Energy poverty: have we got the measure of it?

At the macro-level it has long been accepted that there is a strong relationship between energy and economic growth (IDS, 2003). In the 1990s, the development discourse began to focus on the effects that economic growth has had on poverty. However, an interest in the links between energy and poverty took more time to emerge$^2$ Indeed, energy as an enabling factor in social transformations at the micro-level has not played a major role in the development discourse. Energy, unlike other infrastructure-related sectors such as water, transport and ICT, has also not been a central topic within the social sciences, including anthropology. The recent interest in climate change has focused on energy as the problem not part of the solution, particularly for the poor

Companion Animal Demographics in the United States: A Historical Perspective

Modern American society recognizes the crucial role of data and information in evaluating and effectively addressing societal problems. Americans are bombarded with information on the economy, public health, social and psychological attitude trends, and other matters that are considered important. For example, no self-respecting politician would think of launching a political campaign or initiative without some sense of what the public might be worrying about. Addressing pet population issues should be no different. Data are needed in order to define the nature and scope of the dog and cat demographic challenge. Data can help people to understand the impact of “pet homelessness” on companion animals; to identify some of the characteristics of both successful and failed human-animal relationships; and to develop sound, effective, and long-lasting solutions that will strengthen humans’ relationships with companion animals and enhance companion animals’ welfare. Given the need for reliable data, what is known now about trends concerning the companion animal population and the shelters that help address the “homelessness” problem

Recent Lung Cancer Patterns in Younger Age-Cohorts in Ireland

Background: Smoking causes 85% of all lung cancers in males and 70% in females. Therefore, birth cohort analysis and annual-percent-changes (APC) in age-specific lung cancer mortality rates, particularly in the youngest age cohorts, can explain the beneficial impacts of both past and recent anti-smoking interventions. Methods: A long-term time-trend analysis (1958-2002) in lung cancer mortality rates focusing on the youngest age-cohorts (30-49 years of age) in particular was investigated in Ireland. The rates were standardised to the World Standard Population. Lung cancer mortality data were downloaded from the WHO Cancer Mortality Database to estimate APCs in death rates, using the Joinpoint regression (version 3.0) program. A simple age-cohort modelling (log-linear Poisson model) was also done, using SAS software. Results: The youngest birth cohorts (born after 1965) have almost one-fourth lower lung cancer risk relative to those born around the First World War. A more than 50% relative decline in death rates among those between 35 and 39 years of age was observed in both sexes in recent years. The youngest age-cohorts (30-39 years of age) in males also showed a significant decrease in death rates in 1998-2002 by more than 3% every five years from 1958-1962 onwards. However, death rate declines in females are slower. Conclusions: The youngest birth cohorts had the lowest lung cancer risk and also showed a significant decreasing lung cancer death rate in the most recent years. Such temporal patterns indicate the beneficial impacts of both recent and past tobacco control efforts in Ireland. However, the decline in younger female cohorts is slower. A comprehensive national tobacco control program enforced on evidence-based policies elsewhere can further accelerate a decline in death rates, especially among the younger generations

Sex-Differences in Lung Cancer Cell-Types? An Epidemiologic Study in Ireland

Objective: This study assesses the epidemiological pattern of lung cancer cell-types in Ireland, with identification of any underlying gender variations. Methods: Lung cancer incidence data, including the major cell-types: squamous-cell-carcinoma (SCC), adenocarcinoma (AC), small-cell-lung-carcinoma (SCLC) and large-cell-carcinoma (LCC) were obtained from the national cancer registry (1994–2000), together with individual characteristics, such as age, gender, smoking status, and the year of diagnosis. Age-standardised incidence rates (ASIR), male-to-female (M: F) rate ratios (RR) of ASIR for SCC and AC, as well as RR of AC: SCC according to smoking status for both sexes, were estimated. Estimated-annual-percent-changes for each of the cell-types were calculated. Results: AC incidence in females is rising annually (8.5%, p=0.008) from 1994 to 2000, while SCC is declining (−5.4%, p=0.01) in males. M: F ratios of ASIR are consistently greater than ‘one’, but converging recently. RR of AC: SCC is also approaching ‘unity’ across both sexes, irrespective of the smoking status. Conclusions: An apparent increase in lung AC incidence in females was observed in Ireland that might indicate some local environmental risk factors, in addition to changing smoking habits. The study findings do not support the hypothesis that females in general are at higher risk for lung cancer development, but tobacco and histologic-specific susceptibility cannot be ruled out

Adaptive Multidimensional Integration Based on Rank-1 Lattices

Quasi-Monte Carlo methods are used for numerically integrating multivariate functions. However, the error bounds for these methods typically rely on a priori knowledge of some semi-norm of the integrand, not on the sampled function values. In this article, we propose an error bound based on the discrete Fourier coefficients of the integrand. If these Fourier coefficients decay more quickly, the integrand has less fine scale structure, and the accuracy is higher. We focus on rank-1 lattices because they are a commonly used quasi-Monte Carlo design and because their algebraic structure facilitates an error analysis based on a Fourier decomposition of the integrand. This leads to a guaranteed adaptive cubature algorithm with computational cost $O(mb^m)$, where $b$ is some fixed prime number and $b^m$ is the number of data points

Inflation and initial conditions in the pre-big bang scenario

The pre-big bang scenario describes the evolution of the Universe from an initial state approaching the flat, cold, empty, string perturbative vacuum. The choice of such an initial state is suggested by the present state of our Universe if we accept that the cosmological evolution is (at least partially) duality-symmetric. Recently, the initial conditions of the pre-big bang scenario have been criticized as they introduce large dimensionless parameters allowing the Universe to be "exponentially large from the very beginning". We agree that a set of initial parameters (such as the initial homogeneity scale, the initial entropy) larger than those determined by the initial horizon scale, H^{-1}, would be somewhat unnatural to start with. However, in the pre-big bang scenario, the initial parameters are all bounded by the size of the initial horizon. The basic question thus becomes: is a maximal homogeneity scale of order H^{-1} necessarily unnatural if the initial curvature is small and, consequently, H^{-1} is very large in Planck (or string) units? In the impossibility of experimental information one could exclude "a priori", for large horizons, the maximal homogeneity scale H^{-1} as a natural initial condition. In the pre-big bang scenario, however, pre-Planckian initial conditions are not necessarily washed out by inflation and are accessible (in principle) to observational tests, so that their naturalness could be also analyzed with a Bayesan approach, in terms of "a posteriori" probabilities.Comment: 4 pages, Latex, one figure. Many references added. The text has been improved in many points. To appear in Phys. Rev.

Survey of Academic Field Experiences (SAFE): Trainees Report Harassment and Assault

Little is known about the climate of the scientific fieldwork setting as it relates to gendered experiences, sexual harassment, and sexual assault. We conducted an internet-based survey of field scientists (N = 666) to characterize these experiences. Codes of conduct and sexual harassment policies were not regularly encountered by respondents, while harassment and assault were commonly experienced by respondents during trainee career stages. Women trainees were the primary targets; their perpetrators were predominantly senior to them professionally within the research team. Male trainees were more often targeted by their peers at the research site. Few respondents were aware of mechanisms to report incidents; most who did report were unsatisfied with the outcome. These findings suggest that policies emphasizing safety, inclusivity, and collegiality have the potential to improve field experiences of a diversity of researchers, especially during early career stages. These include better awareness of mechanisms for direct and oblique reporting of harassment and assault and, the implementation of productive response mechanisms when such behaviors are reported. Principal investigators are particularly well positioned to influence workplace culture at their field sites