CoFeD: A visualisation framework for comparative quality evaluation

Evaluation for the purpose of selection can be a challenging task particularly when there is a plethora of choices available. Short-listing, comparisons and eventual choice(s) can be aided by visualisation techniques. In this paper we use Feature Analysis, Tabular and Tree Representations and Composite Features Diagrams (CFDs) for profiling user requirements and for top-down profiling and evaluation of items (methods, tools, techniques, processes and so on) under evaluation. The resulting framework CoFeD enables efficient visual comparison and initial short-listing. The second phase uses bottom-up quantitative evaluation which aids the elimination of the weakest items and hence the effective selection of the most appropriate item. The versatility of the framework is illustrated by a case study comparison and evaluation of two agile methodologies. The paper concludes with limitations and indications of further work

Production of electronics and photovoltaics using a reel-to-reel process

Reel to reel manufacturing is a mature technology that involves the passing of a flexible substrate or web continuously through one or more processes. The web is typically much longer than it is wide, and the width in turn is much greater than its thickness. It is a continuous process that results in high output at a low unit cost when compared with other production methods. Historically this has included newspaper printing and textile manufacture, but more recent research is being conducted in developing printed electronics, such as solar cells (Organic Photo-Voltaic or OPV), and wearable tech and flexible screens (Polymer LEDs or PLEDs). These devices consist of up to five layers, with a separate printing or coating process needed for each. Greater accuracy is necessary than for traditional industries and advances are required in three areas: control of the web; measurement and registration of the printed web; and flexible semi-conductor materials. In this paper we present a new methodology to improve printing accuracy by combining an advanced metrology system with an innovative process design

Heuristic standards for universal design in the face of technological diversity.

CENTRAL PRINCIPLE Important technologies require validated standards for the design heuristics that are used to design and evaluate them, but not necessarily identical heuristics for every technology. BACKGROUND Heuristic standards provide a valuable toolkit with which to evaluate the accessibility of modern information society technologies (IST). But can we apply the same heuristic, generic standards to all types of technological platforms, in the face of their growing diversity e.g. websites, social websites, blogs, virtual reality applications, ambient intelligence etc (Adams, 2007)? Or would it be wiser to expect that different technologies might require different, if overlapping, standards? Can we really expect to design the interface of a modern cell phone on the same basis as for a table computer? Most impartial observers would probably say “no”. How can we introduce a systematic and thorough approach to the diverse technologies that are seen or predicted to be seen? Work in our laboratory has explored two useful questions. First, how to computer literate users perceive the different technologies? Second, how can different heuristic standards be developed where needed

Population assessment and feeding ecology of brown hyenas (hyaena brunnea) in Mountain Zebra National Park, Eastern Cape, South Africa

The development of many small (<400 km2), enclosed game reserves in the Eastern Cape Province of South Africa over the past 25 years has contributed greatly to the conservation of large carnivores. However, the brown hyena (Hyaena brunnea) is one of the least studied large carnivores in South Africa. Nevertheless, the reintroduction of this species (n=3 in 2008) into Mountain Zebra National Park (MZNP) provided the perfect opportunity to broaden our understanding of the role that this carnivore plays in an enclosed system. Camera trap data was collected for just over a year from April 2014 to April 2015 and brown hyena density estimates were calculated using spatially explicit capture-recapture analysis. Left-side images of brown hyenas were used in the analysis and 12 individuals were positively identified. The best model to estimate brown hyena density included a road covariate and estimated brown hyena density to be 6-10 individuals/100 km2 (an absolute abundance of between 12 and 21 individuals), which is higher than densities calculated for brown hyenas in other arid, open systems. In, addition, brown hyena scat samples were collected over a five year period from April 2011 to June 2015 and standard techniques for scat analysis were used to identify prey items. Cheetah (Acinonyx jubatus) and lion (Panthera leo) kill site data were used to investigate the impacts of these species on the diet of brown hyenas. Before the release of lions brown hyenas predominantly scavenged on medium-sized mammals, which was what the cheetahs mainly killed. However, after the release of the lions, brown hyenas predominantly scavenged on large mammals, which was what the lions primarily killed. The results from my study indicate that brown hyenas are most likely reaching high densities in enclosed systems, due to increased scavenging opportunities provided by other large predators. The rapid increase of brown hyena densities from small founder populations in enclosed reserves could result in inbreeding. Therefore, in order to successfully conserve brown hyenas and other large carnivores in South Africa, continual post-release monitoring and possible implementation of meta-population management schemes is required

The Effects of Progesterone Induced Blocking Factor and 17-Hydroxyprogesterone Caproate on the Pathophysiology of Preeclampsia

Preeclampsia (PE) is responsible for about 20% of the 13 million preterm births each year worldwide, including 100,000 cases annually in the United States. Despite being a leading cause of maternal and perinatal morbidity, the mechanisms of pathogenesis are still largely unknown. PE is progesterone deficient state characterized by hypertension, chronic immune activation, endothelial dysfunction and severe forms can lead to seizures. Treatment of seizures includes the administration of magnesium sulfate (MgSO4) though not all PE patients are responsive, and it does not decrease PE-associated hypertension. To resolve these conditions, PE patients are delivered early thereby making PE the leading cause for fetal mortality and morbidity worldwide. Hypothesizing that the reduced progesterone levels accounted for the onset of hypertension in PE, the effects of both 17-hydroxyprogesterone caproate (17-OHPC) and Progesterone Induced Blocking Factor (PIBF) were studied in hypertensive pregnant rat models of PE. Following the intervention, blood pressure and endothelin-1 were reduced while nitric oxide was elevated

An enhanced deep learning architecture for classification of Tuberculosis types from CT lung images

In this work, an enhanced ResNet deep learning network, depth-ResNet, has been developed to classify the five types of Tuberculosis (TB) lung CT images. Depth-ResNet takes 3D CT images as a whole and processes the volumatic blocks along depth directions. It builds on the ResNet-50 model to obtain 2D features on each frame and injects depth information at each process block. As a result, the averaged accuracy for classification is 71.60% for depth-ResNet and 68.59% for ResNet. The datasets are collected from the ImageCLEF 2018 competition with 1008 training data in total, where the top reported accuracy was 42.27%

Optical flow estimation via steered-L1 norm

Global variational methods for estimating optical flow are among the best performing methods due to the subpixel accuracy and the ‘fill-in’ effect they provide. The fill-in effect allows optical flow displacements to be estimated even in low and untextured areas of the image. The estimation of such displacements are induced by the smoothness term. The L1 norm provides a robust regularisation term for the optical flow energy function with a very good performance for edge-preserving. However this norm suffers from several issues, among these is the isotropic nature of this norm which reduces the fill-in effect and eventually the accuracy of estimation in areas near motion boundaries. In this paper we propose an enhancement to the L1 norm that improves the fill-in effect for this smoothness term. In order to do this we analyse the structure tensor matrix and use its eigenvectors to steer the smoothness term into components that are ‘orthogonal to’ and ‘aligned with’ image structures. This is done in primal-dual formulation. Results show a reduced end-point error and improved accuracy compared to the conventional L1 norm