Advance research on control systems for the Saturn launch vehicle Final report, Jan., 1964 - May, 1965

Minimax problem in control systems for Saturn launch vehicl

Rural and Small Town Population is Growing in the 1990s

Rural and small town Canada continues to grow. Rural and small town growth rates vary widely among the provinces. Much of the growth within rural and small town areas is in the small towns. Sub-provincial data show wide regional differences within each province. The population in larger urban centres is growing faster. Thus, the share of Canada's population living in rural and small town areas has declined to 22 percent in 1996. Newfoundland is the only province with over 50 percent of its population living in rural and small town areas.Community/Rural/Urban Development,

Application of boundary integral method to elastoplastic analysis of V-notched beams

The boundary integral equation method was applied in the solution of the plane elastoplastic problem. The use of this method was illustrated by obtaining stress and strain distributions for a number of specimens with a single-edge notch and subjected to pure bending. The boundary integral equation method reduced the inhomogeneous biharmonic equation to two coupled Fredholm-type integral equations. These integral equations were replaced by a system of simultaneous algebraic equations and solved numerically in conjunction with a method of successive elastic solutions

Well-to-Well Log Correlation Using Knowledge-Based Systems and Dynamic Depth Warping

We present a novel system for well-to-well log correlation using knowledge-based systems and dynamic depth warping techniques. This approach overcomes a major drawback inherent in previous methods, namely the difficulty in correlating missing or discontinuous rock units. The system has three components: (1) A Dynamic Programming algorithm to correlate the logs and to find the minimum-cost or "best" match; (2) A set of "rules" to guide the correlation; (3) A data base that contains the logs and other relevant geologic and seismic information. The Dynamic Programming algorithm calculates the cost of correlating each point in the first well with each of the points in the second well. The resulting matrix of dissimilarity contains cost information about every possible operation which matches the well logs. The cost of matching the two wells is measured by the difference in the log values. The dynamic programming approach allows correlation across geologic structures, thinning beds, and missing or discontinuous units. A path finding algorithm then traces through the matrix to define a function which maps the first well onto the second. The minimum cost path is the optimal correlation between the wells. The system's database contains the well logs themselves and other relevant data including information about the geologic setting, seismic ties, interpreted lithologies, and dipmeter information. Rules operating on the data affect the dynamic programming and path finding algorithms in several ways: (1) Seismic ties or marker beds define a point in the warping path, thereby removing calculations over large portions of the search space; (2) Dipmeter results and knowledge of geologic structure further constrain the path to certain global areas and save calculation time; (3) The system assigns weights to different logs based on log quality and sensitivity; (4) Knowledge of the paleoenvironment allows the program to choose a set of rules (model) which accounts for changes in sediment type or thickness within a field. For example, when the program is operating in a deltaic environment, it will correlate the shales before attempting to correlate the sands. We demonstrate the method with synthetic examples in which the program successfully correlates across geologic structures and pinch-outs. We also applied the program to field examples from two widely separated oil provinces. In both cases, the automated correlation agreed very well with correlations provided by geologic experts

Convex recovery of a structured signal from independent random linear measurements

This chapter develops a theoretical analysis of the convex programming method for recovering a structured signal from independent random linear measurements. This technique delivers bounds for the sampling complexity that are similar with recent results for standard Gaussian measurements, but the argument applies to a much wider class of measurement ensembles. To demonstrate the power of this approach, the paper presents a short analysis of phase retrieval by trace-norm minimization. The key technical tool is a framework, due to Mendelson and coauthors, for bounding a nonnegative empirical process.Comment: 18 pages, 1 figure. To appear in "Sampling Theory, a Renaissance." v2: minor corrections. v3: updated citations and increased emphasis on Mendelson's contribution

Passing the baton to pharmacists and nurses: New models of antibiotic stewardship for South Africa?

