Investigations on maar-diatreme volcanoes by inversion of magnetic and gravity data from the Eifel area, Germany

A study of maar-diatreme volcanoes has been perfomed by inversion of gravity and magnetic data. The geophysical inverse problem has been solved by means of the damped nonlinear least-squares method. To ensure stability and convergence of the solution of the inverse problem, a mathematical tool, consisting in data weighting and model scaling, has been worked out. Theoretical gravity and magnetic modeling of maar-diatreme volcanoes has been conducted in order to get information, which is used for a simple rough qualitative and/or quantitative interpretation. The information also serves as a priori information to design models for the inversion and/or to assist the interpretation of inversion results. The results of theoretical modeling have been used to roughly estimate the heights and the dip angles of the walls of eight Eifel maar-diatremes — each taken as a whole. Inversemodeling has been conducted for the Schönfeld Maar (magnetics) and the Hausten-Morswiesen Maar (gravity and magnetics). The geometrical parameters of these maars, as well as the density and magnetic properties of the rocks filling them, have been estimated. For a reliable interpretation of the inversion results, beside the knowledge from theoretical modeling, it was resorted to other tools such like field transformations and spectral analysis for complementary information. Geologic models, based on thesynthesis of the respective interpretation results, are presented for the two maars mentioned above. The results gave more insight into the genesis, physics and posteruptive development of the maar-diatreme volcanoes. A classification of the maar-diatreme volcanoes into three main types has been elaborated. Relatively high magnetic anomalies are indicative of scoria cones embeded within maar-diatremes if they are not caused by a strong remanent component of the magnetization. Smaller (weaker) secondary gravity and magnetic anomalies on the background of the main anomaly of a maar-diatreme — especially in the boundary areas — are indicative for subsidence processes, which probably occurred in the late sedimentation phase of the posteruptive development. Contrary to postulates referring to kimberlite pipes, there exists no generalized systematics between diameter and height nor between geophysical anomaly and the dimensions of the maar-diatreme volcanoes. Although both maar-diatreme volcanoes and kimberlite pipes are products of phreatomagmatism, they probably formed in different thermodynamic and hydrogeological environments. In the case of kimberlite pipes, large amounts of magma and groundwater, certainly supplied by deep and large reservoirs, interacted under high pressure and temperature conditions. This led to a long period phreatomagmatic process and hence to the formation of large structures. Concerning the maar-diatreme and tuff-ring-diatreme volcanoes, the phreatomagmatic process takes place due to an interaction between magma from small and shallow magma chambers (probably segregated magmas) and small amounts of near-surface groundwater under low pressure and temperature conditions. This leads to shorter time eruptions and consequently to structures of smaller size in comparison with kimberlite pipes. Nevertheless, the results show that the diameter to height ratio for 50% of the studied maar-diatremes is around 1, whereby the dip angle of the diatreme walls is similar to that of the kimberlite pipes and lies between 70 and 85°. Note that these numerical characteristics, especially the dip angle, hold for the maars the diatremes of which — estimated by modeling — have the shape of a truncated cone. This indicates that the diatreme can not be completely resolved by inversion.

