Influence of Low Energy Hadronic Interactions on Air-shower Simulations

Experiments measuring cosmic rays above an energy of 10^14 eV deduce the energy and mass of the primary cosmic ray particles from air-shower simulations. We investigate the importance of hadronic interactions at low and high energies on the distributions of muons and electrons in showers on ground. In air shower simulation programs, hadronic interactions below an energy threshold in the range from 80 GeV to 500 GeV are simulated by low energy interaction models, like Fluka or Gheisha, and above that energy by high energy interaction models, e.g. Sibyll or QGJSJet. We find that the impact on shower development obtained by switching the transition energy from 80 GeV to 500 GeV is comparable to the difference obtained by switching between Fluka and Gheisha.Comment: 4 pages, 6 figures, ISVHECRI 200

Atmospheric effects on extensive air showers observed with the Surface Detector of the Pierre Auger Observatory

Atmospheric parameters, such as pressure (P), temperature (T) and density, affect the development of extensive air showers initiated by energetic cosmic rays. We have studied the impact of atmospheric variations on extensive air showers by means of the surface detector of the Pierre Auger Observatory. The rate of events shows a ~10% seasonal modulation and ~2% diurnal one. We find that the observed behaviour is explained by a model including the effects associated with the variations of pressure and density. The former affects the longitudinal development of air showers while the latter influences the Moliere radius and hence the lateral distribution of the shower particles. The model is validated with full simulations of extensive air showers using atmospheric profiles measured at the site of the Pierre Auger Observatory.Comment: 24 pages, 9 figures, accepted for publication in Astroparticle Physic

Integrated global assessment of the natural forest carbon potential

Forests are a substantial terrestrial carbon sink, but anthropogenic changes in land use and climate have considerably reduced the scale of this system1. Remote-sensing estimates to quantify carbon losses from global forests2,3,4,5 are characterized by considerable uncertainty and we lack a comprehensive ground-sourced evaluation to benchmark these estimates. Here we combine several ground-sourced6 and satellite-derived approaches2,7,8 to evaluate the scale of the global forest carbon potential outside agricultural and urban lands. Despite regional variation, the predictions demonstrated remarkable consistency at a global scale, with only a 12% difference between the ground-sourced and satellite-derived estimates. At present, global forest carbon storage is markedly under the natural potential, with a total deficit of 226 Gt (model range = 151–363 Gt) in areas with low human footprint. Most (61%, 139 Gt C) of this potential is in areas with existing forests, in which ecosystem protection can allow forests to recover to maturity. The remaining 39% (87 Gt C) of potential lies in regions in which forests have been removed or fragmented. Although forests cannot be a substitute for emissions reductions, our results support the idea2,3,9 that the conservation, restoration and sustainable management of diverse forests offer valuable contributions to meeting global climate and biodiversity targets

The global distribution and drivers of wood density and their impact on forest carbon stocks.

The density of wood is a key indicator of the carbon investment strategies of trees, impacting productivity and carbon storage. Despite its importance, the global variation in wood density and its environmental controls remain poorly understood, preventing accurate predictions of global forest carbon stocks. Here we analyse information from 1.1 million forest inventory plots alongside wood density data from 10,703 tree species to create a spatially explicit understanding of the global wood density distribution and its drivers. Our findings reveal a pronounced latitudinal gradient, with wood in tropical forests being up to 30% denser than that in boreal forests. In both angiosperms and gymnosperms, hydrothermal conditions represented by annual mean temperature and soil moisture emerged as the primary factors influencing the variation in wood density globally. This indicates similar environmental filters and evolutionary adaptations among distinct plant groups, underscoring the essential role of abiotic factors in determining wood density in forest ecosystems. Additionally, our study highlights the prominent role of disturbance, such as human modification and fire risk, in influencing wood density at more local scales. Factoring in the spatial variation of wood density notably changes the estimates of forest carbon stocks, leading to differences of up to 21% within biomes. Therefore, our research contributes to a deeper understanding of terrestrial biomass distribution and how environmental changes and disturbances impact forest ecosystems

The global distribution and drivers of wood density and their impact on forest carbon stocks

