Numerical Modelling of Natural Draft Wet-Cooling Towers

Numerical modelling of natural draft wet-cooling towers is considered in this paper. Development of a complete CFD model capable of predicting cooling tower performance under various operating conditions is presented. A step-by-step approach is employed, thus one-dimensional numerical modelling of counterflow heat and mass exchangers is extensively presented first. This is followed by reduced order modelling of heat and mass transfer in cooling tower fills by means of proper orthogonal decomposition radial basis function networks. Then the approaches in modelling of droplet zones (rain and spray zones) are discussed. Finally the two and three-dimensional CFD models are discussed.     

Fabrication of sealed nanofluidic channels using site-selective direct write (maskless) X-ray lithography

Sealed nanofluidic channels with cross-sections as small as 60 nm × 60 nm were created in polymer bilayers using the focused X-rays of a scanning transmission X-ray microscope. These structures were then characterized by near-edge X-ray absorption fine structure spectromicroscopy, atomic force microscopy and scanning electron microscopy. The cross-sectional area of the nanochannels could be tuned by adjusting the area patterned in x and y and/or manipulating the bottom layer thickness. The maximum length was found to be limited by the efficiency of excavation of patterned material out of the channel, and the stability of the polymer overlayer which seals the channel. Schemes toward interfacing these nanochannels with conventional microfluidics are discussed.     

Combining MODIS and Landsat imagery to estimate and map boreal forest cover loss

Estimation of forest cover change is important for boreal forests, one of the most extensive forested biomes, due to its unique role in global timber stock, carbon sequestration and deposition, and high vulnerability to the effects of global climate change. We used time-series data from the MODerate Resolution Imaging Spectroradiometer (MODIS) to produce annual forest cover loss hotspot maps. These maps were used to assign all blocks (18.5 by 18.5 km) partitioning the boreal biome into strata of high, medium and low likelihood of forest cover loss. A stratified random sample of 118 blocks was interpreted for forest cover and forest cover loss using high spatial resolution Landsat imagery from 2000 and 2005. Area of forest cover gross loss from 2000 to 2005 within the boreal biome is estimated to be 1.63% (standard error 0.10%) of the total biome area, and represents a 4.02% reduction in year 2000 forest cover. The proportion of identified forest cover loss relative to regional forest area is much higher in North America than in Eurasia (5.63% to 3.00%). Of the total forest cover loss identified, 58.9% is attributable to wildfires. The MODIS pan-boreal change hotspot estimates reveal significant increases in forest cover loss due to wildfires in 2002 and 2003, with 2003 being the peak year of loss within the 5-year study period. Overall, the precision of the aggregate forest cover loss estimates derived from the Landsat data and the value of the MODIS-derived map displaying the spatial and temporal patterns of forest loss demonstrate the efficacy of this protocol for operational, cost-effective, and timely biome-wide monitoring of gross forest cover loss.     

Evaluating the species energy relationship with the newest measures of ecosystem energy: NDVI versus MODIS primary production

Ecosystem energy has been shown to be a strong correlate with biological diversity at continental scales. Early efforts to characterize this association used the normalized difference vegetation index (NDVI) to represent ecosystem energy. While this spectral vegetation index covaries with measures of ecosystem energy such as net primary production, the covariation is known to degrade in areas of very low vegetation or in areas of dense forest. Two of the new vegetation products from the MODIS sensor, derived by integrating spectral reflectance, climate data, and land cover, are thought to better approximate primary productivity than NDVI. In this study, we determine if the new MODIS derived measures of primary production, gross primary productivity (GPP) and net primary productivity (NPP) better explain variation in bird richness than historically used NDVI. Moreover, we evaluate if the two productivity measures covary more strongly with bird diversity in those vegetation conditions where limitations of NDVI are well recognized.Biodiversity was represented as native landbird species richness derived from the North American Breeding Bird Survey. Analyses included correlation analyses among predictor variables, and univariate regression analyses between each predictor variable and bird species richness. Analyses were done at two levels: for all BBS routes across natural landscapes in North America; and for routes in 10 vegetation classes stratified by vegetated cover along a gradient from bare ground to herbaceous cover to tree cover. We found that NDVI, GPP and NPP were highly correlated and explained similar variation in bird species richness when analyzed for all samples across North America. However, when samples were stratified by vegetated cover, strength of correlation between NDVI and both productivity measures was low for samples with bare ground and for dense forest. The NDVI also explained substantially less variation in bird species richness than the primary production in areas with more bare ground and in areas of dense forest. We conclude that MODIS productivity measures have higher utility in studies of the relationship of species richness and productivity and that MODIS GPP and NPP improve on NDVI, especially for studies with large variation in vegetated cover and density.     

