EXPLORING VIENNESE TUNING AND ITS BENEFITS FOR THE MODERN DOUBLE BASSIST

The role of the double bass in Vienna during the eighteenth century evolved significantly between 1760 and 1812. During these years, Viennese composers began to view the double bass less as an accompanimental instrument and more as a solo voice. Despite the abundance of music written for the double bass during this time, few of these compositions are regularly performed today. This dissertation serves three purposes. I explore how learning eighteenth-century Viennese compositions in the original tuning can influence modern performances of these works. Secondly, I document the arrangement of a lesser-know work for the modern tuned bass using the manuscript as the source material. Finally, by performing a variety of eighteenth-century bass works, I bring this music to the public's attention. The research for this dissertation has been presented in two forms. The recitals present both solo and chamber works from eighteenth-century Vienna. The repertoire for the three recitals was chosen so that each recital addresses one of the three purposes mentioned above. The research paper presents performance practices of the eighteenth century, challenges the modern double bassist faces when playing this literature, as well as a look into how to arrange one of these works for the modern tuned double bass. The three recitals were performed on the campus of the University of Maryland in the Leah M. Smith Hall, Gildenhorn Recital Hall and the Ulrich Recital Hall, respectively. Recordings of all three recitals can be found in the Digital Repository at the University of Maryland (DRUM).

Soft computing based parameter identification in pavements and geomechanical systems

Accurate estimation of road pavement geometry and layer material properties through the use of proper nondestructive testing and sensor technologies is essential for evaluating pavement’s structural condition and determining options for maintenance and rehabilitation. For these purposes, pavement deflection basins produced by the nondestructive Falling Weight Deflectometer (FWD) test data are commonly used. The nondestructive FWD test drops weights on the pavement to simulate traffic loads and measures the created pavement deflection basins. Backcalculation of pavement geometry and layer properties using FWD deflections is a difficult inverse problem, and the solution with conventional mathematical methods is often challenging due to the ill-posed nature of the problem. In this dissertation, a hybrid algorithm was developed to seek robust and fast solutions to this inverse problem. The algorithm is based on soft computing techniques, mainly Artificial Neural Networks (ANNs) and Genetic Algorithms (GAs) as well as the use of numerical analysis techniques to properly simulate the geomechanical system. A widely used pavement layered analysis program ILLI-PAVE was employed in the analyses of flexible pavements of various pavement types; including full-depth asphalt and conventional flexible pavements, were built on either lime stabilized soils or untreated subgrade. Nonlinear properties of the subgrade soil and the base course aggregate as transportation geomaterials were also considered. A computer program, Soft Computing Based System Identifier or SOFTSYS, was developed. In SOFTSYS, ANNs were used as surrogate models to provide faster solutions of the nonlinear finite element program ILLI-PAVE. The deflections obtained from FWD tests in the field were matched with the predictions obtained from the numerical simulations to develop SOFTSYS models. The solution to the inverse problem for multi-layered pavements is computationally hard to achieve and is often not feasible due to field variability and quality of the collected data. The primary difficulty in the analysis arises from the substantial increase in the degree of non-uniqueness of the mapping from the pavement layer parameters to the FWD deflections. The insensitivity of some layer properties lowered SOFTSYS model performances. Still, SOFTSYS models were shown to work effectively with the synthetic data obtained from ILLI-PAVE finite element solutions. In general, SOFTSYS solutions very closely matched the ILLI-PAVE mechanistic pavement analysis results. For SOFTSYS validation, field collected FWD data were successfully used to predict pavement layer thicknesses and layer moduli of in-service flexible pavements. Some of the very promising SOFTSYS results indicated average absolute errors on the order of 2%, 7%, and 4% for the Hot Mix Asphalt (HMA) thickness estimation of full-depth asphalt pavements, full-depth pavements on lime stabilized soils and conventional flexible pavements, respectively. The field validations of SOFTSYS data also produced meaningful results. The thickness data obtained from Ground Penetrating Radar testing matched reasonably well with predictions from SOFTSYS models. The differences observed in the HMA and lime stabilized soil layer thicknesses observed were attributed to deflection data variability from FWD tests. The backcalculated asphalt concrete layer thickness results matched better in the case of full-depth asphalt flexible pavements built on lime stabilized soils compared to conventional flexible pavements. Overall, SOFTSYS was capable of producing reliable thickness estimates despite the variability of field constructed asphalt layer thicknesses.

