Ozonation of cooling tower waters

Continuous ozone injection into water circulating between a cooling tower and heat exchanger with heavy scale deposits inhibits formation of further deposits, promotes flaking of existing deposits, inhibits chemical corrosion and controls algae and bacteria

Remote sensing applications to forest vegetation classification and conifer vigor loss due to dwarf mistletoe

Criteria was established for practical remote sensing of vegetation stress and mortality caused by dwarf mistletoe infections in black spruce subboreal forest stands. The project was accomplished in two stages: (1) A fixed tower-tramway site in an infected black spruce stand was used for periodic multispectral photo coverage to establish basic film/filter/scale/season/weather parameters; (2) The photographic combinations suggested by the tower-tramway tests were used in low, medium, and high altitude aerial photography

AN ECONOMIC ANALYSIS OF POLICIES PROTECTING SMALL BUSINESS IN THE MILK INDUSTRY

Livestock Production/Industries,

Association between low 25(OH) vitamin D levels, antiretroviral therapy and metabolic profile in a cohort of treated ahd therapy naive HIV positive patients in Western Australia

See Attache

Hydrogen Dissociation and Diffusion on Ni and Ti -doped Mg(0001) Surfaces

It is well known, both theoretically and experimentally, that alloying MgH$_2$ with transition elements can significantly improve the thermodynamic and kinetic properties for H$_2$ desorption, as well as the H$_2$ intake by Mg bulk. Here we present a density functional theory investigation of hydrogen dissociation and surface diffusion over Ni-doped surface, and compare the findings to previously investigated Ti-doped Mg(0001) and pure Mg(0001) surfaces. Our results show that the energy barrier for hydrogen dissociation on the pure Mg(0001) surface is high, while it is small/null when Ni/Ti are added to the surface as dopants. We find that the binding energy of the two H atoms near the dissociation site is high on Ti, effectively impeding diffusion away from the Ti site. By contrast, we find that on Ni the energy barrier for diffusion is much reduced. Therefore, although both Ti and Ni promote H$_2$ dissociation, only Ni appears to be a good catalyst for Mg hydrogenation, allowing diffusion away from the catalytic sites. Experimental results corroborate these theoretical findings, i.e. faster hydrogenation of the Ni doped Mg sample as opposed to the reference Mg or Ti doped Mg.Comment: 17 pages, 15 figures, to appear in Journal of Chemical Physic

Preparations for Independence and Financial Security in Later Life: A Conceptual Framework and Application to Canada

In this paper, we develop a conceptual framework to describe an individual's preparations for later life. Situated in the life course perspective, this provides a framework that invites a more comprehensive and systematic study of preparations for later life. It describes a dynamic process that portrays the interplay between social structure and human agency. Through its consideration of collective preparations (the public protection programs offered by the state), individual preparations (financial and non- financial), and the interplay between the two, this framework provides fresh insight into the existing literature on retirement planning, the timing of retirement, savings, and consumption behaviour in later life. Moreover, the model may be used to structure research questions, to guide policy decision making and to point the direction for the design and content of future research studies. While the purpose of this paper is primarily the development of a conceptual model, we draw on empirical examples from the 1991 Survey of Aging and Independence (SAI) to illustrate some aspects of the model to Canada. We conclude by suggesting a number of research and questions that may be generated from the model.retirement planning; savings; SAI

Random matrix analysis of complex networks

We study complex networks under random matrix theory (RMT) framework. Using nearest-neighbor and next-nearest-neighbor spacing distributions we analyze the eigenvalues of adjacency matrix of various model networks, namely, random, scale-free and small-world networks. These distributions follow Gaussian orthogonal ensemble statistic of RMT. To probe long-range correlations in the eigenvalues we study spectral rigidity via $\Delta_3$ statistic of RMT as well. It follows RMT prediction of linear behavior in semi-logarithmic scale with slope being $\sim 1/\pi^2$. Random and scale-free networks follow RMT prediction for very large scale. Small-world network follows it for sufficiently large scale, but much less than the random and scale-free networks.Comment: accepted in Phys. Rev. E (replaced with the final version

Memory Aware Synapses: Learning what (not) to forget

Humans can learn in a continuous manner. Old rarely utilized knowledge can be overwritten by new incoming information while important, frequently used knowledge is prevented from being erased. In artificial learning systems, lifelong learning so far has focused mainly on accumulating knowledge over tasks and overcoming catastrophic forgetting. In this paper, we argue that, given the limited model capacity and the unlimited new information to be learned, knowledge has to be preserved or erased selectively. Inspired by neuroplasticity, we propose a novel approach for lifelong learning, coined Memory Aware Synapses (MAS). It computes the importance of the parameters of a neural network in an unsupervised and online manner. Given a new sample which is fed to the network, MAS accumulates an importance measure for each parameter of the network, based on how sensitive the predicted output function is to a change in this parameter. When learning a new task, changes to important parameters can then be penalized, effectively preventing important knowledge related to previous tasks from being overwritten. Further, we show an interesting connection between a local version of our method and Hebb's rule,which is a model for the learning process in the brain. We test our method on a sequence of object recognition tasks and on the challenging problem of learning an embedding for predicting $$ triplets. We show state-of-the-art performance and, for the first time, the ability to adapt the importance of the parameters based on unlabeled data towards what the network needs (not) to forget, which may vary depending on test conditions.Comment: ECCV 201