Reinforcement Lernen mit Regularisierungsnetzwerken

Die Arbeit behandelt das Problem der Skalierbarkeit von Reinforcement Lernen auf hochdimensionale und komplexe Aufgabenstellungen. Unter Reinforcement Lernen versteht man dabei eine auf approximativem Dynamischen Programmieren basierende Klasse von Lernverfahren, die speziell Anwendung in der Künstlichen Intelligenz findet und zur autonomen Steuerung simulierter Agenten oder realer Hardwareroboter in dynamischen und unwägbaren Umwelten genutzt werden kann. Dazu wird mittels Regression aus Stichproben eine Funktion bestimmt, die die Lösung einer "Optimalitätsgleichung" (Bellman) ist und aus der sich näherungsweise optimale Entscheidungen ableiten lassen. Eine große Hürde stellt dabei die Dimensionalität des Zustandsraums dar, die häufig hoch und daher traditionellen gitterbasierten Approximationsverfahren wenig zugänglich ist. Das Ziel dieser Arbeit ist es, Reinforcement Lernen durch nichtparametrisierte Funktionsapproximation (genauer, Regularisierungsnetze) auf -- im Prinzip beliebig -- hochdimensionale Probleme anwendbar zu machen. Regularisierungsnetze sind eine Verallgemeinerung von gewöhnlichen Basisfunktionsnetzen, die die gesuchte Lösung durch die Daten parametrisieren, wodurch die explizite Wahl von Knoten/Basisfunktionen entfällt und so bei hochdimensionalen Eingaben der "Fluch der Dimension" umgangen werden kann. Gleichzeitig sind Regularisierungsnetze aber auch lineare Approximatoren, die technisch einfach handhabbar sind und für die die bestehenden Konvergenzaussagen von Reinforcement Lernen Gültigkeit behalten (anders als etwa bei Feed-Forward Neuronalen Netzen). Allen diesen theoretischen Vorteilen gegenüber steht allerdings ein sehr praktisches Problem: der Rechenaufwand bei der Verwendung von Regularisierungsnetzen skaliert von Natur aus wie O(n**3), wobei n die Anzahl der Daten ist. Das ist besonders deswegen problematisch, weil bei Reinforcement Lernen der Lernprozeß online erfolgt -- die Stichproben werden von einem Agenten/Roboter erzeugt, während er mit der Umwelt interagiert. Anpassungen an der Lösung müssen daher sofort und mit wenig Rechenaufwand vorgenommen werden. Der Beitrag dieser Arbeit gliedert sich daher in zwei Teile: Im ersten Teil der Arbeit formulieren wir für Regularisierungsnetze einen effizienten Lernalgorithmus zum Lösen allgemeiner Regressionsaufgaben, der speziell auf die Anforderungen von Online-Lernen zugeschnitten ist. Unser Ansatz basiert auf der Vorgehensweise von Recursive Least-Squares, kann aber mit konstantem Zeitaufwand nicht nur neue Daten sondern auch neue Basisfunktionen in das bestehende Modell einfügen. Ermöglicht wird das durch die "Subset of Regressors" Approximation, wodurch der Kern durch eine stark reduzierte Auswahl von Trainingsdaten approximiert wird, und einer gierigen Auswahlwahlprozedur, die diese Basiselemente direkt aus dem Datenstrom zur Laufzeit selektiert. Im zweiten Teil übertragen wir diesen Algorithmus auf approximative Politik-Evaluation mittels Least-Squares basiertem Temporal-Difference Lernen, und integrieren diesen Baustein in ein Gesamtsystem zum autonomen Lernen von optimalem Verhalten. Insgesamt entwickeln wir ein in hohem Maße dateneffizientes Verfahren, das insbesondere für Lernprobleme aus der Robotik mit kontinuierlichen und hochdimensionalen Zustandsräumen sowie stochastischen Zustandsübergängen geeignet ist. Dabei sind wir nicht auf ein Modell der Umwelt angewiesen, arbeiten weitestgehend unabhängig von der Dimension des Zustandsraums, erzielen Konvergenz bereits mit relativ wenigen Agent-Umwelt Interaktionen, und können dank des effizienten Online-Algorithmus auch im Kontext zeitkritischer Echtzeitanwendungen operieren. Wir demonstrieren die Leistungsfähigkeit unseres Ansatzes anhand von zwei realistischen und komplexen Anwendungsbeispielen: dem Problem RoboCup-Keepaway, sowie der Steuerung eines (simulierten) Oktopus-Tentakels.

Entrepreneurship influential factors in development of rural tourism as diversified rural activity in Republic of Macedonia

Rural tourism is relatively new product in the process of diversification of the rural economy in Republic of Macedonia. This study used desk research and life story interviews of rural tourism entrepreneurs as qualitative research method to identify prevalent success influential factors. Further quantitative analysis was applied in order to measure the strength of influence of identified success factors. The primary data for the quantitative research was gathered using telephone questionnaire composed of 37 questions with 5-points Likert scale. The data was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) by SmartPLS 3.1.6. Results indicated that human capital, social capital, entrepreneurial personality and external business environment are predominant influential success factors. However, human capital has non-significant direct effect on success (p 0.493) nonetheless the effect was indirect with high level of partial mediation through entrepreneurial personality as mediator (VAF 73%). Personality of the entrepreneur, social capital and business environment have direct positive affect on entrepreneurial success (p 0.001, 0.003 and 0.045 respectably). Personality also mediates the positive effect of social capital on entrepreneurial success (VAF 28%). Opposite to the theory the data showed no interaction between social and human capital on the entrepreneurial success. This research suggests that rural tourism accommodation entrepreneurs could be more successful if there is increased support in development of social capital in form of conservation of cultural heritage and natural attractions. Priority should be finding the form to encourage and support the establishment of formal and informal associations of entrepreneurs in order to improve the conditions for management and marketing of the sector. Special support of family businesses in the early stages of the operation would have a particularly positive impact on the success of rural tourism. Local infrastructure, access to financial instruments, destination marketing and entrepreneurial personality have positive effect on success.

