Macroseismology as a Component of Seismicity Assessments in an Intraplate Region: Studies of Northern Europe with Emphasis on Finland

This work focuses on the role of macroseismology in the assessment of seismicity and probabilistic seismic hazard in Northern Europe. The main type of data under consideration is a set of macroseismic observations available for a given earthquake. The macroseismic questionnaires used to collect earthquake observations from local residents since the late 1800s constitute a special part of the seismological heritage in the region. Information of the earthquakes felt on the coasts of the Gulf of Bothnia between 31 March and 2 April 1883 and on 28 July 1888 was retrieved from the contemporary Finnish and Swedish newspapers, while the earthquake of 4 November 1898 GMT is an example of an early systematic macroseismic survey in the region. A data set of more than 1200 macroseismic questionnaires is available for the earthquake in Central Finland on 16 November 1931. Basic macroseismic investigations including preparation of new intensity data point (IDP) maps were conducted for these earthquakes. Previously disregarded usable observations were found in the press. The improved collection of IDPs of the 1888 earthquake shows that this event was a rare occurrence in the area. In contrast to earlier notions it was felt on both sides of the Gulf of Bothnia. The data on the earthquake of 4 November 1898 GMT were augmented with historical background information discovered in various archives and libraries. This earthquake was of some concern to the authorities, because extra fire inspections were conducted in three towns at least, i.e. Tornio, Haparanda and Piteå, located in the centre of the area of perceptibility. This event posed the indirect hazard of fire, although its magnitude around 4.6 was minor on the global scale. The distribution of slightly damaging intensities was larger than previously outlined. This may have resulted from the amplification of the ground shaking in the soft soil of the coast and river valleys where most of the population was found. The large data set of the 1931 earthquake provided an opportunity to apply statistical methods and assess methodologies that can be used when dealing with macroseismic intensity. It was evaluated using correspondence analysis. Different approaches such as gridding were tested to estimate the macroseismic field from the intensity values distributed irregularly in space. In general, the characteristics of intensity warrant careful consideration. A more pervasive perception of intensity as an ordinal quantity affected by uncertainties is advocated. A parametric earthquake catalogue comprising entries from both the macroseismic and instrumental era was used for probabilistic seismic hazard assessment. The parametric-historic methodology was applied to estimate seismic hazard at a given site in Finland and to prepare a seismic hazard map for Northern Europe. The interpretation of these results is an important issue, because the recurrence times of damaging earthquakes may well exceed thousands of years in an intraplate setting such as Northern Europe. This application may therefore be seen as an example of short-term hazard assessment.

Asteroid identification using statistical orbital inversion methods

An efficient and statistically robust solution for the identification of asteroids among numerous sets of astrometry is presented. In particular, numerical methods have been developed for the short-term identification of asteroids at discovery, and for the long-term identification of scarcely observed asteroids over apparitions, a task which has been lacking a robust method until now. The methods are based on the solid foundation of statistical orbital inversion properly taking into account the observational uncertainties, which allows for the detection of practically all correct identifications. Through the use of dimensionality-reduction techniques and efficient data structures, the exact methods have a loglinear, that is, O(nlog(n)), computational complexity, where n is the number of included observation sets. The methods developed are thus suitable for future large-scale surveys which anticipate a substantial increase in the astrometric data rate. Due to the discontinuous nature of asteroid astrometry, separate sets of astrometry must be linked to a common asteroid from the very first discovery detections onwards. The reason for the discontinuity in the observed positions is the rotation of the observer with the Earth as well as the motion of the asteroid and the observer about the Sun. Therefore, the aim of identification is to find a set of orbital elements that reproduce the observed positions with residuals similar to the inevitable observational uncertainty. Unless the astrometric observation sets are linked, the corresponding asteroid is eventually lost as the uncertainty of the predicted positions grows too large to allow successful follow-up. Whereas the presented identification theory and the numerical comparison algorithm are generally applicable, that is, also in fields other than astronomy (e.g., in the identification of space debris), the numerical methods developed for asteroid identification can immediately be applied to all objects on heliocentric orbits with negligible effects due to non-gravitational forces in the time frame of the analysis. The methods developed have been successfully applied to various identification problems. Simulations have shown that the methods developed are able to find virtually all correct linkages despite challenges such as numerous scarce observation sets, astrometric uncertainty, numerous objects confined to a limited region on the celestial sphere, long linking intervals, and substantial parallaxes. Tens of previously unknown main-belt asteroids have been identified with the short-term method in a preliminary study to locate asteroids among numerous unidentified sets of single-night astrometry of moving objects, and scarce astrometry obtained nearly simultaneously with Earth-based and space-based telescopes has been successfully linked despite a substantial parallax. Using the long-term method, thousands of realistic 3-linkages typically spanning several apparitions have so far been found among designated observation sets each spanning less than 48 hours.

