Summary of External-Store Drag

The drag problems associated with the addition of external stores to airplanes are reviewed. Current analytic techniques for estimating drag penalties associated with the addition of stores in both subsonic and supersonic flight are discussed. In subsonic flight, the drag penalty caused by the addition of external stores is shown to be a function of the type of store installation. In supersonic flight, the drag is shown to be a function of the type of store installation and also of the location of the store installation with respect to the rest of the airplane components. Special store arrangements and attention to the design of the store itself can reduce the drag penalty of the store installation

How Harmful are Adaptation Restrictions

The dominant assumption in economic models of climate policy remains that adaptation will be implemented in an optimal manner. There are, however, several reasons why optimal levels of adaptation may not be attainable. This paper investigates the effects of suboptimal levels of adaptation, i.e. adaptation restrictions, on the composition and level of climate change costs and on welfare. Several adaptation restrictions are identified and then simulated in a revised DICE model, extended with adaptation (AD-DICE). We find that especially substantial over-investment in adaptation can be very harmful due to sharply increasing marginal adaptation costs. Furthermore the potential of mitigation to offset suboptimal adaptation is investigated. When adaptation is not possible at extreme levels of climate change, it is cost-effective to use more stringent mitigation policies in order to keep climate change limited, thereby making adaptation possible. Furthermore not adjusting the optimal level of mitigation to these adaptation restrictions may double the costs of adaptation restrictions, and thus in general it is very harmful to ignore existing restrictions on adaptation when devising (efficient) climate policies.Integrated Assessment Modelling, Adaptation, Climate Change

Some Aspects of Measurement Error in Linear Regression of Astronomical Data

I describe a Bayesian method to account for measurement errors in linear regression of astronomical data. The method allows for heteroscedastic and possibly correlated measurement errors, and intrinsic scatter in the regression relationship. The method is based on deriving a likelihood function for the measured data, and I focus on the case when the intrinsic distribution of the independent variables can be approximated using a mixture of Gaussians. I generalize the method to incorporate multiple independent variables, non-detections, and selection effects (e.g., Malmquist bias). A Gibbs sampler is described for simulating random draws from the probability distribution of the parameters, given the observed data. I use simulation to compare the method with other common estimators. The simulations illustrate that the Gaussian mixture model outperforms other common estimators and can effectively give constraints on the regression parameters, even when the measurement errors dominate the observed scatter, source detection fraction is low, or the intrinsic distribution of the independent variables is not a mixture of Gaussians. I conclude by using this method to fit the X-ray spectral slope as a function of Eddington ratio using a sample of 39 z < 0.8 radio-quiet quasars. I confirm the correlation seen by other authors between the radio-quiet quasar X-ray spectral slope and the Eddington ratio, where the X-ray spectral slope softens as the Eddington ratio increases.Comment: 39 pages, 11 figures, 1 table, accepted by ApJ. IDL routines (linmix_err.pro) for performing the Markov Chain Monte Carlo are available at the IDL astronomy user's library, http://idlastro.gsfc.nasa.gov/homepage.htm

AD-DICE: an implementation of adaptation in the DICE model

Integrated Assessment Models (IAMS) have helped us over the past decade to understand the interactions between the environment and the economy in the context of climate change. Although it has also long been recognized that adaptation is a powerful and necessary tool to combat the adverse effects of climate change, most IAMs have not explicitly included the option of adaptation in combating climate change. This paper adds to the IAM and climate change literature by explicitly including adaptation in an IAM, thereby making the trade-offs between adaptation and mitigation visible. Specifically, a theoretical framework is created and used to implement adaptation as a decision variable into the DICE model. We use our new AD-DICE model to derive the adaptation cost functions implicit in the DICE model. In our set-up, adaptation and mitigation decisions are separable and AD-DICE can mimic DICE when adaptation is optimal. We find that our specification of the adaptation costs is robust with respect to the mitigation policy scenarios. Our numerical results show that adaptation is a powerful option to combat climate change, as it reduces most of the potential costs of climate change in earlier periods, while mitigation does so in later periods.integrated assessment modelling, adaptation, climate change

International Cooperation on Climate Change Adaptation from an Economic Perspective

This paper investigates the economic incentives of countries to cooperate on international adaptation financing. Adaptation is generally implicitly incorporated in the climate change damage functions as used in Integrated Assessment Models. We replace the implicit decision on adaptation with explicit adaptation in a multi-regional setting by using an adjusted RICE model. We show that making adaptation explicit will not affect the optimal mitigation path when adaptation is set at its optimal level. Sub-optimal adaptation will, however, change the optimal mitigation path. Furthermore this paper studies for different forms of cooperation what effects international adaptation transfers will have on (i) domestic adaptation and (ii) the optimal mitigation path. Adaptation transfers will fully crowd out domestic adaptation in a first best setting. Transfers will decrease overall mitigation in our numerical simulations. An analytical framework is used to analyse the most important mechanisms and a numerical model is used to assess the magnitude of effects.Climate Change, Adaptation Funding, Integrated Assessment Modeling

