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  Mitral flow velocity curves are therefore altered by changes in hemodynamic loading conditions. Energy spectra for particles exiting the sources were calculated from the full phase-space data obtained pritor dosage calculator from the Monte Carlo simulations of the source constructions. One hundred adult Sudanese patients who presented to Soba University Hospital (SUH) with established chronic renal failure (CRF) were studied to determine the aetiology.
  
  Posted on 5 May, 2015 Updated on 5 May, 2015 by astraeaCategories:Climate Change, Health, Holonics, Living, Media and Gatherings	
  Earthlings is a docudrama drawing attention to vile, uncivilised behaviour in which we all play a role. It focuses on the torture of animals which modern society condones and from which we distance ourselves, covering five aspects: pets, food, clothing, entertainment, science.
  Be warned. It is graphic. You might have a pet which you rescued, you might eat only organic produce, you might avoid leather and fur, you might not go to the races and you might not wear make-up, but the atrocities shown in the film still go on today, in your community and are endorsed by governments, leaders and “everyone”.
  The point is: Just say NO. Humanity can not survive if it does not extend the rationale of justice to nature. That means treating animals with care, even if you are going to eat them, though plainly that in itself is far from necessary. In fact, a simple fix for climate change is veganism/vegetarianism because the livestock industry in the number one cause of greenhouse gases and a major contributor to biodiversity loss and environmental contamination. (See “The Facts” about sustainability, the environment and your future.)
  I watched the movie last night (mostly with my eyes closed) because Ellen said “Food Inc is “Disney compared to Earthlings”. She wasn’t kidding.
  I used to make fun of vegetarians and tofu. One day I realised I didn’t like killing animals. (Duh!) So I stopped killing them and became vegetarian. That was nearly two decades ago. I’m still alive, a little overweight in fact. And now it is science that a plant based diet is healthier and the meat industry is killing the planet. It should be that we can eat a bit of meat, but the impact on the biosphere of a meat focused food industry has become so violent, that a simple avoidance policy is really the only option.
  You don’t have to watch Earthlings, but humankind must slow down and drastically reduce its consumption of the biosphere (our habitat which we need for life) and that includes consuming fewer animals.
  Is it going to happen? Well, this is today’s headline: Syria conflict: Aleppo civilians suffer ‘unthinkable atrocities’, in which accounts of civilian torture and massacre are reported. Not a hopeful datapoint …
  
  A number of linux distributions are released at this time of year. This, combined with a warranty claim, became a good excuse to have a look at a few different distributions. I checked out openSuse 10.3, Fedora 7, Mandriva 2008, Sabayon 3.4f and Kubuntu 7.10. All were good offering slightly different installation experiences and user interfaces but overall running high end hardware well and quickly.
  The most relevant experience however is that it was possible to install different operating systems while retaining all the data and settings of previous installations. This is the most telling illustration of why linux systems are built to perform. Each distribution is slightly different and therefore difficult to write generic viruses for. They are all built on similar core software engine, the kernel, which enables integration of different higher level software.
  The look of the desktop is dependent on modules loaded, not the distribution (for example you can run either KDE, my preference, or Gnome desktop on any main distro and on top of that all colours and layouts can be adjusted to user preference.
  Some of the fun things you can do are create a 3d desktop cube (4 desktops, each one one the face of a virtual cube which spins to the desktop you demand – use one for work, one for home, one for AV, etc. demo video here or a cool video of a comparison with windoze) and put a system widget on the desktop (can show usage of drives and processors, network link activity, time, media player integration etc you choose – SuperKaramba).
  One issue you can address is how you like to install. Kubuntu loads live from the disk, so you have a fully functioning desktop running from teh install disk from which you instruct installation – user friendly. Sabayon is a pretty automatic install and provides lots of extras to accommodate weird hardware, like the TI or BCM NICs. Mandriva, Fedora and Suse are more customisable, but Mandriva (its free version) offers fewer install options. Fedora with be releasing Fedora 8 in early November and that might be top cat for a while. I installed openSuse in the end, sticking with what I had before.
  When installing, especially if on x64 or dual core processors, you might need kernel arguments. For openSuse 10.3 I needed noapic. (I’ve found pci=noacpi and noapic to be useful in the past). You may also want to check that you have the latest BIOS for your machine loaded.
  During the R&D I used windows too and I’m soooo glad I made the switch to linux. The system is fine and looks pretty but the fundamental software architechture is dysfunctional and inflexible – no wonder MS is trying to get in to other businesses, like entertainment, because that software is heading for extinction.
  And of course, all the software is free! You are welcome to donate to any project you think worthy – that’s donationware.
  
  (This article is about the British actress. For The Submarines' album, see Honeysuckle Weeks (album).) Honeysuckle Hero Susan Weeks (born 1 August 1979) is a British actress known for her starring role as Samantha Stewart in the ITV wartime drama series Foyle's War beginning in 2002.
  
  Predictive vegetation modeling (PVM), is defined as predicting the distribution of vegetation across a landscape based on the relationship between the spatial distribution of vegetation and environmental variables. PVM requires digital maps of the environmental variables, as well as spatial information on the vegetation attribute of interest (e.g., species, type, abundance), usually from a sample of locations. Often these predictive models are developed using traditional statistical methods and are based on the implicit assumption that the distribution of vegetation is random and, therefore, each observation is independent. This approach violates one of the basic tenets of geography, the direct relationship between distance and likeness, as well as of ecological theory, that elements of an ecosystem close to one another are more likely to be influenced by the same generating process and will therefore be similar. Some of the spatial structure can be explained by the predictor variables used in the model. Environmental variables such as precipitation, temperature and elevation exhibit spatial dependence, some of which is responsible for spatial clustering in vegetation distribution, but remaining spatial dependence can result from either unmeasured environmental variables or biotic processes that cause spatial clustering. Spatial dependence in biogeographical data has been recently identified as an important area of future PVM research, and many studies have begun to explore ways to incorporate spatial dependence in predictive models. Here we review the different approaches to incorporating spatial dependence into predictive vegetation models focusing on four statistical methods: autoregressive models, geostatistics, geographically weighted regression, and parameter estimation models. Autoregressive models may be more capable of describing the fine-scaled spatial dependence that results from local biotic factors, such as disturbance, competition, or dispersal, while geostatistical methods may be more suitable for modeling broad-scale spatial dependence. The other methods focus on global and local parameter estimation in the presence of spatially structured or nonstationary data. While this review focuses on incorporating spatial dependence into statistical models for predictive purposes, explicitly including spatial dependence in models can also aid in clarifying the effect of different explanatory variables, thereby improving inferences.