Machine learning analysis and modeling of interest rate curves

The present research deals with the review of the analysis and modeling of Swiss franc interest rate curves (IRC) by using unsupervised (SOM, Gaussian Mixtures) and supervised machine (MLP) learning algorithms. IRC are considered as objects embedded into different feature spaces: maturities; maturity-date, parameters of Nelson-Siegel model (NSM). Analysis of NSM parameters and their temporal and clustering structures helps to understand the relevance of model and its potential use for the forecasting. Mapping of IRC in a maturity-date feature space is presented and analyzed for the visualization and forecasting purposes.

Debris flow modeling for susceptibility mapping at regional to national scale in Norway

Debris flows and related landslide processes occur in many regions all over Norway and pose a significant hazard to inhabited areas. Within the framework of the development of a national debris flows susceptibility map, we are working on a modeling approach suitable for Norway with a nationwide coverage. The discrimination of source areas is based on an index approach, which includes topographic parameters and hydrological settings. For the runout modeling, we use the Flow-R model (IGAR, University of Lausanne), which is based on combined probabilistic and energetic algorithms for the assessment of the spreading of the flow and maximum runout distances. First results for different test areas have shown that runout distances can be modeled reliably. For the selection of source areas, however, additional factors have to be considered, such as the lithological and quaternary geological setting, in order to accommodate the strong variation in debris flow activity in the different geological, geomorphological and climate regions of Norway.

Analcime crystallization and compositional profiles - Comparing approaches to detect post-depositional alterations in archaeological pottery

The approaches of comparative studies and profile measurements, often used in order to detect post-depositional alterations of ceramics, have been applied simultaneously to two sets of Roman pottery, both of which include altered individuals. As analytical techniques, Neutron Activation Analysis and X-Ray Diffraction have been used. Both approaches lead to substantially different results. This shows that they detect different levels of alteration and should complement each other rather than being used exclusively. For the special process of a glassy phase decomposition followed by a crystallization of the Na-zeolite analcime, the results suggest that it changes high-fired calcareous pottery rapidly, and so fundamentally that the results of various archaeometric techniques can be severely disturbed.

Photopolymerizable hydrogels for implants: Monte-Carlo modeling and experimental in vitro validation.

Photopolymerization is commonly used in a broad range of bioapplications, such as drug delivery, tissue engineering, and surgical implants, where liquid materials are injected and then hardened by means of illumination to create a solid polymer network. However, photopolymerization using a probe, e.g., needle guiding both the liquid and the curing illumination, has not been thoroughly investigated. We present a Monte Carlo model that takes into account the dynamic absorption and scattering parameters as well as solid-liquid boundaries of the photopolymer to yield the shape and volume of minimally invasively injected, photopolymerized hydrogels. In the first part of the article, our model is validated using a set of well-known poly(ethylene glycol) dimethacrylate hydrogels showing an excellent agreement between simulated and experimental volume-growth-rates. In the second part, in situ experimental results and simulations for photopolymerization in tissue cavities are presented. It was found that a cavity with a volume of 152  mm3 can be photopolymerized from the output of a 0.28-mm2 fiber by adding scattering lipid particles while only a volume of 38  mm3 (25%) was achieved without particles. The proposed model provides a simple and robust method to solve complex photopolymerization problems, where the dimension of the light source is much smaller than the volume of the photopolymerizable hydrogel.

Models and approaches for environmental education in Iowa, 1999

This guide provides a variety of tools that can help an educator, building staff or school district decide how to include environmental education in their curriculum.

Flow targeted 3D steady-state free-precession coronary MR angiography: comparison of three different imaging approaches.

