210 research outputs found
Anomaly Detection in Time Series: Theoretical and Practical Improvements for Disease Outbreak Detection
The automatic collection and increasing availability of health data provides a new opportunity for techniques to monitor this information. By monitoring pre-diagnostic data sources, such as over-the-counter cough medicine sales or emergency room chief complaints of cough, there exists the potential to detect disease outbreaks earlier than traditional laboratory disease confirmation results. This research is particularly important for a modern, highly-connected society, where the onset of disease outbreak can be swift and deadly, whether caused by a naturally occurring global pandemic such as swine flu or a targeted act of bioterrorism. In this dissertation, we first describe the problem and current state of research in disease outbreak detection, then provide four main additions to the field.
First, we formalize a framework for analyzing health series data and detecting anomalies: using forecasting methods to predict the next day's value, subtracting the forecast to create residuals, and finally using detection algorithms on the residuals. The formalized framework indicates the link between the forecast accuracy of the forecast method and the performance of the detector, and can be used to quantify and analyze the performance of a variety of heuristic methods.
Second, we describe improvements for the forecasting of health data series. The application of weather as a predictor, cross-series covariates, and ensemble forecasting each provide improvements to forecasting health data.
Third, we describe improvements for detection. This includes the use of multivariate statistics for anomaly detection and additional day-of-week preprocessing to aid detection. Most significantly, we also provide a new method, based on the CuScore, for optimizing detection when the impact of the disease outbreak is known. This method can provide an optimal detector for rapid detection, or for probability of detection within a certain timeframe.
Finally, we describe a method for improved comparison of detection methods. We provide tools to evaluate how well a simulated data set captures the characteristics of the authentic series and time-lag heatmaps, a new way of visualizing daily detection rates or displaying the comparison between two methods in a more informative way
Enumerating -arc-connected orientations
12 pagesWe study the problem of enumerating the -arc-connected orientations of a graph , i.e., generating each exactly once. A first algorithm using submodular flow optimization is easy to state, but intricate to implement. In a second approach we present a simple algorithm with delay and amortized time , which improves over the analysis of the submodular flow algorithm. As ingredients, we obtain enumeration algorithms for the -orientations of a graph in delay and for the outdegree sequences attained by -arc-connected orientations of in delay
Stepped Care for Smoking Cessation: A Cost-Effectiveness Analysis and Simulation of Future Outcomes
It has been well established that smoking is the leading avoidable cause of premature morbidity and mortality in the United States and abroad. Smoking is attributable to over 400,000 annual deaths, and 875.09 and 49,025 and 3,450 per QALY. In sensitivity analysis, incremental cost-effectiveness varied from cost-saving to $13,700 per QALY.
Stepped care was not cost-effective relative to repeat intervention. Quitting at the UTHSC site and among ethnic minorities was low, despite better rates of participation. Higher depression scores may have attributed to these results. Success of repeat care in STEP affirms findings of two recent studies. However, long-term cessation did prove highly cost-effective. Smoking cessation interventions continue to be extremely cost-effective and provide sizable returns on investment to employers and payers alike; enhanced coverage of smoking cessation treatments and programs will likely increase quit attempts and ultimately, cessation
The bias-extension test for the analysis of in-plane shear properties of textile composite reinforcements and prepregs: a review
The bias-extension test is a rather simple experiment aiming to determine in-plane shear properties of textile composite reinforcements. However the mechanics during the test involves fibrous material at large shear strains and large rotations of the fibres. Several aspects are still being studied and are not yet modeled in a consensual manner. The standard analysis of the test is based on two assumptions: inextensibility of the fibers and rotations at the yarn crossovers without slippage. They lead to the development of zones with constant fibre orientations proper to the bias-extension test. Beyond the analysis of the test within these basic assumptions, the paper presents studies that have been carried out on the lack of verification of these hypothesis (slippage, tension in the yarns, effects of fibre bending). The effects of temperature, mesoscopic modeling and tension locking are also considered in the case of the bias-extension test
SKCS-A Separable Kernel Family with Compact Support to improve visual segmentation of handwritten data
Extraction of pertinent data from noisy gray level document images with various and complex backgrounds such as mail envelopes, bank checks, business forms, etc... remains a challenging problem in character recognition applications. It depends on the quality of the character segmentation process. Over the last few decades, mathematical tools have been developed for this purpose. Several authors show that the Gaussian kernel is unique and offers many beneficial properties. In their recent work Remaki and Cheriet proposed a new kernel family with compact supports (KCS) in scale space that achieved good performance in extracting data information with regard to the Gaussian kernel. In this paper, we focus in further improving the KCS efficiency by proposing a new separable version of kernel family namely (SKCS). This new kernel has also a compact support and preserves the most important properties of the Gaussian kernel in order to perform image segmentation efficiently and to make the recognizer task particularly easier. A practical comparison is established between results obtained by using the KCS and the SKCS operators. Our comparison is based on the information loss and the gain in time processing. Experiments, on real life data, for extracting handwritten data, from noisy gray level images, show promising performance of the SKCS kernel, especially in reducing drastically the processing time with regard to the KCS
Numerical modelling of coastal structures armoured with concrete units
Looking to a future where the structural stability of single concrete armour layers is based upon numerical investigation, this thesis addresses the first major task, which is the representation of real structures. Coastal structures armoured with concrete units are created in prototype dimensions in a numerical model with satisfied realism for first time. The available 3D computer model based on FEMDEM (the combined finite-discrete element method), which has the capability for multi-body simulation of complex shaped objects, was used.
A major challenge was to develop a methodology for the numerical creation of concrete armour layers that would satisfy the stringent criteria required by the designers of breakwater units for on-site constructed ârandomâ and âinterlockingâ systems. A novel feature to obtain realistically tight systems is the use of four initial types of regular orientations of units, which are sequenced appropriately on a pre-defined positioning pattern grid. This new methodology enables different armour layer models to be built, characterised and examined.
The scope of the study is limited to dry conditions and performance under oscillatory loading is investigated by means of vibration. Design variables such as initial packing density, underlayer roughness and number of rows are evaluated and the technical criteria are challenged. The use of a different type of unit shape is also examined to show the potential of the developed technology. A set of analysis tools including accurate calculation of packing density on a local and global basis and the distribution of unit displacements after disturbance were developed to evaluate designs.
It is confirmed that the packing density is the most important parameter, which influences the performance of armour layers; the tighter the packs, the less are the displacements of units under disturbance. A single armour layer with low number of rows of units also proved to be stable. It is easier for units placed on a relatively smooth underlayer to find tighter positions, causing higher values of total average packing density. But when disturbed, armour layers placed on a rough underlayer are more stable. The use of a different type of unit shape is also examined in this thesis, with the purpose to present the potential of the developed technology to such applications.
Results may be considered to have limited applicability to the real behavior of structures under wave action. However, they provide some insights into how such complex coastal structures behave. This research constitutes a stepping stone on the way to models that accommodate wave action and will may one day improve the engineering design and understanding of movement of these concrete armour units.Open Acces
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