Process variability-aware statistical hybrid modeling of dynamic power dissipation in 65 nm CMOS designs

A generalized technique is proposed for modeling the effects of process variations on dynamic power by directly relating the variations in process parameters to variations in dynamic power of a digital circuit. The dynamic power of a 2-input NAND gate is characterized by mixed-mode simulations, to be used as a library element for 65mn gate length technology. The proposed methodology is demonstrated with a multiplier circuit built using the NAND gate library, by characterizing its dynamic power through Monte Carlo analysis. The statistical technique of Response. Surface Methodology (RSM) using Design of Experiments (DOE) and Least Squares Method (LSM), are employed to generate a "hybrid model" for gate power to account for simultaneous variations in multiple process parameters. We demonstrate that our hybrid model based statistical design approach results in considerable savings in the power budget of low power CMOS designs with an error of less than 1%, with significant reductions in uncertainty by atleast 6X on a normalized basis, against worst case design.

Author response: "Missed opportunity related to statistical methods"

"We thank MrGilder for his considered comments and suggestions for alternative analyses of our data. We also appreciate Mr Gilder’s support of our call for larger studies to contribute to the evidence base for preoperative loading with high-carbohydrate fluids..."

Statistical tools for analyzing water quality data

Water quality data are often collected at different sites over time to improve water quality management. Water quality data usually exhibit the following characteristics: non-normal distribution, presence of outliers, missing values, values below detection limits (censored), and serial dependence. It is essential to apply appropriate statistical methodology when analyzing water quality data to draw valid conclusions and hence provide useful advice in water management. In this chapter, we will provide and demonstrate various statistical tools for analyzing such water quality data, and will also introduce how to use a statistical software R to analyze water quality data by various statistical methods. A dataset collected from the Susquehanna River Basin will be used to demonstrate various statistical methods provided in this chapter. The dataset can be downloaded from website http://www.srbc.net/programs/CBP/nutrientprogram.htm.

Ethylation of benzene: A statistical study

A central composite rotatable experimental design was constructed for a statistical study of the ethylation of benzene in the liquid phase, with aluminum chloride catalyst, in an agitated tank system. The conversion of benzene and ethylene and the yield of monoethyl- and diethylbenzene are characterized by the response surface technique. In the experimental range studied, agitation rate has no significant effect. Catalyst concentration, rate of ethylene Flow, and temperature are the influential factors. The response surfaces may be adequately approximated by planes.

Applicability of Statistical Learning Algorithms for Spatial Variability of Rock Depth

Two algorithms are outlined, each of which has interesting features for modeling of spatial variability of rock depth. In this paper, reduced level of rock at Bangalore, India, is arrived from the 652 boreholes data in the area covering 220 sqa <.km. Support vector machine (SVM) and relevance vector machine (RVM) have been utilized to predict the reduced level of rock in the subsurface of Bangalore and to study the spatial variability of the rock depth. The support vector machine (SVM) that is firmly based on the theory of statistical learning theory uses regression technique by introducing epsilon-insensitive loss function has been adopted. RVM is a probabilistic model similar to the widespread SVM, but where the training takes place in a Bayesian framework. Prediction results show the ability of learning machine to build accurate models for spatial variability of rock depth with strong predictive capabilities. The paper also highlights the capability ofRVM over the SVM model.