Development of Prognosis in Palliative care Study (PiPS) predictor models to improve prognostication in advanced cancer: prospective cohort study

OBJECTIVE: To develop a novel prognostic indicator for use in patients with advanced cancer that is significantly better than clinicians' estimates of survival. DESIGN: Prospective multicentre observational cohort study. SETTING: 18 palliative care services in the UK (including hospices, hospital support teams, and community teams). PARTICIPANTS: 1018 patients with locally advanced or metastatic cancer, no longer being treated for cancer, and recently referred to palliative care services. MAIN OUTCOME MEASURES: Performance of a composite model to predict whether patients were likely to survive for "days" (0-13 days), "weeks" (14-55 days), or "months+" (>55 days), compared with actual survival and clinicians' predictions. RESULTS: On multivariate analysis, 11 core variables (pulse rate, general health status, mental test score, performance status, presence of anorexia, presence of any site of metastatic disease, presence of liver metastases, C reactive protein, white blood count, platelet count, and urea) independently predicted both two week and two month survival. Four variables had prognostic significance only for two week survival (dyspnoea, dysphagia, bone metastases, and alanine transaminase), and eight variables had prognostic significance only for two month survival (primary breast cancer, male genital cancer, tiredness, loss of weight, lymphocyte count, neutrophil count, alkaline phosphatase, and albumin). Separate prognostic models were created for patients without (PiPS-A) or with (PiPS-B) blood results. The area under the curve for all models varied between 0.79 and 0.86. Absolute agreement between actual survival and PiPS predictions was 57.3% (after correction for over-optimism). The median survival across the PiPS-A categories was 5, 33, and 92 days and survival across PiPS-B categories was 7, 32, and 100.5 days. All models performed as well as, or better than, clinicians' estimates of survival. CONCLUSIONS: In patients with advanced cancer no longer being treated, a combination of clinical and laboratory variables can reliably predict two week and two month survival

DNA methylation of ESR-1 and N-33 in colorectal mucosa of patients with Ulcerative Colitis (UC)

Introduction: Epigenetic marking such as DNA methylation influence gene transcription and chromosomal stability and may also be affected by environmental exposures. Few studies exist on alteration in DNA methylation profiles (genomic and gene specific methylation) in patients with Ulcerative Colitis (UC) and none assessing its relationship with lifestyle exposures. Aims & Methods: To assess genomic methylation and promoter methylation of the ESR-1 (oestrogen receptor - 1) and N-33 (tumour suppressor candidate-3) genes in the macroscopically normal mucosa of UC patients as well as to investigate effects of anthropometric and lifestyle exposures on DNA methylation. Sixty eight subjects were recruited (24 UC and 44 age and sex matched controls). Colorectal mucosal biopsies were obtained and DNA was extracted. Genomic DNA methylation was quantified using the tritium-labelled cytosine extension assay (3[H] dCTP) whilst gene specific methylation was quantified using the COBRA method. Results: The methylation level of both ESR-1 and N-33 genes were significantly higher in UC subjects compared with controls (7.9% vs 5.9%; p = 0.015 and 66% vs 9.3%; p < 0.001 respectively). There was no detectable difference in global DNA methylation between patients with UC and age and sex matched controls. No associations between indices of DNA methylation and anthropometric measures or smoking patterns were detected. Conclusions: For the first time, we have shown increased methylation in the promoter regions of the putative tumour suppressor gene N-33 in macroscopically normal mucosa of patients with UC. In addition, we have confirmed that methylation of ESR-1 promoter is higher in UC patients compared with age and sex matched controls. These findings suggests that, inactivation through methylation of the putative tumour suppressor genes N-33 and ESR-1, may not be associated with colorectal carcinogenesis in UC

Virial Masses of Black Holes from Single Epoch Spectra of AGN

We describe the general problem of estimating black hole masses of AGN by calculating the conditional probability distribution of M_BH given some set of observables. Special attention is given to the case where one uses the AGN continuum luminosity and emission line widths to estimate M_BH, and we outline how to set up the conditional probability distribution of M_BH given the observed luminosity, line width, and redshift. We show how to combine the broad line estimates of M_BH with information from an intrinsic correlation between M_BH and L, and from the intrinsic distribution of M_BH, in a manner that improves the estimates of M_BH. Simulation was used to assess how the distribution of M_BH inferred from the broad line mass estimates differs from the intrinsic distribution, and we find that this can lead to an inferred distribution that is too broad. We use these results and a sample of 25 sources that have recent reverberation mapping estimates of AGN black hole masses to investigate the effectiveness of using the C IV emission line to estimate M_BH and to indirectly probe the C IV region size--luminosity (R--L) relationship. We estimated M_BH from both C IV and H-Beta for a sample of 100 sources, including new spectra of 29 quasars. We find that the two emission lines give consistent estimates if one assumes R \propto L^{1/2}_{UV} for both lines.Comment: 38 pages, 6 figures, accepted by Ap