NLGA based Active Control in Aerospace: fault tolerability, disturbance rejection, and parameter estimation

A new control scheme has been presented in this thesis. Based on the NonLinear Geometric Approach, the proposed Active Control System represents a new way to see the reconfigurable controllers for aerospace applications. The presence of the Diagnosis module (providing the estimation of generic signals which, based on the case, can be faults, disturbances or system parameters), mean feature of the depicted Active Control System, is a characteristic shared by three well known control systems: the Active Fault Tolerant Controls, the Indirect Adaptive Controls and the Active Disturbance Rejection Controls. The standard NonLinear Geometric Approach (NLGA) has been accurately investigated and than improved to extend its applicability to more complex models. The standard NLGA procedure has been modified to take account of feasible and estimable sets of unknown signals. Furthermore the application of the Singular Perturbations approximation has led to the solution of Detection and Isolation problems in scenarios too complex to be solved by the standard NLGA. Also the estimation process has been improved, where multiple redundant measuremtent are available, by the introduction of a new algorithm, here called "Least Squares - Sliding Mode". It guarantees optimality, in the sense of the least squares, and finite estimation time, in the sense of the sliding mode. The Active Control System concept has been formalized in two controller: a nonlinear backstepping controller and a nonlinear composite controller. Particularly interesting is the integration, in the controller design, of the estimations coming from the Diagnosis module. Stability proofs are provided for both the control schemes. Finally, different applications in aerospace have been provided to show the applicability and the effectiveness of the proposed NLGA-based Active Control System.

A photophysical study of excited state dynamics in donor-acceptor copolymer photovoltaic blends

This thesis deals with the investigation of charge generation and recombination processes in three different polymer:fullerene photovoltaic blends by means of ultrafast time-resolved optical spectroscopy. The first donor polymer, namely poly[N-11"-henicosanyl-2,7-carbazole-alt-5,5-(4',7'-di-2-thienyl-2',1',3'-benzothiadiazole)] (PCDTBT), is a mid-bandgap polymer, the other two materials are the low-bandgap donor polymers poly[2,6-(4,4-bis-(2-ethylhexyl)-4H-cyclopenta[2,1-b;3,4-b']-dithiophene)-alt-4,7-(2,1,3-benzothiadiazole) (PCPDTBT) and poly[(4,4'-bis(2-ethylhexyl)dithieno[3,2-b:2',3'-d]silole)-2,6-diyl-alt-(2,1,3-benzothiadiazole)-4,7-diyl] (PSBTBT). Despite their broader absorption, the low-bandgap polymers do not show enhanced photovoltaic efficiencies compared to the mid-bandgap system.rnrnTransient absorption spectroscopy revealed that energetic disorder plays an important role in the photophysics of PCDTBT, and that in a blend with PCBM geminate losses are small. The photophysics of the low-bandgap system PCPDTBT were strongly altered by adding a high boiling point cosolvent to the polymer:fullerene blend due to a partial demixing of the materials. We observed an increase in device performance together with a reduction of geminate recombination upon addition of the cosolvent. By applying model-free multi-variate curve resolution to the spectroscopic data, we found that fast non-geminate recombination due to polymer triplet state formation is a limiting loss channel in the low-bandgap material system PCPDTBT, whereas in PSBTBT triplet formation has a smaller impact on device performance, and thus higher efficiencies are obtained.rn

Microwave breast imaging reconstruction

In this thesis I analyzed the microwave tomography method to recognize breast can- cer. I study how identify the dielectric permittivity, the Helmoltz equation parameter used to model the real physic problem. Through a non linear least squares method I solve a problem of parameters identification; I show the theoric approach and the devel- opment to reach the results. I use the Levenberg-Marquardt algorithm, applied on COMSOL software to multiphysic models; so I do numerical proofs on semplified test problems compared to the specific real problem to solve.

Morphological integration between the cranial base and the face in children and adults

The primary aim of the present study was to assess morphological covariation between the face and the basicranium (midline and lateral), and to evaluate patterns of integration at two specific developmental stages. A group of 71 children (6-10 years) was compared with a group of 71 adults (20-35 years). Lateral cephalometric radiographs were digitized and a total of 28 landmarks were placed on three areas; the midline cranial base, the lateral cranial base and the face. Geometric morphometric methods were applied and partial least squares analysis was used to evaluate correlation between the three shape blocks. Morphological integration was tested both with and without removing the effect of allometry. In children, mainly the midline and, to a lesser extent, the lateral cranial base were moderately correlated to the face. In adults, the correlation between the face and the midline cranial base, which ceases development earlier than the lateral base, was reduced. However, the lateral cranial base retained and even strengthened its correlation to the face. This suggests that the duration of common developmental timing is an important factor that influences integration between craniofacial structures. However, despite the apparent switch of primary roles between the cranial bases during development, the patterns of integration remained stable, thereby supporting the role of genetics over function in the establishment and development of craniofacial shape.