VISIM: Sequential simulation for linear inverse problems

Linear inverse Gaussian problems are traditionally solved using least squares-based inversion. The center of the posterior Gaussian probability distribution is often chosen as the solution to such problems, while the solution is in fact the posterior Gaussian probability distribution itself. We present an algorithm, based on direct sequential simulation, which can be used to efficiently draw samples of the posterior probability distribution for linear inverse problems. There is no Gaussian restriction on the distribution in the model parameter space, as inherent in traditional least squares-based algorithms.As data for linear inverse problems can be seen as weighed linear averages over some volume, block kriging can be used to perform both estimation (i.e. finding the center of the posterior Gaussian pdf) and simulation (drawing samples of the posterior Gaussian pdf). We present the kriging system which we use to implement a flexible GSLIB-based algorithm for solving linear inverse problems.We show how we implement such a simulation program conditioned to linear average data. The program is called VISIM as an acronym for Volume average Integration SIMulation. An effort has been made to make the program efficient, even for larger scale problems, and the computational efficiency and accuracy of the code is investigated.Using a synthetic cross-borehole tomography case study, we show how the program can be used to generate realizations of the a posteriori distributions (i.e. solutions) from a linear tomography problem. Both Gaussian and non-Gaussian a priori model parameter distributions are considered.     

Family psychosocial screening: should we focus on high-risk settings?

The objective of this study was to address the question of whether or not psychosocial screening should be focused on "high-risk" populations. A cross-sectional survey of mothers of young children was conducted in various clinics: 758 in teaching clinics, 444 in private practices, and 202 at a military clinic. The self-administered questionnaire covered demographic factors, problems in mothers family of origin, maternal depression, and substance abuse. Mothers in the teaching clinics were younger and had less education and lower incomes than mothers in private practices, with intermediate levels in the military clinic. However, a substantial proportion of mothers seen in all sites reported psychosocial problems. Approximately 20% of mothers in all sites reported a family history of alcoholism. Positive screens for maternal depression ranged from about 15% to 35%. Binge drinking was reported by 10% to 20% at different sites. Psychosocial problems were common even among families seen in "low-risk" settings. Focusing screening only on high-risk clinics would miss many families with psychosocial problems.     

What if computers could think?

The question whether or not computers can think was first asked in print by Alan Turing in his seminal 1950 article. In order to avoid defining what a computer is or what thinking is, Turing resorts to the imitation game which is a test that allows us to determine whether or not a machine can think. That is, if an interrogator is unable to tell whether responses to his questions come from a human being or from a machine, the machine is imitating a human being so well that it has to be acknowledged that these responses result from its thinking. However, then as now, it is not an indisputable claim that machines could think, and an unceasing stream of papers discussing the validity of the test proves this point. There are many arguments in favour of, as well as against, the claims borne by the test, and Turing himself discusses some of them. In his view, there are mice possible objections to the concept of a thinking machine, which he eventually dismisses as weak, irrelevant, or plain false. However, as he admits, he can present no very convincing arguments of a positive nature to support my views. If I had I should not have taken such pains to point out the fallacies in contrary views.     

Hybrid image steganography method using Lempel Ziv Welch and genetic algorithms for hiding confidential data

Multidimensional Systems and Signal Processing - Digital images are commonly used in steganography due to the popularity of digital image transfer and exchange through the Internet. However, the...     

Containerization technologies: taxonomies,applications and challenges

Modern scientific research challenges require new technologies, integrated tools, reusable and complex experiments in distributed computing infrastructures. But above all, computing power for efficient data processing and analyzing. Containers technologies have emerged as a new paradigm to address such intensive scientific applications problems. Their easy deployment in a reasonable amount of time and the few required computational resource make them more suitable. Containers are considered light virtualization solutions. They enable performance isolation and flexible deployment of complex, parallel, and high-performance systems. Moreover, they gained popularity to modernize and migrate scientific applications in computing infrastructure management. Additionally, they reduce computational time processing. In this paper, we first give an overview of virtualization and containerization technologies. We discuss the taxonomies of containerization technologies of the literature, and then we provide a new one that covers and completes those proposed in the literature. We identify the most important application domains of containerization and their technological progress. Furthermore, we discuss the performance metrics used in most containerization techniques. Finally, we point out research gaps in the related aspects of containerization technology that require more research.