Response of mouse epidermal cells to single doses of heavy-particles

The survival of mouse epidermal cells to heavy-particles has been studied In Vivo by the Withers clone technique. Experiments with accelerated helium, lithium and carbon ions were performed. The survival curve for the helium ion irradiations used a modified Bragg curve method with a maximum tissue penetration of 465 microns, and indicated that the dose needed to reduce the original cell number to 1 surviving cell/square centimeters was 1525 rads with a D sub o of 95 rads. The LET at the basal cell layer was 28.6 keV per micron. Preliminary experiments with lithium and carbon used treatment doses of 1250 rads with LET's at the surface of the skin of 56 and 193 keV per micron respectively. Penetration depths in skin were 350 and 530 microns for the carbon and lithium ions whose Bragg curves were unmodified. Results indicate a maximum RBE for skin of about 2 using the skin cloning technique. An attempt has been made to relate the epidermal cell survival curve to mortality of the whole animal for helium ions

A Deep \u3cem\u3eChandra\u3c/em\u3e ACIS Survey of M83

We have obtained a series of deep X-ray images of the nearby galaxy M83 using Chandra, with a total exposure of 729 ks. Combining the new data with earlier archival observations totaling 61 ks, we find 378 point sources within the D25 contour of the galaxy. We find 80 more sources, mostly background active galactic nuclei (AGNs), outside of the D25 contour. Of the X-ray sources, 47 have been detected in a new radio survey of M83 obtained using the Australia Telescope Compact Array. Of the X-ray sources, at least 87 seem likely to be supernova remnants (SNRs), based on a combination of their properties in X-rays and at other wavelengths. We attempt to classify the point source population of M83 through a combination of spectral and temporal analysis. As part of this effort, we carry out an initial spectral analysis of the 29 brightest X-ray sources. The soft X-ray sources in the disk, many of which are SNRs, are associated with the spiral arms, while the harder X-ray sources, mostly X-ray binaries (XRBs), do not appear to be. After eliminating AGNs, foreground stars, and identified SNRs from the sample, we construct the cumulative luminosity function (CLF) of XRBs brighter than 8 × 1035 erg s–1. Despite M83\u27s relatively high star formation rate, the CLF indicates that most of the XRBs in the disk are low mass XRBs

A Deep \u3cem\u3eChandra\u3c/em\u3e ACIS Survey of M83

We have obtained a series of deep X-ray images of the nearby galaxy M83 using Chandra, with a total exposure of 729 ks. Combining the new data with earlier archival observations totaling 61 ks, we find 378 point sources within the D25 contour of the galaxy. We find 80 more sources, mostly background active galactic nuclei (AGNs), outside of the D25 contour. Of the X-ray sources, 47 have been detected in a new radio survey of M83 obtained using the Australia Telescope Compact Array. Of the X-ray sources, at least 87 seem likely to be supernova remnants (SNRs), based on a combination of their properties in X-rays and at other wavelengths. We attempt to classify the point source population of M83 through a combination of spectral and temporal analysis. As part of this effort, we carry out an initial spectral analysis of the 29 brightest X-ray sources. The soft X-ray sources in the disk, many of which are SNRs, are associated with the spiral arms, while the harder X-ray sources, mostly X-ray binaries (XRBs), do not appear to be. After eliminating AGNs, foreground stars, and identified SNRs from the sample, we construct the cumulative luminosity function (CLF) of XRBs brighter than 8 × 1035 erg s–1. Despite M83\u27s relatively high star formation rate, the CLF indicates that most of the XRBs in the disk are low mass XRBs

WLAN Channel Selection Without Communication

In this paper we consider how a group of wireless access-points can self-configure their channel choice so as to avoid interference between one another and thereby maximise network capacity. We make the observation that communication between access points is not necessary, although it is a feature of almost all published channel allocation algorithms. We argue that this observation is of key practical importance as, except in special circumstances, interfering WLANs need not all lie in the same administrative domain and/or may be beyond wireless communication distance (although within interference distance). We demonstrate the feasibility of the communicationfree paradigm via a new class of decentralized algorithms that are simple, robust and provably correct for arbitrary interference graphs. The algorithm requires only standard hardware and we demonstrate its effectiveness via experimental measurements

An Adaptive Capacity Guide Book: assessing, building and evaluating the capacity of communities to adapt in a changing climate

A capacity to adapt to change is essential for managing Australia’s natural resources. The individuals, communities and organisations who manage our natural resources all have an innate capacity to adapt to change. Changes in climate, markets and technology have shaped the way we adapt the management of natural resources in urban, rural and coastal landscapes. Some of these changes are predictable and easy to manage. Others are expected, but their timing and magnitude are uncertain. Whatever the future holds, this guide can be used to build our capacity to meet future change with confidence

Experimental implementation of optimal WLAN channel selection without communication

Proposed a simple decentralised algorithm for channel allocation that is provably correct and requires no message passing or common administrative control between interfering WLANs. In this paper we implement this algorithm using a standard 802.11 hardware testbed and demonstrate that it does indeed offer the potential for effective channel allocation in realistic environments. This includes environments with complex, spatially varying noise and channel dependent propagation behaviour and with time-varying load