The density of wood is a key indicator of the carbon investment strategies of trees, impacting productivity and carbon storage. Despite its importance, the global variation in wood density and its environmental controls remain poorly understood, preventing accurate predictions of global forest carbon stocks. Here we analyse information from 1.1 million forest inventory plots alongside wood density data from 10,703 tree species to create a spatially explicit understanding of the global wood density distribution and its drivers. Our findings reveal a pronounced latitudinal gradient, with wood in tropical forests being up to 30% denser than that in boreal forests. In both angiosperms and gymnosperms, hydrothermal conditions represented by annual mean temperature and soil moisture emerged as the primary factors influencing the variation in wood density globally. This indicates similar environmental filters and evolutionary adaptations among distinct plant groups, underscoring the essential role of abiotic factors in determining wood density in forest ecosystems. Additionally, our study highlights the prominent role of disturbance, such as human modification and fire risk, in influencing wood density at more local scales. Factoring in the spatial variation of wood density notably changes the estimates of forest carbon stocks, leading to differences of up to 21% within biomes. Therefore, our research contributes to a deeper understanding of terrestrial biomass distribution and how environmental changes and disturbances impact forest ecosystems

Plasma progesterone levels in lactating ewes after hormone-induced ovulation during the non-breeding season

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Monte Carlo simulations of the ion-cascade process in the ESEM

Our Monte Carlo simulations and experimental measurements show the Townsend Gas Capacitor (TGC) model to be highly inappropriate for describing the electron cascade process in the Environmental SEM (ESEM). Previous workers have described the signal collected by the Gaseous Secondary Electron Detector (GSED) as having contributions from secondary as well as backscattered and primary electrons, all being amplified by gas cascade. Although these models are qualitatively correct, they require a more sophisticated description of Townsend’s First Ionisation Coefficient, α. Figure 1 illustrates the short-comings of the TGC models when compared to experimentally obtained amplification curves. (Details of the amplification measurements made with various imaging gases will be given elsewhere, along with specifics of the Monte Carlo Calculations.)The major flaw in applying the TGC model to the ESEM stems from the assumption that the secondary electrons and their environmental daughters reach a steady-state kinetic energy distribution en-route to the detector.</jats:p

A survey of patients with surgical wounds healing by secondary intention; an assessment of prevalence, aetiology, duration and management

BACKGROUND: Surgical wounds healing by secondary intention (SWHSI) are often difficult and costly to treat. There is a dearth of clinical and research information regarding SWHSI. The aim of this survey was to estimate the prevalence of SWHSI and to characterise the aetiology, duration and management of these wounds.METHODS: Anonymised data were collected from patients with SWHSI receiving treatment in primary, secondary and community settings. Over a two weeks period, data were collected on the patients, their SWHSI, clinical and treatment details.RESULTS: Data were collected from 187 patients with a median age of 58.0 (95% CI = 55 to 61) years. The prevalence of SWHSI was 0.41 (95% CI = 0.35 to 0.47) per 1000 population. More patients with SWHSI were being treated in community (109/187, 58.3%) than in secondary (56/187, 29.9%) care settings. Most patients (164/187, 87.7%) had one SWHSI and the median duration of wounds was 28.0 (95% CI = 21 to 35) days. The most common surgical specialities associated with SWHSI were colorectal (80/187, 42.8%), plastics (24/187, 12.8%) and vascular (22/187, 11.8%) surgery. Nearly half of SWHSI were planned to heal by secondary intention (90/187, 48.1%) and 77/187 (41.2%) were wounds that had dehisced. Dressings were the most common single treatment for SWHSI, received by 169/181 (93.4%) patients. Eleven (6.1%) patients were receiving negative pressure wound therapy.CONCLUSIONS: This survey provides a previously unknown insight into the occurrence, duration, treatment and types of surgery that lead to SWHSI. This information will be of value to patients, health care providers and researchers.</p

Sigmoid Data Fitting by Least Squares Adjustment of Second and Third Divided Differences

We consider the performance of two data smoothing methods that provide sigmoid fits by adjustment of divided differences on some test problems. Thus we investigate the accuracy and the efficiency of the methods for smoothing a variety of data points, our conclusions being drawn from numerical results. The first method is a least squares data smoothing calculation subject to nonnegative third divided differences. The second method is a non-linear least squares data smoothing calculation subject to one sign change in the second divided differences. Both methods employ structured quadratic programming calculations, which take into account the form of the constraints and make efficient use of the banded matrices that occur in the subproblems during the iterations of the quadratic programming calculations. The total work of each method, in practice, is of quadratic complexity with respect to the number of data. Our results expose some weaknesses of the methods. Therefore they may be helpful to the development of new algorithms that are particularly suitable for sigmoid data fitting calculations. Our results expose also some strengths of the methods, which they may be useful to particular scientific analyses, e.g. sigmoid phenomena, and to strategic management practices, i.e. economic substitution. © Springer International Publishing Switzerland 2014