustained regionally throughout a group of connected villages even when individual village conditions appear not to support endemicity. Optimum transmission is determined by an interplay between different transport pathways, and not necessarily by those that are the most dispersive (e.g. disperse social contacts may not be optimal for transmission). We show that the traditional targeting of villages with high infection, without regard to village interconnections, may not lead to optimum control. These findings have major implications for effective disease control, which needs to go beyond considering local variations in disease intensity, to also consider the degree to which populations are interconnected
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