PURPOSE: To compare 3 different flow targeted magnetization preparation strategies for coronary MR angiography (cMRA), which allow selective visualization of the vessel lumen. MATERIAL AND METHODS: The right coronary artery of 10 healthy subjects was investigated on a 1.5 Tesla MR system (Gyroscan ACS-NT, Philips Healthcare, Best, NL). A navigator-gated and ECG-triggered 3D radial steady-state free-precession (SSFP) cMRA sequence with 3 different magnetization preparation schemes was performed referred to as projection SSFP (selective labeling of the aorta, subtraction of 2 data sets), LoReIn SSFP (double-inversion preparation, selective labeling of the aorta, 1 data set), and inflow SSFP (inversion preparation, selective labeling of the coronary artery, 1 data set). Signal-to-noise ratio (SNR) of the coronary artery and aorta, contrast-to-noise ratio (CNR) between the coronary artery and epicardial fat, vessel length and vessel sharpness were analyzed. RESULTS: All cMRA sequences were successfully obtained in all subjects. Both projection SSFP and LoReIn SSFP allowed for selective visualization of the coronary arteries with excellent background suppression. Scan time was doubled in projection SSFP because of the need for subtraction of 2 data sets. In inflow SSFP, background suppression was limited to the tissue included in the inversion volume. Projection SSFP (SNR(coro): 25.6 +/- 12.1; SNR(ao): 26.1 +/- 16.8; CNR(coro-fat): 22.0 +/- 11.7) and inflow SSFP (SNR(coro): 27.9 +/- 5.4; SNR(ao): 37.4 +/- 9.2; CNR(coro-fat): 24.9 +/- 4.8) yielded significantly increased SNR and CNR compared with LoReIn SSFP (SNR(coro): 12.3 +/- 5.4; SNR(ao): 11.8 +/- 5.8; CNR(coro-fat): 9.8 +/- 5.5; P < 0.05 for both). Longest visible vessel length was found with projection SSFP (79.5 mm +/- 18.9; P < 0.05 vs. LoReIn) whereas vessel sharpness was best in inflow SSFP (68.2% +/- 4.5%; P < 0.05 vs. LoReIn). Consistently good image quality was achieved using inflow SSFP likely because of the simple planning procedure and short scanning time. CONCLUSION: Three flow targeted cMRA approaches are presented, which provide selective visualization of the coronary vessel lumen and in addition blood flow information without the need of contrast agent administration. Inflow SSFP yielded highest SNR, CNR and vessel sharpness and may prove useful as a fast and efficient approach for assessing proximal and mid vessel coronary blood flow, whereas requiring less planning skills than projection SSFP or LoReIn SSFP.

Modeling storm events to investigate the influence of the stream¿catchment interface zone on stream biogeochemistry

We formulate a new mixing model to explore hydrological and chemical conditions under which the interface between the stream and catchment interface (SCI) influences the release of reactive solutes into stream water during storms. Physically, the SCI corresponds to the hyporheic/riparian sediments. In the new model this interface is coupled through a bidirectional water exchange to the conventional two components mixing model. Simulations show that the influence of the SCI on stream solute dynamics during storms is detectable when the runoff event is dominated by the infiltrated groundwater component that flows through the SCI before entering the stream and when the flux of solutes released from SCI sediments is similar to, or higher than, the solute flux carried by the groundwater. Dissolved organic carbon (DOC) and nitrate data from two small Mediterranean streams obtained during storms are compared to results from simulations using the new model to discern the circumstances under which the SCI is likely to control the dynamics of reactive solutes in streams. The simulations and the comparisons with empirical data suggest that the new mixing model may be especially appropriate for streams in which the periodic, or persistent, abrupt changes in the level of riparian groundwater exert hydrologic control on flux of biologically reactive fluxes between the riparian/hyporheic compartment and the stream water.

Using toxicokinetic modeling approach to determine biological reference values (BRVs) and to assess human exposure to pesticides