The Incumbent Spending Puzzle

Objective This article seeks to explain the puzzle of why incumbents spend so much on campaigns despite most research finding that their spending has almost no effect on voters. Methods The article uses ordinary least squares, instrumental variables, and fixed-effects regression to estimate the impact of incumbent spending on election outcomes. The estimation includes an interaction term between incumbent and challenger spending to allow the effect of incumbent spending to depend on the level of challenger spending. Results The estimation provides strong evidence that spending by the incumbent has a larger positive impact on votes received the more money the challenger spends. Conclusion Campaign spending by incumbents is most valuable in the races where the incumbent faces a serious challenge. Raising large sums of money to be used in close races is thus a rational choice by incumbents.

Broadband Fourier transform rotational spectroscopy for structure determination: The water heptamer

Over the recent years chirped-pulse, Fourier-transform microwave (CP-FTMW) spectrometers have chan- ged the scope of rotational spectroscopy. The broad frequency and large dynamic range make possible structural determinations in molecular systems of increasingly larger size from measurements of heavy atom (13C, 15N, 18O) isotopes recorded in natural abundance in the same spectrum as that of the parent isotopic species. The design of a broadband spectrometer operating in the 2–8 GHz frequency range with further improvements in sensitivity is presented. The current CP-FTMW spectrometer performance is benchmarked in the analyses of the rotational spectrum of the water heptamer, (H2O)7, in both 2– 8 GHz and 6–18 GHz frequency ranges. Two isomers of the water heptamer have been observed in a pulsed supersonic molecular expansion. High level ab initio structural searches were performed to pro- vide plausible low-energy candidates which were directly compared with accurate structures provided from broadband rotational spectra. The full substitution structure of the most stable species has been obtained through the analysis of all possible singly-substituted isotopologues (H218O and HDO), and a least-squares rm(1) geometry of the oxygen framework determined from 16 different isotopic species compares with the calculated O–O equilibrium distances at the 0.01 Å level.

Broadband Fourier transform rotational spectroscopy for structure determination: The water heptamer

Over the recent years chirped-pulse, Fourier-transform microwave (CP-FTMW) spectrometers have changed the scope of rotational spectroscopy. The broad frequency and large dynamic range make possible structural determinations in molecular systems of increasingly larger size from measurements of heavy atom (C-13, N-15, O-18) isotopes recorded in natural abundance in the same spectrum as that of the parent isotopic species. The design of a broadband spectrometer operating in the 2-8 GHz frequency range with further improvements in sensitivity is presented. The current CP-FTMW spectrometer performance is benchmarked in the analyses of the rotational spectrum of the water heptamer, (H2O)(7), in both 2-8 GHz and 6-18 GHz frequency ranges. Two isomers of the water heptamer have been observed in a pulsed supersonic molecular expansion. High level ab initio structural searches were performed to provide plausible low-energy candidates which were directly compared with accurate structures provided from broadband rotational spectra. The full substitution structure of the most stable species has been obtained through the analysis of all possible singly-substituted isotopologues ((H2O)-O-18 and HDO), and a least-squares r(m)((1)) geometry of the oxygen framework determined from 16 different isotopic species compares with the calculated O-O equilibrium distances at the 0.01 angstrom level. (C) 2013 Elsevier B.V. All rights reserved.

The Effects of Interstate Speed Limit Increases: Fatality Rates and Traffic Diversion Versus Speed Spillover

This thesis examines two panel data sets of 48 states from 1981 to 2009 and utilizes ordinary least squares (OLS) and fixed effects models to explore the relationship between rural Interstate speed limits and fatality rates and whether rural Interstate speed limits affect non-Interstate safety. Models provide evidence that rural Interstate speed limits higher than 55 MPH lead to higher fatality rates on rural Interstates though this effect is somewhat tempered by reductions in fatality rates for roads other than rural Interstates. These results provide some but not unanimous support for the traffic diversion hypothesis that rural Interstate speed limit increases lead to decreases in fatality rates of other roads. To the author’s knowledge, this paper is the first econometric study to differentiate between the effects of 70 MPH speed limits and speed limits above 70 MPH on fatality rates using a multi-state data set. Considering both rural Interstates and other roads, rural Interstate speed limit increases above 55 MPH are responsible for 39,700 net fatalities, 4.1 percent of total fatalities from 1987, the year limits were first raised, to 2009.