A Learning Function for Parameter Reduction in Spiking Neural Networks with Radial Basis Function

Spiking neural networks - networks that encode information in the timing of spikes - are arising as a new approach in the artificial neural networks paradigm, emergent from cognitive science. One of these new models is the pulsed neural network with radial basis function, a network able to store information in the axonal propagation delay of neurons. Learning algorithms have been proposed to this model looking for mapping input pulses into output pulses. Recently, a new method was proposed to encode constant data into a temporal sequence of spikes, stimulating deeper studies in order to establish abilities and frontiers of this new approach. However, a well known problem of this kind of network is the high number of free parameters - more that 15 - to be properly configured or tuned in order to allow network convergence. This work presents for the first time a new learning function for this network training that allow the automatic configuration of one of the key network parameters: the synaptic weight decreasing factor.

ARL1 of the Attribute c Control Chart with Estimated Parameter

International audience

Systematic Improvement of Density Functionals through Parameter-Free Hybridization Schemes

It is suggested here that the ultimate accuracy of DFT methods arises from the type of hybridization scheme followed. This idea can be cast into a mathematical formulation utilizing an integrand connecting the noninteracting and the interacting particle system. We consider two previously developed models for it, dubbed as HYB0 and QIDH, and assess a large number of exchange-correlation functionals against the AE6, G2/148, and S22 reference data sets. An interesting consequence of these hybridization schemes is that the error bars, including the standard deviation, are found to markedly decrease with respect to the density-based (nonhybrid) case. This improvement is substantially better than variations due to the underlying density functional used. We thus finally hypothesize about the universal character of the HYB0 and QIDH models.

Materials design for ambipolar devices: tuning orbital energetics in oligothiophene-naphthalimides semiconductors

Ambipolar organic field-effect transistors (OFETs), which can efficiently transport both holes and electrons, using a single type of electrode, are currently of great interest due to their possible applications in complementary metal oxide semiconductor (CMOS)-like circuits, sensors, and in light-emitting transistors. Several theoretical and experimental studies have argued that most organic semiconductors should be able to transport both types of carrier, although typically unipolar behavior is observed. One factor that can compromise ambipolar transport in organic semiconductors is poor solid state overlap between the HOMO (p-type) or LUMO (n-type) orbitals of neighboring molecules in the semiconductor thin film. In the search of low-bandgap ambipolar materials, where the absence of skeletal distortions allows closer intermolecular π-π stacking and enhanced intramolecular π-conjugation, a new family of oligothiophene-naphthalimide assemblies have been synthesized and characterized, in which both donor and acceptor moieties are directly conjugated through rigid linkers. In previous works we found that oligothiophene-napthalimide assemblies connected through amidine linkers (NDI derivates) exhibit skeletal distortions (50-60º) arising from steric hindrance between the carbonyl group of the arylene core and the sulphur atom of the neighbored thiophene ring (see Figure 1). In the present work we report novel oligo- and polythiophene–naphthalimide analogues NAI-3T, NAI-5T and poly-NAI-8C-3T, in which the connections of the amidine linkage have been inverted in order to prevent steric interactions. Thus, the nitrogen atoms are directly connected to the naphthalene moiety in NAI derivatives while they were attached directly to the thiophene moiety in the previously investigated NDI-3T and NDI-5T. In Figure 1 is depicted the calculated molecular structure of NAI-3T together with that of NDI-3T showing how the steric interactions are not present in the novel NAI derivative. The planar skeletons in these new family induce higher degree of crystallinity and the carrier charge transport can be switched from n-type to ambipolar behaviour. The highest FET performance is achieved for vapor-deposited films of NAI-3T with mobilities of 1.95x10-4cm2V-1s-1 and 2.00x10-4cm2V-1s-1 for electrons and holes, respectively. Finally, these planar semiconductors are compared with their NDI derivates analogues, which exhibit only n-type mobility, in order to understand the origin of the ambipolarity in this new series of molecular semiconductors.