The optimisation of antibiotic use to maximise patient care and safety through antibiotic stewardship forms one of the cornerstones of the global response to antibiotic resistance. Stewardship efforts in low- and middle-income countries are challenged by lack of healthcare professionals trained in infection. Therefore, in resource-poor settings, the traditional model of infection specialist-led stewardship may be impractical, requiring new models to be developed. A recent South African study across 47 Netcare hospitals nationally highlighted the role of pharmacists in this regard, proving that non-specialised pharmacists can drive a prospective audit, feedback collaborative strategy and a range of improvement science principles to reduce antibiotic consumption by the same levels as that documented in high-resource, infection specialist-led stewardship programmes. This editorial discusses the hidden opportunities to engage non-infection specialists in programmes to combat antibiotic resistance, expanding the cadres of healthcare professionals to lead stewardship programmes

Accurate multimodal probabilistic prediction of conversion to Alzheimer's disease in patients with mild cognitive impairment

Accurately identifying the patients that have mild cognitive impairment (MCI) who will go on to develop Alzheimer's disease (AD) will become essential as new treatments will require identification of AD patients at earlier stages in the disease process. Most previous work in this area has centred around the same automated techniques used to diagnose AD patients from healthy controls, by coupling high dimensional brain image data or other relevant biomarker data to modern machine learning techniques. Such studies can now distinguish between AD patients and controls as accurately as an experienced clinician. Models trained on patients with AD and control subjects can also distinguish between MCI patients that will convert to AD within a given timeframe (MCI-c) and those that remain stable (MCI-s), although differences between these groups are smaller and thus, the corresponding accuracy is lower. The most common type of classifier used in these studies is the support vector machine, which gives categorical class decisions. In this paper, we introduce Gaussian process (GP) classification to the problem. This fully Bayesian method produces naturally probabilistic predictions, which we show correlate well with the actual chances of converting to AD within 3 years in a population of 96 MCI-s and 47 MCI-c subjects. Furthermore, we show that GPs can integrate multimodal data (in this study volumetric MRI, FDG-PET, cerebrospinal fluid, and APOE genotype with the classification process through the use of a mixed kernel). The GP approach aids combination of different data sources by learning parameters automatically from training data via type-II maximum likelihood, which we compare to a more conventional method based on cross validation and an SVM classifier. When the resulting probabilities from the GP are dichotomised to produce a binary classification, the results for predicting MCI conversion based on the combination of all three types of data show a balanced accuracy of 74%. This is a substantially higher accuracy than could be obtained using any individual modality or using a multikernel SVM, and is competitive with the highest accuracy yet achieved for predicting conversion within three years on the widely used ADNI dataset

The Viscous Nonlinear Dynamics of Twist and Writhe

Exploiting the "natural" frame of space curves, we formulate an intrinsic dynamics of twisted elastic filaments in viscous fluids. A pair of coupled nonlinear equations describing the temporal evolution of the filament's complex curvature and twist density embodies the dynamic interplay of twist and writhe. These are used to illustrate a novel nonlinear phenomenon: ``geometric untwisting" of open filaments, whereby twisting strains relax through a transient writhing instability without performing axial rotation. This may explain certain experimentally observed motions of fibers of the bacterium B. subtilis [N.H. Mendelson, et al., J. Bacteriol. 177, 7060 (1995)].Comment: 9 pages, 4 figure

Application of Design of Experiments (DOE) on the Processing of Rapid Prototyped Samples

The purpose of this experiment was to improve the Fused Deposition Modeling Process by examining the tensile strength of samples fabricated in a Stratasys FDM 1650 Machine utilizing the methods of Design of Experiments. A two-level, four-factor, full factorial experiment was conducted. The selected factors were temperature, air gap, slice thickness, and raster orientation. A regression equation determined the level each factor should be set in order to optimize the FDM machine settings. It was found that single factors - small air gap, small layer thickness and low raster orientation, as well as the interaction between high temperature and small layer thickness yielded the greatest effect the response.Mechanical Engineerin