Sonic and Vibration Environments for Ground Facilities: a Design Manual

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Sound absorption at the soil surface

The properties of a soil structure may be examined in various manners. As well as a study of the stability, a knowledge of the geometry of the volume of air filled pores is often needed. The most common measurements, like those of porosity and flow resistance to gases do not permit a detailed description of this pore volume. Since wave phenomena are characterized by three independent variables, viz. frequency, amplitude and phase, with frequency chosen freely, the measurement of acoustical characteristics of the air in the soil offers new opportunities. Also a determination of the acoustical properties of a porous material is non-destructive.In chapter 1, a description is given of an interferometric method of measurement following the derivation of the wave equation. The propagation velocity of sound in air and the specific mass of air are the important physical quantities. The change in these quantities is studied from variations in the experimental conditions, such as temperature and humidity. Next the principles of the propagation of sound in porous materials are presented. For a sample of thickness l and having a rigid backing, the specific acoustic impedance Z at the free surface is given by Z = W m coth(γ m l), where γ m is the propagation constant for acoustical waves in the sample and W m is the specific acoustic wave impedance. Z, W m and γ m are complex quantities. Z may be measured in an interferometer and W m and γ m characterize the sample material. γ m and W m considered as functions of frequency give more information on pore geometry than may be obtained from static measurements. The loci of the function in two types of a complex plane is studied. Finally the behaviour of this function in the complex planes is shown with some examples.Chapter 2 contains a discussion of the measuring equipment used and of the calibration of the measuring set-up. After a discussion of the measuring techniques, the sources of error are evaluated.Chapter 3 deals with the propagation of waves in porous materials. Independent determination of W m and γ m proves impossible for soil samples. A method for this, described in the literature, is rejected on the grounds of inadequate accuracy. An alternative approach is followed: the material is described by a mathematical model and the parameters in the model are considered as the characteristic quantities for pore geometry. The models assume comparatively simple geometries and may be considered an extension of the work of previous authors. In addition a new projection plane for the determination of γ m and W m by a graphical method is discussed. Use of the plane is confined to cases where the sample thickness may be varied. Also, formulas are derived with which the acoustical properties of prismatic of structures soils can be studied. Finally, the applicability of scale rules and the possibility of an electric- acoustical equivalent network are examined for the sample material. Neither approach seems promising.Chapter 4 starts with a discussion of the problems to be expected on the com parison of calculated and measured curves for Z. Somes series of measurements are discussed. The mathematical models selected yield a reasonably good relation ship between the theoretical and measured values. A short critical discussion is given on the feasibility of an extension of the mathematical model.In conclusion a brief discussion is devoted to measurements on layers whose solid phases can no longer be considered as rigid, such as layers of mulch and straw. Some results obtained with straw are dealt with

Bioinformatical approaches to ranking of anti-HIV combination therapies and planning of treatment schedules