Surface irregularity factor as a parameter to evaluate the fatigue damage state of CFRP

This work presents an optical non-contact technique to evaluate the fatigue damage state of CFRP structures measuring the irregularity factor of the surface. This factor includes information about surface topology and can be measured easily on field, by techniques such as optical perfilometers. The surface irregularity factor has been correlated with stiffness degradation, which is a well-accepted parameter for the evaluation of the fatigue damage state of composite materials. Constant amplitude fatigue loads (CAL) and realistic variable amplitude loads (VAL), representative of real in- flight conditions, have been applied to “dog bone” shaped tensile specimens. It has been shown that the measurement of the surface irregularity parameters can be applied to evaluate the damage state of a structure, and that it is independent of the type of fatigue load that has caused the damage. As a result, this measurement technique is applicable for a wide range of inspections of composite material structures, from pressurized tanks with constant amplitude loads, to variable amplitude loaded aeronautical structures such as wings and empennages, up to automotive and other industrial applications.

Impact of single parameter changes on Ceph cloud storage performance

In a general purpose cloud system efficiencies are yet to be had from supporting diverse applications and their requirements within a storage system used for a private cloud. Supporting such diverse requirements poses a significant challenge in a storage system that supports fine grained configuration on a variety of parameters. This paper uses the Ceph distributed file system, and in particular its global parameters, to show how a single changed parameter can effect the performance for a range of access patterns when tested with an OpenStack cloud system.

On The Use of Genetic Algorithms for Parameter Identification in Anaerobic Digestion Modelling

Anaerobic digestion (AD) of wastewater is a very interesting option for waste valorization, energy production and environment protection. It is a complex, naturally occurring process that can take place inside bioreactors. The capability of predicting the operation of such bioreactors is important to optimize the design and the operation conditions of the reactors, which, in part, justifies the numerous AD models presently available. The existing AD models are not universal, have to be inferred from prior knowledge and rely on existing experimental data. Among the tasks involved in the process of developing a dynamical model for AD, the estimation of parameters is one of the most challenging. This paper presents the identifiability analysis of a nonlinear dynamical model for a batch reactor. Particular attention is given to the structural identifiability of the model, which considers the uniqueness of the estimated parameters. To perform this analysis, the GenSSI toolbox was used. The estimation of the model parameters is achieved with genetic algorithms (GA) which have already been used in the context of AD modelling, although not commonly. The paper discusses its advantages and disadvantages.

Confidence intervals for large non- centrality parameter

We use asymptotic linearity to derive confidence intervals for large noncentrality parameters. These results enable us to measure relevance of effects and interactions in multifactors models when we get highly statistically significant the values of F tests statistics. We show how to use our approach by considering two sets of data as application examples.

Cloud parameter retrievals from Meteosat and their effects on the shortwave radiation at the surface

A method based on Spinning Enhanced Visible and Infrared Imager (SEVIRI) measured reflectance at 0.6 and 3.9 µm is used to retrieve the cloud optical thickness (COT) and cloud effective radius (re) over the Iberian Peninsula. A sensitivity analysis of simulated retrievals to the input parameters demonstrates that the cloud top height is an important factor in satellite retrievals of COT and re with uncertainties around 10% for small values of COT and re; for water clouds these uncertainties can be greater than 10% for small values of re. The uncertainties found related with geometries are around 3%. The COT and re are assessed using well-known satellite cloud products, showing that the method used characterize the cloud field with more than 80% (82%) of the absolute differences between COT (re) mean values of all clouds (water plus ice clouds) centred in the range from ±10 (±10 µm), with absolute bias lower than 2 (2 μm) for COT (re) and root mean square error values lower than 10 (8 μm) for COT (re). The cloud water path (CWP), derived from satellite retrievals, and the shortwave cloud radiative effect at the surface (CRESW) are related for high fractional sky covers (Fsc >0.8), showing that water clouds produce more negative CRESW than ice clouds. The COT retrieved was also related to the cloud modification factor, which exhibits reductions and enhancements of the surface SW radiation of the order of 80% and 30%, respectively, for COT values lower than 10. A selected case study shows, using a ground-based sky camera that some situations classified by the satellite with high Fsc values correspond to situations of broken clouds where the enhancements actually occur. For this case study, a closure between the liquid water path (LWP) obtained from the satellite retrievals and the same cloud quantity obtained from ground-based microwave measurements was performed showing a good agreement between both LWP data set values.