Vibrational Relaxation of 13CO2(v2) by Atomic Oxygen

Carbon dioxide (CO2) has been of recent interest due to the issue of greenhouse cooling in the upper atmosphere by species such as CO2 and NO. In the Earth’s upper atmosphere, between altitudes of 75 and 110 km, a collisional energy exchange occurs between CO2 and atomic oxygen, which promotes a population of ground state CO2 to the bend excited state. The relaxation of CO2 following this excitation is characterized by spontaneous emission of 15-μm. Most of this energy is emitted away from Earth. Due to the low density in the upper atmosphere, most of this energy is not reabsorbed and thus escapes into space, leading to a local cooling effect in the upper atmosphere. To determine the efficiency of the CO2- O atom collisional energy exchange, transient diode laser absorption spectroscopy was used to monitor the population of the first vibrationally excited state, 13CO2(0110) or ν2, as a function of time. The rate coefficient, kO(ν2), for the vibrational relaxation 13CO2 (ν2)-O was determined by fitting laboratory measurements using a home-written linear least squares algorithm. The rate coefficient, kO(ν2), of the vibrational relaxation of 13CO2(ν2), by atomic oxygen at room temperature was determined to be (1.6 ± 0.3 x 10-12 cm3 s-1), which is within the uncertainty of the rate coefficient previously found in this group for 12CO2(ν2) relaxation. The cold temperature kO(ν2) values were determined to be: (2.1 ± 0.8) x 10-12 cm3 s-1 at Tfinal = 274 K, (1.8 ± 0.3) x 10-12 cm3 s-1 at Tfinal = 239 K, (2 ± 1) x 10-12 cm3 s-1 at Tfinal = 208 K, and (1.7 ± 0.3) x 10-12 cm3 s-1 at Tfinal = 186 K. These data did not show a definitive negative temperature dependence comparable to that found for 12CO2 previously.

Geographical partitioning of goat diversity in Europe and the Middle East

Thirty microsatellite markers were analysed in 1426 goats from 45 traditional or rare breeds in 15 European and Middle Eastern countries. In all populations inbreeding was indicated by heterozygosity deficiency (mean FIS = 0.10). Genetic differentiation between breeds was moderate with a mean FST value of 0.07, but for most (c. 71%) northern and central European breeds, individuals could be assigned to their breeds with a success rate of more than 80%. Bayesian-based clustering analysis of allele frequencies and multivariate analysis revealed at least four discrete clusters: eastern Mediterranean (Middle East), central Mediterranean, western Mediterranean and central/northern Europe. About 41% of the genetic variability among the breeds could be explained by their geographical origin. A decrease in genetic diversity from the south-east to the north-west was accompanied by an increase in the level of differentiation at the breed level. These observations support the hypothesis that domestic livestock migrated from the Middle East towards western and northern Europe and indicate that breed formation was more systematic in north-central Europe than in the Middle East. We propose that breed differentiation and molecular diversity are independent criteria for conservation.

Application and comparison of classification algorithms for recognition of Alzheimer's disease in electrical brain activity (EEG)

The early detection of subjects with probable Alzheimer's disease (AD) is crucial for effective appliance of treatment strategies. Here we explored the ability of a multitude of linear and non-linear classification algorithms to discriminate between the electroencephalograms (EEGs) of patients with varying degree of AD and their age-matched control subjects. Absolute and relative spectral power, distribution of spectral power, and measures of spatial synchronization were calculated from recordings of resting eyes-closed continuous EEGs of 45 healthy controls, 116 patients with mild AD and 81 patients with moderate AD, recruited in two different centers (Stockholm, New York). The applied classification algorithms were: principal component linear discriminant analysis (PC LDA), partial least squares LDA (PLS LDA), principal component logistic regression (PC LR), partial least squares logistic regression (PLS LR), bagging, random forest, support vector machines (SVM) and feed-forward neural network. Based on 10-fold cross-validation runs it could be demonstrated that even tough modern computer-intensive classification algorithms such as random forests, SVM and neural networks show a slight superiority, more classical classification algorithms performed nearly equally well. Using random forests classification a considerable sensitivity of up to 85% and a specificity of 78%, respectively for the test of even only mild AD patients has been reached, whereas for the comparison of moderate AD vs. controls, using SVM and neural networks, values of 89% and 88% for sensitivity and specificity were achieved. Such a remarkable performance proves the value of these classification algorithms for clinical diagnostics.