The human immunodeficiency virus (HIV) pandemic is one of the most serious health challenges humanity is facing today. Combination therapy comprising multiple antiretroviral drugs resulted in a dramatic decline in HIV-related mortality in the developed countries. However, the emergence of drug resistant HIV variants during treatment remains a problem for permanent treatment success and seriously hampers the composition of new active regimens. In this thesis we use statistical learning for developing novel methods that rank combination therapies according to their chance of achieving treatment success. These depend on information regarding the treatment composition, the viral genotype, features of viral evolution, and the patient's therapy history. Moreover, we investigate different definitions of response to antiretroviral therapy and their impact on prediction performance of our method. We address the problem of extending purely data-driven approaches to support novel drugs with little available data. In addition, we explore the prospect of prediction systems that are centered on the patient's treatment history instead of the viral genotype. We present a framework for rapidly simulating resistance development during combination therapy that will eventually allow application of combination therapies in the best order. Finally, we analyze surface proteins of HIV regarding their susceptibility to neutralizing antibodies with the aim of supporting HIV vaccine development.Die Humane Immundefizienz-Virus (HIV) Pandemie ist eine der schwerwiegendsten gesundheitlichen Herausforderungen weltweit. Kombinationstherapien bestehend aus mehreren Medikamenten führten in entwickelten Ländern zu einem drastischen Rückgang der HIV-bedingten Sterblichkeit. Die Entstehung von Arzneimittel-resistenten Varianten während der Behandlung stellt allerdings ein Problem für den anhaltenden Behandlungserfolg dar und erschwert die Zusammenstellung von neuen aktiven Kombinationen. In dieser Arbeit verwenden wir statistisches Lernen zur Entwicklung neuer Methoden, welche Kombinationstherapien bezüglich ihres erwarteten Behandlungserfolgs sortieren. Dabei nutzen wir Informationen über die Medikamente, das virale Erbgut, die Virus Evolution und die Therapiegeschichte des Patienten. Außerdem untersuchen wir unterschiedliche Definitionen für Therapieerfolg und ihre Auswirkungen auf die Güte unserer Modelle. Wir gehen das Problem der Erweiterung von daten-getriebenen Modellen bezüglich neuer Wirkstoffen an, und untersuchen weiterhin die Therapiegeschichte des Patienten als Ersatz für das virale Genom bei der Vorhersage. Wir stellen das Rahmenwerk für die schnelle Simulation von Resistenzentwicklung vor, welches schließlich erlaubt, die bestmögliche Reihenfolge von Kombinationstherapien zu suchen. Schließlich analysieren wir das HIV Oberflächenprotein im Hinblick auf seine Anfälligkeit für neutralisierende Antikörper mit dem Ziel die Impfstoff Entwicklung zu unterstützen

Bioinformatical approaches to ranking of anti-HIV combination therapies and planning of treatment schedules

The human immunodeficiency virus (HIV) pandemic is one of the most serious health challenges humanity is facing today. Combination therapy comprising multiple antiretroviral drugs resulted in a dramatic decline in HIV-related mortality in the developed countries. However, the emergence of drug resistant HIV variants during treatment remains a problem for permanent treatment success and seriously hampers the composition of new active regimens. In this thesis we use statistical learning for developing novel methods that rank combination therapies according to their chance of achieving treatment success. These depend on information regarding the treatment composition, the viral genotype, features of viral evolution, and the patient's therapy history. Moreover, we investigate different definitions of response to antiretroviral therapy and their impact on prediction performance of our method. We address the problem of extending purely data-driven approaches to support novel drugs with little available data. In addition, we explore the prospect of prediction systems that are centered on the patient's treatment history instead of the viral genotype. We present a framework for rapidly simulating resistance development during combination therapy that will eventually allow application of combination therapies in the best order. Finally, we analyze surface proteins of HIV regarding their susceptibility to neutralizing antibodies with the aim of supporting HIV vaccine development.Die Humane Immundefizienz-Virus (HIV) Pandemie ist eine der schwerwiegendsten gesundheitlichen Herausforderungen weltweit. Kombinationstherapien bestehend aus mehreren Medikamenten führten in entwickelten Ländern zu einem drastischen Rückgang der HIV-bedingten Sterblichkeit. Die Entstehung von Arzneimittel-resistenten Varianten während der Behandlung stellt allerdings ein Problem für den anhaltenden Behandlungserfolg dar und erschwert die Zusammenstellung von neuen aktiven Kombinationen. In dieser Arbeit verwenden wir statistisches Lernen zur Entwicklung neuer Methoden, welche Kombinationstherapien bezüglich ihres erwarteten Behandlungserfolgs sortieren. Dabei nutzen wir Informationen über die Medikamente, das virale Erbgut, die Virus Evolution und die Therapiegeschichte des Patienten. Außerdem untersuchen wir unterschiedliche Definitionen für Therapieerfolg und ihre Auswirkungen auf die Güte unserer Modelle. Wir gehen das Problem der Erweiterung von daten-getriebenen Modellen bezüglich neuer Wirkstoffen an, und untersuchen weiterhin die Therapiegeschichte des Patienten als Ersatz für das virale Genom bei der Vorhersage. Wir stellen das Rahmenwerk für die schnelle Simulation von Resistenzentwicklung vor, welches schließlich erlaubt, die bestmögliche Reihenfolge von Kombinationstherapien zu suchen. Schließlich analysieren wir das HIV Oberflächenprotein im Hinblick auf seine Anfälligkeit für neutralisierende Antikörper mit dem Ziel die Impfstoff Entwicklung zu unterstützen