Cosmological solutions of supercritical string theory

We study quintessence-driven, spatially flat, expanding FRW cosmologies that arise naturally from string theory formulated in a supercritical number of spacetime dimensions. The tree-level potential of the string theory produces an equation of state at the threshold between accelerating and decelerating cosmologies, and the resulting spacetime is globally conformally equivalent to Minkowski space. We demonstrate that exact solutions exist with a condensate of the closed-string tachyon, the simplest of which is a Liouville wall moving at the speed of light. We rely on the existence of this solution to derive constraints on the couplings of the tachyon to the dilaton and metric in the string theory effective action. In particular, we show that the tachyon dependence of the Einstein term must be nontrivial.Comment: v2: typos corrected and references added; v3: minor corrections; 35 pages, 9 figure

Dark Matter and The Anthropic Principle

We evaluate the problem of galaxy formation in the landscape approach to phenomenology of the axion sector. With other parameters of standard LambdaCDM cosmology held fixed, the density of cold dark matter is bounded below relative to the density of baryonic matter by the requirement that structure should form before the era of cosmological constant domination of the universe. Galaxies comparable to the Milky Way can only form if the ratio also satisfies an upper bound. The resulting constraint on the density of dark matter is too loose to select a low axion decay constant or small initial displacement angle on anthropic grounds.Comment: 17 pages, 1 figur

The Impact of Community Violence on African American Children and Families

This thesis will attempt to increase awareness of the significant impact of community violence on the social and emotional development of African American children and families and to examine the role that mental health and maternal and child health agencies could play in the implementation of effective prevention and intervention strategies. The purpose of the study is to give an overview of the scope of the problem, identifying the extent, prevalence, and nature of the community violence in African American communities. Additionally, the study will review the theoretical and conceptual frameworks that have guided research in the area of violence and its impact on children. This research specifically addresses the impact of community violence on the social and emotional development of African American children. The study gives an overview of prevention programs that have been developed and are presented in this study. Also, it addresses the issue of the evaluation of violence prevention efforts. It also provides information on three violence prevention programs in the state of Massachusetts, California, and Ohio. The study addresses the lack of involvement from mental health professionals in the area of community violence and suggests that mental health professionals may have to develop a new treatment paradigm to meet the needs of African American children who are victims of community violence. The summary presents three discussions of new directions that must be taken to more effectively address violence in the African American community. The major theme is that, in order to understand and intervene in African American violence, it bas to be understood in the political and social context of African American communities

BAYES' ESTIMATES OF THE DOUBLE HURDLE MODEL IN THE PRESENCE OF FIXED COSTS

We present a model of market adoption (participation) where the presence of non-negligible fixed costs leads to non-zero censoring of the traditional double-hurdle regression. Fixed costs arise due to household resources that must be devoted a priori to the decision to participate in the market. These costs-usually a cost of time-motivate two-step decision-making and focus attentions on the minimum-efficient scale of operations (the minimum amount of milk sales) at which market entry becomes viable. This focus, in turn, motivates a non-zero-censored Tobit regression estimated through routine application of Markov chain Monte Carlo Methods.market participation, fixed costs, double-hurdle model, censored regression., Financial Economics, O1, O11, C34, O13, Q16, D1,

HOW BIG IS YOUR NEIGHBORHOOD? SPATIAL IMPLICATIONS OF MARKET PARTICIPATION BY SMALLHOLDER LIVESTOCK PRODUCERS

Identifying ways to increase market participation by smallholder producers requires identifying variables that influence market access. This is usually achieved using probit estimation. An important phenomenon affecting entry decision-making is the entry decision of a 'similar' household, where similarity is measured in terms of 'location.' When neighborhood influences are significant, it is important to allow for them in discrete decision contexts, such as probit estimation. This paper, therefore, assesses the magnitude of neighborhood influences in smallholder decisions concerning market entry. The empirical model is based on a cross-section of (110) farms situated in northern Philippines, visited (twice) in the 2000-2001 production year (a panel of 220 observations). The vehicle for analysis is a Bayesian formulation of a standard probit model, but one that allows for spatial autoregression in the decision vector. Estimation requires a Metropolis-step addition to a basic Gibbs sampling algorithm and generates useful insights concerning quantities that are important for market-access policy.Livestock Production/Industries,

Consumer Willingness to Pay for Breads Marketed as "Low-Carbohydrate"

Bread producers are taking advantage of healthy feeding habits by developing new "low carbohydrate" products to entice customers. These low carbohydrate breads are generally more expensive than conventional types. This study tests the hypothesis that consumers are willing to pay higher premium for "low carbohydrate" breads at various locations and markets. We use retail data in a hedonic pricing framework to estimate the premium paid for the "low carbohydrate" attribute of bread. Results show that the implicit price of the "low carbohydrate" attribute of bread ranges from about 0.06¢ to 1.1¢ per gram, reflecting the amount consumers are willing to pay above the price of conventional bread.low carbohydrate bread, hedonic price, willingness to pay, Institutional and Behavioral Economics, D12,

Preventing and managing plant diseases (2008)

"New 4/08/2M.""Master Gardener.

Interpretability of machine learning solutions in public healthcare : the CRISP-ML approach

Public healthcare has a history of cautious adoption for artificial intelligence (AI) systems. The rapid growth of data collection and linking capabilities combined with the increasing diversity of the data-driven AI techniques, including machine learning (ML), has brought both ubiquitous opportunities for data analytics projects and increased demands for the regulation and accountability of the outcomes of these projects. As a result, the area of interpretability and explainability of ML is gaining significant research momentum. While there has been some progress in the development of ML methods, the methodological side has shown limited progress. This limits the practicality of using ML in the health domain: the issues with explaining the outcomes of ML algorithms to medical practitioners and policy makers in public health has been a recognized obstacle to the broader adoption of data science approaches in this domain. This study builds on the earlier work which introduced CRISP-ML, a methodology that determines the interpretability level required by stakeholders for a successful real-world solution and then helps in achieving it. CRISP-ML was built on the strengths of CRISP-DM, addressing the gaps in handling interpretability. Its application in the Public Healthcare sector follows its successful deployment in a number of recent real-world projects across several industries and fields, including credit risk, insurance, utilities, and sport. This study elaborates on the CRISP-ML methodology on the determination, measurement, and achievement of the necessary level of interpretability of ML solutions in the Public Healthcare sector. It demonstrates how CRISP-ML addressed the problems with data diversity, the unstructured nature of data, and relatively low linkage between diverse data sets in the healthcare domain. The characteristics of the case study, used in the study, are typical for healthcare data, and CRISP-ML managed to deliver on these issues, ensuring the required level of interpretability of the ML solutions discussed in the project. The approach used ensured that interpretability requirements were met, taking into account public healthcare specifics, regulatory requirements, project stakeholders, project objectives, and data characteristics. The study concludes with the three main directions for the development of the presented cross-industry standard process