Semi-supervised learning of WLAN radio maps

In this thesis a manifold learning method is applied to the problem of WLAN positioning and automatic radio map creation. Due to the nature of WLAN signal strength measurements, a signal map created from raw measurements results in non-linear distance relations between measurement points. These signal strength vectors reside in a high-dimensioned coordinate system. With the help of the so called Isomap-algorithm the dimensionality of this map can be reduced, and thus more easily processed. By embedding position-labeled strategic key points, we can automatically adjust the mapping to match the surveyed environment. The environment is thus learned in a semi-supervised way; gathering training points and embedding them in a two-dimensional manifold gives us a rough mapping of the measured environment. After a calibration phase, where the labeled key points in the training data are used to associate coordinates in the manifold representation with geographical locations, we can perform positioning using the adjusted map. This can be achieved through a traditional supervised learning process, which in our case is a simple nearest neighbors matching of a sampled signal strength vector. We deployed this system in two locations in the Kumpula campus in Helsinki, Finland. Results indicate that positioning based on the learned radio map can achieve good accuracy, especially in hallways or other areas in the environment where the WLAN signal is constrained by obstacles such as walls.

Future chemistry teachers use of knowledge dimensions and high-order cognitive skills in pre-laboratory concept maps

This poster describes a pilot case study, which aim is to study how future chemistry teachers use knowledge dimensions and high-order cognitive skills (HOCS) in their pre-laboratory concept maps to support chemistry laboratory work. The research data consisted of 168 pre-laboratory concept maps that 29 students constructed as a part of their chemistry laboratory studies. Concept maps were analyzed by using a theory based content analysis through Anderson & Krathwohls' learning taxonomy (2001). This study implicates that novice concept mapper students use all knowledge dimensions and applying, analyzing and evaluating HOCS to support the pre-laboratory work.

Data fusion and matching by maximizing statistical dependencies

The core aim of machine learning is to make a computer program learn from the experience. Learning from data is usually defined as a task of learning regularities or patterns in data in order to extract useful information, or to learn the underlying concept. An important sub-field of machine learning is called multi-view learning where the task is to learn from multiple data sets or views describing the same underlying concept. A typical example of such scenario would be to study a biological concept using several biological measurements like gene expression, protein expression and metabolic profiles, or to classify web pages based on their content and the contents of their hyperlinks. In this thesis, novel problem formulations and methods for multi-view learning are presented. The contributions include a linear data fusion approach during exploratory data analysis, a new measure to evaluate different kinds of representations for textual data, and an extension of multi-view learning for novel scenarios where the correspondence of samples in the different views or data sets is not known in advance. In order to infer the one-to-one correspondence of samples between two views, a novel concept of multi-view matching is proposed. The matching algorithm is completely data-driven and is demonstrated in several applications such as matching of metabolites between humans and mice, and matching of sentences between documents in two languages.

Enhancement of workability in AZ31 alloy-processing maps: Part I,castmaterial

Processing maps developed on the basis of the Dynamic Materials Model provide valuable information that might help the metal working industry in solving problems related to workability and microstructural control in commercial alloys. In this research, the processing maps for an as-cast AZ31 magnesium alloy are presented. The results are validated via microstructural observations, clearly delineating safe and unsafe regimes for further process design of this alloy.

High temperature deformation processing maps for boron modified Ti-6Al-4V alloys

The alloy, Ti-6Al-4V is an alpha + beta Ti alloy that has large prior beta grain size (similar to 2 mm) in the as cast state. Minor addition of B (about 0.1 wt.%) to it refines the grain size significantly as well as produces in-situ TiB needles. The role played by these microstructural modifications on high temperature deformation processing maps of B-modified Ti64 alloys is examined in this paper.Power dissipation efficiency and instability maps have been generated within the temperature range of 750-1000 degrees C and strain rate range of 10(-3)-10(+1) s(-1). Various deformation mechanisms, which operate in different temperature-strain rate regimes, were identified with the aid of the maps and complementary microstructural analysis of the deformed specimens. Results indicate four distinct deformation domains within the range of experimental conditions examined, with the combination of 900-1000 degrees C and 10(-3)-10(-2) s(-1) being the optimum for hot working. In that zone, dynamic globularization of alpha laths is the principle deformation mechanism. The marked reduction in the prior beta grain size, achieved with the addition of B, does not appear to alter this domain markedly. The other domains, with negative values of instability parameter, show undesirable microstructural features such as extensive kinking/bending of alpha laths and breaking of beta laths for Ti64-0.0B as well as generation of voids and cracks in the matrix and TiB needles in the B-modified alloys. (C) 2010 Elsevier B.V. All rights reserved.