Exposure to various pesticides has been characterized in workers and the general population, but interpretation and assessment of biomonitoring data from a health risk perspective remains an issue. For workers, a Biological Exposure Index (BEI®) has been proposed for some substances, but most BEIs are based on urinary biomarker concentrations at Threshold Limit Value - Time Weighted Average (TLV-TWA) airborne exposure while occupational exposure can potentially occurs through multiple routes, particularly by skin contact (i.e.captan, chlorpyrifos, malathion). Similarly, several biomonitoring studies have been conducted to assess environmental exposure to pesticides in different populations, but dose estimates or health risks related to these environmental exposures (mainly through the diet), were rarely characterized. Recently, biological reference values (BRVs) in the form of urinary pesticide metabolites have been proposed for both occupationally exposed workers and children. These BRVs were established using toxicokinetic models developed for each substance, and correspond to safe levels of absorption in humans, regardless of the exposure scenario. The purpose of this chapter is to present a review of a toxicokinetic modeling approach used to determine biological reference values. These are then used to facilitate health risk assessments and decision-making on occupational and environmental pesticide exposures. Such models have the ability to link absorbed dose of the parent compound to exposure biomarkers and critical biological effects. To obtain the safest BRVs for the studied population, simulations of exposure scenarios were performed using a conservative reference dose such as a no-observed-effect level (NOEL). The various examples discussed in this chapter show the importance of knowledge on urine collections (i.e. spot samples and complete 8-h, 12-h or 24-h collections), sampling strategies, metabolism, relative proportions of the different metabolites in urine, absorption fraction, route of exposure and background contribution of prior exposures. They also show that relying on urinary measurements of specific metabolites appears more accurate when applying this approach to the case of occupational exposures. Conversely, relying on semi-specific metabolites (metabolites common to a category of pesticides) appears more accurate for the health risk assessment of environmental exposures given that the precise pesticides to which subjects are exposed are often unknown. In conclusion, the modeling approach to define BRVs for the relevant pesticides may be useful for public health authorities for managing issues related to health risks resulting from environmental and occupational exposures to pesticides.

Analysis and spatial modeling of the indoor radon Swiss data

The present research deals with an important public health threat, which is the pollution created by radon gas accumulation inside dwellings. The spatial modeling of indoor radon in Switzerland is particularly complex and challenging because of many influencing factors that should be taken into account. Indoor radon data analysis must be addressed from both a statistical and a spatial point of view. As a multivariate process, it was important at first to define the influence of each factor. In particular, it was important to define the influence of geology as being closely associated to indoor radon. This association was indeed observed for the Swiss data but not probed to be the sole determinant for the spatial modeling. The statistical analysis of data, both at univariate and multivariate level, was followed by an exploratory spatial analysis. Many tools proposed in the literature were tested and adapted, including fractality, declustering and moving windows methods. The use of Quan-tité Morisita Index (QMI) as a procedure to evaluate data clustering in function of the radon level was proposed. The existing methods of declustering were revised and applied in an attempt to approach the global histogram parameters. The exploratory phase comes along with the definition of multiple scales of interest for indoor radon mapping in Switzerland. The analysis was done with a top-to-down resolution approach, from regional to local lev¬els in order to find the appropriate scales for modeling. In this sense, data partition was optimized in order to cope with stationary conditions of geostatistical models. Common methods of spatial modeling such as Κ Nearest Neighbors (KNN), variography and General Regression Neural Networks (GRNN) were proposed as exploratory tools. In the following section, different spatial interpolation methods were applied for a par-ticular dataset. A bottom to top method complexity approach was adopted and the results were analyzed together in order to find common definitions of continuity and neighborhood parameters. Additionally, a data filter based on cross-validation was tested with the purpose of reducing noise at local scale (the CVMF). At the end of the chapter, a series of test for data consistency and methods robustness were performed. This lead to conclude about the importance of data splitting and the limitation of generalization methods for reproducing statistical distributions. The last section was dedicated to modeling methods with probabilistic interpretations. Data transformation and simulations thus allowed the use of multigaussian models and helped take the indoor radon pollution data uncertainty into consideration. The catego-rization transform was presented as a solution for extreme values modeling through clas-sification. Simulation scenarios were proposed, including an alternative proposal for the reproduction of the global histogram based on the sampling domain. The sequential Gaussian simulation (SGS) was presented as the method giving the most complete information, while classification performed in a more robust way. An error measure was defined in relation to the decision function for data classification hardening. Within the classification methods, probabilistic neural networks (PNN) show to be better adapted for modeling of high threshold categorization and for automation. Support vector machines (SVM) on the contrary performed well under balanced category conditions. In general, it was concluded that a particular prediction or estimation method is not better under all conditions of scale and neighborhood definitions. Simulations should be the basis, while other methods can provide complementary information to accomplish an efficient indoor radon decision making.